Francesco Audrino
Citations
Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.Wikipedia or ReplicationWiki mentions
(Only mentions on Wikipedia that link back to a page on a RePEc service)- Francesco Audrino & Marcelo C. Medeiros, 2011.
"Modeling and forecasting short‐term interest rates: The benefits of smooth regimes, macroeconomic variables, and bagging,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(6), pages 999-1022, September.
- Francesco Audrino & Marcelo Cunha Medeiros, 2010. "Modeling and Forecasting Short-term Interest Rates: The Benefits of Smooth Regimes, Macroeconomic Variables, and Bagging," Textos para discussão 570, Department of Economics PUC-Rio (Brazil).
Mentioned in:
- Modeling and forecasting short-term interest rates: The benefits of smooth regimes, Macroeconomic variables, and bagging (Journal of Applied Econometrics 2011) in ReplicationWiki ()
- Modelling and forecasting Multivariate realized volatility (Journal of Applied Econometrics 2011) in ReplicationWiki ()
Working papers
- Audrino, Francesco & Camponovo, Lorenzo & Roth, Constantin, 2015.
"Testing the lag structure of assets’ realized volatility dynamics,"
Economics Working Paper Series
1501, University of St. Gallen, School of Economics and Political Science.
Cited by:
- Arnaud Dufays & Jeroen V.K. Rombouts, 2016.
"Sparse Change-point HAR Models for Realized Variance,"
Cahiers de recherche
1607, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.
- Arnaud Dufays & Jeroen V. K. Rombouts, 2019. "Sparse Change-point HAR Models for Realized Variance," Econometric Reviews, Taylor & Francis Journals, vol. 38(8), pages 857-880, September.
- Yao, Xingzhi & Izzeldin, Marwan & Li, Zhenxiong, 2019. "A novel cluster HAR-type model for forecasting realized volatility," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1318-1331.
- Tian Xie, 2019. "Forecast Bitcoin Volatility with Least Squares Model Averaging," Econometrics, MDPI, vol. 7(3), pages 1-20, September.
- Niu, Zibo & Ma, Feng & Zhang, Hongwei, 2022. "The role of uncertainty measures in volatility forecasting of the crude oil futures market before and during the COVID-19 pandemic," Energy Economics, Elsevier, vol. 112(C).
- Arnaud Dufays & Jeroen V.K. Rombouts, 2016.
"Sparse Change-point HAR Models for Realized Variance,"
Cahiers de recherche
1607, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.
- Audrino, Francesco & Huitema, Robert & Ludwig, Markus, 2014.
"An Empirical Analysis of the Ross Recovery Theorem,"
Economics Working Paper Series
1411, University of St. Gallen, School of Economics and Political Science.
Cited by:
- Jensen, Christian Skov & Lando, David & Pedersen, Lasse Heje, 2019.
"Generalized recovery,"
Journal of Financial Economics, Elsevier, vol. 133(1), pages 154-174.
- Lasse Pedersen & David Lando & Christian Skov Jensen, 2016. "Generalized Recovery," 2016 Meeting Papers 935, Society for Economic Dynamics.
- Pedersen, Lasse Heje & Skov Jensen, Christian & Lando, David, 2018. "Generalized Recovery," CEPR Discussion Papers 12665, C.E.P.R. Discussion Papers.
- Jihun Han & Hyungbin Park, 2014. "The Intrinsic Bounds on the Risk Premium of Markovian Pricing Kernels," Papers 1411.4606, arXiv.org, revised Sep 2015.
- Likuan Qin & Vadim Linetsky, 2014. "Positive Eigenfunctions of Markovian Pricing Operators: Hansen-Scheinkman Factorization, Ross Recovery and Long-Term Pricing," Papers 1411.3075, arXiv.org, revised Sep 2015.
- Johan Walden, 2017. "Recovery with Unbounded Diffusion Processes," Review of Finance, European Finance Association, vol. 21(4), pages 1403-1444.
- Alex Backwell, 2015. "State Prices and Implementation of the Recovery Theorem," JRFM, MDPI, vol. 8(1), pages 1-15, January.
- Greg Orosi, 2017. "Information content of right option tails: Evidence from S&P 500 index options," Journal of Asset Management, Palgrave Macmillan, vol. 18(7), pages 516-526, December.
- Horatio Cuesdeanu & Jens Carsten Jackwerth, 2018. "The pricing kernel puzzle: survey and outlook," Annals of Finance, Springer, vol. 14(3), pages 289-329, August.
- Carr, Peter & Wu, Liuren, 2016. "Analyzing volatility risk and risk premium in option contracts: A new theory," Journal of Financial Economics, Elsevier, vol. 120(1), pages 1-20.
- Likuan Qin & Vadim Linetsky, 2016. "Positive Eigenfunctions of Markovian Pricing Operators: Hansen-Scheinkman Factorization, Ross Recovery, and Long-Term Pricing," Operations Research, INFORMS, vol. 64(1), pages 99-117, February.
- Yuan Hu & W. Brent Lindquist & Svetlozar T. Rachev & Frank J. Fabozzi, 2023. "Option pricing using a skew random walk pricing tree," Papers 2303.17014, arXiv.org.
- Han, Jihun & Park, Hyungbin, 2015. "The intrinsic bounds on the risk premium of Markovian pricing kernels," Finance Research Letters, Elsevier, vol. 13(C), pages 36-44.
- Jensen, Christian Skov & Lando, David & Pedersen, Lasse Heje, 2019.
"Generalized recovery,"
Journal of Financial Economics, Elsevier, vol. 133(1), pages 154-174.
- Audrino, Francesco & Fengler, Matthias, 2013.
"Are classical option pricing models consistent with observed option second-order moments? Evidence from high-frequency data,"
Economics Working Paper Series
1311, University of St. Gallen, School of Economics and Political Science.
- Audrino, Francesco & Fengler, Matthias R., 2015. "Are classical option pricing models consistent with observed option second-order moments? Evidence from high-frequency data," Journal of Banking & Finance, Elsevier, vol. 61(C), pages 46-63.
Cited by:
- Diego Amaya & Jean-François Bégin & Geneviève Gauthier, 2022. "The Informational Content of High-Frequency Option Prices," Management Science, INFORMS, vol. 68(3), pages 2166-2201, March.
- Dalderop, Jeroen, 2020. "Nonparametric filtering of conditional state-price densities," Journal of Econometrics, Elsevier, vol. 214(2), pages 295-325.
- Stephen J. Taylor & Chi‐Feng Tzeng & Martin Widdicks, 2018. "Information about price and volatility jumps inferred from options prices," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(10), pages 1206-1226, October.
- Francesco Audrino & Lorenzo Camponovo, 2013.
"Oracle Properties and Finite Sample Inference of the Adaptive Lasso for Time Series Regression Models,"
Papers
1312.1473, arXiv.org.
- Audrino, Francesco & Camponovo, Lorenzo, 2013. "Oracle Properties and Finite Sample Inference of the Adaptive Lasso for Time Series Regression Models," Economics Working Paper Series 1327, University of St. Gallen, School of Economics and Political Science.
Cited by:
- Francesco Audrino & Simon D. Knaus, 2016.
"Lassoing the HAR Model: A Model Selection Perspective on Realized Volatility Dynamics,"
Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1485-1521, December.
- Audrino, Francesco & Knaus, Simon, 2012. "Lassoing the HAR model: A Model Selection Perspective on Realized Volatility Dynamics," Economics Working Paper Series 1224, University of St. Gallen, School of Economics and Political Science.
- Medeiros, Marcelo C. & Mendes, Eduardo F., 2016. "ℓ1-regularization of high-dimensional time-series models with non-Gaussian and heteroskedastic errors," Journal of Econometrics, Elsevier, vol. 191(1), pages 255-271.
- Ding, Yi & Kambouroudis, Dimos & McMillan, David G., 2021. "Forecasting realised volatility: Does the LASSO approach outperform HAR?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
- Marcelo C. Medeiros & Eduardo F. Mendes, 2015. "l1-Regularization of High-Dimensional Time-Series Models with Flexible Innovations," Textos para discussão 636, Department of Economics PUC-Rio (Brazil).
- Audrino, Francesco & Camponovo, Lorenzo & Roth, Constantin, 2015. "Testing the lag structure of assets’ realized volatility dynamics," Economics Working Paper Series 1501, University of St. Gallen, School of Economics and Political Science.
- Corsi, Fulvio & Peluso, Stefano & Audrino, Francesco, 2012.
"Missing in Asynchronicity: A Kalman-EM Approach for Multivariate Realized Covariance Estimation,"
Economics Working Paper Series
1202, University of St. Gallen, School of Economics and Political Science.
- Fulvio Corsi & Stefano Peluso & Francesco Audrino, 2015. "Missing in Asynchronicity: A Kalman‐em Approach for Multivariate Realized Covariance Estimation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(3), pages 377-397, April.
Cited by:
- Yuta Koike, 2013. "Limit Theorems for the Pre-averaged Hayashi-Yoshida Estimator with Random Sampling," Global COE Hi-Stat Discussion Paper Series gd12-276, Institute of Economic Research, Hitotsubashi University.
- Donelli, Nicola & Peluso, Stefano & Mira, Antonietta, 2021. "A Bayesian semiparametric vector Multiplicative Error Model," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
- Tim Bollerslev & Andrew J. Patton & Wenjing Wang, 2016.
"Daily House Price Indices: Construction, Modeling, and Longer‐run Predictions,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(6), pages 1005-1025, September.
- Tim Bollerslev & Andrew J. Patton & Wang Wenjing, 2013. "Daily House Price Indexes: Construction, Modeling, and Longer-Run Predictions," Working Papers 13-29, Duke University, Department of Economics.
- Tim Bollerslev & Andrew J. Patton & Wenjing Wang, 2015. "Daily House Price Indices: Construction, Modeling, and Longer-Run Predictions," CREATES Research Papers 2015-02, Department of Economics and Business Economics, Aarhus University.
- Xin Jin & John M. Maheu, 2014.
"Bayesian Semiparametric Modeling of Realized Covariance Matrices,"
Working Paper series
34_14, Rimini Centre for Economic Analysis.
- Jin, Xin & Maheu, John M., 2016. "Bayesian semiparametric modeling of realized covariance matrices," Journal of Econometrics, Elsevier, vol. 192(1), pages 19-39.
- Jin, Xin & Maheu, John M, 2014. "Bayesian Semiparametric Modeling of Realized Covariance Matrices," MPRA Paper 60102, University Library of Munich, Germany.
- Kris Boudt & Sébastien Laurent & Asger Lunde & Rogier Quaedvlieg & Orimar Sauri, 2017.
"Positive semidefinite integrated covariance estimation, factorizations and asynchronicity,"
Post-Print
hal-01505775, HAL.
- Boudt, Kris & Laurent, Sébastien & Lunde, Asger & Quaedvlieg, Rogier & Sauri, Orimar, 2017. "Positive semidefinite integrated covariance estimation, factorizations and asynchronicity," Journal of Econometrics, Elsevier, vol. 196(2), pages 347-367.
- Kris Boudt & Sébastien Laurent & Asger Lunde & Rogier Quaedvlieg, 2014. "Positive Semidefinite Integrated Covariance Estimation, Factorizations and Asynchronicity," CREATES Research Papers 2014-05, Department of Economics and Business Economics, Aarhus University.
- Liu, Cheng & Tang, Cheng Yong, 2014. "A quasi-maximum likelihood approach for integrated covariance matrix estimation with high frequency data," Journal of Econometrics, Elsevier, vol. 180(2), pages 217-232.
- Christian Brownlees & Eulàlia Nualart & Yucheng Sun, 2018. "Realized networks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(7), pages 986-1006, November.
- Vander Elst, Harry & Veredas, David, 2014.
"Disentangled jump-robust realized covariances and correlations with non-synchronous prices,"
DES - Working Papers. Statistics and Econometrics. WS
ws142416, Universidad Carlos III de Madrid. Departamento de EstadÃstica.
- Harry-Paul Vander Elst & David Veredas, 2014. "Disentangled Jump-Robust Realized Covariances and Correlations with Non-Synchronous Prices," Working Papers ECARES ECARES 2014-35, ULB -- Universite Libre de Bruxelles.
- Neil Shephard & Dacheng Xiu, 2012.
"Econometric analysis of multivariate realised QML: efficient positive semi-definite estimators of the covariation of equity prices,"
Economics Papers
2012-W04, Economics Group, Nuffield College, University of Oxford.
- Neil Shephard & Dacheng Xiu, 2012. "Econometric analysis of multivariate realised QML: efficient positive semi-definite estimators of the covariation of equity prices," Economics Series Working Papers 604, University of Oxford, Department of Economics.
- Matthias R. Fengler & Ostap Okhrin, 2012.
"Realized Copula,"
SFB 649 Discussion Papers
SFB649DP2012-034, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
- Fengler, Matthias & Okhrin, Ostap, 2012. "Realized Copula," Economics Working Paper Series 1214, University of St. Gallen, School of Economics and Political Science.
- Carlo Campajola & Fabrizio Lillo & Daniele Tantari, 2019. "Unveiling the relation between herding and liquidity with trader lead-lag networks," Papers 1909.10807, arXiv.org, revised Mar 2020.
- Audrino, Francesco, 2011.
"Forecasting correlations during the late-2000s financial crisis: short-run component, long-run component, and structural breaks,"
Economics Working Paper Series
1112, University of St. Gallen, School of Economics and Political Science.
- Audrino, Francesco, 2014. "Forecasting correlations during the late-2000s financial crisis: The short-run component, the long-run component, and structural breaks," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 43-60.
- Qu, Hui & Zhang, Yi, 2022. "Asymmetric multivariate HAR models for realized covariance matrix: A study based on volatility timing strategies," Economic Modelling, Elsevier, vol. 106(C).
- Joel Hasbrouck, 2021. "Rejoinder on: Price Discovery in High Resolution," Journal of Financial Econometrics, Oxford University Press, vol. 19(3), pages 465-471.
- Bahcivan, Hulusi & Karahan, Cenk C., 2022. "High frequency correlation dynamics and day-of-the-week effect: A score-driven approach in an emerging market stock exchange," International Review of Financial Analysis, Elsevier, vol. 80(C).
- Ulrich Hounyo, 2014. "Bootstrapping integrated covariance matrix estimators in noisy jump-diffusion models with non-synchronous trading," CREATES Research Papers 2014-35, Department of Economics and Business Economics, Aarhus University.
- Yaojie Zhang & Yu Wei & Li Liu, 2019. "Improving forecasting performance of realized covariance with extensions of HAR-RCOV model: statistical significance and economic value," Quantitative Finance, Taylor & Francis Journals, vol. 19(9), pages 1425-1438, September.
- Yongheng Deng & Eric Girardin & Roselyne Joyeux, 2015. "Fundamentals and the Volatility of Real Estate Prices in China: A Sequential Modelling Strategy," Working Papers 222015, Hong Kong Institute for Monetary Research.
- Deng, Yongheng & Girardin, Eric & Joyeux, Roselyne, 2018.
"Fundamentals and the volatility of real estate prices in China: A sequential modelling strategy,"
China Economic Review, Elsevier, vol. 48(C), pages 205-222.
- Yongheng Deng & Eric Girardin & Roselyne Joyeux, 2018. "Fundamentals and the volatility of real estate prices in China: A sequential modelling strategy," Post-Print hal-01996210, HAL.
- Stefano Peluso & Fulvio Corsi & Antonietta Mira, 2015. "A Bayesian High-Frequency Estimator of the Multivariate Covariance of Noisy and Asynchronous Returns," Journal of Financial Econometrics, Oxford University Press, vol. 13(3), pages 665-697.
- Shephard, Neil & Xiu, Dacheng, 2017. "Econometric analysis of multivariate realised QML: Estimation of the covariation of equity prices under asynchronous trading," Journal of Econometrics, Elsevier, vol. 201(1), pages 19-42.
- Giuseppe Buccheri & Giacomo Bormetti & Fulvio Corsi & Fabrizio Lillo, 2018. "A Score-Driven Conditional Correlation Model for Noisy and Asynchronous Data: an Application to High-Frequency Covariance Dynamics," Papers 1803.04894, arXiv.org, revised Mar 2019.
- Danilo Vassallo & Giacomo Bormetti & Fabrizio Lillo, 2019. "A tale of two sentiment scales: Disentangling short-run and long-run components in multivariate sentiment dynamics," Papers 1910.01407, arXiv.org, revised Sep 2020.
- Calypso Herrera & Florian Krach & Anastasis Kratsios & Pierre Ruyssen & Josef Teichmann, 2020. "Denise: Deep Robust Principal Component Analysis for Positive Semidefinite Matrices," Papers 2004.13612, arXiv.org, revised Jun 2023.
- Michael Ho & Jack Xin, 2016. "Sparse Kalman Filtering Approaches to Covariance Estimation from High Frequency Data in the Presence of Jumps," Papers 1602.02185, arXiv.org, revised Apr 2016.
- Audrino, Francesco & Meier, Pirmin, 2012.
"Empirical pricing kernel estimation using a functional gradient descent algorithm based on splines,"
Economics Working Paper Series
1210, University of St. Gallen, School of Economics and Political Science.
Cited by:
- Jiao, Yuhan & Liu, Qiang & Guo, Shuxin, 2021. "Pricing kernel monotonicity and term structure: Evidence from China," Journal of Banking & Finance, Elsevier, vol. 123(C).
- Horatio Cuesdeanu & Jens Carsten Jackwerth, 2018. "The pricing kernel puzzle: survey and outlook," Annals of Finance, Springer, vol. 14(3), pages 289-329, August.
- Audrino, Francesco & Knaus, Simon, 2012.
"Lassoing the HAR model: A Model Selection Perspective on Realized Volatility Dynamics,"
Economics Working Paper Series
1224, University of St. Gallen, School of Economics and Political Science.
- Francesco Audrino & Simon D. Knaus, 2016. "Lassoing the HAR Model: A Model Selection Perspective on Realized Volatility Dynamics," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1485-1521, December.
Cited by:
- Audrino, Francesco & Fengler, Matthias, 2013.
"Are classical option pricing models consistent with observed option second-order moments? Evidence from high-frequency data,"
Economics Working Paper Series
1311, University of St. Gallen, School of Economics and Political Science.
- Audrino, Francesco & Fengler, Matthias R., 2015. "Are classical option pricing models consistent with observed option second-order moments? Evidence from high-frequency data," Journal of Banking & Finance, Elsevier, vol. 61(C), pages 46-63.
- Zhu, Haibin & Bai, Lu & He, Lidan & Liu, Zhi, 2023. "Forecasting realized volatility with machine learning: Panel data perspective," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 251-271.
- Naimoli, Antonio, 2022. "Modelling the persistence of Covid-19 positivity rate in Italy," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).
- Chun, Dohyun & Cho, Hoon & Ryu, Doojin, 2023. "Discovering the drivers of stock market volatility in a data-rich world," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 82(C).
- Arnaud Dufays & Jeroen V.K. Rombouts, 2016.
"Sparse Change-point HAR Models for Realized Variance,"
Cahiers de recherche
1607, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.
- Arnaud Dufays & Jeroen V. K. Rombouts, 2019. "Sparse Change-point HAR Models for Realized Variance," Econometric Reviews, Taylor & Francis Journals, vol. 38(8), pages 857-880, September.
- Zhang, Yaojie & Wei, Yu & Zhang, Yi & Jin, Daxiang, 2019. "Forecasting oil price volatility: Forecast combination versus shrinkage method," Energy Economics, Elsevier, vol. 80(C), pages 423-433.
- Yao, Xingzhi & Izzeldin, Marwan & Li, Zhenxiong, 2019. "A novel cluster HAR-type model for forecasting realized volatility," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1318-1331.
- Wilms, Ines & Rombouts, Jeroen & Croux, Christophe, 2021. "Multivariate volatility forecasts for stock market indices," International Journal of Forecasting, Elsevier, vol. 37(2), pages 484-499.
- Chao Liang & Yongan Xu & Zhonglu Chen & Xiafei Li, 2023. "Forecasting China's stock market volatility with shrinkage method: Can Adaptive Lasso select stronger predictors from numerous predictors?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 3689-3699, October.
- Richard T. Baillie & Dooyeon Cho & Seunghwa Rho, 2023. "Approximating long-memory processes with low-order autoregressions: Implications for modeling realized volatility," Empirical Economics, Springer, vol. 64(6), pages 2911-2937, June.
- Zongwu Cai & Chaoqun Ma & Xianhua Mi, 2020. "Realized Volatility Forecasting Based on Dynamic Quantile Model Averaging," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202016, University of Kansas, Department of Economics, revised Sep 2020.
- Ding, Shusheng & Cui, Tianxiang & Zhang, Yongmin, 2022. "Futures volatility forecasting based on big data analytics with incorporating an order imbalance effect," International Review of Financial Analysis, Elsevier, vol. 83(C).
- Francesco Audrino & Lorenzo Camponovo, 2013.
"Oracle Properties and Finite Sample Inference of the Adaptive Lasso for Time Series Regression Models,"
Papers
1312.1473, arXiv.org.
- Audrino, Francesco & Camponovo, Lorenzo, 2013. "Oracle Properties and Finite Sample Inference of the Adaptive Lasso for Time Series Regression Models," Economics Working Paper Series 1327, University of St. Gallen, School of Economics and Political Science.
- Santos, Douglas G. & Candido, Osvaldo & Tófoli, Paula V., 2022. "Forecasting risk measures using intraday and overnight information," The North American Journal of Economics and Finance, Elsevier, vol. 60(C).
- Zhifeng Dai & Tingyu Li & Mi Yang, 2022. "Forecasting stock return volatility: The role of shrinkage approaches in a data‐rich environment," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 980-996, August.
- Chanatásig-Niza, Evelyn & Ciarreta, Aitor & Zarraga, Ainhoa, 2022. "A volatility spillover analysis with realized semi(co)variances in Australian electricity markets," Energy Economics, Elsevier, vol. 111(C).
- Wei Zhang & Kai Yan & Dehua Shen, 2021. "Can the Baidu Index predict realized volatility in the Chinese stock market?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-31, December.
- Liu, Guangqiang & Guo, Xiaozhu, 2022. "Forecasting stock market volatility using commodity futures volatility information," Resources Policy, Elsevier, vol. 75(C).
- Deev, Oleg & Plíhal, Tomáš, 2022. "How to calm down the markets? The effects of COVID-19 economic policy responses on financial market uncertainty," Research in International Business and Finance, Elsevier, vol. 60(C).
- Opschoor, Anne & Lucas, André, 2023. "Time-varying variance and skewness in realized volatility measures," International Journal of Forecasting, Elsevier, vol. 39(2), pages 827-840.
- Pedersen, Lasse Heje & Bollerslev, Tim & Hood, Benjamin & Huss, John, 2018.
"Risk Everywhere: Modeling and Managing Volatility,"
CEPR Discussion Papers
12687, C.E.P.R. Discussion Papers.
- Tim Bollerslev & Benjamin Hood & John Huss & Lasse Heje Pedersen, 2018. "Risk Everywhere: Modeling and Managing Volatility," The Review of Financial Studies, Society for Financial Studies, vol. 31(7), pages 2729-2773.
- Laurent A. F. Callot & Anders B. Kock & Marcelo C. Medeiros, 2014.
"Estimation and Forecasting of Large Realized Covariance Matrices and Portfolio Choice,"
CREATES Research Papers
2014-42, Department of Economics and Business Economics, Aarhus University.
- Laurent Callot & Anders B. Kock & Marcelo C. Medeiros, 2014. "Estimation and Forecasting of Large Realized Covariance Matrices and Portfolio Choice," Tinbergen Institute Discussion Papers 14-147/III, Tinbergen Institute.
- Liu, Min & Lee, Chien-Chiang, 2021. "Capturing the dynamics of the China crude oil futures: Markov switching, co-movement, and volatility forecasting," Energy Economics, Elsevier, vol. 103(C).
- Ding, Yi & Kambouroudis, Dimos & McMillan, David G., 2021. "Forecasting realised volatility: Does the LASSO approach outperform HAR?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
- Niu, Zibo & Liu, Yuanyuan & Gao, Wang & Zhang, Hongwei, 2021. "The role of coronavirus news in the volatility forecasting of crude oil futures markets: Evidence from China," Resources Policy, Elsevier, vol. 73(C).
- Zargar, Faisal Nazir & Kumar, Dilip, 2020. "Modeling unbiased extreme value volatility estimator in presence of heterogeneity and jumps: A study with economic significance analysis," International Review of Economics & Finance, Elsevier, vol. 67(C), pages 25-41.
- Kim Christensen & Mathias Siggaard & Bezirgen Veliyev, 2021. "A machine learning approach to volatility forecasting," CREATES Research Papers 2021-03, Department of Economics and Business Economics, Aarhus University.
- Marcelo C. Medeiros & Eduardo F. Mendes, 2015. "l1-Regularization of High-Dimensional Time-Series Models with Flexible Innovations," Textos para discussão 636, Department of Economics PUC-Rio (Brazil).
- Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2020.
"Machine Learning Advances for Time Series Forecasting,"
Papers
2012.12802, arXiv.org, revised Apr 2021.
- Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2023. "Machine learning advances for time series forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 76-111, February.
- António Rua & Fátima Cardoso, 2023. "Gone with the wind: A structural decomposition of carbon emissions," Working Papers w202312, Banco de Portugal, Economics and Research Department.
- Taylor, Nick, 2017. "Realised variance forecasting under Box-Cox transformations," International Journal of Forecasting, Elsevier, vol. 33(4), pages 770-785.
- Massimiliano Caporin & Francesco Poli, 2017. "Building News Measures from Textual Data and an Application to Volatility Forecasting," Econometrics, MDPI, vol. 5(3), pages 1-46, August.
- Tian Xie, 2019. "Forecast Bitcoin Volatility with Least Squares Model Averaging," Econometrics, MDPI, vol. 7(3), pages 1-20, September.
- Fengler, Matthias R. & Mammen, Enno & Vogt, Michael, 2013. "Additive modeling of realized variance: tests for parametric specifications and structural breaks," Economics Working Paper Series 1332, University of St. Gallen, School of Economics and Political Science.
- Michael Frömmel & Eyup Kadioglu, 2023. "Impact of trading hours extensions on foreign exchange volatility: intraday evidence from the Moscow exchange," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-23, December.
- Audrino, Francesco & Camponovo, Lorenzo & Roth, Constantin, 2015. "Testing the lag structure of assets’ realized volatility dynamics," Economics Working Paper Series 1501, University of St. Gallen, School of Economics and Political Science.
- Qianjie Geng & Xianfeng Hao & Yudong Wang, 2024. "Forecasting the volatility of crude oil futures: A time‐dependent weighted least squares with regularization constraint," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 309-325, March.
- Lehrer, Steven & Xie, Tian & Zhang, Xinyu, 2021. "Social media sentiment, model uncertainty, and volatility forecasting," Economic Modelling, Elsevier, vol. 102(C).
- Niu, Zibo & Ma, Feng & Zhang, Hongwei, 2022. "The role of uncertainty measures in volatility forecasting of the crude oil futures market before and during the COVID-19 pandemic," Energy Economics, Elsevier, vol. 112(C).
- Alain Hecq & Marie Ternes & Ines Wilms, 2023. "Hierarchical Regularizers for Reverse Unrestricted Mixed Data Sampling Regressions," Papers 2301.10592, arXiv.org.
- Audrino, Francesco & Tetereva, Anastasija, 2019. "Sentiment spillover effects for US and European companies," Journal of Banking & Finance, Elsevier, vol. 106(C), pages 542-567.
- Min Liu & Wei‐Chong Choo & Chi‐Chuan Lee & Chien‐Chiang Lee, 2023. "Trading volume and realized volatility forecasting: Evidence from the China stock market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 76-100, January.
- Audrino, Francesco, 2011.
"Forecasting correlations during the late-2000s financial crisis: short-run component, long-run component, and structural breaks,"
Economics Working Paper Series
1112, University of St. Gallen, School of Economics and Political Science.
- Audrino, Francesco, 2014. "Forecasting correlations during the late-2000s financial crisis: The short-run component, the long-run component, and structural breaks," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 43-60.
Cited by:
- ROMBOUTS, Jeroen V. K. & STENTOFT, Lars & VIOLANTE, Francesco, 2012.
"The value of multivariate model sophistication: an application to pricing Dow Jones Industrial Average options,"
LIDAM Discussion Papers CORE
2012003, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Jeroen Rombouts & Lars Stentoft & Francesco Violente, 2012. "The Value of Multivariate Model Sophistication: An Application to pricing Dow Jones Industrial Average Options," CIRANO Working Papers 2012s-05, CIRANO.
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- Jeroen V.K. Rombouts & Lars Stentoft & Francesco Violante, 2012. "The Value of Multivariate Model Sophistication: An Application to pricing Dow Jones Industrial Average options," CREATES Research Papers 2012-04, Department of Economics and Business Economics, Aarhus University.
- de Almeida, Daniel & Hotta, Luiz K. & Ruiz, Esther, 2018.
"MGARCH models: Trade-off between feasibility and flexibility,"
International Journal of Forecasting, Elsevier, vol. 34(1), pages 45-63.
- Almeida, Daniel de & Hotta, Luiz & Ruiz Ortega, Esther, 2015. "MGARCH models: tradeoff between feasibility and flexibility," DES - Working Papers. Statistics and Econometrics. WS ws1516, Universidad Carlos III de Madrid. Departamento de EstadÃstica.
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"Dynamic functional time-series forecasts of foreign exchange implied volatility surfaces,"
International Journal of Forecasting, Elsevier, vol. 38(3), pages 1025-1049.
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"The uncertainty of conditional returns, volatilities and correlations in DCC models,"
DES - Working Papers. Statistics and Econometrics. WS
ws140202, Universidad Carlos III de Madrid. Departamento de EstadÃstica.
- Fresoli, Diego E. & Ruiz, Esther, 2016. "The uncertainty of conditional returns, volatilities and correlations in DCC models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 170-185.
- Audrino, Francesco & Hu, Yujia, 2011.
"Volatility Forecasting: Downside Risk, Jumps and Leverage Effect,"
Economics Working Paper Series
1138, University of St. Gallen, School of Economics and Political Science.
- Francesco Audrino & Yujia Hu, 2016. "Volatility Forecasting: Downside Risk, Jumps and Leverage Effect," Econometrics, MDPI, vol. 4(1), pages 1-24, February.
Cited by:
- Chao Liang & Yin Liao & Feng Ma & Bo Zhu, 2022. "United States Oil Fund volatility prediction: the roles of leverage effect and jumps," Empirical Economics, Springer, vol. 62(5), pages 2239-2262, May.
- Christophe Chorro & Florian Ielpo & Benoît Sévi, 2020. "The contribution of intraday jumps to forecasting the density of returns," Post-Print halshs-02505861, HAL.
- Fengler, Matthias R. & Gisler, Katja I. M., 2014.
"A variance spillover analysis without covariances: what do we miss?,"
Economics Working Paper Series
1409, University of St. Gallen, School of Economics and Political Science.
- Fengler, Matthias R. & Gisler, Katja I.M., 2015. "A variance spillover analysis without covariances: What do we miss?," Journal of International Money and Finance, Elsevier, vol. 51(C), pages 174-195.
- Li, Xiafei & Liao, Yin & Lu, Xinjie & Ma, Feng, 2022. "An oil futures volatility forecast perspective on the selection of high-frequency jump tests," Energy Economics, Elsevier, vol. 116(C).
- Chorro, Christophe & Ielpo, Florian & Sévi, Benoît, 2020. "The contribution of intraday jumps to forecasting the density of returns," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).
- Chao Liang & Yaojie Zhang & Xiafei Li & Feng Ma, 2022. "Which predictor is more predictive for Bitcoin volatility? And why?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 1947-1961, April.
- Xu Gong & Boqiang Lin, 2018. "Structural breaks and volatility forecasting in the copper futures market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(3), pages 290-339, March.
- Jerome L Kreuser & Didier Sornette, 2017. "Super-Exponential RE Bubble Model with Efficient Crashes," Swiss Finance Institute Research Paper Series 17-33, Swiss Finance Institute.
- Maki, Daiki & Ota, Yasushi, 2021. "Impacts of asymmetry on forecasting realized volatility in Japanese stock markets," Economic Modelling, Elsevier, vol. 101(C).
- Feng Ma & M. I. M. Wahab & Julien Chevallier & Ziyang Li, 2023. "A tug of war of forecasting the US stock market volatility: Oil futures overnight versus intraday information," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 60-75, January.
- Matthias R. Fengler & Ostap Okhrin, 2012.
"Realized Copula,"
SFB 649 Discussion Papers
SFB649DP2012-034, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
- Fengler, Matthias & Okhrin, Ostap, 2012. "Realized Copula," Economics Working Paper Series 1214, University of St. Gallen, School of Economics and Political Science.
- Papantonis, Ioannis & Rompolis, Leonidas & Tzavalis, Elias, 2023. "Improving variance forecasts: The role of Realized Variance features," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1221-1237.
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- Chao Liang & Yan Li & Feng Ma & Yaojie Zhang, 2022. "Forecasting international equity market volatility: A new approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1433-1457, November.
- Fengler, Matthias R. & Okhrin, Ostap, 2016. "Managing risk with a realized copula parameter," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 131-152.
- Wang, Yajing & Liang, Fang & Wang, Tianyi & Huang, Zhuo, 2020. "Does measurement error matter in volatility forecasting? Empirical evidence from the Chinese stock market," Economic Modelling, Elsevier, vol. 87(C), pages 148-157.
- Gbenga Ibikunle & Vito Mollica & Qiao Sun, 2021. "Jumps in foreign exchange spot rates and the informational efficiency of currency forwards," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(8), pages 1201-1219, August.
- Papantonis Ioannis & Tzavalis Elias & Agapitos Orestis & Rompolis Leonidas S., 2023. "Augmenting the Realized-GARCH: the role of signed-jumps, attenuation-biases and long-memory effects," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 27(2), pages 171-198, April.
- Chao Liang & Feng Ma & Lu Wang & Qing Zeng, 2021. "The information content of uncertainty indices for natural gas futures volatility forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(7), pages 1310-1324, November.
- Xu, Yanyan & Huang, Dengshi & Ma, Feng & Qiao, Gaoxiu, 2019. "The heterogeneous impact of liquidity on volatility in Chinese stock index futures market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 73-85.
- Vitali Alexeev & Mardi Dungey & Wenying Yao, 2016. "Continuous and Jump Betas: Implications for Portfolio Diversification," Econometrics, MDPI, vol. 4(2), pages 1-15, June.
- Cheng, Mingmian & Swanson, Norman R. & Yang, Xiye, 2021. "Forecasting volatility using double shrinkage methods," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 46-61.
- Christophe Chorro & Florian Ielpo & Benoît Sévi, 2020. "The contribution of intraday jumps to forecasting the density of returns," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02505861, HAL.
- Fengler, Matthias R. & Mammen, Enno & Vogt, Michael, 2013. "Additive modeling of realized variance: tests for parametric specifications and structural breaks," Economics Working Paper Series 1332, University of St. Gallen, School of Economics and Political Science.
- Xu Gong & Boqiang Lin, 2021. "Effects of structural changes on the prediction of downside volatility in futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(7), pages 1124-1153, July.
- Gong, Xu & Lin, Boqiang, 2017. "Forecasting the good and bad uncertainties of crude oil prices using a HAR framework," Energy Economics, Elsevier, vol. 67(C), pages 315-327.
- Liang, Chao & Li, Yan & Ma, Feng & Wei, Yu, 2021. "Global equity market volatilities forecasting: A comparison of leverage effects, jumps, and overnight information," International Review of Financial Analysis, Elsevier, vol. 75(C).
- Daiki Maki & Yasushi Ota, 2020. "The impacts of asymmetry on modeling and forecasting realized volatility in Japanese stock markets," Papers 2006.00158, arXiv.org.
- Chen, Yixiang & Ma, Feng & Zhang, Yaojie, 2019. "Good, bad cojumps and volatility forecasting: New evidence from crude oil and the U.S. stock markets," Energy Economics, Elsevier, vol. 81(C), pages 52-62.
- Francesco Audrino & Marcelo Cunha Medeiros, 2010.
"Modeling and Forecasting Short-term Interest Rates: The Benefits of Smooth Regimes, Macroeconomic Variables, and Bagging,"
Textos para discussão
570, Department of Economics PUC-Rio (Brazil).
- Francesco Audrino & Marcelo C. Medeiros, 2011. "Modeling and forecasting short‐term interest rates: The benefits of smooth regimes, macroeconomic variables, and bagging," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(6), pages 999-1022, September.
Cited by:
- Li, Dongxin & Zhang, Li & Li, Lihong, 2023. "Forecasting stock volatility with economic policy uncertainty: A smooth transition GARCH-MIDAS model," International Review of Financial Analysis, Elsevier, vol. 88(C).
- Tae-Hwy Lee & Huiyu Huang, 2014.
"Forecasting Value-at-Risk Using High Frequency Information,"
Working Papers
201409, University of California at Riverside, Department of Economics.
- Huiyu Huang & Tae-Hwy Lee, 2013. "Forecasting Value-at-Risk Using High-Frequency Information," Econometrics, MDPI, vol. 1(1), pages 1-14, June.
- Erik Hillebrand & Tae-Hwy Lee & Marcelo Cunha Medeiros, 2012.
"Let´s do it again: bagging equity premium predictors,"
Textos para discussão
604, Department of Economics PUC-Rio (Brazil).
- Eric Hillebrand & Tae-Hwy Lee & Marcelo C. Medeiros, 2012. "Let's Do It Again: Bagging Equity Premium Predictors," CREATES Research Papers 2012-41, Department of Economics and Business Economics, Aarhus University.
- Francesco Audrino, 2012. "What Drives Short Rate Dynamics? A Functional Gradient Descent Approach," Computational Economics, Springer;Society for Computational Economics, vol. 39(3), pages 315-335, March.
- Luo, Jiawen & Klein, Tony & Walther, Thomas & Ji, Qiang, 2021. "Forecasting Realized Volatility of Crude Oil Futures Prices based on Machine Learning," QBS Working Paper Series 2021/04, Queen's University Belfast, Queen's Business School.
- Tae-Hwy Lee & Eric Hillebrand & Marcelo Medeiros, 2014. "Bagging Constrained Equity Premium Predictors," Working Papers 201421, University of California at Riverside, Department of Economics, revised Feb 2013.
- Yang, Ke & Tian, Fengping & Chen, Langnan & Li, Steven, 2017. "Realized volatility forecast of agricultural futures using the HAR models with bagging and combination approaches," International Review of Economics & Finance, Elsevier, vol. 49(C), pages 276-291.
- Xiaojing Xi & Rogemar Mamon, 2014. "Capturing the Regime-Switching and Memory Properties of Interest Rates," Computational Economics, Springer;Society for Computational Economics, vol. 44(3), pages 307-337, October.
- Fulvio Corsi & Francesco Audrino, 2008.
"Realized Covariance Tick-by-Tick in Presence of Rounded Time Stamps and General Microstructure Effects,"
University of St. Gallen Department of Economics working paper series 2008
2008-04, Department of Economics, University of St. Gallen.
- Fulvio Corsi & Francesco Audrino, 2012. "Realized Covariance Tick-by-Tick in Presence of Rounded Time Stamps and General Microstructure Effects," Journal of Financial Econometrics, Oxford University Press, vol. 10(4), pages 591-616, September.
Cited by:
- Márcio Gomes Pinto Garcia & Marcelo Cunha Medeiros & Francisco Eduardo de Luna e Almeida Santos, 2014.
"Economic gains of realized volatility in the Brazilian stock market,"
Brazilian Review of Finance, Brazilian Society of Finance, vol. 12(3), pages 319-349.
- Marcio Garcia & Marcelo Medeiros & Francisco Eduardo de Luna e Almeida Santos, 2014. "Economic gains of realized volatility in the Brazilian stock market," Textos para discussão 624, Department of Economics PUC-Rio (Brazil).
- Audrino, Francesco & Corsi, Fulvio, 2010.
"Modeling tick-by-tick realized correlations,"
Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2372-2382, November.
- Fulvio Corsi & Francesco Audrino, 2008. "Modeling Tick-by-Tick Realized Correlations," University of St. Gallen Department of Economics working paper series 2008 2008-05, Department of Economics, University of St. Gallen.
- Arnerić, Josip & Matković, Mario & Sorić, Petar, 2019. "Comparison of range-based volatility estimators against integrated volatility in European emerging markets," Finance Research Letters, Elsevier, vol. 28(C), pages 118-124.
- de Almeida, Daniel & Hotta, Luiz K. & Ruiz, Esther, 2018.
"MGARCH models: Trade-off between feasibility and flexibility,"
International Journal of Forecasting, Elsevier, vol. 34(1), pages 45-63.
- Almeida, Daniel de & Hotta, Luiz & Ruiz Ortega, Esther, 2015. "MGARCH models: tradeoff between feasibility and flexibility," DES - Working Papers. Statistics and Econometrics. WS ws1516, Universidad Carlos III de Madrid. Departamento de EstadÃstica.
- Audrino, Francesco, 2011.
"Forecasting correlations during the late-2000s financial crisis: short-run component, long-run component, and structural breaks,"
Economics Working Paper Series
1112, University of St. Gallen, School of Economics and Political Science.
- Audrino, Francesco, 2014. "Forecasting correlations during the late-2000s financial crisis: The short-run component, the long-run component, and structural breaks," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 43-60.
- Fulvio Corsi & Stefano Peluso & Francesco Audrino, 2015.
"Missing in Asynchronicity: A Kalman‐em Approach for Multivariate Realized Covariance Estimation,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(3), pages 377-397, April.
- Corsi, Fulvio & Peluso, Stefano & Audrino, Francesco, 2012. "Missing in Asynchronicity: A Kalman-EM Approach for Multivariate Realized Covariance Estimation," Economics Working Paper Series 1202, University of St. Gallen, School of Economics and Political Science.
- Chiranjit Dutta & Kara Karpman & Sumanta Basu & Nalini Ravishanker, 2023. "Review of Statistical Approaches for Modeling High-Frequency Trading Data," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 1-48, May.
- Fresoli, Diego Eduardo & Ruiz Ortega, Esther, 2014.
"The uncertainty of conditional returns, volatilities and correlations in DCC models,"
DES - Working Papers. Statistics and Econometrics. WS
ws140202, Universidad Carlos III de Madrid. Departamento de EstadÃstica.
- Fresoli, Diego E. & Ruiz, Esther, 2016. "The uncertainty of conditional returns, volatilities and correlations in DCC models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 170-185.
- Arnab Chakrabarti & Rituparna Sen, 2023. "Copula Estimation for Nonsynchronous Financial Data," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 116-149, May.
- Mike Buckle & Jing Chen & Julian Williams, 2014. "How Predictable Are Equity Covariance Matrices? Evidence from High‐Frequency Data for Four Markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(7), pages 542-557, November.
- Fulvio Corsi & Francesco Audrino, 2008.
"Modeling Tick-by-Tick Realized Correlations,"
University of St. Gallen Department of Economics working paper series 2008
2008-05, Department of Economics, University of St. Gallen.
- Audrino, Francesco & Corsi, Fulvio, 2010. "Modeling tick-by-tick realized correlations," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2372-2382, November.
Cited by:
- Aslanidis, Nektarios & Christiansen, Charlotte, 2012.
"Smooth transition patterns in the realized stock–bond correlation,"
Journal of Empirical Finance, Elsevier, vol. 19(4), pages 454-464.
- Aslanidis, Nektarios & Christiansen, Charlotte, 2011. "Smooth Transition Patterns in the Realized Stock- Bond Correlation," Working Papers 2072/152138, Universitat Rovira i Virgili, Department of Economics.
- Nektarios Aslanidis & Charlotte Christiansen, 2010. "Smooth Transition Patterns in the Realized Stock Bond Correlation," CREATES Research Papers 2010-15, Department of Economics and Business Economics, Aarhus University.
- Liu, Wei & Garrett, Ian, 2023. "Regime-dependent effects of macroeconomic uncertainty on realized volatility in the U.S. stock market," Economic Modelling, Elsevier, vol. 128(C).
- Gribisch, Bastian, 2013. "A latent dynamic factor approach to forecasting multivariate stock market volatility," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79823, Verein für Socialpolitik / German Economic Association.
- Denisa BANULESCU-RADU & Elena Ivona DUMITRESCU, 2019.
"Do High-frequency-based Measures Improve Conditional Covariance Forecasts?,"
LEO Working Papers / DR LEO
2709, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
- Elena Ivona Dumitrescu & Georgiana-Denisa Banulescu, 2019. "Do High-frequency-based Measures Improve Conditional Covariance Forecasts?," Post-Print hal-03331122, HAL.
- Paolo Gorgi & Siem Jan Koopman, 2020. "Beta observation-driven models with exogenous regressors: a joint analysis of realized correlation and leverage effects," Tinbergen Institute Discussion Papers 20-004/III, Tinbergen Institute.
- Roxana Halbleib & Valeri Voev, 2012.
"Forecasting Covariance Matrices: A Mixed Frequency Approach,"
Working Paper Series of the Department of Economics, University of Konstanz
2012-30, Department of Economics, University of Konstanz.
- Roxana Halbleib & Valeri Voev, 2011. "Forecasting Covariance Matrices: A Mixed Frequency Approach," CREATES Research Papers 2011-03, Department of Economics and Business Economics, Aarhus University.
- Roxana Halbleib & Valerie Voev, 2011. "Forecasting Covariance Matrices: A Mixed Frequency Approach," Working Papers ECARES ECARES 2011-002, ULB -- Universite Libre de Bruxelles.
- Philip Bertram & Robinson Kruse & Philipp Sibbertsen, 2013. "Fractional integration versus level shifts: the case of realized asset correlations," Statistical Papers, Springer, vol. 54(4), pages 977-991, November.
- Markopoulou, Chrysi E. & Skintzi, Vasiliki D. & Refenes, Apostolos-Paul N., 2016. "Realized hedge ratio: Predictability and hedging performance," International Review of Financial Analysis, Elsevier, vol. 45(C), pages 121-133.
- Harvey, A. & Palumbo, D., 2019. "Score-Driven Models for Realized Volatility," Cambridge Working Papers in Economics 1950, Faculty of Economics, University of Cambridge.
- Luo, Jiawen & Chen, Langnan, 2020. "Realized volatility forecast with the Bayesian random compressed multivariate HAR model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 781-799.
- Ostap Okhrin & Anastasija Tetereva, 2017. "The Realized Hierarchical Archimedean Copula in Risk Modelling," Econometrics, MDPI, vol. 5(2), pages 1-31, June.
- Tsunehiro Ishihara & Yasuhiro Omori & Manabu Asai, 2011.
"Matrix Exponential Stochastic Volatility with Cross Leverage,"
CIRJE F-Series
CIRJE-F-812, CIRJE, Faculty of Economics, University of Tokyo.
- Tsunehiro Ishihara & Yasuhiro Omori & Manabu Asai, 2014. "Matrix Exponential Stochastic Volatility with Cross Leverage," CIRJE F-Series CIRJE-F-938, CIRJE, Faculty of Economics, University of Tokyo.
- Ishihara, Tsunehiro & Omori, Yasuhiro & Asai, Manabu, 2016. "Matrix exponential stochastic volatility with cross leverage," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 331-350.
- Tsunehiro Ishihara & Yasuhiro Omori & Manabu Asai, 2013. "Matrix Exponential Stochastic Volatility with Cross Leverage," CIRJE F-Series CIRJE-F-904, CIRJE, Faculty of Economics, University of Tokyo.
- Tsunehiro Ishihara & Yasuhiro Omori & Manabu Asai, 2014. "Matrix Exponential Stochastic Volatility with Cross Leverage," CIRJE F-Series CIRJE-F-932, CIRJE, Faculty of Economics, University of Tokyo.
- Dark, Jonathan, 2018. "Multivariate models with long memory dependence in conditional correlation and volatility," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 162-180.
- Boudt, Kris & Cornelissen, Jonathan & Croux, Christophe, 2012. "Jump robust daily covariance estimation by disentangling variance and correlation components," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 2993-3005.
- Roxana Halbleib & Valeri Voev, 2016. "Forecasting Covariance Matrices: A Mixed Approach," Journal of Financial Econometrics, Oxford University Press, vol. 14(2), pages 383-417.
- Manabu Asai & Rangan Gupta & Michael McAleer, 2019.
"Forecasting Volatility and Co-volatility of Crude Oil and Gold Futures: Effects of Leverage, Jumps, Spillovers, and Geopolitical Risks,"
Working Papers
201951, University of Pretoria, Department of Economics.
- Asai, Manabu & Gupta, Rangan & McAleer, Michael, 2020. "Forecasting volatility and co-volatility of crude oil and gold futures: Effects of leverage, jumps, spillovers, and geopolitical risks," International Journal of Forecasting, Elsevier, vol. 36(3), pages 933-948.
- Kim Christensen & Mathias Siggaard & Bezirgen Veliyev, 2021. "A machine learning approach to volatility forecasting," CREATES Research Papers 2021-03, Department of Economics and Business Economics, Aarhus University.
- Fengler, Matthias R. & Okhrin, Ostap, 2016. "Managing risk with a realized copula parameter," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 131-152.
- Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 7(2), pages 174-196, Spring.
- Yaojie Zhang & Yu Wei & Li Liu, 2019. "Improving forecasting performance of realized covariance with extensions of HAR-RCOV model: statistical significance and economic value," Quantitative Finance, Taylor & Francis Journals, vol. 19(9), pages 1425-1438, September.
- Jiawen Luo & Langnan Chen, 2019. "Multivariate realized volatility forecasts of agricultural commodity futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(12), pages 1565-1586, December.
- Asgharian, Hossein & Christiansen, Charlotte & Hou, Ai Jun, 2023. "The effect of uncertainty on stock market volatility and correlation," Journal of Banking & Finance, Elsevier, vol. 154(C).
- Konstantinos Gkillas & Christoforos Konstantatos & Costas Siriopoulos, 2021. "Uncertainty Due to Infectious Diseases and Stock–Bond Correlation," Econometrics, MDPI, vol. 9(2), pages 1-18, April.
- Francesco Audrino & Marcelo C. Medeiros, 2008.
"Smooth Regimes, Macroeconomic Variables, and Bagging for the Short-Term Interest Rate Process,"
University of St. Gallen Department of Economics working paper series 2008
2008-16, Department of Economics, University of St. Gallen.
Cited by:
- Francesco Audrino & Kameliya Filipova, 2009. "Yield Curve Predictability, Regimes, and Macroeconomic Information: A Data-Driven Approach," University of St. Gallen Department of Economics working paper series 2009 2009-10, Department of Economics, University of St. Gallen.
- Francesco Audrino & Fabio Trojani, 2007.
"Accurate Short-Term Yield Curve Forecasting using Functional Gradient Descent,"
University of St. Gallen Department of Economics working paper series 2007
2007-24, Department of Economics, University of St. Gallen.
- Fabio Trojani, 2007. "Accurate Short-Term Yield Curve Forecasting using Functional Gradient Descent," Journal of Financial Econometrics, Oxford University Press, vol. 5(4), pages 591-623, Fall.
Cited by:
- Teresa Buchen & Klaus Wohlrabe, 2013.
"Assessing the Macroeconomic Forecasting Performance of Boosting - Evidence for the United States, the Euro Area, and Germany,"
CESifo Working Paper Series
4148, CESifo.
- Klaus Wohlrabe & Teresa Buchen, 2014. "Assessing the Macroeconomic Forecasting Performance of Boosting: Evidence for the United States, the Euro Area and Germany," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(4), pages 231-242, July.
- Teresa, Buchen & Wohlrabe, Klaus, 2014. "Assessing the Macroeconomic Forecasting Performance of Boosting: Evidence for the United States, the Euro Area, and Germany," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100626, Verein für Socialpolitik / German Economic Association.
- Francesco Audrino, 2012. "What Drives Short Rate Dynamics? A Functional Gradient Descent Approach," Computational Economics, Springer;Society for Computational Economics, vol. 39(3), pages 315-335, March.
- Audrino, Francesco, 2011.
"Forecasting correlations during the late-2000s financial crisis: short-run component, long-run component, and structural breaks,"
Economics Working Paper Series
1112, University of St. Gallen, School of Economics and Political Science.
- Audrino, Francesco, 2014. "Forecasting correlations during the late-2000s financial crisis: The short-run component, the long-run component, and structural breaks," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 43-60.
- Emrich, Eike & Pierdzioch, Christian, 2015. "Public goods, private consumption, and human-capital formation: On the economics of volunteer labour supply," Working Papers of the European Institute for Socioeconomics 14, European Institute for Socioeconomics (EIS), Saarbrücken.
- Emrich Eike & Pierdzioch Christian, 2016. "Public Goods, Private Consumption, and Human Capital: Using Boosted Regression Trees to Model Volunteer Labour Supply," Review of Economics, De Gruyter, vol. 67(3), pages 263-283, December.
- Fulvio Corsi & Francesco Audrino, 2007.
"Realized Correlation Tick-by-Tick,"
University of St. Gallen Department of Economics working paper series 2007
2007-02, Department of Economics, University of St. Gallen.
Cited by:
- Chiriac, Roxana & Voev, Valeri, 2008.
"Modelling and forecasting multivariate realized volatility,"
CoFE Discussion Papers
08/06, University of Konstanz, Center of Finance and Econometrics (CoFE).
- Roxana Chiriac & Valeri Voev, 2011. "Modelling and forecasting multivariate realized volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(6), pages 922-947, September.
- Roxana Chiriac & Valeri Voev, 2008. "Modelling and Forecasting Multivariate Realized Volatility," CREATES Research Papers 2008-39, Department of Economics and Business Economics, Aarhus University.
- Audrino, Francesco & Trojani, Fabio, 2011.
"A General Multivariate Threshold GARCH Model With Dynamic Conditional Correlations,"
Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 138-149.
- Francesco Audrino & Fabio Trojani, 2007. "A general multivariate threshold GARCH model with dynamic conditional correlations," University of St. Gallen Department of Economics working paper series 2007 2007-25, Department of Economics, University of St. Gallen.
- Fabio Trojani & Francesco Audrino, 2005. "A general multivariate threshold GARCH model with dynamic conditional correlations," University of St. Gallen Department of Economics working paper series 2005 2005-04, Department of Economics, University of St. Gallen.
- Francesco Audrino & Fabio Trojani, 2011. "A General Multivariate Threshold GARCH Model With Dynamic Conditional Correlations," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(1), pages 138-149, January.
- Nolte, Ingmar & Voev, Valeri, 2007.
"Estimating high-frequency based (co-) variances: A unified approach,"
CoFE Discussion Papers
07/07, University of Konstanz, Center of Finance and Econometrics (CoFE).
- Ingmar Nolte & Valeri Voev, 2008. "Estimating High-Frequency Based (Co-) Variances: A Unified Approach," CREATES Research Papers 2008-31, Department of Economics and Business Economics, Aarhus University.
- Christensen, Kim & Kinnebrock, Silja & Podolskij, Mark, 2010.
"Pre-averaging estimators of the ex-post covariance matrix in noisy diffusion models with non-synchronous data,"
Journal of Econometrics, Elsevier, vol. 159(1), pages 116-133, November.
- Kim Christensen & Silja Kinnebrock & Mark Podolskij, 2010. "Pre-averaging estimators of the ex-post covariance matrix in noisy diffusion models with non-synchronous data," Post-Print hal-00732537, HAL.
- Kim Christensen & Silja Kinnebrock & Mark Podolskij, 2009. "Pre-averaging estimators of the ex-post covariance matrix in noisy diffusion models with non-synchronous data," CREATES Research Papers 2009-45, Department of Economics and Business Economics, Aarhus University.
- Audrino, Francesco, 2011.
"Forecasting correlations during the late-2000s financial crisis: short-run component, long-run component, and structural breaks,"
Economics Working Paper Series
1112, University of St. Gallen, School of Economics and Political Science.
- Audrino, Francesco, 2014. "Forecasting correlations during the late-2000s financial crisis: The short-run component, the long-run component, and structural breaks," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 43-60.
- Vortelinos, Dimitrios I., 2010. "The properties of realized correlation: Evidence from the French, German and Greek equity markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 50(3), pages 273-290, August.
- Griffin, Jim E. & Oomen, Roel C.A., 2011. "Covariance measurement in the presence of non-synchronous trading and market microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 58-68, January.
- Fresoli, Diego Eduardo & Ruiz Ortega, Esther, 2014.
"The uncertainty of conditional returns, volatilities and correlations in DCC models,"
DES - Working Papers. Statistics and Econometrics. WS
ws140202, Universidad Carlos III de Madrid. Departamento de EstadÃstica.
- Fresoli, Diego E. & Ruiz, Esther, 2016. "The uncertainty of conditional returns, volatilities and correlations in DCC models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 170-185.
- William H. Press, 2023. "Optimal Cross-Correlation Estimates from Asynchronous Tick-by-Tick Trading Data," Papers 2303.16153, arXiv.org.
- Münnix, Michael C. & Schäfer, Rudi & Guhr, Thomas, 2010. "Compensating asynchrony effects in the calculation of financial correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(4), pages 767-779.
- Chiriac, Roxana & Voev, Valeri, 2008.
"Modelling and forecasting multivariate realized volatility,"
CoFE Discussion Papers
08/06, University of Konstanz, Center of Finance and Econometrics (CoFE).
- Francesco Audrino & Peter Bühlmann, 2007.
"Splines for Financial Volatility,"
University of St. Gallen Department of Economics working paper series 2007
2007-11, Department of Economics, University of St. Gallen.
- Francesco Audrino & Peter Bühlmann, 2009. "Splines for financial volatility," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(3), pages 655-670, June.
Cited by:
- Mittnik, Stefan & Robinzonov, Nikolay & Spindler, Martin, 2015. "Stock market volatility: Identifying major drivers and the nature of their impact," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 1-14.
- Yujiao Yang & Yuhang Xu & Qiongxia Song, 2012. "Spline confidence bands for variance functions in nonparametric time series regressive models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(3), pages 699-714.
- Cristina Amado & Annastiina Silvennoinen & Timo Ter¨asvirta, 2018.
"Models with Multiplicative Decomposition of Conditional Variances and Correlations,"
NIPE Working Papers
07/2018, NIPE - Universidade do Minho.
- Cristina Amado & Annastiina Silvennoinen & Timo Teräsvirta, 2018. "Models with Multiplicative Decomposition of Conditional Variances and Correlations," CREATES Research Papers 2018-14, Department of Economics and Business Economics, Aarhus University.
- Barrow, Devon K. & Crone, Sven F., 2016. "A comparison of AdaBoost algorithms for time series forecast combination," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1103-1119.
- Ozer Ozdemir & Memmedaga Memmedli & Akhlitdin Nizamitdinov, 2013. "ANN Models and Bayesian Spline Models for Analysis of Exchange Rates and Gold Price," International Econometric Review (IER), Econometric Research Association, vol. 5(2), pages 53-69, September.
- Wilson Ye Chen & Richard H. Gerlach, 2017. "Semiparametric GARCH via Bayesian model averaging," Papers 1708.07587, arXiv.org.
- Hiroyuki Kawakatsu, 2022. "Local projection variance impulse response," Empirical Economics, Springer, vol. 62(3), pages 1219-1244, March.
- Meister, Alexander & Kreiß, Jens-Peter, 2016. "Statistical inference for nonparametric GARCH models," Stochastic Processes and their Applications, Elsevier, vol. 126(10), pages 3009-3040.
- Souhaib Ben Taieb & Rob J Hyndman, 2014. "Boosting multi-step autoregressive forecasts," Monash Econometrics and Business Statistics Working Papers 13/14, Monash University, Department of Econometrics and Business Statistics.
- Hugh Christensen & Simon Godsill & Richard E Turner, 2020. "Hidden Markov Models Applied To Intraday Momentum Trading With Side Information," Papers 2006.08307, arXiv.org.
- Nikolay Robinzonov & Gerhard Tutz & Torsten Hothorn, 2012. "Boosting techniques for nonlinear time series models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(1), pages 99-122, January.
- Christan Francq & Jean-Michel Zakoian, 2012.
"Optimal Predictions of Powers of Conditionally Heteroskedastic Processes,"
Working Papers
2012-17, Center for Research in Economics and Statistics.
- Francq, Christian & Zakoian, Jean-Michel, 2010. "Optimal predictions of powers of conditionally heteroskedastic processes," MPRA Paper 22155, University Library of Munich, Germany.
- Christian Francq & Jean-Michel Zakoïan, 2013. "Optimal predictions of powers of conditionally heteroscedastic processes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(2), pages 345-367, March.
- VAN BELLEGEM, Sébastien, 2011. "Locally stationary volatility modelling," LIDAM Discussion Papers CORE 2011041, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Audrino, Francesco & Meier, Pirmin, 2012. "Empirical pricing kernel estimation using a functional gradient descent algorithm based on splines," Economics Working Paper Series 1210, University of St. Gallen, School of Economics and Political Science.
- Fabio Trojani & Francesco Audrino, 2005.
"A general multivariate threshold GARCH model with dynamic conditional correlations,"
University of St. Gallen Department of Economics working paper series 2005
2005-04, Department of Economics, University of St. Gallen.
- Audrino, Francesco & Trojani, Fabio, 2011. "A General Multivariate Threshold GARCH Model With Dynamic Conditional Correlations," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 138-149.
- Francesco Audrino & Fabio Trojani, 2011. "A General Multivariate Threshold GARCH Model With Dynamic Conditional Correlations," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(1), pages 138-149, January.
- Francesco Audrino & Fabio Trojani, 2007. "A general multivariate threshold GARCH model with dynamic conditional correlations," University of St. Gallen Department of Economics working paper series 2007 2007-25, Department of Economics, University of St. Gallen.
Cited by:
- ROMBOUTS, Jeroen V. K. & STENTOFT, Lars & VIOLANTE, Francesco, 2012.
"The value of multivariate model sophistication: an application to pricing Dow Jones Industrial Average options,"
LIDAM Discussion Papers CORE
2012003, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Jeroen Rombouts & Lars Stentoft & Francesco Violente, 2012. "The Value of Multivariate Model Sophistication: An Application to pricing Dow Jones Industrial Average Options," CIRANO Working Papers 2012s-05, CIRANO.
- Rombouts, Jeroen & Stentoft, Lars & Violante, Franceso, 2014. "The value of multivariate model sophistication: An application to pricing Dow Jones Industrial Average options," International Journal of Forecasting, Elsevier, vol. 30(1), pages 78-98.
- Jeroen V.K. Rombouts & Lars Stentoft & Francesco Violante, 2012. "The Value of Multivariate Model Sophistication: An Application to pricing Dow Jones Industrial Average options," CREATES Research Papers 2012-04, Department of Economics and Business Economics, Aarhus University.
- Fulvio Corsi & Francesco Audrino, 2012.
"Realized Covariance Tick-by-Tick in Presence of Rounded Time Stamps and General Microstructure Effects,"
Journal of Financial Econometrics, Oxford University Press, vol. 10(4), pages 591-616, September.
- Fulvio Corsi & Francesco Audrino, 2008. "Realized Covariance Tick-by-Tick in Presence of Rounded Time Stamps and General Microstructure Effects," University of St. Gallen Department of Economics working paper series 2008 2008-04, Department of Economics, University of St. Gallen.
- Audrino, Francesco & Corsi, Fulvio, 2010.
"Modeling tick-by-tick realized correlations,"
Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2372-2382, November.
- Fulvio Corsi & Francesco Audrino, 2008. "Modeling Tick-by-Tick Realized Correlations," University of St. Gallen Department of Economics working paper series 2008 2008-05, Department of Economics, University of St. Gallen.
- Chia-Lin Chang & Michael McAleer & Roengchai Tansuchat, 2009.
"Modelling Conditional Correlations for Risk Diversification in Crude Oil Markets,"
CIRJE F-Series
CIRJE-F-640, CIRJE, Faculty of Economics, University of Tokyo.
- Chia-Lin Chang & Michael McAleer & Roengchai Tansuchat, 2009. "Modelling Conditional Correlations for Risk Diversification in Crude Oil Markets," CARF F-Series CARF-F-162, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
- Chang, C-L. & McAleer, M.J. & Tansuchat, R., 2009. "Modelling conditional correlations for risk diversification in crude oil markets," Econometric Institute Research Papers EI 2009-11, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
- Paolo Gorgi & Siem Jan Koopman, 2020. "Beta observation-driven models with exogenous regressors: a joint analysis of realized correlation and leverage effects," Tinbergen Institute Discussion Papers 20-004/III, Tinbergen Institute.
- de Almeida, Daniel & Hotta, Luiz K. & Ruiz, Esther, 2018.
"MGARCH models: Trade-off between feasibility and flexibility,"
International Journal of Forecasting, Elsevier, vol. 34(1), pages 45-63.
- Almeida, Daniel de & Hotta, Luiz & Ruiz Ortega, Esther, 2015. "MGARCH models: tradeoff between feasibility and flexibility," DES - Working Papers. Statistics and Econometrics. WS ws1516, Universidad Carlos III de Madrid. Departamento de EstadÃstica.
- Kang‐Soek Lee & Richard A. Werner, 2023. "Are lower interest rates really associated with higher growth? New empirical evidence on the interest rate thesis from 19 countries," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 3960-3975, October.
- So, Mike K.P. & Chan, Thomas W.C. & Chu, Amanda M.Y., 2022. "Efficient estimation of high-dimensional dynamic covariance by risk factor mapping: Applications for financial risk management," Journal of Econometrics, Elsevier, vol. 227(1), pages 151-167.
- Rezitis Anthony N & Stavropoulos Konstantinos S, 2011. "Price Transmission and Volatility in the Greek Broiler Sector: A Threshold Cointegration Analysis," Journal of Agricultural & Food Industrial Organization, De Gruyter, vol. 9(1), pages 1-37, July.
- Bollerslev, Tim & Patton, Andrew J. & Quaedvlieg, Rogier, 2020. "Multivariate leverage effects and realized semicovariance GARCH models," Journal of Econometrics, Elsevier, vol. 217(2), pages 411-430.
- Audrino, Francesco, 2011.
"Forecasting correlations during the late-2000s financial crisis: short-run component, long-run component, and structural breaks,"
Economics Working Paper Series
1112, University of St. Gallen, School of Economics and Political Science.
- Audrino, Francesco, 2014. "Forecasting correlations during the late-2000s financial crisis: The short-run component, the long-run component, and structural breaks," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 43-60.
- Jian, Zhihong & Deng, Pingjun & Zhu, Zhican, 2018. "High-dimensional covariance forecasting based on principal component analysis of high-frequency data," Economic Modelling, Elsevier, vol. 75(C), pages 422-431.
- Boudt, Kris & Daníelsson, Jón & Laurent, Sébastien, 2013. "Robust forecasting of dynamic conditional correlation GARCH models," International Journal of Forecasting, Elsevier, vol. 29(2), pages 244-257.
- Stefano Peluso & Fulvio Corsi & Antonietta Mira, 2015. "A Bayesian High-Frequency Estimator of the Multivariate Covariance of Noisy and Asynchronous Returns," Journal of Financial Econometrics, Oxford University Press, vol. 13(3), pages 665-697.
- Kuruppuarachchi, Duminda & Premachandra, I.M., 2016. "Information spillover dynamics of the energy futures market sector: A novel common factor approach," Energy Economics, Elsevier, vol. 57(C), pages 277-294.
- Li, Johnny Siu-Hang & Ng, Andrew C.Y. & Chan, Wai-Sum, 2015. "Managing financial risk in Chinese stock markets: Option pricing and modeling under a multivariate threshold autoregression," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 217-230.
- Sarantis Tsiaplias & Chew Lian Chua, 2013. "A Multivariate GARCH Model Incorporating the Direct and Indirect Transmission of Shocks," Econometric Reviews, Taylor & Francis Journals, vol. 32(2), pages 244-271, February.
- Francesco Audrino & Enrico De Giorgi, "undated".
"Beta Regimes for the Yield Curve,"
IEW - Working Papers
244, Institute for Empirical Research in Economics - University of Zurich.
- Francesco Audrino & Enrico De Giorgi, 0. "Beta Regimes for the Yield Curve," Journal of Financial Econometrics, Oxford University Press, vol. 5(3), pages 456-490.
Cited by:
- Wolfgang Lemke & Theofanis Archontakis, 2008.
"Bond pricing when the short-term interest rate follows a threshold process,"
Quantitative Finance, Taylor & Francis Journals, vol. 8(8), pages 811-822.
- Lemke, Wolfgang & Archontakis, Theofanis, 2006. "Bond pricing when the short term interest rate follows a threshold process," Discussion Paper Series 1: Economic Studies 2006,06, Deutsche Bundesbank.
- Francesco Audrino, 2012. "What Drives Short Rate Dynamics? A Functional Gradient Descent Approach," Computational Economics, Springer;Society for Computational Economics, vol. 39(3), pages 315-335, March.
- Tania Singer & Ernst Fehr, 2005.
"The Neuroeconomics of Mind Reading and Empathy,"
American Economic Review, American Economic Association, vol. 95(2), pages 340-345, May.
- Fehr, Ernst & ,, 2005. "The Neuroconomics of Mind Reading and Empathy," CEPR Discussion Papers 5128, C.E.P.R. Discussion Papers.
- Singer, Tania & Fehr, Ernst, 2005. "The Neuroeconomics of Mind Reading and Empathy," IZA Discussion Papers 1647, Institute of Labor Economics (IZA).
- Francesco Audrino & Kameliya Filipova, 2009. "Yield Curve Predictability, Regimes, and Macroeconomic Information: A Data-Driven Approach," University of St. Gallen Department of Economics working paper series 2009 2009-10, Department of Economics, University of St. Gallen.
- Fehr, Ernst & Falk, Armin & Zehnder, Christian, 2005.
"The Behavioural Effects of Minimum Wages,"
CEPR Discussion Papers
5115, C.E.P.R. Discussion Papers.
- Falk, Armin & Fehr, Ernst & Zehnder, Christian, 2005. "The Behavioral Effects of Minimum Wages," IZA Discussion Papers 1625, Institute of Labor Economics (IZA).
- Armin Falk & Ernst Fehr & Christian Zehnder, "undated". "The Behavioral Effects of Minimum Wages," IEW - Working Papers 247, Institute for Empirical Research in Economics - University of Zurich.
- Francesco Audrino & Marcelo C. Medeiros, 2011.
"Modeling and forecasting short‐term interest rates: The benefits of smooth regimes, macroeconomic variables, and bagging,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(6), pages 999-1022, September.
- Francesco Audrino & Marcelo Cunha Medeiros, 2010. "Modeling and Forecasting Short-term Interest Rates: The Benefits of Smooth Regimes, Macroeconomic Variables, and Bagging," Textos para discussão 570, Department of Economics PUC-Rio (Brazil).
- Fehr, Ernst & Fischbacher, Urs & Kosfeld, Michael, 2005.
"Neuroeconomic Foundation of Trust and Social Preferences,"
CEPR Discussion Papers
5127, C.E.P.R. Discussion Papers.
- Fehr, Ernst & Fischbacher, Urs & Kosfeld, Michael, 2005. "Neuroeconomic Foundations of Trust and Social Preferences," IZA Discussion Papers 1641, Institute of Labor Economics (IZA).
- Francesco Audrino & Marcelo C. Medeiros, 2008. "Smooth Regimes, Macroeconomic Variables, and Bagging for the Short-Term Interest Rate Process," University of St. Gallen Department of Economics working paper series 2008 2008-16, Department of Economics, University of St. Gallen.
- Jaramillo, Laura & Weber, Anke, 2013.
"Bond yields in emerging economies: It matters what state you are in,"
Emerging Markets Review, Elsevier, vol. 17(C), pages 169-185.
- Laura Jaramillo & Miss Anke Weber, 2012. "Bond Yields in Emerging Economies: It Matters What State You Are In," IMF Working Papers 2012/198, International Monetary Fund.
- Theofanis Archontakis & Wolfgang Lemke, 2008.
"Threshold Dynamics of Short‐term Interest Rates: Empirical Evidence and Implications for the Term Structure,"
Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 37(1), pages 75-117, February.
- Archontakis, Theofanis & Lemke, Wolfgang, 2007. "Threshold dynmamics of short-term interest rates: empirical evidence and implications for the term structure," Discussion Paper Series 1: Economic Studies 2007,02, Deutsche Bundesbank.
- Filipova, Kameliya & Audrino, Francesco & De Giorgi, Enrico, 2014. "Monetary policy regimes: Implications for the yield curve and bond pricing," Journal of Financial Economics, Elsevier, vol. 113(3), pages 427-454.
Articles
- Ballinari, Daniele & Audrino, Francesco & Sigrist, Fabio, 2022.
"When does attention matter? The effect of investor attention on stock market volatility around news releases,"
International Review of Financial Analysis, Elsevier, vol. 82(C).
Cited by:
- Lyócsa, Štefan & Halousková, Martina & Haugom, Erik, 2023. "The US banking crisis in 2023: Intraday attention and price variation of banks at risk," Finance Research Letters, Elsevier, vol. 57(C).
- Audrino, Francesco & Sigrist, Fabio & Ballinari, Daniele, 2020.
"The impact of sentiment and attention measures on stock market volatility,"
International Journal of Forecasting, Elsevier, vol. 36(2), pages 334-357.
Cited by:
- Jaydip Sen & Subhasis Dasgupta, 2023. "Portfolio Optimization: A Comparative Study," Papers 2307.05048, arXiv.org.
- Martina Halouskov'a & Daniel Stav{s}ek & Mat'uv{s} Horv'ath, 2022. "The role of investor attention in global asset price variation during the invasion of Ukraine," Papers 2205.05985, arXiv.org, revised Aug 2022.
- Ballinari, Daniele & Audrino, Francesco & Sigrist, Fabio, 2022. "When does attention matter? The effect of investor attention on stock market volatility around news releases," International Review of Financial Analysis, Elsevier, vol. 82(C).
- Ding, Hui & Huang, Yisu & Wang, Jiqian, 2023. "Have the predictability of oil changed during the COVID-19 pandemic: Evidence from international stock markets," International Review of Financial Analysis, Elsevier, vol. 87(C).
- Anastasiou, Dimitris & Ballis, Antonis & Drakos, Konstantinos, 2022. "Constructing a positive sentiment index for COVID-19: Evidence from G20 stock markets," International Review of Financial Analysis, Elsevier, vol. 81(C).
- Zhu, Haibin & Bai, Lu & He, Lidan & Liu, Zhi, 2023. "Forecasting realized volatility with machine learning: Panel data perspective," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 251-271.
- Alin Marius Andries & Steven Ongena & Nicu Sprincean & Radu Tunaru, 2020.
"Risk Spillovers and Interconnectedness between Systemically Important Institutions,"
Swiss Finance Institute Research Paper Series
20-40, Swiss Finance Institute.
- Andrieş, Alin Marius & Ongena, Steven & Sprincean, Nicu & Tunaru, Radu, 2022. "Risk spillovers and interconnectedness between systemically important institutions," Journal of Financial Stability, Elsevier, vol. 58(C).
- Chun, Dohyun & Cho, Hoon & Ryu, Doojin, 2023. "Discovering the drivers of stock market volatility in a data-rich world," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 82(C).
- Qingjie Zhou & Panpan Zhu & Yinpeng Zhang, 2023. "Contagion Spillover from Bitcoin to Carbon Futures Pricing: Perspective from Investor Attention," Energies, MDPI, vol. 16(2), pages 1-22, January.
- Doris Chenguang Wu & Shiteng Zhong & Richard T R Qiu & Ji Wu, 2022. "Are customer reviews just reviews? Hotel forecasting using sentiment analysis," Tourism Economics, , vol. 28(3), pages 795-816, May.
- Wilms, Ines & Rombouts, Jeroen & Croux, Christophe, 2021. "Multivariate volatility forecasts for stock market indices," International Journal of Forecasting, Elsevier, vol. 37(2), pages 484-499.
- Tim Matthies & Thomas Lohden & Stephan Leible & Jun-Patrick Raabe, 2023. "To the Moon: Analyzing Collective Trading Events on the Wings of Sentiment Analysis," Papers 2308.09968, arXiv.org.
- Zhang, Zhikai & He, Mengxi & Zhang, Yaojie & Wang, Yudong, 2021. "Realized skewness and the short-term predictability for aggregate stock market volatility," Economic Modelling, Elsevier, vol. 103(C).
- Liang, Chao & Tang, Linchun & Li, Yan & Wei, Yu, 2020. "Which sentiment index is more informative to forecast stock market volatility? Evidence from China," International Review of Financial Analysis, Elsevier, vol. 71(C).
- Gong, Xue & Zhang, Weiguo & Wang, Junbo & Wang, Chao, 2022. "Investor sentiment and stock volatility: New evidence," International Review of Financial Analysis, Elsevier, vol. 80(C).
- Mathieu Rosenbaum & Jianfei Zhang, 2022. "On the universality of the volatility formation process: when machine learning and rough volatility agree," Papers 2206.14114, arXiv.org.
- Wenting Liu & Zhaozhong Gui & Guilin Jiang & Lihua Tang & Lichun Zhou & Wan Leng & Xulong Zhang & Yujiang Liu, 2023. "Stock Volatility Prediction Based on Transformer Model Using Mixed-Frequency Data," Papers 2309.16196, arXiv.org.
- Roland Füss & Massimo Guidolin & Christian Koeppel, 2019. "Sentiment Risk Premia In The Cross-Section of Global Equity," Working Papers on Finance 1913, University of St. Gallen, School of Finance, revised May 2020.
- Wei Zhang & Kai Yan & Dehua Shen, 2021. "Can the Baidu Index predict realized volatility in the Chinese stock market?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-31, December.
- Huang, Yisu & Xu, Weiju & Huang, Dengshi & Zhao, Chenchen, 2023. "Chinese crude oil futures volatility and sustainability: An uncertainty indices perspective," Resources Policy, Elsevier, vol. 80(C).
- Vladimir Pyrlik & Pavel Elizarov & Aleksandra Leonova, 2021. "Forecasting Realized Volatility Using Machine Learning and Mixed-Frequency Data (the Case of the Russian Stock Market)," CERGE-EI Working Papers wp713, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
- Gaoshan Wang & Guangjin Yu & Xiaohong Shen, 2020. "The Effect of Online Investor Sentiment on Stock Movements: An LSTM Approach," Complexity, Hindawi, vol. 2020, pages 1-11, December.
- Weiguo Zhang & Xue Gong & Chao Wang & Xin Ye, 2021. "Predicting stock market volatility based on textual sentiment: A nonlinear analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1479-1500, December.
- Mikhaylov, Dmitry, 2023. "Macroeconomic Forecasting with the Use of News Data," Working Papers w20220250, Russian Presidential Academy of National Economy and Public Administration.
- Filip, Angela Maria & Pochea, Maria Miruna, 2023. "Intentional and spurious herding behavior: A sentiment driven analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 38(C).
- Lyócsa, Štefan & Baumöhl, Eduard & Výrost, Tomáš & Molnár, Peter, 2020.
"Fear of the coronavirus and the stock markets,"
EconStor Preprints
219336, ZBW - Leibniz Information Centre for Economics.
- Lyócsa, Štefan & Baumöhl, Eduard & Výrost, Tomáš & Molnár, Peter, 2020. "Fear of the coronavirus and the stock markets," Finance Research Letters, Elsevier, vol. 36(C).
- Wang, Gaoshan & Yu, Guangjin & Shen, Xiaohong, 2021. "The effect of online environmental news on green industry stocks: The mediating role of investor sentiment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
- Liu, Yuanyuan & Niu, Zibo & Suleman, Muhammad Tahir & Yin, Libo & Zhang, Hongwei, 2022. "Forecasting the volatility of crude oil futures: The role of oil investor attention and its regime switching characteristics under a high-frequency framework," Energy, Elsevier, vol. 238(PA).
- Bickley, Steve J. & Brumpton, Martin & Chan, Ho Fai & Colthurst, Richard & Torgler, Benno, 2021. "The stabilizing effect of social distancing: Cross-country differences in financial market response to COVID-19 pandemic policies," Research in International Business and Finance, Elsevier, vol. 58(C).
- Wang, Ping & Han, Wei & Huang, Chengcheng & Duong, Duy, 2022. "Forecasting realised volatility from search volume and overnight sentiment: Evidence from China," Research in International Business and Finance, Elsevier, vol. 62(C).
- Daniele Ballinari & Simon Behrendt, 2021. "How to gauge investor behavior? A comparison of online investor sentiment measures," Digital Finance, Springer, vol. 3(2), pages 169-204, June.
- Yongan Xu & Jianqiong Wang & Zhonglu Chen & Chao Liang, 2023. "Sentiment indices and stock returns: Evidence from China," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 1063-1080, January.
- Guglielmo Maria Caporale & Kyriacos Kyriacou & Nicola Spagnolo, 2023.
"Aggregate Insider Trading and Stock Market Volatility in the UK,"
CESifo Working Paper Series
10511, CESifo.
- Caporale, Guglielmo Maria & Kyriacou, Kyriacos & Spagnolo, Nicola, 2023. "Aggregate insider trading and stock market volatility in the UK," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 89(C).
- v{S}tefan Ly'ocsa & Tom'av{s} Pl'ihal, 2022. "Russia's Ruble during the onset of the Russian invasion of Ukraine in early 2022: The role of implied volatility and attention," Papers 2205.09179, arXiv.org.
- Yan Li & Weiping Li, 2021. "Empirical Analysis of MSCI China A-Shares," JRFM, MDPI, vol. 14(11), pages 1-25, October.
- Bai, Xiwen & Lam, Jasmine Siu Lee & Jakher, Astha, 2021. "Shipping sentiment and the dry bulk shipping freight market: New evidence from newspaper coverage," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 155(C).
- Luo, Qin & Bu, Jinfeng & Xu, Weiju & Huang, Dengshi, 2023. "Stock market volatility prediction: Evidence from a new bagging model," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 445-456.
- Wang, Hua & Xu, Liao & Sharma, Susan Sunila, 2021. "Does investor attention increase stock market volatility during the COVID-19 pandemic?," Pacific-Basin Finance Journal, Elsevier, vol. 69(C).
- Aromi, J. Daniel & Clements, Adam, 2021. "Facial expressions and the business cycle," Economic Modelling, Elsevier, vol. 102(C).
- Lansing, Kevin J. & LeRoy, Stephen F. & Ma, Jun, 2022.
"Examining the sources of excess return predictability: Stochastic volatility or market inefficiency?,"
Journal of Economic Behavior & Organization, Elsevier, vol. 197(C), pages 50-72.
- Kevin J. Lansing & Stephen F. LeRoy & Jun Ma, 2022. "Examining the Sources of Excess Return Predictability: Stochastic Volatility or Market Inefficiency?," Working Paper Series 2018-14, Federal Reserve Bank of San Francisco.
- Kim Christensen & Mathias Siggaard & Bezirgen Veliyev, 2021. "A machine learning approach to volatility forecasting," CREATES Research Papers 2021-03, Department of Economics and Business Economics, Aarhus University.
- Zhu, Xuehong & Niu, Zibo & Zhang, Hongwei & Huang, Jiaxin & Zuo, Xuguang, 2022. "Can gold and bitcoin hedge against the COVID-19 related news sentiment risk? New evidence from a NARDL approach," Resources Policy, Elsevier, vol. 79(C).
- Ballinari, Daniele & Behrendt, Simon, 2020. "Structural breaks in online investor sentiment: A note on the nonstationarity of financial chatter," Finance Research Letters, Elsevier, vol. 35(C).
- Halousková, Martina & Stašek, Daniel & Horváth, Matúš, 2022. "The role of investor attention in global asset price variation during the invasion of Ukraine," Finance Research Letters, Elsevier, vol. 50(C).
- Yang Gao & Chengjie Zhao & Bianxia Sun & Wandi Zhao, 2022. "Effects of investor sentiment on stock volatility: new evidences from multi-source data in China’s green stock markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-30, December.
- Fernando Díaz & Pablo A Henríquez, 2021. "Social sentiment segregation: Evidence from Twitter and Google Trends in Chile during the COVID-19 dynamic quarantine strategy," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-29, July.
- Lyócsa, Štefan & Plíhal, Tomáš, 2022. "Russia’s ruble during the onset of the Russian invasion of Ukraine in early 2022: The role of implied volatility and attention," Finance Research Letters, Elsevier, vol. 48(C).
- Naimoli, Antonio, 2022. "The information content of sentiment indices for forecasting Value at Risk and Expected Shortfall in equity markets," MPRA Paper 112588, University Library of Munich, Germany.
- Xiaohong Shen & Gaoshan Wang & Yue Wang & Alfred Peris, 2021. "The Influence of Research Reports on Stock Returns: The Mediating Effect of Machine-Learning-Based Investor Sentiment," Discrete Dynamics in Nature and Society, Hindawi, vol. 2021, pages 1-14, December.
- Lyócsa, Štefan & Baumöhl, Eduard & Výrost, Tomáš, 2022.
"YOLO trading: Riding with the herd during the GameStop episode,"
Finance Research Letters, Elsevier, vol. 46(PA).
- Lyócsa, Štefan & Baumöhl, Eduard & Vŷrost, Tomáš, 2021. "YOLO trading: Riding with the herd during the GameStop episode," EconStor Preprints 230679, ZBW - Leibniz Information Centre for Economics.
- Roland Fuess & Massimo Guidolin & Christian Koeppel, 2019. "Sentiment Risk Premia in the Cross-Section of Global Equity and Currency Returns," BAFFI CAREFIN Working Papers 19116, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
- Ruzita Abdul-Rahim & Airil Khalid & Zulkefly Abdul Karim & Mamunur Rashid, 2022. "Exploring the Driving Forces of Stock-Cryptocurrency Comovements during COVID-19 Pandemic: An Analysis Using Wavelet Coherence and Seemingly Unrelated Regression," Mathematics, MDPI, vol. 10(12), pages 1-19, June.
- Tseng‐Chan Tseng & Hung‐Cheng Lai & Jih‐Kuang Chen, 2022. "Impacts of relatively rational and irrational investor sentiment on realized volatility," Asian Economic Journal, East Asian Economic Association, vol. 36(4), pages 458-478, December.
- Adam Clements & Yin Liao & Yusui Tang, 2022. "Moving beyond Volatility Index (VIX): HARnessing the term structure of implied volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 86-99, January.
- Lyócsa, Štefan & Halousková, Martina & Haugom, Erik, 2023. "The US banking crisis in 2023: Intraday attention and price variation of banks at risk," Finance Research Letters, Elsevier, vol. 57(C).
- Ying Wang & Hongwei Zhang & Wang Gao & Cai Yang, 2023. "Spillover effects from news to travel and leisure stocks during the COVID-19 pandemic: Evidence from the time and frequency domains," Tourism Economics, , vol. 29(2), pages 460-487, March.
- Ma, Feng & Wang, Jiqian & Wahab, M.I.M. & Ma, Yuanhui, 2023. "Stock market volatility predictability in a data-rich world: A new insight," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1804-1819.
- Michele Costola & Michael Donadelli & Luca Gerotto & Ivan Gufler, 2022. "Global risks, the macroeconomy, and asset prices," Empirical Economics, Springer, vol. 63(5), pages 2357-2388, November.
- Izzeldin, Marwan & Muradoğlu, Yaz Gülnur & Pappas, Vasileios & Sivaprasad, Sheeja, 2021. "The impact of Covid-19 on G7 stock markets volatility: Evidence from a ST-HAR model," International Review of Financial Analysis, Elsevier, vol. 74(C).
- Jaydip Sen & Abhishek Dutta & Sidra Mehtab, 2021. "Stock Portfolio Optimization Using a Deep Learning LSTM Model," Papers 2111.04709, arXiv.org.
- Jung, Sang Hoon & Jeong, Yong Jin, 2021. "Examining stock markets and societal mood using Internet memes," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
- Hussain, Shahzad & Akbar, Muhammad & Malik, Qaisar & Ahmad, Tanveer & Abbas, Nasir, 2021. "Downside Systematic Risk in Pakistani Stock Market: Role of Corporate Governance, Financial Liberalization and Investor Sentiment," CAFE Working Papers 14, Centre for Accountancy, Finance and Economics (CAFE), Birmingham City Business School, Birmingham City University.
- Chu, Xiaojun & Wan, Xinmin & Qiu, Jianying, 2023. "The relative importance of overnight sentiment versus trading-hour sentiment in volatility forecasting," Journal of Behavioral and Experimental Finance, Elsevier, vol. 39(C).
- Herrera, Gabriel Paes & Constantino, Michel & Su, Jen-Je & Naranpanawa, Athula, 2022. "Renewable energy stocks forecast using Twitter investor sentiment and deep learning," Energy Economics, Elsevier, vol. 114(C).
- Audrino, Francesco & Tetereva, Anastasija, 2019.
"Sentiment spillover effects for US and European companies,"
Journal of Banking & Finance, Elsevier, vol. 106(C), pages 542-567.
Cited by:
- Das, Prashant & Füss, Roland & Hanle, Benjamin & Russ, Isabel Nina, 2020. "The cross-over effect of irrational sentiments in housing, commercial property, and stock markets," Journal of Banking & Finance, Elsevier, vol. 114(C).
- Samitas, Aristeidis & Kampouris, Elias & Polyzos, Stathis, 2022. "Covid-19 pandemic and spillover effects in stock markets: A financial network approach," International Review of Financial Analysis, Elsevier, vol. 80(C).
- Huynh, Toan Luu Duc & Foglia, Matteo & Nasir, Muhammad Ali & Angelini, Eliana, 2021. "Feverish sentiment and global equity markets during the COVID-19 pandemic," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 1088-1108.
- Parhizgari, A.M. & Padungsaksawasdi, Chaiyuth, 2021. "Global equity market leadership positions through implied volatility measures," Journal of Empirical Finance, Elsevier, vol. 61(C), pages 180-205.
- Niţoi, Mihai & Pochea, Maria Miruna, 2022. "The nexus between bank connectedness and investors’ sentiment," Finance Research Letters, Elsevier, vol. 44(C).
- Pedro Manuel Nogueira Reis & Carlos Pinho, 2021. "A Reappraisal of the Causal Relationship between Sentiment Proxies and Stock Returns," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 22(4), pages 420-442, October.
- Roland Füss & Massimo Guidolin & Christian Koeppel, 2019. "Sentiment Risk Premia In The Cross-Section of Global Equity," Working Papers on Finance 1913, University of St. Gallen, School of Finance, revised May 2020.
- Alexander Koch & Toan Luu Duc Huynh & Mei Wang, 2024. "News sentiment and international equity markets during BREXIT period: A textual and connectedness analysis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(1), pages 5-34, January.
- Caporale, Guglielmo Maria & Menla Ali, Faek & Spagnolo, Fabio & Spagnolo, Nicola, 2022.
"Cross-border portfolio flows and news media coverage,"
Journal of International Money and Finance, Elsevier, vol. 126(C).
- Guglielmo Maria Caporale & Faek Menla Ali & Fabio Spagnolo & Nicola Spagnolo, 2020. "Cross-Border Portfolio Flows and News Media Coverage," CESifo Working Paper Series 8112, CESifo.
- Aysan, Ahmet Faruk & Polat, Ali Yavuz & Tekin, Hasan & Tunali, Ahmet Semih, 2021.
"Bitcoin-specific fear sentiment and bitcoin returns in the COVID-19 outbreak,"
MPRA Paper
110013, University Library of Munich, Germany.
- Ahmet Faruk Aysan & Ali Yavuz Polat & Hasan Tekin & Ahmet Semih Tunali, 2021. "Bitcoin-specific fear sentiment and bitcoin returns in the COVID-19 outbreak," Working Papers hal-03354930, HAL.
- Chernozhukov, Victor & Härdle, Wolfgang Karl & Huang, Chen & Wang, Weining, 2018.
"LASSO-Driven Inference in Time and Space,"
IRTG 1792 Discussion Papers
2018-021, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
- Victor Chernozhukov & Wolfgang Härdle & Chen Huang & Weining Wang, 2019. "LASSO-Driven Inference in Time and Space," CeMMAP working papers CWP20/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Chernozhukov, V. & Härdle, W.K. & Huang, C. & Wang, W., 2018. "LASSO-Driven Inference in Time and Space," Working Papers 18/04, Department of Economics, City University London.
- Victor Chernozhukov & Wolfgang K. Hardle & Chen Huang & Weining Wang, 2018. "LASSO-Driven Inference in Time and Space," Papers 1806.05081, arXiv.org, revised May 2020.
- Victor Chernozhukov & Wolfgang Härdle & Chen Huang & Weining Wang, 2018. "LASSO-driven inference in time and space," CeMMAP working papers CWP36/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Ballinari, Daniele & Behrendt, Simon, 2020. "Structural breaks in online investor sentiment: A note on the nonstationarity of financial chatter," Finance Research Letters, Elsevier, vol. 35(C).
- Wang, Wenzhao & Su, Chen & Duxbury, Darren, 2022. "The conditional impact of investor sentiment in global stock markets: A two-channel examination," Journal of Banking & Finance, Elsevier, vol. 138(C).
- Caporin, Massimiliano & Poli, Francesco, 2022. "News and intraday jumps: Evidence from regularization and class imbalance," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
- Roland Fuess & Massimo Guidolin & Christian Koeppel, 2019. "Sentiment Risk Premia in the Cross-Section of Global Equity and Currency Returns," BAFFI CAREFIN Working Papers 19116, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
- Alomari, Mohammad & Al Rababa’a, Abdel Razzaq & El-Nader, Ghaith & Alkhataybeh, Ahmad & Ur Rehman, Mobeen, 2021. "Examining the effects of news and media sentiments on volatility and correlation: Evidence from the UK," The Quarterly Review of Economics and Finance, Elsevier, vol. 82(C), pages 280-297.
- Danilo Vassallo & Giacomo Bormetti & Fabrizio Lillo, 2019. "A tale of two sentiment scales: Disentangling short-run and long-run components in multivariate sentiment dynamics," Papers 1910.01407, arXiv.org, revised Sep 2020.
- Audrino Francesco & Huang Chen & Okhrin Ostap, 2019.
"Flexible HAR model for realized volatility,"
Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 23(3), pages 1-22, June.
Cited by:
- Mei, Dexiang & Ma, Feng & Liao, Yin & Wang, Lu, 2020. "Geopolitical risk uncertainty and oil future volatility: Evidence from MIDAS models," Energy Economics, Elsevier, vol. 86(C).
- Naimoli, Antonio, 2022. "Modelling the persistence of Covid-19 positivity rate in Italy," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).
- Chun, Dohyun & Cho, Hoon & Ryu, Doojin, 2023. "Discovering the drivers of stock market volatility in a data-rich world," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 82(C).
- Zhifeng Dai & Tingyu Li & Mi Yang, 2022. "Forecasting stock return volatility: The role of shrinkage approaches in a data‐rich environment," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 980-996, August.
- Golosnoy, Vasyl & Gribisch, Bastian, 2022. "Modeling and forecasting realized portfolio weights," Journal of Banking & Finance, Elsevier, vol. 138(C).
- Ding, Yi & Kambouroudis, Dimos & McMillan, David G., 2021. "Forecasting realised volatility: Does the LASSO approach outperform HAR?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
- Tian Xie, 2019. "Forecast Bitcoin Volatility with Least Squares Model Averaging," Econometrics, MDPI, vol. 7(3), pages 1-20, September.
- Verena Monschang & Bernd Wilfling, 2022. "A procedure for upgrading linear-convex combination forecasts with an application to volatility prediction," CQE Working Papers 9722, Center for Quantitative Economics (CQE), University of Muenster.
- Won-Tak Hong & Jiwon Lee & Eunju Hwang, 2020. "A Note on the Asymptotic Normality Theory of the Least Squares Estimates in Multivariate HAR-RV Models," Mathematics, MDPI, vol. 8(11), pages 1-18, November.
- Audrino, Francesco & Kostrov, Alexander & Ortega, Juan-Pablo, 2019.
"Predicting U.S. Bank Failures with MIDAS Logit Models,"
Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 54(6), pages 2575-2603, December.
Cited by:
- Jiang, Cuixia & Xiong, Wei & Xu, Qifa & Liu, Yezheng, 2021. "Predicting default of listed companies in mainland China via U-MIDAS Logit model with group lasso penalty," Finance Research Letters, Elsevier, vol. 38(C).
- Patel, Ajay & Sorokina, Nonna & Thornton, John H., 2022. "Liquidity and bank capital structure," Journal of Financial Stability, Elsevier, vol. 62(C).
- Lee, Kangbok & Joo, Sunghoon & Baik, Hyeoncheol & Han, Sumin & In, Joonhwan, 2020. "Unbalanced data, type II error, and nonlinearity in predicting M&A failure," Journal of Business Research, Elsevier, vol. 109(C), pages 271-287.
- Zhou, Ying & Shen, Long & Ballester, Laura, 2023. "A two-stage credit scoring model based on random forest: Evidence from Chinese small firms," International Review of Financial Analysis, Elsevier, vol. 89(C).
- Kristóf, Tamás & Virág, Miklós, 2022. "EU-27 bank failure prediction with C5.0 decision trees and deep learning neural networks," Research in International Business and Finance, Elsevier, vol. 61(C).
- Audrino Francesco, 2018.
"Do match officials give preferential treatment to the strongest football teams? An analysis of four top European clubs,"
Journal of Quantitative Analysis in Sports, De Gruyter, vol. 14(4), pages 185-199, December.
Cited by:
- Carlos Alberto Belchior, 2020. "Fans and Match Results: Evidence From a Natural Experiment in Brazil," Journal of Sports Economics, , vol. 21(7), pages 663-687, October.
- Francesco Audrino & Lorenzo Camponovo, 2018.
"Oracle Properties, Bias Correction, and Bootstrap Inference for Adaptive Lasso for Time Series M†Estimators,"
Journal of Time Series Analysis, Wiley Blackwell, vol. 39(2), pages 111-128, March.
Cited by:
- Caporin, Massimiliano & Poli, Francesco, 2022. "News and intraday jumps: Evidence from regularization and class imbalance," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
- Audrino, Francesco & Tetereva, Anastasija, 2019. "Sentiment spillover effects for US and European companies," Journal of Banking & Finance, Elsevier, vol. 106(C), pages 542-567.
- Audrino, Francesco & Sigrist, Fabio & Ballinari, Daniele, 2020. "The impact of sentiment and attention measures on stock market volatility," International Journal of Forecasting, Elsevier, vol. 36(2), pages 334-357.
- Francesco Audrino & Yujia Hu, 2016.
"Volatility Forecasting: Downside Risk, Jumps and Leverage Effect,"
Econometrics, MDPI, vol. 4(1), pages 1-24, February.
See citations under working paper version above.
- Audrino, Francesco & Hu, Yujia, 2011. "Volatility Forecasting: Downside Risk, Jumps and Leverage Effect," Economics Working Paper Series 1138, University of St. Gallen, School of Economics and Political Science.
- Francesco Audrino & Simon D. Knaus, 2016.
"Lassoing the HAR Model: A Model Selection Perspective on Realized Volatility Dynamics,"
Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1485-1521, December.
See citations under working paper version above.
- Audrino, Francesco & Knaus, Simon, 2012. "Lassoing the HAR model: A Model Selection Perspective on Realized Volatility Dynamics," Economics Working Paper Series 1224, University of St. Gallen, School of Economics and Political Science.
- Audrino, Francesco & Fengler, Matthias R., 2015.
"Are classical option pricing models consistent with observed option second-order moments? Evidence from high-frequency data,"
Journal of Banking & Finance, Elsevier, vol. 61(C), pages 46-63.
See citations under working paper version above.
- Audrino, Francesco & Fengler, Matthias, 2013. "Are classical option pricing models consistent with observed option second-order moments? Evidence from high-frequency data," Economics Working Paper Series 1311, University of St. Gallen, School of Economics and Political Science.
- Fulvio Corsi & Stefano Peluso & Francesco Audrino, 2015.
"Missing in Asynchronicity: A Kalman‐em Approach for Multivariate Realized Covariance Estimation,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(3), pages 377-397, April.
See citations under working paper version above.
- Corsi, Fulvio & Peluso, Stefano & Audrino, Francesco, 2012. "Missing in Asynchronicity: A Kalman-EM Approach for Multivariate Realized Covariance Estimation," Economics Working Paper Series 1202, University of St. Gallen, School of Economics and Political Science.
- Audrino, Francesco, 2014.
"Forecasting correlations during the late-2000s financial crisis: The short-run component, the long-run component, and structural breaks,"
Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 43-60.
See citations under working paper version above.
- Audrino, Francesco, 2011. "Forecasting correlations during the late-2000s financial crisis: short-run component, long-run component, and structural breaks," Economics Working Paper Series 1112, University of St. Gallen, School of Economics and Political Science.
- Filipova, Kameliya & Audrino, Francesco & De Giorgi, Enrico, 2014.
"Monetary policy regimes: Implications for the yield curve and bond pricing,"
Journal of Financial Economics, Elsevier, vol. 113(3), pages 427-454.
Cited by:
- Shang, Fei, 2022. "The effect of uncertainty on the sensitivity of the yield curve to monetary policy surprises," Journal of Economic Dynamics and Control, Elsevier, vol. 137(C).
- Francesco Audrino & Lorenzo Camponovo, 2013.
"Oracle Properties and Finite Sample Inference of the Adaptive Lasso for Time Series Regression Models,"
Papers
1312.1473, arXiv.org.
- Audrino, Francesco & Camponovo, Lorenzo, 2013. "Oracle Properties and Finite Sample Inference of the Adaptive Lasso for Time Series Regression Models," Economics Working Paper Series 1327, University of St. Gallen, School of Economics and Political Science.
- Gozluklu, Arie & Morin, Annaïg, 2019. "Stock vs. Bond yields and demographic fluctuations," Journal of Banking & Finance, Elsevier, vol. 109(C).
- Zheng Qiao & Yangshu Liu, 2017. "Open Market Operation Effectiveness in China," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 53(8), pages 1706-1719, August.
- Francesco Audrino, 2012.
"What Drives Short Rate Dynamics? A Functional Gradient Descent Approach,"
Computational Economics, Springer;Society for Computational Economics, vol. 39(3), pages 315-335, March.
Cited by:
- Ranik Raaen Wahlstrøm & Florentina Paraschiv & Michael Schürle, 2022. "A Comparative Analysis of Parsimonious Yield Curve Models with Focus on the Nelson-Siegel, Svensson and Bliss Versions," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 967-1004, March.
- Nikolay Robinzonov & Gerhard Tutz & Torsten Hothorn, 2012. "Boosting techniques for nonlinear time series models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(1), pages 99-122, January.
- Fulvio Corsi & Francesco Audrino, 2012.
"Realized Covariance Tick-by-Tick in Presence of Rounded Time Stamps and General Microstructure Effects,"
Journal of Financial Econometrics, Oxford University Press, vol. 10(4), pages 591-616, September.
See citations under working paper version above.
- Fulvio Corsi & Francesco Audrino, 2008. "Realized Covariance Tick-by-Tick in Presence of Rounded Time Stamps and General Microstructure Effects," University of St. Gallen Department of Economics working paper series 2008 2008-04, Department of Economics, University of St. Gallen.
- Audrino, Francesco & Trojani, Fabio, 2011.
"A General Multivariate Threshold GARCH Model With Dynamic Conditional Correlations,"
Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 138-149.
- Francesco Audrino & Fabio Trojani, 2011. "A General Multivariate Threshold GARCH Model With Dynamic Conditional Correlations," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(1), pages 138-149, January.
See citations under working paper version above.- Francesco Audrino & Fabio Trojani, 2007. "A general multivariate threshold GARCH model with dynamic conditional correlations," University of St. Gallen Department of Economics working paper series 2007 2007-25, Department of Economics, University of St. Gallen.
- Fabio Trojani & Francesco Audrino, 2005. "A general multivariate threshold GARCH model with dynamic conditional correlations," University of St. Gallen Department of Economics working paper series 2005 2005-04, Department of Economics, University of St. Gallen.
- Francesco Audrino & Marcelo C. Medeiros, 2011.
"Modeling and forecasting short‐term interest rates: The benefits of smooth regimes, macroeconomic variables, and bagging,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(6), pages 999-1022, September.
See citations under working paper version above.
- Francesco Audrino & Marcelo Cunha Medeiros, 2010. "Modeling and Forecasting Short-term Interest Rates: The Benefits of Smooth Regimes, Macroeconomic Variables, and Bagging," Textos para discussão 570, Department of Economics PUC-Rio (Brazil).
- Audrino, Francesco & Corsi, Fulvio, 2010.
"Modeling tick-by-tick realized correlations,"
Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2372-2382, November.
See citations under working paper version above.
- Fulvio Corsi & Francesco Audrino, 2008. "Modeling Tick-by-Tick Realized Correlations," University of St. Gallen Department of Economics working paper series 2008 2008-05, Department of Economics, University of St. Gallen.
- Francesco Audrino & Peter Bühlmann, 2009.
"Splines for financial volatility,"
Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(3), pages 655-670, June.
See citations under working paper version above.
- Francesco Audrino & Peter Bühlmann, 2007. "Splines for Financial Volatility," University of St. Gallen Department of Economics working paper series 2007 2007-11, Department of Economics, University of St. Gallen.
- Francesco Audrino & Robert Fernholz & Roberto Ferretti, 2007.
"A Forecasting Model for Stock Market Diversity,"
Annals of Finance, Springer, vol. 3(2), pages 213-240, March.
Cited by:
- Andrey Sarantsev, 2014. "On a class of diverse market models," Annals of Finance, Springer, vol. 10(2), pages 291-314, May.
- Giovanni Barone-Adesi & Francesco Audrino, 2006.
"Average conditional correlation and tree structures for multivariate GARCH models,"
Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(8), pages 579-600.
Cited by:
- Jacobs, Michael & Karagozoglu, Ahmet K., 2014. "On the characteristics of dynamic correlations between asset pairs," Research in International Business and Finance, Elsevier, vol. 32(C), pages 60-82.
- Audrino, Francesco, 2006. "The impact of general non-parametric volatility functions in multivariate GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 50(11), pages 3032-3052, July.
- K. Triantafyllopoulos, 2008. "Multivariate stochastic volatility with Bayesian dynamic linear models," Papers 0802.0214, arXiv.org.
- Audrino, Francesco, 2006.
"The impact of general non-parametric volatility functions in multivariate GARCH models,"
Computational Statistics & Data Analysis, Elsevier, vol. 50(11), pages 3032-3052, July.
Cited by:
- Hartz, Christoph & Mittnik, Stefan & Paolella, Marc, 2006. "Accurate value-at-risk forecasting based on the normal-GARCH model," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2295-2312, December.
- Teresa Serra & José M. Gil, 2013.
"Price volatility in food markets: can stock building mitigate price fluctuations?,"
European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 40(3), pages 507-528, July.
- Serra, Teresa & Gil, Jose Maria, 2012. "Price volatility in food markets: can stock building mitigate price fluctuations?," 2012 Conference, August 18-24, 2012, Foz do Iguacu, Brazil 126055, International Association of Agricultural Economists.
- de Almeida, Daniel & Hotta, Luiz K. & Ruiz, Esther, 2018.
"MGARCH models: Trade-off between feasibility and flexibility,"
International Journal of Forecasting, Elsevier, vol. 34(1), pages 45-63.
- Almeida, Daniel de & Hotta, Luiz & Ruiz Ortega, Esther, 2015. "MGARCH models: tradeoff between feasibility and flexibility," DES - Working Papers. Statistics and Econometrics. WS ws1516, Universidad Carlos III de Madrid. Departamento de EstadÃstica.
- Barrow, Devon K. & Crone, Sven F., 2016. "A comparison of AdaBoost algorithms for time series forecast combination," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1103-1119.
- Audrino, Francesco, 2011.
"Forecasting correlations during the late-2000s financial crisis: short-run component, long-run component, and structural breaks,"
Economics Working Paper Series
1112, University of St. Gallen, School of Economics and Political Science.
- Audrino, Francesco, 2014. "Forecasting correlations during the late-2000s financial crisis: The short-run component, the long-run component, and structural breaks," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 43-60.
- Hartz, Christoph & Mittnik, Stefan & Paolella, Marc S., 2006. "Accurate Value-at-Risk forecast with the (good old) normal-GARCH model," CFS Working Paper Series 2006/23, Center for Financial Studies (CFS).
- Serra, Teresa, 2011.
"Volatility spillovers between food and energy markets: A semiparametric approach,"
Energy Economics, Elsevier, vol. 33(6), pages 1155-1164.
- Serra, Teresa, 2011. "Volatility Spillovers between Food and Energy Markets, A Semiparametric Approach," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 115997, European Association of Agricultural Economists.
- Elezovic, Suad, 2009. "Functional modelling of volatility in the Swedish limit order book," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2107-2118, April.
- Gulpinar, Nalan & Rustem, Berc, 2007. "Robust optimal decisions with imprecise forecasts," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3595-3611, April.
- Ane, Thierry, 2006. "An analysis of the flexibility of Asymmetric Power GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 1293-1311, November.
- Audrino, Francesco & Barone-Adesi, Giovanni, 2006.
"A dynamic model of expected bond returns: A functional gradient descent approach,"
Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2267-2277, December.
Cited by:
- Chen, Baoline & Zadrozny, Peter A., 2009. "Multi-step perturbation solution of nonlinear differentiable equations applied to an econometric analysis of productivity," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2061-2074, April.
- Nikolay Robinzonov & Gerhard Tutz & Torsten Hothorn, 2012. "Boosting techniques for nonlinear time series models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(1), pages 99-122, January.
- Fabio Trojani & Francesco Audrino, 2006.
"Estimating and predicting multivariate volatility thresholds in global stock markets,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(3), pages 345-369.
- Francesco Audrino & Fabio Trojani, 2006. "Estimating and predicting multivariate volatility thresholds in global stock markets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(3), pages 345-369, April.
Cited by:
- Audrino, Francesco & Corsi, Fulvio, 2010.
"Modeling tick-by-tick realized correlations,"
Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2372-2382, November.
- Fulvio Corsi & Francesco Audrino, 2008. "Modeling Tick-by-Tick Realized Correlations," University of St. Gallen Department of Economics working paper series 2008 2008-05, Department of Economics, University of St. Gallen.
- Audrino, Francesco & Trojani, Fabio, 2011.
"A General Multivariate Threshold GARCH Model With Dynamic Conditional Correlations,"
Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 138-149.
- Francesco Audrino & Fabio Trojani, 2007. "A general multivariate threshold GARCH model with dynamic conditional correlations," University of St. Gallen Department of Economics working paper series 2007 2007-25, Department of Economics, University of St. Gallen.
- Fabio Trojani & Francesco Audrino, 2005. "A general multivariate threshold GARCH model with dynamic conditional correlations," University of St. Gallen Department of Economics working paper series 2005 2005-04, Department of Economics, University of St. Gallen.
- Francesco Audrino & Fabio Trojani, 2011. "A General Multivariate Threshold GARCH Model With Dynamic Conditional Correlations," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(1), pages 138-149, January.
- Marcelo Cunha Medeiros & Alvaro Veiga, 2004. "Modelling multiple regimes in financial volatility with a flexible coefficient GARCH model," Textos para discussão 486, Department of Economics PUC-Rio (Brazil).
- Audrino, Francesco, 2011.
"Forecasting correlations during the late-2000s financial crisis: short-run component, long-run component, and structural breaks,"
Economics Working Paper Series
1112, University of St. Gallen, School of Economics and Political Science.
- Audrino, Francesco, 2014. "Forecasting correlations during the late-2000s financial crisis: The short-run component, the long-run component, and structural breaks," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 43-60.
- Francesco Audrino & Kameliya Filipova, 2009. "Yield Curve Predictability, Regimes, and Macroeconomic Information: A Data-Driven Approach," University of St. Gallen Department of Economics working paper series 2009 2009-10, Department of Economics, University of St. Gallen.
- Philippe Charlot & Vêlayoudom Marimoutou, 2008. "Hierarchical hidden Markov structure for dynamic correlations: the hierarchical RSDC model," Working Papers halshs-00285866, HAL.
- Giovanni Barone-Adesi & Francesco Audrino, 2006. "Average conditional correlation and tree structures for multivariate GARCH models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(8), pages 579-600.
- Kung, Ling-Ming & Yu, Shang-Wu, 2008. "Prediction of index futures returns and the analysis of financial spillovers--A comparison between GARCH and the grey theorem," European Journal of Operational Research, Elsevier, vol. 186(3), pages 1184-1200, May.
- Nidhal Mgadmi & Khemaies Bougatef, 2017. "Modeling volatility of the French stock market," Economics Bulletin, AccessEcon, vol. 37(2), pages 988-998.
- Francesco Audrino & Robert Fernholz & Roberto Ferretti, 2007. "A Forecasting Model for Stock Market Diversity," Annals of Finance, Springer, vol. 3(2), pages 213-240, March.
- Sarantis Tsiaplias & Chew Lian Chua, 2013. "A Multivariate GARCH Model Incorporating the Direct and Indirect Transmission of Shocks," Econometric Reviews, Taylor & Francis Journals, vol. 32(2), pages 244-271, February.
- Audrino, Francesco, 2006.
"Tree-Structured Multiple Regimes in Interest Rates,"
Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 338-353, July.
Cited by:
- David I. Harvey & Stephen J. Leybourne & Bin Xiao, 2007.
"A powerful test for linearity when the order of integration is unknown,"
Discussion Papers
07/01, University of Nottingham, Granger Centre for Time Series Econometrics.
- Harvey David I & Leybourne Stephen J & Xiao Bin, 2008. "A Powerful Test for Linearity When the Order of Integration is Unknown," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 12(3), pages 1-24, September.
- David I. Harvey & Stephen J. Leybourne & Bin Xiao, 2007. "A powerful test for linearity when the order of integration is unknown," Discussion Papers 07/06, University of Nottingham, Granger Centre for Time Series Econometrics.
- Audrino, Francesco & Trojani, Fabio, 2011.
"A General Multivariate Threshold GARCH Model With Dynamic Conditional Correlations,"
Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 138-149.
- Francesco Audrino & Fabio Trojani, 2007. "A general multivariate threshold GARCH model with dynamic conditional correlations," University of St. Gallen Department of Economics working paper series 2007 2007-25, Department of Economics, University of St. Gallen.
- Fabio Trojani & Francesco Audrino, 2005. "A general multivariate threshold GARCH model with dynamic conditional correlations," University of St. Gallen Department of Economics working paper series 2005 2005-04, Department of Economics, University of St. Gallen.
- Francesco Audrino & Fabio Trojani, 2011. "A General Multivariate Threshold GARCH Model With Dynamic Conditional Correlations," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(1), pages 138-149, January.
- Francesco Audrino, 2012. "What Drives Short Rate Dynamics? A Functional Gradient Descent Approach," Computational Economics, Springer;Society for Computational Economics, vol. 39(3), pages 315-335, March.
- Francesco Audrino & Kameliya Filipova, 2009. "Yield Curve Predictability, Regimes, and Macroeconomic Information: A Data-Driven Approach," University of St. Gallen Department of Economics working paper series 2009 2009-10, Department of Economics, University of St. Gallen.
- Francesco Audrino & Enrico De Giorgi, "undated".
"Beta Regimes for the Yield Curve,"
IEW - Working Papers
244, Institute for Empirical Research in Economics - University of Zurich.
- Francesco Audrino & Enrico De Giorgi, 0. "Beta Regimes for the Yield Curve," Journal of Financial Econometrics, Oxford University Press, vol. 5(3), pages 456-490.
- Francesco Audrino & Marcelo C. Medeiros, 2011.
"Modeling and forecasting short‐term interest rates: The benefits of smooth regimes, macroeconomic variables, and bagging,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(6), pages 999-1022, September.
- Francesco Audrino & Marcelo Cunha Medeiros, 2010. "Modeling and Forecasting Short-term Interest Rates: The Benefits of Smooth Regimes, Macroeconomic Variables, and Bagging," Textos para discussão 570, Department of Economics PUC-Rio (Brazil).
- Francesco Audrino & Marcelo C. Medeiros, 2008. "Smooth Regimes, Macroeconomic Variables, and Bagging for the Short-Term Interest Rate Process," University of St. Gallen Department of Economics working paper series 2008 2008-16, Department of Economics, University of St. Gallen.
- Filipova, Kameliya & Audrino, Francesco & De Giorgi, Enrico, 2014. "Monetary policy regimes: Implications for the yield curve and bond pricing," Journal of Financial Economics, Elsevier, vol. 113(3), pages 427-454.
- David I. Harvey & Stephen J. Leybourne & Bin Xiao, 2007.
"A powerful test for linearity when the order of integration is unknown,"
Discussion Papers
07/01, University of Nottingham, Granger Centre for Time Series Econometrics.
- Audrino, Francesco & Barone-Adesi, Giovanni, 2005.
"Functional gradient descent for financial time series with an application to the measurement of market risk,"
Journal of Banking & Finance, Elsevier, vol. 29(4), pages 959-977, April.
Cited by:
- Teresa Buchen & Klaus Wohlrabe, 2013.
"Assessing the Macroeconomic Forecasting Performance of Boosting - Evidence for the United States, the Euro Area, and Germany,"
CESifo Working Paper Series
4148, CESifo.
- Klaus Wohlrabe & Teresa Buchen, 2014. "Assessing the Macroeconomic Forecasting Performance of Boosting: Evidence for the United States, the Euro Area and Germany," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(4), pages 231-242, July.
- Teresa, Buchen & Wohlrabe, Klaus, 2014. "Assessing the Macroeconomic Forecasting Performance of Boosting: Evidence for the United States, the Euro Area, and Germany," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100626, Verein für Socialpolitik / German Economic Association.
- Audrino, Francesco, 2011.
"Forecasting correlations during the late-2000s financial crisis: short-run component, long-run component, and structural breaks,"
Economics Working Paper Series
1112, University of St. Gallen, School of Economics and Political Science.
- Audrino, Francesco, 2014. "Forecasting correlations during the late-2000s financial crisis: The short-run component, the long-run component, and structural breaks," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 43-60.
- Fengler, Matthias R. & Okhrin, Ostap, 2016. "Managing risk with a realized copula parameter," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 131-152.
- Fabio Trojani, 2007.
"Accurate Short-Term Yield Curve Forecasting using Functional Gradient Descent,"
Journal of Financial Econometrics, Oxford University Press, vol. 5(4), pages 591-623, Fall.
- Francesco Audrino & Fabio Trojani, 2007. "Accurate Short-Term Yield Curve Forecasting using Functional Gradient Descent," University of St. Gallen Department of Economics working paper series 2007 2007-24, Department of Economics, University of St. Gallen.
- Alexandros Agapitos & Anthony Brabazon & Michael O’Neill, 2017. "Regularised gradient boosting for financial time-series modelling," Computational Management Science, Springer, vol. 14(3), pages 367-391, July.
- Fresoli, Diego Eduardo & Ruiz Ortega, Esther, 2014.
"The uncertainty of conditional returns, volatilities and correlations in DCC models,"
DES - Working Papers. Statistics and Econometrics. WS
ws140202, Universidad Carlos III de Madrid. Departamento de EstadÃstica.
- Fresoli, Diego E. & Ruiz, Esther, 2016. "The uncertainty of conditional returns, volatilities and correlations in DCC models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 170-185.
- Fries, Christian P. & Nigbur, Tobias & Seeger, Norman, 2017. "Displaced relative changes in historical simulation: Application to risk measures of interest rates with phases of negative rates," Journal of Empirical Finance, Elsevier, vol. 42(C), pages 175-198.
- Katarina Valaskova & Tomas Kliestik & Lucia Svabova & Peter Adamko, 2018. "Financial Risk Measurement and Prediction Modelling for Sustainable Development of Business Entities Using Regression Analysis," Sustainability, MDPI, vol. 10(7), pages 1-15, June.
- Teresa Buchen & Klaus Wohlrabe, 2013.
"Assessing the Macroeconomic Forecasting Performance of Boosting - Evidence for the United States, the Euro Area, and Germany,"
CESifo Working Paper Series
4148, CESifo.
- Francesco Audrino & Giovanni Barone-Adesi, 2005.
"A multivariate FGD technique to improve VaR computation in equity markets,"
Computational Management Science, Springer, vol. 2(2), pages 87-106, March.
Cited by:
- Pritsker, Matthew, 2006. "The hidden dangers of historical simulation," Journal of Banking & Finance, Elsevier, vol. 30(2), pages 561-582, February.
- Fabio Trojani & Francesco Audrino, 2005. "Accurate Yield Curve Scenarios Generation using Functional Gradient Descent," Computing in Economics and Finance 2005 14, Society for Computational Economics.
- Audrino, Francesco & Barone-Adesi, Giovanni, 2005. "Functional gradient descent for financial time series with an application to the measurement of market risk," Journal of Banking & Finance, Elsevier, vol. 29(4), pages 959-977, April.
- Fabio Trojani, 2007.
"Accurate Short-Term Yield Curve Forecasting using Functional Gradient Descent,"
Journal of Financial Econometrics, Oxford University Press, vol. 5(4), pages 591-623, Fall.
- Francesco Audrino & Fabio Trojani, 2007. "Accurate Short-Term Yield Curve Forecasting using Functional Gradient Descent," University of St. Gallen Department of Economics working paper series 2007 2007-24, Department of Economics, University of St. Gallen.
- Fries, Christian P. & Nigbur, Tobias & Seeger, Norman, 2017. "Displaced relative changes in historical simulation: Application to risk measures of interest rates with phases of negative rates," Journal of Empirical Finance, Elsevier, vol. 42(C), pages 175-198.
- Francesco Audrino, 2005.
"Local Likelihood for non‐parametric ARCH(1) models,"
Journal of Time Series Analysis, Wiley Blackwell, vol. 26(2), pages 251-278, March.
Cited by:
- Francesco Audrino & Peter Bühlmann, 2007.
"Splines for Financial Volatility,"
University of St. Gallen Department of Economics working paper series 2007
2007-11, Department of Economics, University of St. Gallen.
- Francesco Audrino & Peter Bühlmann, 2009. "Splines for financial volatility," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(3), pages 655-670, June.
- Arash Nademi & Rahman Farnoosh, 2014. "Mixtures of autoregressive-autoregressive conditionally heteroscedastic models: semi-parametric approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(2), pages 275-293, February.
- Francesco Audrino & Peter Bühlmann, 2007.
"Splines for Financial Volatility,"
University of St. Gallen Department of Economics working paper series 2007
2007-11, Department of Economics, University of St. Gallen.
- Francesco Audrino, 2005.
"The Stability of Factor Models of Interest Rates,"
Journal of Financial Econometrics, Oxford University Press, vol. 3(3), pages 422-441.
Cited by:
- Oliver Blaskowitz & Helmut Herwartz, 2009.
"Adaptive forecasting of the EURIBOR swap term structure,"
Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(7), pages 575-594.
- Oliver Blaskowitz & Helmut Herwatz, 2008. "Adaptive Forecasting of the EURIBOR Swap Term Structure," SFB 649 Discussion Papers SFB649DP2008-017, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
- Zema, Sebastiano Michele, 2022. "Directed acyclic graph based information shares for price discovery," Journal of Economic Dynamics and Control, Elsevier, vol. 139(C).
- Chihwa Kao & Lorenzo Trapani & Giovanni Urga, 2007.
"Modelling and Testing for Structural Changes in Panel Cointegration Models with Common and Idiosyncratic Stochastic Trends,"
Working Papers
0708, Department of Management, Information and Production Engineering, University of Bergamo.
- Chihwa Kao & Lorenzo Trapani & Giovanni Urga, 2007. "Modelling and Testing for Structural Changes in Panel Cointegration Models with Common and Idiosyncratic Stochastic Trend," Center for Policy Research Working Papers 92, Center for Policy Research, Maxwell School, Syracuse University.
- Blomvall, Jörgen & Hagenbjörk, Johan, 2019. "A generic framework for monetary performance attribution," Journal of Banking & Finance, Elsevier, vol. 105(C), pages 121-133.
- Francesco Audrino & Dominik Colagelo, 2007. "Forecasting Implied Volatility Surfaces," University of St. Gallen Department of Economics working paper series 2007 2007-42, Department of Economics, University of St. Gallen.
- Audrino, Francesco & Barone-Adesi, Giovanni, 2006. "A dynamic model of expected bond returns: A functional gradient descent approach," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2267-2277, December.
- Sebastiano Michele Zema, 2020. "Directed Acyclic Graph based Information Shares for Price Discovery," LEM Papers Series 2020/28, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
- Oliver Blaskowitz & Helmut Herwartz, 2009.
"Adaptive forecasting of the EURIBOR swap term structure,"
Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(7), pages 575-594.
- Francesco Audrino & Peter Bühlmann, 2001.
"Tree‐structured generalized autoregressive conditional heteroscedastic models,"
Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(4), pages 727-744.
Cited by:
- Liu, Wei & Garrett, Ian, 2023. "Regime-dependent effects of macroeconomic uncertainty on realized volatility in the U.S. stock market," Economic Modelling, Elsevier, vol. 128(C).
- Amaya, Johanna & Arellana, Julian & Delgado-Lindeman, Maira, 2020. "Stakeholders perceptions to sustainable urban freight policies in emerging markets," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 329-348.
- Francesco Audrino & Peter Bühlmann, 2007.
"Splines for Financial Volatility,"
University of St. Gallen Department of Economics working paper series 2007
2007-11, Department of Economics, University of St. Gallen.
- Francesco Audrino & Peter Bühlmann, 2009. "Splines for financial volatility," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(3), pages 655-670, June.
- Meister, Alexander & Kreiß, Jens-Peter, 2016. "Statistical inference for nonparametric GARCH models," Stochastic Processes and their Applications, Elsevier, vol. 126(10), pages 3009-3040.
- Lee, Paul H. & Yu, Philip L.H., 2010. "Distance-based tree models for ranking data," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1672-1682, June.
- Ioannis Papageorgiou & Ioannis Kontoyiannis, 2023. "The Bayesian Context Trees State Space Model for time series modelling and forecasting," Papers 2308.00913, arXiv.org, revised Oct 2023.
- Andrew J. Patton & Yasin Simsek, 2023. "Generalized Autoregressive Score Trees and Forests," Papers 2305.18991, arXiv.org.
- Francesco Audrino & Enrico De Giorgi, 0.
"Beta Regimes for the Yield Curve,"
Journal of Financial Econometrics, Oxford University Press, vol. 5(3), pages 456-490.
See citations under working paper version above.
- Francesco Audrino & Enrico De Giorgi, "undated". "Beta Regimes for the Yield Curve," IEW - Working Papers 244, Institute for Empirical Research in Economics - University of Zurich.