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Daniele Massacci

Personal Details

First Name:Daniele
Middle Name:
Last Name:Massacci
Suffix:
RePEc Short-ID:pma1104
[This author has chosen not to make the email address public]
https://sites.google.com/site/danielemassacci/home

Affiliation

Business School
King's College London

London, United Kingdom
http://www.kcl.ac.uk/business
RePEc:edi:dmkcluk (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Alessandro Giovannelli & Daniele Massacci & Stefano Soccorsi, 2020. "Forecasting Stock Returns with Large Dimensional Factor Models," Working Papers 305661169, Lancaster University Management School, Economics Department.
  2. Fullwood, Jonathan & Massacci, Daniele, 2018. "Liquidity resilience in the UK gilt futures market: evidence from the order book," Bank of England working papers 744, Bank of England.

Articles

  1. Daniele Massacci, 2019. "Unstable Diffusion Indexes: With an Application to Bond Risk Premia," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(6), pages 1376-1400, December.
  2. Massacci, Daniele, 2017. "Least squares estimation of large dimensional threshold factor models," Journal of Econometrics, Elsevier, vol. 197(1), pages 101-129.
  3. Daniele Massacci, 2017. "Tail Risk Dynamics in Stock Returns: Links to the Macroeconomy and Global Markets Connectedness," Management Science, INFORMS, vol. 63(9), pages 3072-3089, September.
  4. Daniele Massacci, 2015. "Predicting the Distribution of Stock Returns: Model Formulation, Statistical Evaluation, VaR Analysis and Economic Significance," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(3), pages 191-208, April.
  5. Massacci, Daniele, 2014. "A two-regime threshold model with conditional skewed Student t distributions for stock returns," Economic Modelling, Elsevier, vol. 43(C), pages 9-20.
  6. Massacci, Daniele, 2013. "A switching model with flexible threshold variable: With an application to nonlinear dynamics in stock returns," Economics Letters, Elsevier, vol. 119(2), pages 199-203.
  7. Massacci, Daniele, 2013. "A variable addition test for exogeneity in structural threshold models," Economics Letters, Elsevier, vol. 120(1), pages 5-9.
  8. Massacci, Daniele, 2012. "A simple test for linearity against exponential smooth transition models with endogenous variables," Economics Letters, Elsevier, vol. 117(3), pages 851-856.

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. Alessandro Giovannelli & Daniele Massacci & Stefano Soccorsi, 2020. "Forecasting Stock Returns with Large Dimensional Factor Models," Working Papers 305661169, Lancaster University Management School, Economics Department.

    Cited by:

    1. Carlos Cesar Trucios-Maza & João H. G Mazzeu & Luis K. Hotta & Pedro L. Valls Pereira & Marc Hallin, 2019. "On the robustness of the general dynamic factor model with infinite-dimensional space: identification, estimation, and forecasting," Working Papers ECARES 2019-32, ULB -- Universite Libre de Bruxelles.
    2. Trucíos Maza, Carlos César & Mazzeu, João H. G. & Hotta, Luiz Koodi & Pereira, Pedro L. Valls & Hallin, Marc, 2020. "Robustness and the general dynamic factor model with infinite-dimensional space: identification, estimation, and forecasting," Textos para discussão 521, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    3. Marc Hallin & Luis K. Hotta & João H. G Mazzeu & Carlos Cesar Trucios-Maza & Pedro L. Valls Pereira & Mauricio Zevallos, 2019. "Forecasting Conditional Covariance Matrices in High-Dimensional Time Series: a General Dynamic Factor Approach," Working Papers ECARES 2019-14, ULB -- Universite Libre de Bruxelles.
    4. Matteo Barigozzi & Marc Hallin & Stefano Soccorsi, 2017. "Identification of Global and National Shocks in International Financial Markets via General Dynamic Factor Models," Working Papers ECARES ECARES 2017-10, ULB -- Universite Libre de Bruxelles.

Articles

  1. Massacci, Daniele, 2017. "Least squares estimation of large dimensional threshold factor models," Journal of Econometrics, Elsevier, vol. 197(1), pages 101-129.

    Cited by:

    1. Thomas Despois & Catherine Doz, 2021. "Identifying and interpreting the factors in factor models via sparsity: Different approaches," PSE Working Papers halshs-02235543, HAL.
    2. Matteo Barigozzi & Lorenzo Trapani, 2018. "Sequential testing for structural stability in approximate factor models," Discussion Papers 18/04, University of Nottingham, Granger Centre for Time Series Econometrics.
    3. Bai, Jushan & Han, Xu & Shi, Yutang, 2020. "Estimation and inference of change points in high-dimensional factor models," Journal of Econometrics, Elsevier, vol. 219(1), pages 66-100.
    4. Barigozzi, Matteo & Cho, Haeran & Fryzlewicz, Piotr, 2018. "Simultaneous multiple change-point and factor analysis for high-dimensional time series," LSE Research Online Documents on Economics 88110, London School of Economics and Political Science, LSE Library.
    5. Aslanidis, Nektarios & Hartigan, Luke, 2021. "Is the assumption of constant factor loadings too strong in practice?," Economic Modelling, Elsevier, vol. 98(C), pages 100-108.
    6. Alessandro Casini & Pierre Perron, 2018. "Structural Breaks in Time Series," Boston University - Department of Economics - Working Papers Series WP2019-02, Boston University - Department of Economics.
    7. Jianqing Fan & Kunpeng Li & Yuan Liao, 2020. "Recent Developments on Factor Models and its Applications in Econometric Learning," Papers 2009.10103, arXiv.org.
    8. Baltagi, Badi H. & Kao, Chihwa & Wang, Fa, 2016. "Estimating and testing high dimensional factor models with multiple structural changes," MPRA Paper 98489, University Library of Munich, Germany, revised 26 Jul 2019.
    9. Thomas Despois & Catherine Doz, 2021. "Identifying and interpreting the factors in factor models via sparsity: Different approaches," Working Papers halshs-02235543, HAL.
    10. Wu, Jianhong, 2021. "Estimation of high dimensional factor model with multiple threshold-type regime shifts," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).

  2. Daniele Massacci, 2017. "Tail Risk Dynamics in Stock Returns: Links to the Macroeconomy and Global Markets Connectedness," Management Science, INFORMS, vol. 63(9), pages 3072-3089, September.

    Cited by:

    1. Song, Shijia & Tian, Fei & Li, Handong, 2021. "An intraday-return-based Value-at-Risk model driven by dynamic conditional score with censored generalized Pareto distribution," Journal of Asian Economics, Elsevier, vol. 74(C).
    2. Polanski, Arnold & Stoja, Evarist, 2017. "Forecasting multidimensional tail risk at short and long horizons," Bank of England working papers 660, Bank of England.
    3. Palumbo, D., 2021. "Testing and Modelling Time Series with Time Varying Tails," Cambridge Working Papers in Economics 2111, Faculty of Economics, University of Cambridge.
    4. Marco Bee & Luca Trapin, 2018. "Estimating and Forecasting Conditional Risk Measures with Extreme Value Theory: A Review," Risks, MDPI, Open Access Journal, vol. 6(2), pages 1-16, April.

  3. Daniele Massacci, 2015. "Predicting the Distribution of Stock Returns: Model Formulation, Statistical Evaluation, VaR Analysis and Economic Significance," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(3), pages 191-208, April.

    Cited by:

    1. Leopoldo Catania & Nima Nonejad, 2016. "Density Forecasts and the Leverage Effect: Some Evidence from Observation and Parameter-Driven Volatility Models," Papers 1605.00230, arXiv.org, revised Nov 2016.
    2. Giovannelli, Alessandro & Massacci, Daniele & Soccorsi, Stefano, 2021. "Forecasting stock returns with large dimensional factor models," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 252-269.
    3. Richard K. Crump & Domenico Giannone & Sean Hundtofte, 2018. "Changing risk-return profiles," Staff Reports 850, Federal Reserve Bank of New York.
    4. Pal, Shanoli Samui & Kar, Samarjit, 2019. "Time series forecasting for stock market prediction through data discretization by fuzzistics and rule generation by rough set theory," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 162(C), pages 18-30.
    5. Kaihua Deng, 2015. "Predicting By Learning: An Adaptive Rationale," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 10(02), pages 1-14, December.

  4. Massacci, Daniele, 2014. "A two-regime threshold model with conditional skewed Student t distributions for stock returns," Economic Modelling, Elsevier, vol. 43(C), pages 9-20.

    Cited by:

    1. 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. Massacci, Daniele, 2013. "A switching model with flexible threshold variable: With an application to nonlinear dynamics in stock returns," Economics Letters, Elsevier, vol. 119(2), pages 199-203.

    Cited by:

    1. Massacci, Daniele, 2014. "A two-regime threshold model with conditional skewed Student t distributions for stock returns," Economic Modelling, Elsevier, vol. 43(C), pages 9-20.
    2. Leppin, Julia S. & Reitz, Stefan, 2014. "The Role of a Changing Market Environment for Credit Default Swap Pricing," FinMaP-Working Papers 7, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.

  6. Massacci, Daniele, 2012. "A simple test for linearity against exponential smooth transition models with endogenous variables," Economics Letters, Elsevier, vol. 117(3), pages 851-856.

    Cited by:

    1. Massacci, Daniele, 2013. "A variable addition test for exogeneity in structural threshold models," Economics Letters, Elsevier, vol. 120(1), pages 5-9.

More information

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Statistics

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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 2 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-FMK: Financial Markets (1) 2020-10-26. Author is listed
  2. NEP-FOR: Forecasting (1) 2020-10-26. Author is listed
  3. NEP-MST: Market Microstructure (1) 2018-08-27. Author is listed
  4. NEP-ORE: Operations Research (1) 2020-10-26. Author is listed

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