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Matthias R. Fengler

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.

Working papers

  1. Fengler, Matthias & Polivka, Jeannine, 2022. "Structural Volatility Impulse Response Analysis," Economics Working Paper Series 2211, University of St. Gallen, School of Economics and Political Science.

    Cited by:

    1. Hafner, Christian M. & Herwartz, Helmut, 2023. "Correlation impulse response functions," Finance Research Letters, Elsevier, vol. 57(C).

  2. Fengler, Matthias & Polivka, Jeannine, 2021. "Identifying structural shocks to volatility through a proxy-MGARCH model," Economics Working Paper Series 2103, University of St. Gallen, School of Economics and Political Science, revised May 2021.

    Cited by:

    1. Fengler, Matthias & Polivka, Jeannine, 2022. "Structural Volatility Impulse Response Analysis," Economics Working Paper Series 2211, University of St. Gallen, School of Economics and Political Science.

  3. Chen, Cathy Yi-Hsuan & Fengler, Matthias R. & Härdle, Wolfgang Karl & Liu, Yanchu, 2019. "Media-expressed tone, Option Characteristics, and Stock Return Predictability," IRTG 1792 Discussion Papers 2019-015, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".

    Cited by:

    1. Fengler, Matthias & Phan, Minh Tri, 2023. "A Topic Model for 10-K Management Disclosures," Economics Working Paper Series 2307, University of St. Gallen, School of Economics and Political Science.

  4. Yi-Hsuan Chen, Cathy & Fengler, Matthias & Härdle, Wolfgang Karl & Liu, Yanchu, 2018. "Textual Sentiment, Option Characteristics, and Stock Return Predictability," Economics Working Paper Series 1808, University of St. Gallen, School of Economics and Political Science.

    Cited by:

    1. Fan, Qingliang & Zhong, Wei, 2018. "Nonparametric Additive Instrumental Variable Estimator: A Group Shrinkage Estimation Perspective," IRTG 1792 Discussion Papers 2018-052, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    2. Joshua Zoen Git Hiew & Xin Huang & Hao Mou & Duan Li & Qi Wu & Yabo Xu, 2019. "BERT-based Financial Sentiment Index and LSTM-based Stock Return Predictability," Papers 1906.09024, arXiv.org, revised Jul 2022.
    3. Zhong, Wei & Liu, Xi & Ma, Shuangge, 2018. "Variable selection and direction estimation for single-index models via DC-TGDR method," IRTG 1792 Discussion Papers 2018-050, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    4. Guo, Li & Tao, Yubo & Härdle, Wolfgang Karl, 2018. "Understanding Latent Group Structure of Cryptocurrencies Market: A Dynamic Network Perspective," IRTG 1792 Discussion Papers 2018-032, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    5. Packham, Natalie & Woebbeking, Fabian, 2018. "A factor-model approach for correlation scenarios and correlation stress-testing," IRTG 1792 Discussion Papers 2018-034, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    6. Xiaojia Bao & Qingliang Fan, 2020. "The impact of temperature on gaming productivity: evidence from online games," Empirical Economics, Springer, vol. 58(2), pages 835-867, February.
    7. Packham, Natalie & Kalkbrener, Michael & Overbeck, Ludger, 2018. "Default probabilities and default correlations under stress," IRTG 1792 Discussion Papers 2018-037, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    8. Kuczmaszewska, Anna & Yan, Ji Gao, 2018. "On complete convergence in Marcinkiewicz-Zygmund type SLLN for random variables," IRTG 1792 Discussion Papers 2018-041, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    9. Chen, Haiqiang & Li, Yingxing & Lin, Ming & Zhu, Yanli, 2018. "A Regime Shift Model with Nonparametric Switching Mechanism," IRTG 1792 Discussion Papers 2018-048, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    10. Yatracos, Yannis G., 2018. "Residual'S Influence Index (Rinfin), Bad Leverage And Unmasking In High Dimensional L2-Regression," IRTG 1792 Discussion Papers 2018-060, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    11. Zbonakova, Lenka & Li, Xinjue & Härdle, Wolfgang Karl, 2018. "Penalized Adaptive Forecasting with Large Information Sets and Structural Changes," IRTG 1792 Discussion Papers 2018-039, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    12. Packham, Natalie, 2018. "Optimal contracts under competition when uncertainty from adverse selection and moral hazard are present," IRTG 1792 Discussion Papers 2018-033, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    13. Cai, Zongwu & Fang, Ying & Lin, Ming & Su, Jia, 2018. "Inferences for a Partially Varying Coefficient Model With Endogenous Regressors," IRTG 1792 Discussion Papers 2018-047, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    14. Wang, Honglin & Yu, Fan & Zhou, Yinggang, 2018. "Property Investment and Rental Rate under Housing Price Uncertainty: A Real Options Approach," IRTG 1792 Discussion Papers 2018-051, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    15. Yan, Ji Gao, 2018. "Complete Convergence and Complete Moment Convergence for Maximal Weighted Sums of Extended Negatively Dependent Random Variables," IRTG 1792 Discussion Papers 2018-040, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    16. Kalkbrener, Michael & Packham, Natalie, 2018. "Correlation Under Stress In Normal Variance Mixture Models," IRTG 1792 Discussion Papers 2018-035, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    17. Chiu, Hsin-Yu & Chiang, Mi-Hsiu & Kuo, Wei-Yu, 2018. "Predicative Ability of Similarity-based Futures Trading Strategies," IRTG 1792 Discussion Papers 2018-045, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    18. Guo, Shaojun & Li, Dong & Li, Muyi, 2018. "Strict Stationarity Testing and GLAD Estimation of Double Autoregressive Models," IRTG 1792 Discussion Papers 2018-049, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    19. Koziuk, Andzhey & Spokoiny, Vladimir, 2018. "Toolbox: Gaussian comparison on Eucledian balls," IRTG 1792 Discussion Papers 2018-028, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    20. Nasekin, Sergey & Chen, Cathy Yi-Hsuan, 2018. "Deep learning-based cryptocurrency sentiment construction," IRTG 1792 Discussion Papers 2018-066, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".

  5. Fengler, Matthias R. & Hin, Lin-Yee, 2014. "A simple and general approach to fitting the discount curve under no-arbitrage constraints," Economics Working Paper Series 1423, University of St. Gallen, School of Economics and Political Science.

    Cited by:

    1. Cousin, Areski & Maatouk, Hassan & Rullière, Didier, 2016. "Kriging of financial term-structures," European Journal of Operational Research, Elsevier, vol. 255(2), pages 631-648.
    2. Damir Filipović & Sander Willems, 2016. "Exact Smooth Term Structure Estimation," Swiss Finance Institute Research Paper Series 16-38, Swiss Finance Institute.

  6. 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.

    Cited by:

    1. Mensi, Walid & Shafiullah, Muhammad & Vo, Xuan Vinh & Kang, Sang Hoon, 2021. "Volatility spillovers between strategic commodity futures and stock markets and portfolio implications: Evidence from developed and emerging economies," Resources Policy, Elsevier, vol. 71(C).
    2. Cubadda, Gianluca & Guardabascio, Barbara & Hecq, Alain, 2017. "A vector heterogeneous autoregressive index model for realized volatility measures," International Journal of Forecasting, Elsevier, vol. 33(2), pages 337-344.
    3. Thomas F. P. Wiesen & Lakshya Bharadwaj, 2023. "Cryptocurrency Connectedness: Does Controlling for the Cross-Correlations Matter?," Applied Economics Letters, Taylor & Francis Journals, vol. 30(20), pages 2873-2880, November.
    4. Liow, Kim Hiang & Huang, Yuting, 2018. "The dynamics of volatility connectedness in international real estate investment trusts," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 55(C), pages 195-210.
    5. Jozef Baruník & Evžen Kocenda & Lukáš Vácha, 2015. "Asymmetric Connectedness on the U.S. Stock Market: Bad and Good Volatility Spillover," CESifo Working Paper Series 5305, CESifo.
    6. Nyberg, Henri & Pönkä, Harri, 2016. "International sign predictability of stock returns: The role of the United States," Economic Modelling, Elsevier, vol. 58(C), pages 323-338.
    7. Evžen Kočenda, 2017. "Survey of volatility and spillovers on financial markets," Working Papers 363, Leibniz Institut für Ost- und Südosteuropaforschung (Institute for East and Southeast European Studies).
    8. Lyócsa, Štefan & Molnár, Peter & Todorova, Neda, 2017. "Volatility forecasting of non-ferrous metal futures: Covariances, covariates or combinations?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 51(C), pages 228-247.
    9. Baruník, Jozef & Kočenda, Evžen & Vácha, Lukáš, 2017. "Asymmetric volatility connectedness on the forex market," Journal of International Money and Finance, Elsevier, vol. 77(C), pages 39-56.
    10. Riza Demirer & Konstantinos Gkillas & Christos Kountzakis & Amaryllis Mavragani, 2020. "Risk Appetite and Jumps in Realized Correlation," Mathematics, MDPI, vol. 8(12), pages 1-11, December.
    11. Evzen Kocenda & Michala Moravcova, 2017. "Exchange Rate Co-movements, Hedging and Volatility Spillovers in New EU Forex Markets," Working Papers IES 2017/27, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Nov 2017.
    12. Iwanicz-Drozdowska, Małgorzata & Rogowicz, Karol & Kurowski, Łukasz & Smaga, Paweł, 2021. "Two decades of contagion effect on stock markets: Which events are more contagious?," Journal of Financial Stability, Elsevier, vol. 55(C).
    13. Apergis, Nicholas & Baruník, Jozef & Lau, Marco Chi Keung, 2017. "Good volatility, bad volatility: What drives the asymmetric connectedness of Australian electricity markets?," Energy Economics, Elsevier, vol. 66(C), pages 108-115.
    14. Buncic, Daniel & Gisler, Katja I.M., 2016. "Global equity market volatility spillovers: A broader role for the United States," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1317-1339.
    15. Gianluca Cubadda & Alain Hecq & Antonio Riccardo, 2018. "Forecasting Realized Volatility Measures with Multivariate and Univariate Models: The Case of The US Banking Sector," CEIS Research Paper 445, Tor Vergata University, CEIS, revised 30 Oct 2018.
    16. Guan, Bo & Mazouz, Khelifa & Xu, Yongdeng, 2023. "Asymmetric volatility spillover between crude oil and other asset markets," Cardiff Economics Working Papers E2023/27, Cardiff University, Cardiff Business School, Economics Section.
    17. Lukas Boeckelmann & Arthur Stalla-Bourdillon, 2021. "Structural Estimation of Time-Varying Spillovers:an Application to International Credit Risk Transmission," Working Papers hal-03338209, HAL.
    18. Mensi, Walid & Shafiullah, Muhammad & Vo, Xuan Vinh & Kang, Sang Hoon, 2022. "Asymmetric spillovers and connectedness between crude oil and currency markets using high-frequency data," Resources Policy, Elsevier, vol. 77(C).
    19. Nishimura, Yusaku & Sun, Bianxia, 2018. "The intraday volatility spillover index approach and an application in the Brexit vote," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 55(C), pages 241-253.
    20. Caloia, Francesco Giuseppe & Cipollini, Andrea & Muzzioli, Silvia, 2018. "Asymmetric semi-volatility spillover effects in EMU stock markets," International Review of Financial Analysis, Elsevier, vol. 57(C), pages 221-230.
    21. Mensi, Walid & Vo, Xuan Vinh & Kang, Sang Hoon, 2023. "Quantile spillovers and connectedness analysis between oil and African stock markets," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 60-83.
    22. Andrea Cipollini & Iolanda Lo Cascio & Silvia Muzzioli, 2015. "Financial connectedness among European volatility risk premia," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 0058, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    23. Shahzad, Syed Jawad Hussain & Ferrer, Román & Ballester, Laura & Umar, Zaghum, 2017. "Risk transmission between Islamic and conventional stock markets: A return and volatility spillover analysis," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 9-26.
    24. Syed Jawad Hussain Shahzad & Román Ferrer, 2020. "Dynamic spillover effects among tourism, economic growth and macro-finance risk factors," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 19(3), pages 173-194, September.
    25. Baklaci, Hasan Fehmi & Aydoğan, Berna & Yelkenci, Tezer, 2020. "Impact of stock market trading on currency market volatility spillovers," Research in International Business and Finance, Elsevier, vol. 52(C).
    26. Nekhili, Ramzi & Bouri, Elie, 2023. "Higher-order moments and co-moments' contribution to spillover analysis and portfolio risk management," Energy Economics, Elsevier, vol. 119(C).
    27. Fengler, Matthias R. & Herwartz, Helmut, 2015. "Measuring spot variance spillovers when (co)variances are time-varying – the case of multivariate GARCH models," Economics Working Paper Series 1517, University of St. Gallen, School of Economics and Political Science.
    28. Lastrapes, William D. & Wiesen, Thomas F.P., 2021. "The joint spillover index," Economic Modelling, Elsevier, vol. 94(C), pages 681-691.
    29. Yin, Kedong & Liu, Zhe & Jin, Xue, 2020. "Interindustry volatility spillover effects in China’s stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    30. Bonato, Matteo, 2019. "Realized correlations, betas and volatility spillover in the agricultural commodity market: What has changed?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 62(C), pages 184-202.
    31. Helmut Herwartz & Alberto Saucedo, 2020. "Food–oil volatility spillovers and the impact of distinct biofuel policies on price uncertainties on feedstock markets," Agricultural Economics, International Association of Agricultural Economists, vol. 51(3), pages 387-402, May.
    32. Licheng Sun & Liang Meng & Mohammad Najand, 2017. "The Role of U.S. Market on International Risk-Return Tradeoff Relations," The Financial Review, Eastern Finance Association, vol. 52(3), pages 499-526, August.
    33. Stefan Lyocsa & Peter Molnar & Igor Fedorko, 2016. "Forecasting Exchange Rate Volatility: The Case of the Czech Republic, Hungary and Poland," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 66(5), pages 453-475, October.
    34. Mohammad Isleimeyyeh & Amine Ben Amar & Stéphane Goutte & Ramzi Benkraiem, 2022. "Commodity markets dynamics: What do cross-commodities over different nearest-to-maturities tell us?," Post-Print hal-03674806, HAL.
    35. Dimitrios Vortelinos & Konstantinos Gkillas (Gillas) & Costas Syriopoulos & Argyro Svingou, 2017. "Asymmetric and nonlinear inter-relations of US stock indices," International Journal of Managerial Finance, Emerald Group Publishing Limited, vol. 14(1), pages 78-129, December.
    36. 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).
    37. Cipollini, Andrea & Lo Cascio, Iolanda & Muzzioli, Silvia, 2018. "Risk aversion connectedness in five European countries," Economic Modelling, Elsevier, vol. 71(C), pages 68-79.
    38. Ben Amar, Amine & Goutte, Stéphane & Isleimeyyeh, Mohammad, 2022. "Asymmetric cyclical connectedness on the commodity markets: Further insights from bull and bear markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 85(C), pages 386-400.
    39. Kim Hiang LIOW & Jeongseop SONG, 2019. "Market Integration Among the US and Asian Real Estate Investment Trusts in Crisis Times," International Real Estate Review, Global Social Science Institute, vol. 22(4), pages 463-512.
    40. Pedro Pires Ribeiro & José Dias Curto, 2017. "Volatility spillover effects in interbank money markets," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 153(1), pages 105-136, February.
    41. Chen, Yufeng & Li, Wenqi & Qu, Fang, 2019. "Dynamic asymmetric spillovers and volatility interdependence on China’s stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 825-838.
    42. Sachapon Tungsong & Fabio Caccioli & Tomaso Aste, 2017. "Relation between regional uncertainty spillovers in the global banking system," Papers 1702.05944, arXiv.org.
    43. Chung, Chien-Ping & Liao, Tzu-Hsiang & Lee, Hsiu-Chuan, 2021. "Volatility spillovers of A- and B-shares for the Chinese stock market and its impact on the Chinese index returns," Pacific-Basin Finance Journal, Elsevier, vol. 65(C).
    44. Abosedra, Salah & Arayssi, Mahmoud & Ben Sita, Bernard & Mutshinda, Crispin, 2020. "Exploring GDP growth volatility spillovers across countries," Economic Modelling, Elsevier, vol. 89(C), pages 577-589.
    45. Bouri, Elie, 2023. "Spillovers in the joint system of conditional higher-order moments: US evidence from green energy, brown energy, and technology stocks," Renewable Energy, Elsevier, vol. 210(C), pages 507-523.

  7. 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.

    Cited by:

    1. 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.
    2. 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.
    3. Dalderop, Jeroen, 2020. "Nonparametric filtering of conditional state-price densities," Journal of Econometrics, Elsevier, vol. 214(2), pages 295-325.

  8. 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.

    Cited by:

    1. Buncic, Daniel & Gisler, Katja I.M., 2016. "Global equity market volatility spillovers: A broader role for the United States," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1317-1339.

  9. Matthias R. Fengler & Ostap Okhrin, 2012. "Realized Copula," SFB 649 Discussion Papers SFB649DP2012-034, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.

    Cited by:

    1. 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.
    2. De Lira Salvatierra, Irving & Patton, Andrew J., 2015. "Dynamic copula models and high frequency data," Journal of Empirical Finance, Elsevier, vol. 30(C), pages 120-135.
    3. Jean-David Fermanian, 2017. "Recent Developments in Copula Models," Econometrics, MDPI, vol. 5(3), pages 1-3, July.
    4. Ostap Okhrin & Anastasija Tetereva, 2017. "The Realized Hierarchical Archimedean Copula in Risk Modelling," Econometrics, MDPI, vol. 5(2), pages 1-31, June.
    5. Thorsten Dickhaus & Jakob Gierl, 2012. "Simultaneous test procedures in terms of p-value copulae," SFB 649 Discussion Papers SFB649DP2012-049, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.

  10. Fengler, Matthias & Hin, Lin-Yee, 2011. "Semi-nonparametric estimation of the call price surface under strike and time-to-expiry no-arbitrage constraints," Economics Working Paper Series 1136, University of St. Gallen, School of Economics and Political Science, revised May 2013.

    Cited by:

    1. Fengler, Matthias R. & Hin, Lin-Yee, 2015. "A simple and general approach to fitting the discount curve under no-arbitrage constraints," Finance Research Letters, Elsevier, vol. 15(C), pages 78-84.

  11. Matthias Fengler, 2010. "Option data and modeling BSM implied volatility," University of St. Gallen Department of Economics working paper series 2010 2010-32, Department of Economics, University of St. Gallen.

    Cited by:

    1. Noshaba Zulfiqar & Saqib Gulzar, 2021. "Implied volatility estimation of bitcoin options and the stylized facts of option pricing," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-30, December.
    2. Wolfgang Karl Härdle & Yarema Okhrin & Weining Wang, 2015. "Uniform Confidence Bands for Pricing Kernels," Journal of Financial Econometrics, Oxford University Press, vol. 13(2), pages 376-413.
    3. Cristian Homescu, 2011. "Implied Volatility Surface: Construction Methodologies and Characteristics," Papers 1107.1834, arXiv.org.
    4. Bo Zhao & Stewart Hodges, 2013. "Parametric modeling of implied smile functions: a generalized SVI model," Review of Derivatives Research, Springer, vol. 16(1), pages 53-77, April.

  12. Matthias Fengler & Helmut Herwartz & Christian Werner, 2010. "A dynamic copula approach to recovering the index implied volatility skew," University of St. Gallen Department of Economics working paper series 2010 1132, Department of Economics, University of St. Gallen, revised Nov 2011.

    Cited by:

    1. 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).
    2. Herwartz, Helmut & Raters, Fabian H.C., 2015. "Copula-MGARCH with continuous covariance decomposition," Economics Letters, Elsevier, vol. 133(C), pages 73-76.
    3. Saldías, Martín, 2013. "Systemic risk analysis using forward-looking Distance-to-Default series," Journal of Financial Stability, Elsevier, vol. 9(4), pages 498-517.
    4. Dahiru A. Balaa & Taro Takimotob, 2017. "Stock markets volatility spillovers during financial crises: A DCC-MGARCH with skewed-t density approach," Borsa Istanbul Review, Research and Business Development Department, Borsa Istanbul, vol. 17(1), pages 25-48, March.
    5. Fengler, Matthias & Melnikov, Alexander, 2017. "GARCH option pricing models with Meixner innovations," Economics Working Paper Series 1702, University of St. Gallen, School of Economics and Political Science.
    6. Félix, Luiz & Kräussl, Roman & Stork, Philip, 2013. "The 2011 European short sale ban on financial stocks: A cure or a curse?," CFS Working Paper Series 2013/17, Center for Financial Studies (CFS).

  13. Fengler, Matthias R. & Winter, Joachim, 2007. "Price variability and price dispersion in a stable monetary environment: Evidence from German retail markets," Munich Reprints in Economics 20338, University of Munich, Department of Economics.

    Cited by:

    1. Levy, Daniel, 2007. "Price Rigidity and Flexibility: New Empirical Evidence," MPRA Paper 2762, University Library of Munich, Germany.
    2. Ater, Itai & Gerlitz, Omri, 2017. "Round prices and price rigidity: Evidence from outlawing odd prices," Journal of Economic Behavior & Organization, Elsevier, vol. 144(C), pages 188-203.
    3. Daniel Levy, 2007. "Price rigidity and flexibility: new empirical evidence - Introduction to the Special Issue," Post-Print hal-02387441, HAL.

  14. Matthias R. Fengler, 2005. "Arbitrage-Free Smoothing of the Implied Volatility Surface," SFB 649 Discussion Papers SFB649DP2005-019, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.

    Cited by:

    1. Olena Burkovska & Maximilian Ga{ss} & Kathrin Glau & Mirco Mahlstedt & Wim Schoutens & Barbara Wohlmuth, 2016. "Calibration to American Options: Numerical Investigation of the de-Americanization," Papers 1611.06181, arXiv.org.
    2. Orcan Ogetbil & Bernhard Hientzsch, 2020. "Extensions of Dupire Formula: Stochastic Interest Rates and Stochastic Local Volatility," Papers 2005.05530, arXiv.org, revised Feb 2023.
    3. Martin Tegn'er & Stephen Roberts, 2019. "A Probabilistic Approach to Nonparametric Local Volatility," Papers 1901.06021, arXiv.org, revised Jan 2019.
    4. Kai Yin & Anirban Mondal, 2023. "Bayesian uncertainty quantification of local volatility model," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 290-324, May.
    5. H. Peter Boswijk & Roger J. A. Laeven & Evgenii Vladimirov, 2022. "Estimating Option Pricing Models Using a Characteristic Function Based Linear State Space Representation," Tinbergen Institute Discussion Papers 22-000/III, Tinbergen Institute.
    6. Gianluca Cassese, 2019. "Nonparametric Estimates Of Option Prices And Related Quantities," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(07), pages 1-29, November.
    7. Mnacho Echenim & Emmanuel Gobet & Anne-Claire Maurice, 2022. "Unbiasing and robustifying implied volatility calibration in a cryptocurrency market with large bid-ask spreads and missing quotes," Papers 2207.02989, arXiv.org.
    8. Vedant Choudhary & Sebastian Jaimungal & Maxime Bergeron, 2023. "FuNVol: A Multi-Asset Implied Volatility Market Simulator using Functional Principal Components and Neural SDEs," Papers 2303.00859, arXiv.org, revised Dec 2023.
    9. Laurini, Márcio P., 2007. "Imposing No-Arbitrage Conditions In Implied Volatility Surfaces Using Constrained Smoothing Splines," Insper Working Papers wpe_89, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
    10. Salazar Celis, Oliver & Liang, Lingzhi & Lemmens, Damiaan & Tempère, Jacques & Cuyt, Annie, 2015. "Determining and benchmarking risk neutral distributions implied from option prices," Applied Mathematics and Computation, Elsevier, vol. 258(C), pages 372-387.
    11. Frédéric Vrins & Linqi Wang, 2023. "Asymmetric short-rate model without lower bound," Quantitative Finance, Taylor & Francis Journals, vol. 23(2), pages 279-295, February.
    12. Pascal Albert & Michael Herold & Matthias Muck, 2023. "Estimation of rare disaster concerns from option prices—An arbitrage‐free RND‐based smile construction approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(12), pages 1807-1835, December.
    13. Beer, Simone & Braun, Alexander, 2022. "Market-consistent valuation of natural catastrophe risk," Journal of Banking & Finance, Elsevier, vol. 134(C).
    14. Maxim Ulrich & Simon Walther, 2020. "Option-implied information: What’s the vol surface got to do with it?," Review of Derivatives Research, Springer, vol. 23(3), pages 323-355, October.
    15. Arindam Kundu & Sumit Kumar & Nutan Kumar Tomar, 2019. "Option Implied Risk-Neutral Density Estimation: A Robust and Flexible Method," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 705-728, August.
    16. Michal Benko & Wolfgang Härdle & Alois Kneip, 2006. "Common Functional Principal Components," SFB 649 Discussion Papers SFB649DP2006-010, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    17. Fengler, Matthias & Hin, Lin-Yee, 2011. "Semi-nonparametric estimation of the call price surface under strike and time-to-expiry no-arbitrage constraints," Economics Working Paper Series 1136, University of St. Gallen, School of Economics and Political Science, revised May 2013.
    18. Johannes Rauch & Carol Alexander, 2016. "Tail Risk Premia for Long-Term Equity Investors," Papers 1602.00865, arXiv.org.
    19. Samuel N. Cohen & Christoph Reisinger & Sheng Wang, 2020. "Detecting and repairing arbitrage in traded option prices," Papers 2008.09454, arXiv.org.
    20. Miloš Kopa & Sebastiano Vitali & Tomáš Tichý & Radek Hendrych, 2017. "Implied volatility and state price density estimation: arbitrage analysis," Computational Management Science, Springer, vol. 14(4), pages 559-583, October.
    21. Sigurd Emil Rømer & Rolf Poulsen, 2020. "How Does the Volatility of Volatility Depend on Volatility?," Risks, MDPI, vol. 8(2), pages 1-18, June.
    22. Wenyong Zhang & Lingfei Li & Gongqiu Zhang, 2021. "A Two-Step Framework for Arbitrage-Free Prediction of the Implied Volatility Surface," Papers 2106.07177, arXiv.org, revised Jan 2022.
    23. Fengler, Matthias R. & Hin, Lin-Yee, 2015. "Semi-nonparametric estimation of the call-option price surface under strike and time-to-expiry no-arbitrage constraints," Journal of Econometrics, Elsevier, vol. 184(2), pages 242-261.
    24. Kim, Namhyoung & Lee, Jaewook, 2013. "No-arbitrage implied volatility functions: Empirical evidence from KOSPI 200 index options," Journal of Empirical Finance, Elsevier, vol. 21(C), pages 36-53.
    25. Pascal François & Rémi Galarneau‐Vincent & Geneviève Gauthier & Frédéric Godin, 2022. "Venturing into uncharted territory: An extensible implied volatility surface model," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(10), pages 1912-1940, October.
    26. Samuel N. Cohen & Christoph Reisinger & Sheng Wang, 2021. "Arbitrage-free neural-SDE market models," Papers 2105.11053, arXiv.org, revised Aug 2021.
    27. Hirbod Assa & Mostafa Pouralizadeh & Abdolrahim Badamchizadeh, 2019. "Sound Deposit Insurance Pricing Using a Machine Learning Approach," Risks, MDPI, vol. 7(2), pages 1-18, April.
    28. Mnacho Echenim & Emmanuel Gobet & Anne-Claire Maurice, 2023. "Unbiasing and robustifying implied volatility calibration in a cryptocurrency market with large bid-ask spreads and missing quotes," Post-Print hal-03715921, HAL.
    29. Shengli Chen & Zili Zhang, 2019. "Forecasting Implied Volatility Smile Surface via Deep Learning and Attention Mechanism," Papers 1912.11059, arXiv.org.
    30. Judith Glaser & Pascal Heider, 2012. "Arbitrage-free approximation of call price surfaces and input data risk," Quantitative Finance, Taylor & Francis Journals, vol. 12(1), pages 61-73, August.
    31. Mnacho Echenim & Emmanuel Gobet & Anne-Claire Maurice, 2022. "Unbiasing and robustifying implied volatility calibration in a cryptocurrency market with large bid-ask spreads and missing quotes," Working Papers hal-03715921, HAL.
    32. Jim Gatheral & Antoine Jacquier, 2012. "Arbitrage-free SVI volatility surfaces," Papers 1204.0646, arXiv.org, revised Mar 2013.
    33. Stefano Galluccio & Yann Le Cam, 2005. "Implied Calibration of Stochastic Volatility Jump Diffusion Models," Finance 0510028, University Library of Munich, Germany.
    34. Wolfgang Härdle & Zdenek Hlavka, 2005. "Dynamics of State Price Densities," SFB 649 Discussion Papers SFB649DP2005-021, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    35. Yanlin Qu & Randall R. Rojas, 2017. "Closed-form Solutions of Relativistic Black-Scholes Equations," Papers 1711.04219, arXiv.org.
    36. Jan Maruhn & Morten Nalholm & Matthias Fengler, 2011. "Static hedges for reverse barrier options with robustness against skew risk: an empirical analysis," Quantitative Finance, Taylor & Francis Journals, vol. 11(5), pages 711-727.
    37. Carol Alexander & Johannes Rauch, 2014. "Model-Free Discretisation-Invariant Swaps and S&P 500 Higher-Moment Risk Premia," Papers 1404.1351, arXiv.org, revised Feb 2016.
    38. Gabriel Drimus & Walter Farkas, 2013. "Local volatility of volatility for the VIX market," Review of Derivatives Research, Springer, vol. 16(3), pages 267-293, October.
    39. Pierre M. Blacque-Florentin & Badr Missaoui, 2015. "Nonparametric and arbitrage-free construction of call surfaces using l1-recovery," Papers 1506.06997, arXiv.org, revised Aug 2016.
    40. Курочкин С.В., 2016. "Выпуклость Множества Цен Опционов Как Необходимое И Достаточное Условие Отсутствия Арбитража," Журнал Экономика и математические методы (ЭММ), Центральный Экономико-Математический Институт (ЦЭМИ), vol. 52(2), pages 103-111, апрель.
    41. Itkin, Andrey, 2015. "To sigmoid-based functional description of the volatility smile," The North American Journal of Economics and Finance, Elsevier, vol. 31(C), pages 264-291.
    42. Bo Zhao & Stewart Hodges, 2013. "Parametric modeling of implied smile functions: a generalized SVI model," Review of Derivatives Research, Springer, vol. 16(1), pages 53-77, April.
    43. Bernd Engelmann & Matthias Fengler & Morten Nalholm & Peter Schwendner, 2006. "Static versus dynamic hedges: an empirical comparison for barrier options," Review of Derivatives Research, Springer, vol. 9(3), pages 239-264, November.
    44. Dilip B. Madan & Wim Schoutens, 2019. "Arbitrage Free Approximations to Candidate Volatility Surface Quotations," JRFM, MDPI, vol. 12(2), pages 1-21, April.
    45. Alan L. Lewis, 2019. "Option-based Equity Risk Premiums," Papers 1910.14522, arXiv.org, revised Apr 2020.
    46. Bernales, Alejandro & Guidolin, Massimo, 2015. "Learning to smile: Can rational learning explain predictable dynamics in the implied volatility surface?," Journal of Financial Markets, Elsevier, vol. 26(C), pages 1-37.
    47. Seung Hwan Lee, 2014. "Estimation of risk-neutral measures using quartic B-spline cumulative distribution functions with power tails," Quantitative Finance, Taylor & Francis Journals, vol. 14(10), pages 1857-1879, October.
    48. Carol Alexander & Alexander Rubinov & Markus Kalepky & Stamatis Leontsinis, 2010. "Regime-Dependent Smile-Adjusted Delta Hedging," ICMA Centre Discussion Papers in Finance icma-dp2010-10, Henley Business School, University of Reading.
    49. Bender Christian & Thiel Matthias, 2020. "Arbitrage-free interpolation of call option prices," Statistics & Risk Modeling, De Gruyter, vol. 37(1-2), pages 55-78, January.
    50. Thomas Mazzoni, 2018. "Asymptotic Expansion of Risk-Neutral Pricing Density," IJFS, MDPI, vol. 6(1), pages 1-26, March.
    51. Abdulwahab Animoku & Ömür Uğur & Yeliz Yolcu-Okur, 2018. "Modeling and implementation of local volatility surfaces in Bayesian framework," Computational Management Science, Springer, vol. 15(2), pages 239-258, June.
    52. Tahar Ferhati, 2020. "Robust Calibration For SVI Model Arbitrage Free," Working Papers hal-02490029, HAL.
    53. Tahar Ferhati, 2020. "SVI Model Free Wings," Working Papers hal-02517572, HAL.
    54. Sylvain Corlay, 2013. "B-spline techniques for volatility modeling," Papers 1306.0995, arXiv.org, revised Jun 2015.
    55. Gaoyue Guo & Antoine Jacquier & Claude Martini & Leo Neufcourt, 2012. "Generalised arbitrage-free SVI volatility surfaces," Papers 1210.7111, arXiv.org, revised May 2016.
    56. Areski Cousin & Djibril Gueye, 2021. "Kriging For Implied Volatility Surface," Working Papers hal-03274026, HAL.

  15. Matthias Fengler & Wolfgang Härdle & Enno Mammen, 2005. "A Dynamic Semiparametric Factor Model for Implied Volatility String Dynamics," SFB 649 Discussion Papers SFB649DP2005-020, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.

    Cited by:

    1. Härdle, Wolfgang & Hlávka, Zdenek, 2009. "Dynamics of state price densities," Journal of Econometrics, Elsevier, vol. 150(1), pages 1-15, May.
    2. Liu, Xialu & Xiao, Han & Chen, Rong, 2016. "Convolutional autoregressive models for functional time series," Journal of Econometrics, Elsevier, vol. 194(2), pages 263-282.
    3. Michal Benko & Wolfgang Härdle & Alois Kneip, 2006. "Common Functional Principal Components," SFB 649 Discussion Papers SFB649DP2006-010, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    4. Ralf Brüggemann & Wolfgang Härdle & Julius Mungo & Carsten Trenkler, 2006. "VAR Modeling for Dynamic Semiparametric Factors of Volatility Strings," SFB 649 Discussion Papers SFB649DP2006-011, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    5. Stefan Trück & Wolfgang Härdle & Rafal Weron, 2012. "The relationship between spot and futures CO2 emission allowance prices in the EU-ETS," HSC Research Reports HSC/12/02, Hugo Steinhaus Center, Wroclaw University of Technology.
    6. Wolfgang Karl Härdle,Piotr Majer & Melanie Schienle, 2012. "Yield Curve Modeling and Forecasting using Semiparametric Factor Dynamics," SFB 649 Discussion Papers SFB649DP2012-048, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    7. Szymon Borak & Matthias Fengler & Wolfgang Härdle, 2005. "DSFM fitting of Implied Volatility Surfaces," SFB 649 Discussion Papers SFB649DP2005-022, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    8. Lam, Clifford & Yao, Qiwei & Bathia, Neil, 2011. "Estimation of latent factors for high-dimensional time series," LSE Research Online Documents on Economics 31549, London School of Economics and Political Science, LSE Library.

  16. Szymon Borak & Matthias Fengler & Wolfgang Härdle, 2005. "DSFM fitting of Implied Volatility Surfaces," SFB 649 Discussion Papers SFB649DP2005-022, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.

    Cited by:

    1. Ralf Brüggemann & Wolfgang Härdle & Julius Mungo & Carsten Trenkler, 2006. "VAR Modeling for Dynamic Semiparametric Factors of Volatility Strings," SFB 649 Discussion Papers SFB649DP2006-011, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    2. Wolfgang Härdle & Julius Mungo, 2007. "Long Memory Persistence in the Factor of Implied Volatility Dynamics," SFB 649 Discussion Papers SFB649DP2007-027, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.

  17. Fengler, Matthias R. & Härdle, Wolfgang & Mammen, Enno, 2003. "Implied volatility string dynamics," SFB 373 Discussion Papers 2003,54, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

    Cited by:

    1. Matthias Fengler & Wolfgang Härdle & Christophe Villa, 2003. "The Dynamics of Implied Volatilities: A Common Principal Components Approach," Review of Derivatives Research, Springer, vol. 6(3), pages 179-202, October.
    2. Enno Mammen & Byeong U. Park & Melanie Schienle, 2012. "Additive Models: Extensions and Related Models," SFB 649 Discussion Papers SFB649DP2012-045, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    3. Enzo Giacomini & Wolfgang Härdle, 2007. "Statistics of Risk Aversion," SFB 649 Discussion Papers SFB649DP2007-025, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    4. Hans Buehler, 2006. "Consistent Variance Curve Models," Finance and Stochastics, Springer, vol. 10(2), pages 178-203, April.
    5. René Carmona & Sergey Nadtochiy, 2009. "Local volatility dynamic models," Finance and Stochastics, Springer, vol. 13(1), pages 1-48, January.
    6. Agnieszka Janek & Tino Kluge & Rafal Weron & Uwe Wystup, 2010. "FX Smile in the Heston Model," Papers 1010.1617, arXiv.org.
    7. Damiano Brigo & Francesco Rapisarda & Abir Sridi, 2013. "The arbitrage-free Multivariate Mixture Dynamics Model: Consistent single-assets and index volatility smiles," Papers 1302.7010, arXiv.org, revised Sep 2014.
    8. Song Song & Peter J. Bickel, 2011. "Large Vector Auto Regressions," Papers 1106.3915, arXiv.org.
    9. Bernales, Alejandro & Guidolin, Massimo, 2014. "Can we forecast the implied volatility surface dynamics of equity options? Predictability and economic value tests," Journal of Banking & Finance, Elsevier, vol. 46(C), pages 326-342.
    10. Michel van der Wel & Sait R. Ozturk & Dick van Dijk, 2015. "Dynamic Factor Models for the Volatility Surface," CREATES Research Papers 2015-13, Department of Economics and Business Economics, Aarhus University.
    11. Matthias R. Fengler, 2005. "Arbitrage-Free Smoothing of the Implied Volatility Surface," SFB 649 Discussion Papers SFB649DP2005-019, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    12. Enzo Giacomini & Wolfgang Härdle & Volker Krätschmer, 2008. "Dynamic Semiparametric Factor Models in Risk Neutral Density Estimation," SFB 649 Discussion Papers SFB649DP2008-038, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    13. Szymon Borak & Wolfgang Härdle & Enno Mammen & Byeong U. Park, 2007. "Time Series Modelling with Semiparametric Factor Dynamics," SFB 649 Discussion Papers SFB649DP2007-023, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    14. Denis Belomestny & Markus Reiß, 2006. "Spectral calibration of exponential Lévy Models [1]," SFB 649 Discussion Papers SFB649DP2006-034, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    15. Hanousek, Jan & Novotný, Jan, 2012. "Price jumps in Visegrad-country stock markets: An empirical analysis," Emerging Markets Review, Elsevier, vol. 13(2), pages 184-201.
    16. Hans Buehler, 2006. "Consistent Variance Curve Models," Finance and Stochastics, Springer, vol. 10(2), pages 178-203, April.
    17. Lena Boneva (Körber) & Oliver Linton & Michael Vogt, 2013. "A semiparametric model for heterogeneous panel data with fixed effects," CeMMAP working papers CWP02/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    18. Wolfgang Karl Härdle & Elena Silyakova, 2012. "Implied Basket Correlation Dynamics," SFB 649 Discussion Papers SFB649DP2012-066, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    19. Song Song & Wolfgang K. Härdle & Ya'acov Ritov, 2010. "High Dimensional Nonstationary Time Series Modelling with Generalized Dynamic Semiparametric Factor Model," SFB 649 Discussion Papers SFB649DP2010-039, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    20. Borak, Szymon & Weron, Rafal, 2008. "A semiparametric factor model for electricity forward curve dynamics," MPRA Paper 10421, University Library of Munich, Germany.
    21. Wolfgang Härdle & Zdenek Hlavka, 2005. "Dynamics of State Price Densities," SFB 649 Discussion Papers SFB649DP2005-021, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    22. Michal Benko & Alois Kneip, 2005. "Common functional component modelling," SFB 649 Discussion Papers SFB649DP2005-016, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    23. M. Benko & M. Fengler & W. Härdle & M. Kopa, 2007. "On extracting information implied in options," Computational Statistics, Springer, vol. 22(4), pages 543-553, December.
    24. Bo Zhao & Stewart Hodges, 2013. "Parametric modeling of implied smile functions: a generalized SVI model," Review of Derivatives Research, Springer, vol. 16(1), pages 53-77, April.
    25. Bernd Engelmann & Matthias Fengler & Morten Nalholm & Peter Schwendner, 2006. "Static versus dynamic hedges: an empirical comparison for barrier options," Review of Derivatives Research, Springer, vol. 9(3), pages 239-264, November.
    26. Wallmeier, Martin, 2012. "Smile in Motion: An Intraday Analysis of Asymmetric Implied Volatility," FSES Working Papers 427, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    27. Maria Grith & Wolfgang Karl Härdle & Melanie Schienle, 2010. "Nonparametric Estimation of Risk-Neutral Densities," SFB 649 Discussion Papers SFB649DP2010-021, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.

  18. Fengler, Matthias R. & Schwendner, Peter, 2003. "Correlation Risk Premia for Multi-Asset Equity Options," SFB 373 Discussion Papers 2003,10, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

    Cited by:

    1. Aydınlı, Gökhan & Härdle, Wolfgang Karl & Neuwirth, E., 2003. "Computational Statistics with Spreadsheets Towards Efficiency, Reproducibility and Security," SFB 373 Discussion Papers 2003,26, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    2. Farnaz Farzan & Khashayar Mahani & Kaveh Gharieh & Mohsen Jafari, 2015. "Microgrid investment under uncertainty: a real option approach using closed form contingent analysis," Annals of Operations Research, Springer, vol. 235(1), pages 259-276, December.
    3. Pellegrino, Tommaso & Sabino, Piergiacomo, 2014. "On the use of the moment-matching technique for pricing and hedging multi-asset spread options," Energy Economics, Elsevier, vol. 45(C), pages 172-185.

  19. Fengler, Matthias R. & Wang, Qihua, 2003. "Fitting the Smile Revisited: A Least Squares Kernel Estimator for the Implied Volatility Surface," SFB 373 Discussion Papers 2003,25, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

    Cited by:

    1. Matthias Fengler & Wolfgang Härdle & Christophe Villa, 2003. "The Dynamics of Implied Volatilities: A Common Principal Components Approach," Review of Derivatives Research, Springer, vol. 6(3), pages 179-202, October.
    2. Vedant Choudhary & Sebastian Jaimungal & Maxime Bergeron, 2023. "FuNVol: A Multi-Asset Implied Volatility Market Simulator using Functional Principal Components and Neural SDEs," Papers 2303.00859, arXiv.org, revised Dec 2023.
    3. Matthias Fengler & Wolfgang Härdle & Enno Mammen, 2005. "A Dynamic Semiparametric Factor Model for Implied Volatility String Dynamics," SFB 649 Discussion Papers SFB649DP2005-020, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    4. Fengler, Matthias R. & Härdle, Wolfgang & Mammen, Enno, 2003. "Implied volatility string dynamics," SFB 373 Discussion Papers 2003,54, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

  20. Christophe Villa & M.R. Fengler & W.K. Hardle, 2003. "The dynamics of implied volatilities : a common principal components approach," Post-Print halshs-00069509, HAL.

    Cited by:

    1. Fearghal Kearney & Han Lin Shang & Lisa Sheenan, 2019. "Implied volatility surface predictability: the case of commodity markets," Papers 1909.11009, arXiv.org.
    2. Vähämaa, Sami & Krylova, Elizaveta & Nikkinen, Jussi, 2005. "Cross-dynamics of volatility term structures implied by foreign exchange options," Working Paper Series 530, European Central Bank.
    3. Francesco Audrino & Dominik Colangelo, 2009. "Option trading strategies based on semi-parametric implied volatility surface prediction," University of St. Gallen Department of Economics working paper series 2009 2009-24, Department of Economics, University of St. Gallen.
    4. T. F. Coleman & Y. Kim & Y. Li & M. Patron, 2007. "Robustly Hedging Variable Annuities With Guarantees Under Jump and Volatility Risks," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 74(2), pages 347-376, June.
    5. He, Xin-Jiang & Zhu, Song-Ping, 2017. "How should a local regime-switching model be calibrated?," Journal of Economic Dynamics and Control, Elsevier, vol. 78(C), pages 149-163.
    6. Pavel Cizek & Karel Komorad, 2005. "Implied Trinomial Trees," SFB 649 Discussion Papers SFB649DP2005-007, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    7. Vedant Choudhary & Sebastian Jaimungal & Maxime Bergeron, 2023. "FuNVol: A Multi-Asset Implied Volatility Market Simulator using Functional Principal Components and Neural SDEs," Papers 2303.00859, arXiv.org, revised Dec 2023.
    8. Kim, Alisa & Trimborn, Simon & Härdle, Wolfgang Karl, 2021. "VCRIX — A volatility index for crypto-currencies," International Review of Financial Analysis, Elsevier, vol. 78(C).
    9. Laurini, Márcio P., 2007. "Imposing No-Arbitrage Conditions In Implied Volatility Surfaces Using Constrained Smoothing Splines," Insper Working Papers wpe_89, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
    10. Beer, Simone & Braun, Alexander, 2022. "Market-consistent valuation of natural catastrophe risk," Journal of Banking & Finance, Elsevier, vol. 134(C).
    11. Kun Li & Joseph D. Cursio & Yunchuan Sun, 2018. "Principal Component Analysis of Price Fluctuation in the Smart Grid Electricity Market," Sustainability, MDPI, vol. 10(11), pages 1-16, November.
    12. Cizek, P. & Tamine, J. & Härdle, W.K., 2006. "Smoothed L-estimation of Regression Function," Discussion Paper 2006-20, Tilburg University, Center for Economic Research.
    13. Matthias Fengler & Wolfgang Härdle & Enno Mammen, 2005. "A Dynamic Semiparametric Factor Model for Implied Volatility String Dynamics," SFB 649 Discussion Papers SFB649DP2005-020, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    14. Tanha, Hassan & Dempsey, Michael, 2016. "The evolving dynamics of the Australian SPI 200 implied volatility surface," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 43(C), pages 44-57.
    15. Michal Benko & Wolfgang Härdle & Alois Kneip, 2006. "Common Functional Principal Components," SFB 649 Discussion Papers SFB649DP2006-010, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    16. Bernales, Alejandro & Guidolin, Massimo, 2014. "Can we forecast the implied volatility surface dynamics of equity options? Predictability and economic value tests," Journal of Banking & Finance, Elsevier, vol. 46(C), pages 326-342.
    17. Fengler, Matthias & Hin, Lin-Yee, 2011. "Semi-nonparametric estimation of the call price surface under strike and time-to-expiry no-arbitrage constraints," Economics Working Paper Series 1136, University of St. Gallen, School of Economics and Political Science, revised May 2013.
    18. Michel van der Wel & Sait R. Ozturk & Dick van Dijk, 2015. "Dynamic Factor Models for the Volatility Surface," CREATES Research Papers 2015-13, Department of Economics and Business Economics, Aarhus University.
    19. Ralf Brüggemann & Wolfgang Härdle & Julius Mungo & Carsten Trenkler, 2006. "VAR Modeling for Dynamic Semiparametric Factors of Volatility Strings," SFB 649 Discussion Papers SFB649DP2006-011, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    20. Barletta, Andrea & Santucci de Magistris, Paolo & Sloth, David, 2019. "It only takes a few moments to hedge options," Journal of Economic Dynamics and Control, Elsevier, vol. 100(C), pages 251-269.
    21. Martin Magris & Perttu Barholm & Juho Kanniainen, 2017. "Implied volatility smile dynamics in the presence of jumps," Papers 1711.02925, arXiv.org, revised May 2020.
    22. Miloš Kopa & Sebastiano Vitali & Tomáš Tichý & Radek Hendrych, 2017. "Implied volatility and state price density estimation: arbitrage analysis," Computational Management Science, Springer, vol. 14(4), pages 559-583, October.
    23. Wenyong Zhang & Lingfei Li & Gongqiu Zhang, 2021. "A Two-Step Framework for Arbitrage-Free Prediction of the Implied Volatility Surface," Papers 2106.07177, arXiv.org, revised Jan 2022.
    24. Sergey Nasekin & Wolfgang Karl Hardle, 2020. "Model-driven statistical arbitrage on LETF option markets," Papers 2009.09713, arXiv.org.
    25. Han Lin Shang, 2011. "A survey of functional principal component analysis," Monash Econometrics and Business Statistics Working Papers 6/11, Monash University, Department of Econometrics and Business Statistics.
    26. Shengli Chen & Zili Zhang, 2019. "Forecasting Implied Volatility Smile Surface via Deep Learning and Attention Mechanism," Papers 1912.11059, arXiv.org.
    27. Carol Alexander & Leonardo M. Nogueira, 2004. "Hedging with Stochastic and Local Volatility," ICMA Centre Discussion Papers in Finance icma-dp2004-10, Henley Business School, University of Reading, revised Dec 2004.
    28. Da Fonseca, José & Gottschalk, Katrin, 2014. "Cross-hedging strategies between CDS spreads and option volatility during crises," Journal of International Money and Finance, Elsevier, vol. 49(PB), pages 386-400.
    29. Fengler, Matthias R. & Wang, Qihua, 2003. "Fitting the Smile Revisited: A Least Squares Kernel Estimator for the Implied Volatility Surface," SFB 373 Discussion Papers 2003,25, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    30. Gang Li & Chu Zhang, 2010. "On the Number of State Variables in Options Pricing," Management Science, INFORMS, vol. 56(11), pages 2058-2075, November.
    31. Michal Benko & Alois Kneip, 2005. "Common functional component modelling," SFB 649 Discussion Papers SFB649DP2005-016, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    32. Xiu Xu & Cathy Yi-Hsuan Chen & Wolfgang Karl Härdle, 2019. "Dynamic credit default swap curves in a network topology," Quantitative Finance, Taylor & Francis Journals, vol. 19(10), pages 1705-1726, October.
    33. Bali, Juan Lucas & Boente, Graciela, 2017. "Robust estimators under a functional common principal components model," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 424-440.
    34. Chantziara, Thalia & Skiadopoulos, George, 2008. "Can the dynamics of the term structure of petroleum futures be forecasted? Evidence from major markets," Energy Economics, Elsevier, vol. 30(3), pages 962-985, May.
    35. Massimo Guidolin & Kai Wang, 2022. "The Empirical Performance of Option Implied Volatility Surface-Driven Optimal Portfolios," BAFFI CAREFIN Working Papers 22190, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    36. Mónica Fuentes & Sergio Godoy, 2005. "Sovereign Spread in Emerging Markets: A Principal Component Analysis," Working Papers Central Bank of Chile 333, Central Bank of Chile.
    37. Yueh-Neng Lin & Shih-Kuo Yeh & Shih-Ching Chuan & Steven J. Jordan, 2008. "The link between intraday signals and call warrant mispricing," The Service Industries Journal, Taylor & Francis Journals, vol. 30(13), pages 2273-2288, November.
    38. Chen, Si & Zhou, Zhen & Li, Shenghong, 2016. "An efficient estimate and forecast of the implied volatility surface: A nonlinear Kalman filter approach," Economic Modelling, Elsevier, vol. 58(C), pages 655-664.
    39. Itkin, Andrey, 2015. "To sigmoid-based functional description of the volatility smile," The North American Journal of Economics and Finance, Elsevier, vol. 31(C), pages 264-291.
    40. Szymon Borak & Matthias Fengler & Wolfgang Härdle, 2005. "DSFM fitting of Implied Volatility Surfaces," SFB 649 Discussion Papers SFB649DP2005-022, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    41. Bernd Engelmann & Matthias Fengler & Morten Nalholm & Peter Schwendner, 2006. "Static versus dynamic hedges: an empirical comparison for barrier options," Review of Derivatives Research, Springer, vol. 9(3), pages 239-264, November.
    42. Fengler, Matthias R. & Härdle, Wolfgang & Mammen, Enno, 2003. "Implied volatility string dynamics," SFB 373 Discussion Papers 2003,54, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    43. Wallmeier, Martin, 2012. "Smile in Motion: An Intraday Analysis of Asymmetric Implied Volatility," FSES Working Papers 427, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    44. Sebastiano Vitali & Miloš Kopa & Gabriele Giana, 2023. "Implied volatility smoothing at COVID-19 times," Computational Management Science, Springer, vol. 20(1), pages 1-42, December.
    45. Panigirtzoglou, Nikolaos & Skiadopoulos, George, 2004. "A new approach to modeling the dynamics of implied distributions: Theory and evidence from the S&P 500 options," Journal of Banking & Finance, Elsevier, vol. 28(7), pages 1499-1520, July.

  21. Fengler, Matthias R. & Herwartz, Helmut, 2001. "Multivariate volatility models," SFB 373 Discussion Papers 2001,74, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

    Cited by:

    1. Hafner, C.M. & Herwartz, H., 2002. "Testing for vector autoregressive dynamics under heteroskedasticity," Econometric Institute Research Papers EI 2002-36, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    2. Fengler, Matthias R. & Schwendner, Peter, 2003. "Correlation Risk Premia for Multi-Asset Equity Options," SFB 373 Discussion Papers 2003,10, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

  22. Fengler, Matthias R. & Härdle, Wolfgang & Schmidt, Peter, 2001. "The analysis of implied volatilities," SFB 373 Discussion Papers 2001,73, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

    Cited by:

    1. Härdle, Wolfgang & Schmidt, Peter, 2000. "Common factors governing VDAX movements and the maximum loss," SFB 373 Discussion Papers 2000,97, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    2. Fengler, Matthias R. & Wang, Qihua, 2003. "Fitting the Smile Revisited: A Least Squares Kernel Estimator for the Implied Volatility Surface," SFB 373 Discussion Papers 2003,25, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

Articles

  1. Chen, Cathy Yi-Hsuan & Fengler, Matthias R. & Härdle, Wolfgang Karl & Liu, Yanchu, 2022. "Media-expressed tone, option characteristics, and stock return predictability," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    See citations under working paper version above.
  2. Matthias R. Fengler & Helmut Herwartz, 2018. "Measuring Spot Variance Spillovers when (Co)variances are Time†varying – The Case of Multivariate GARCH Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 80(1), pages 135-159, February.

    Cited by:

    1. Francesco Caloia & Andrea Cipollini & Silvia Muzzioli, 2018. "On the financial connectedness of the commodity market: a replication of the Diebold and Yilmaz (2012) study," Department of Economics 0131, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
    2. BenSaïda, Ahmed, 2019. "Good and bad volatility spillovers: An asymmetric connectedness," Journal of Financial Markets, Elsevier, vol. 43(C), pages 78-95.
    3. Apostolakis, George N. & Floros, Christos & Gkillas, Konstantinos & Wohar, Mark, 2021. "Political uncertainty, COVID-19 pandemic and stock market volatility transmission," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
    4. Lukas Boeckelmann & Arthur Stalla-Bourdillon, 2021. "Structural Estimation of Time-Varying Spillovers:an Application to International Credit Risk Transmission," Working Papers hal-03338209, HAL.
    5. Hafner, Christian & Herwartz, Helmut & Maxand, Simone, 2020. "Identification of structural multivariate GARCH models," LIDAM Reprints ISBA 2020032, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    6. Fengler, Matthias & Polivka, Jeannine, 2021. "Identifying structural shocks to volatility through a proxy-MGARCH model," Economics Working Paper Series 2103, University of St. Gallen, School of Economics and Political Science, revised May 2021.
    7. Caloia, Francesco Giuseppe & Cipollini, Andrea & Muzzioli, Silvia, 2019. "How do normalization schemes affect net spillovers? A replication of the Diebold and Yilmaz (2012) study," Energy Economics, Elsevier, vol. 84(C).
    8. Helmut Herwartz & Alberto Saucedo, 2020. "Food–oil volatility spillovers and the impact of distinct biofuel policies on price uncertainties on feedstock markets," Agricultural Economics, International Association of Agricultural Economists, vol. 51(3), pages 387-402, May.

  3. Fengler, Matthias R. & Okhrin, Ostap, 2016. "Managing risk with a realized copula parameter," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 131-152.

    Cited by:

    1. Quanrui Song & Jianxu Liu & Songsak Sriboonchitta, 2019. "Risk Measurement of Stock Markets in BRICS, G7, and G20: Vine Copulas versus Factor Copulas," Mathematics, MDPI, vol. 7(3), pages 1-16, March.
    2. Niels Gillmann & Ostap Okhrin, 2023. "Adaptive local VAR for dynamic economic policy uncertainty spillover," Papers 2302.02808, arXiv.org.
    3. Katja Ignatieva & Natalia Ponomareva, 2017. "Commodity currencies and commodity prices: modelling static and time-varying dependence," Applied Economics, Taylor & Francis Journals, vol. 49(15), pages 1491-1512, March.
    4. Jean-David Fermanian, 2017. "Recent Developments in Copula Models," Econometrics, MDPI, vol. 5(3), pages 1-3, July.
    5. Rewat Khanthaporn, 2022. "Analysis of Nonlinear Comovement of Benchmark Thai Government Bond Yields," PIER Discussion Papers 183, Puey Ungphakorn Institute for Economic Research.
    6. Ostap Okhrin & Anastasija Tetereva, 2017. "The Realized Hierarchical Archimedean Copula in Risk Modelling," Econometrics, MDPI, vol. 5(2), pages 1-31, June.

  4. 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.
    See citations under working paper version above.
  5. 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.
  6. Fengler, Matthias R. & Hin, Lin-Yee, 2015. "Semi-nonparametric estimation of the call-option price surface under strike and time-to-expiry no-arbitrage constraints," Journal of Econometrics, Elsevier, vol. 184(2), pages 242-261.

    Cited by:

    1. Fearghal Kearney & Han Lin Shang & Lisa Sheenan, 2019. "Implied volatility surface predictability: the case of commodity markets," Papers 1909.11009, arXiv.org.
    2. Dillschneider, Yannick & Maurer, Raimond, 2019. "Functional Ross recovery: Theoretical results and empirical tests," Journal of Economic Dynamics and Control, Elsevier, vol. 108(C).
    3. Kentaro Hoshisashi & Carolyn E. Phelan & Paolo Barucca, 2023. "No-Arbitrage Deep Calibration for Volatility Smile and Skewness," Papers 2310.16703, arXiv.org, revised Jan 2024.
    4. Taboga, Marco, 2016. "Option-implied probability distributions: How reliable? How jagged?," International Review of Economics & Finance, Elsevier, vol. 45(C), pages 453-469.
    5. Arindam Kundu & Sumit Kumar & Nutan Kumar Tomar, 2019. "Option Implied Risk-Neutral Density Estimation: A Robust and Flexible Method," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 705-728, August.
    6. Cao, Yi & Liu, Xiaoquan & Zhai, Jia, 2021. "Option valuation under no-arbitrage constraints with neural networks," European Journal of Operational Research, Elsevier, vol. 293(1), pages 361-374.
    7. Gianluca Cassese, 2015. "Nonparametric Estimates of Option Prices Using Superhedging," Working Papers 293, University of Milano-Bicocca, Department of Economics, revised Feb 2015.
    8. Samuel N. Cohen & Christoph Reisinger & Sheng Wang, 2020. "Detecting and repairing arbitrage in traded option prices," Papers 2008.09454, arXiv.org.
    9. Fengler, Matthias R. & Hin, Lin-Yee, 2015. "A simple and general approach to fitting the discount curve under no-arbitrage constraints," Finance Research Letters, Elsevier, vol. 15(C), pages 78-84.
    10. Miloš Kopa & Sebastiano Vitali & Tomáš Tichý & Radek Hendrych, 2017. "Implied volatility and state price density estimation: arbitrage analysis," Computational Management Science, Springer, vol. 14(4), pages 559-583, October.
    11. Horatio Cuesdeanu & Jens Carsten Jackwerth, 2018. "The pricing kernel puzzle: survey and outlook," Annals of Finance, Springer, vol. 14(3), pages 289-329, August.
    12. Wenyong Zhang & Lingfei Li & Gongqiu Zhang, 2021. "A Two-Step Framework for Arbitrage-Free Prediction of the Implied Volatility Surface," Papers 2106.07177, arXiv.org, revised Jan 2022.
    13. Samuel N. Cohen & Christoph Reisinger & Sheng Wang, 2021. "Arbitrage-free neural-SDE market models," Papers 2105.11053, arXiv.org, revised Aug 2021.
    14. Pierre M. Blacque-Florentin & Badr Missaoui, 2015. "Nonparametric and arbitrage-free construction of call surfaces using l1-recovery," Papers 1506.06997, arXiv.org, revised Aug 2016.
    15. Dalderop, Jeroen, 2020. "Nonparametric filtering of conditional state-price densities," Journal of Econometrics, Elsevier, vol. 214(2), pages 295-325.
    16. Bender Christian & Thiel Matthias, 2020. "Arbitrage-free interpolation of call option prices," Statistics & Risk Modeling, De Gruyter, vol. 37(1-2), pages 55-78, January.
    17. Ana M. Monteiro & Antonio A. F. Santos, 2020. "Conditional risk-neutral density from option prices by local polynomial kernel smoothing with no-arbitrage constraints," Review of Derivatives Research, Springer, vol. 23(1), pages 41-61, April.
    18. Sebastiano Vitali & Miloš Kopa & Gabriele Giana, 2023. "Implied volatility smoothing at COVID-19 times," Computational Management Science, Springer, vol. 20(1), pages 1-42, December.
    19. Ana M. Monteiro & António A. F. Santos, 2022. "Option prices for risk‐neutral density estimation using nonparametric methods through big data and large‐scale problems," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(1), pages 152-171, January.

  7. Fengler, M.R. & Mammen, E. & Vogt, M., 2015. "Specification and structural break tests for additive models with applications to realized variance data," Journal of Econometrics, Elsevier, vol. 188(1), pages 196-218.

    Cited by:

    1. Peter Malec, 2016. "A Semiparametric Intraday GARCH Model," Cambridge Working Papers in Economics 1633, Faculty of Economics, University of Cambridge.
    2. Buncic, Daniel & Gisler, Katja I.M., 2016. "Global equity market volatility spillovers: A broader role for the United States," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1317-1339.
    3. Luo, Jiawen & Demirer, Riza & Gupta, Rangan & Ji, Qiang, 2022. "Forecasting oil and gold volatilities with sentiment indicators under structural breaks," Energy Economics, Elsevier, vol. 105(C).
    4. Mammen, Enno & Martínez-Miranda, María Dolores & Nielsen, Jens Perch & Vogt, Michael, 2021. "Calendar effect and in-sample forecasting," Insurance: Mathematics and Economics, Elsevier, vol. 96(C), pages 31-52.
    5. Xu Gong & Boqiang Lin, 2022. "Predicting the volatility of crude oil futures: The roles of leverage effects and structural changes," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 610-640, January.
    6. 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.
    7. Lu, Botao & Ma, Feng & Wang, Jiqian & Ding, Hui & Wahab, M.I.M., 2021. "Harnessing the decomposed realized measures for volatility forecasting: Evidence from the US stock market," International Review of Economics & Finance, Elsevier, vol. 72(C), pages 672-689.

  8. Fengler, Matthias R. & Hin, Lin-Yee, 2015. "A simple and general approach to fitting the discount curve under no-arbitrage constraints," Finance Research Letters, Elsevier, vol. 15(C), pages 78-84.
    See citations under working paper version above.
  9. Matthias R. Fengler & Helmut Herwartz & Christian Werner, 2012. "A Dynamic Copula Approach to Recovering the Index Implied Volatility Skew," Journal of Financial Econometrics, Oxford University Press, vol. 10(3), pages 457-493, June.
    See citations under working paper version above.
  10. Matthias Fengler, 2009. "Arbitrage-free smoothing of the implied volatility surface," Quantitative Finance, Taylor & Francis Journals, vol. 9(4), pages 417-428.
    See citations under working paper version above.
  11. M. Benko & M. Fengler & W. Härdle & M. Kopa, 2007. "On extracting information implied in options," Computational Statistics, Springer, vol. 22(4), pages 543-553, December.

    Cited by:

    1. Martin Tegn'er & Stephen Roberts, 2019. "A Probabilistic Approach to Nonparametric Local Volatility," Papers 1901.06021, arXiv.org, revised Jan 2019.
    2. Arindam Kundu & Sumit Kumar & Nutan Kumar Tomar, 2019. "Option Implied Risk-Neutral Density Estimation: A Robust and Flexible Method," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 705-728, August.
    3. Fengler, Matthias & Hin, Lin-Yee, 2011. "Semi-nonparametric estimation of the call price surface under strike and time-to-expiry no-arbitrage constraints," Economics Working Paper Series 1136, University of St. Gallen, School of Economics and Political Science, revised May 2013.
    4. Matthias Fengler, 2010. "Option data and modeling BSM implied volatility," University of St. Gallen Department of Economics working paper series 2010 2010-32, Department of Economics, University of St. Gallen.
    5. Miloš Kopa & Sebastiano Vitali & Tomáš Tichý & Radek Hendrych, 2017. "Implied volatility and state price density estimation: arbitrage analysis," Computational Management Science, Springer, vol. 14(4), pages 559-583, October.
    6. Maciak, Matúš, 2021. "Quantile LASSO with changepoints in panel data models applied to option pricing," Econometrics and Statistics, Elsevier, vol. 20(C), pages 166-175.
    7. Fengler, Matthias R. & Hin, Lin-Yee, 2015. "Semi-nonparametric estimation of the call-option price surface under strike and time-to-expiry no-arbitrage constraints," Journal of Econometrics, Elsevier, vol. 184(2), pages 242-261.
    8. Kim, Namhyoung & Lee, Jaewook, 2013. "No-arbitrage implied volatility functions: Empirical evidence from KOSPI 200 index options," Journal of Empirical Finance, Elsevier, vol. 21(C), pages 36-53.
    9. Dietmar P. J. Leisen, 2017. "The shape of small sample biases in pricing kernel estimations," Quantitative Finance, Taylor & Francis Journals, vol. 17(6), pages 943-958, June.
    10. Cristian Homescu, 2011. "Implied Volatility Surface: Construction Methodologies and Characteristics," Papers 1107.1834, arXiv.org.
    11. Judith Glaser & Pascal Heider, 2012. "Arbitrage-free approximation of call price surfaces and input data risk," Quantitative Finance, Taylor & Francis Journals, vol. 12(1), pages 61-73, August.
    12. David Volkmann, 2021. "Explaining S&P500 option returns: an implied risk-adjusted approach," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(2), pages 665-685, June.
    13. Zdeněk Drábek & Miloš Kopa & Matúš Maciak & Michal Pešta & Sebastiano Vitali, 2023. "Investment disputes and their explicit role in option market uncertainty and overall risk instability," Computational Management Science, Springer, vol. 20(1), pages 1-25, December.
    14. Tahar Ferhati, 2020. "Robust Calibration For SVI Model Arbitrage Free," Working Papers hal-02490029, HAL.
    15. Tahar Ferhati, 2020. "SVI Model Free Wings," Working Papers hal-02517572, HAL.
    16. Sebastiano Vitali & Miloš Kopa & Gabriele Giana, 2023. "Implied volatility smoothing at COVID-19 times," Computational Management Science, Springer, vol. 20(1), pages 1-42, December.
    17. Maciak, Matúš, 2021. "Quantile LASSO in arbitrage-free option markets," Econometrics and Statistics, Elsevier, vol. 18(C), pages 106-116.

  12. Matthias R. Fengler & Joachim K. Winter, 2007. "Price variability and price dispersion in a stable monetary environment: evidence from German retail markets," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 28(7), pages 789-801.
    See citations under working paper version above.
  13. Bernd Engelmann & Matthias Fengler & Morten Nalholm & Peter Schwendner, 2006. "Static versus dynamic hedges: an empirical comparison for barrier options," Review of Derivatives Research, Springer, vol. 9(3), pages 239-264, November.

    Cited by:

    1. Lu, Xiaoping & Putri, Endah R.M., 2020. "A semi-analytic valuation of American options under a two-state regime-switching economy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 538(C).
    2. Leonidas S. Rompolis & Elias Tzavalis, 2017. "Pricing and hedging contingent claims using variance and higher order moment swaps," Quantitative Finance, Taylor & Francis Journals, vol. 17(4), pages 531-550, April.
    3. Lee, Hangsuck & Choi, Yang Ho & Lee, Gaeun, 2022. "Multi-step barrier products and static hedging," The North American Journal of Economics and Finance, Elsevier, vol. 61(C).
    4. Jeonggyu Huh & Jaegi Jeon & Yong-Ki Ma, 2020. "Static Hedges of Barrier Options Under Fast Mean-Reverting Stochastic Volatility," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 185-210, January.
    5. Thorsten Rheinlander & Michael Schmutz, 2012. "Quasi self-dual exponential L\'evy processes," Papers 1201.5132, arXiv.org.
    6. Yunbi An & Wulin Suo, 2009. "An Empirical Comparison of Option‐Pricing Models in Hedging Exotic Options," Financial Management, Financial Management Association International, vol. 38(4), pages 889-914, December.
    7. Ilya Molchanov & Michael Schmutz, 2009. "Exchangeability type properties of asset prices," Papers 0901.4914, arXiv.org, revised Apr 2011.
    8. Grzegorz Krzy.zanowski & Marcin Magdziarz, 2020. "A computational weighted finite difference method for American and barrier options in subdiffusive Black-Scholes model," Papers 2003.05358, arXiv.org, revised Dec 2020.
    9. Augusto Blanc-Blocquel & Luis Ortiz-Gracia & Rodolfo Oviedo, 2023. "Hedging At-the-money Digital Options Near Maturity," Methodology and Computing in Applied Probability, Springer, vol. 25(1), pages 1-18, March.
    10. Philipp Mayer & Natalie Packham & Wolfgang Schmidt, 2015. "Static hedging under maturity mismatch," Finance and Stochastics, Springer, vol. 19(3), pages 509-539, July.
    11. Jan Maruhn & Morten Nalholm & Matthias Fengler, 2011. "Static hedges for reverse barrier options with robustness against skew risk: an empirical analysis," Quantitative Finance, Taylor & Francis Journals, vol. 11(5), pages 711-727.
    12. Johannes Siven & Rolf Poulsen, 2009. "Auto-static for the people: risk-minimizing hedges of barrier options," Review of Derivatives Research, Springer, vol. 12(3), pages 193-211, October.
    13. Hansjörg Albrecher & Philipp Mayer, 2010. "Semi-Static Hedging Strategies For Exotic Options," World Scientific Book Chapters, in: Rüdiger Kiesel & Matthias Scherer & Rudi Zagst (ed.), Alternative Investments And Strategies, chapter 14, pages 345-373, World Scientific Publishing Co. Pte. Ltd..

  14. Matthias Fengler & Wolfgang Härdle & Christophe Villa, 2003. "The Dynamics of Implied Volatilities: A Common Principal Components Approach," Review of Derivatives Research, Springer, vol. 6(3), pages 179-202, October.
    See citations under working paper version above.
  15. Matthias R. Fengler & Wolfgang K. Härdle & Enno Mammen, 0. "A semiparametric factor model for implied volatility surface dynamics," Journal of Financial Econometrics, Oxford University Press, vol. 5(2), pages 189-218.

    Cited by:

    1. Fearghal Kearney & Han Lin Shang & Lisa Sheenan, 2019. "Implied volatility surface predictability: the case of commodity markets," Papers 1909.11009, arXiv.org.
    2. Francesco Audrino & Dominik Colangelo, 2009. "Option trading strategies based on semi-parametric implied volatility surface prediction," University of St. Gallen Department of Economics working paper series 2009 2009-24, Department of Economics, University of St. Gallen.
    3. 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.
    4. Enno Mammen & Byeong U. Park & Melanie Schienle, 2012. "Additive Models: Extensions and Related Models," SFB 649 Discussion Papers SFB649DP2012-045, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    5. Härdle Wolfgang Karl & Silyakova Elena, 2016. "Implied basket correlation dynamics," Statistics & Risk Modeling, De Gruyter, vol. 33(1-2), pages 1-20, September.
    6. Enzo Giacomini & Wolfgang Härdle, 2007. "Statistics of Risk Aversion," SFB 649 Discussion Papers SFB649DP2007-025, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    7. Barbara Choroś-Tomczyk & Wolfgang Karl Härdle & Ostap Okhrin, 2013. "CDO Surfaces Dynamics," SFB 649 Discussion Papers SFB649DP2013-032, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    8. Georgios Chalamandaris & Andrianos Tsekrekos, 2013. "Explanatory Factors and Causality in the Dynamics of Volatility Surfaces Implied from OTC Asian–Pacific Currency Options," Computational Economics, Springer;Society for Computational Economics, vol. 41(3), pages 327-358, March.
    9. Taboga, Marco, 2016. "Option-implied probability distributions: How reliable? How jagged?," International Review of Economics & Finance, Elsevier, vol. 45(C), pages 453-469.
    10. Song Song & Peter J. Bickel, 2011. "Large Vector Auto Regressions," Papers 1106.3915, arXiv.org.
    11. Bernales, Alejandro & Guidolin, Massimo, 2014. "Can we forecast the implied volatility surface dynamics of equity options? Predictability and economic value tests," Journal of Banking & Finance, Elsevier, vol. 46(C), pages 326-342.
    12. Fengler, Matthias & Hin, Lin-Yee, 2011. "Semi-nonparametric estimation of the call price surface under strike and time-to-expiry no-arbitrage constraints," Economics Working Paper Series 1136, University of St. Gallen, School of Economics and Political Science, revised May 2013.
    13. Bastien Baldacci, 2020. "High-frequency dynamics of the implied volatility surface," Papers 2012.10875, arXiv.org.
    14. Michel van der Wel & Sait R. Ozturk & Dick van Dijk, 2015. "Dynamic Factor Models for the Volatility Surface," CREATES Research Papers 2015-13, Department of Economics and Business Economics, Aarhus University.
    15. Enzo Giacomini & Wolfgang Härdle & Volker Krätschmer, 2008. "Dynamic Semiparametric Factor Models in Risk Neutral Density Estimation," SFB 649 Discussion Papers SFB649DP2008-038, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    16. Szymon Borak & Wolfgang Härdle & Enno Mammen & Byeong U. Park, 2007. "Time Series Modelling with Semiparametric Factor Dynamics," SFB 649 Discussion Papers SFB649DP2007-023, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    17. Hanousek, Jan & Novotný, Jan, 2012. "Price jumps in Visegrad-country stock markets: An empirical analysis," Emerging Markets Review, Elsevier, vol. 13(2), pages 184-201.
    18. Wenyong Zhang & Lingfei Li & Gongqiu Zhang, 2021. "A Two-Step Framework for Arbitrage-Free Prediction of the Implied Volatility Surface," Papers 2106.07177, arXiv.org, revised Jan 2022.
    19. Fengler, Matthias R. & Hin, Lin-Yee, 2015. "Semi-nonparametric estimation of the call-option price surface under strike and time-to-expiry no-arbitrage constraints," Journal of Econometrics, Elsevier, vol. 184(2), pages 242-261.
    20. Choroś-Tomczyk, Barbara & Härdle, Wolfgang Karl & Okhrin, Ostap, 2016. "A semiparametric factor model for CDO surfaces dynamics," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 151-163.
    21. Lena Boneva (Körber) & Oliver Linton & Michael Vogt, 2013. "A semiparametric model for heterogeneous panel data with fixed effects," CeMMAP working papers CWP02/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    22. Oliver Linton & Jens Perch Nielsen & Søren Feodor Nielsen, 2009. "Non-parametric regression with a latent time series," Econometrics Journal, Royal Economic Society, vol. 12(2), pages 187-207, July.
    23. Sergey Nasekin & Wolfgang Karl Hardle, 2020. "Model-driven statistical arbitrage on LETF option markets," Papers 2009.09713, arXiv.org.
    24. Da Fonseca, José & Gottschalk, Katrin, 2014. "Cross-hedging strategies between CDS spreads and option volatility during crises," Journal of International Money and Finance, Elsevier, vol. 49(PB), pages 386-400.
    25. Yaxiong Zeng & Diego Klabjan, 2017. "Online Adaptive Machine Learning Based Algorithm for Implied Volatility Surface Modeling," Papers 1706.01833, arXiv.org, revised Jun 2018.
    26. Likai Chen & Weining Wang & Wei Biao Wu, 2017. "Dynamic Semiparametric Factor Model with a Common Break," SFB 649 Discussion Papers SFB649DP2017-026, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    27. Stefan Trück & Wolfgang Härdle & Rafal Weron, 2012. "The relationship between spot and futures CO2 emission allowance prices in the EU-ETS," HSC Research Reports HSC/12/02, Hugo Steinhaus Center, Wroclaw University of Technology.
    28. Wolfgang Karl Härdle & Elena Silyakova, 2012. "Implied Basket Correlation Dynamics," SFB 649 Discussion Papers SFB649DP2012-066, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    29. Song Song & Wolfgang K. Härdle & Ya'acov Ritov, 2010. "High Dimensional Nonstationary Time Series Modelling with Generalized Dynamic Semiparametric Factor Model," SFB 649 Discussion Papers SFB649DP2010-039, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    30. Borak, Szymon & Weron, Rafal, 2008. "A semiparametric factor model for electricity forward curve dynamics," MPRA Paper 10421, University Library of Munich, Germany.
    31. Jan Maruhn & Morten Nalholm & Matthias Fengler, 2011. "Static hedges for reverse barrier options with robustness against skew risk: an empirical analysis," Quantitative Finance, Taylor & Francis Journals, vol. 11(5), pages 711-727.
    32. Xiaofeng Cao & Ostap Okhrin & Martin Odening & Matthias Ritter, 2015. "Modelling spatio-temporal variability of temperature," Computational Statistics, Springer, vol. 30(3), pages 745-766, September.
    33. Ostap Okhrin & Stefan Trück, 2015. "Editorial to the special issue on Applicable semiparametrics of computational statistics," Computational Statistics, Springer, vol. 30(3), pages 641-646, September.
    34. Bernd Engelmann & Matthias Fengler & Morten Nalholm & Peter Schwendner, 2006. "Static versus dynamic hedges: an empirical comparison for barrier options," Review of Derivatives Research, Springer, vol. 9(3), pages 239-264, November.
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