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Shock-triggered asymmetric response stochastic volatility

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  • Marín Díazaraque, Juan Miguel
  • Lopes Moreira da Veiga, María Helena

Abstract

We propose a novel asymmetric stochastic volatility model (STAR-SV) in which the leverage parameter adjusts to the magnitude of past shocks. This flexible specification captures both the leverage effects and their propagation more effectively than standard asymmetric volatility models. To estimate the STAR-SV parameters, we implement a data cloning algorithm that approximates the maximum likelihood estimates and their asymptotic variances. In finite-sample simulations, data cloning consistently leads to reliable estimates and small standard errors. Empirically, we fit the model to Bitcoin, Nasdaq and S&P 500 returns and evaluate 1 and 10-day volatility forecasts, using unconditional and conditional tests of predictive ability. STAR-SV using data cloning proves to be the most adequate forecaster, outperforming the most stringent confidence thresholds and weakly dominating in several variance regimes. Finally, we show the performance of the model in predicting the 99% Value-at-Risk. STAR-SV, using data cloning, seems to respond quickly to volatility spikes and passes backtests for both time horizons.

Suggested Citation

  • Marín Díazaraque, Juan Miguel & Lopes Moreira da Veiga, María Helena, 2023. "Shock-triggered asymmetric response stochastic volatility," DES - Working Papers. Statistics and Econometrics. WS 36569, Universidad Carlos III de Madrid. Departamento de Estadística.
    • Handle: RePEc:cte:wsrepe:36569
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    References listed on IDEAS

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    1. Bandi, Federico M. & Renò, Roberto, 2012. "Time-varying leverage effects," Journal of Econometrics, Elsevier, vol. 169(1), pages 94-113.
    2. Yu, Jun, 2012. "A semiparametric stochastic volatility model," Journal of Econometrics, Elsevier, vol. 167(2), pages 473-482.
    3. Leopoldo Catania, 2022. "A Stochastic Volatility Model With a General Leverage Specification," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(2), pages 678-689, April.
    4. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    5. Hoang Nguyen & Trong-Nghia Nguyen & Minh-Ngoc Tran, 2023. "A dynamic leverage stochastic volatility model," Applied Economics Letters, Taylor & Francis Journals, vol. 30(1), pages 97-102, January.
    6. Yu, Jun, 2005. "On leverage in a stochastic volatility model," Journal of Econometrics, Elsevier, vol. 127(2), pages 165-178, August.
    7. P. de Zea Bermudez & J. Miguel Marín & Helena Veiga, 2020. "Data cloning estimation for asymmetric stochastic volatility models," Econometric Reviews, Taylor & Francis Journals, vol. 39(10), pages 1057-1074, November.
    8. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    9. Renate Meyer & Jun Yu, 2000. "BUGS for a Bayesian analysis of stochastic volatility models," Econometrics Journal, Royal Economic Society, vol. 3(2), pages 198-215.
    10. Harvey, Andrew C & Shephard, Neil, 1996. "Estimation of an Asymmetric Stochastic Volatility Model for Asset Returns," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(4), pages 429-434, October.
    11. Mao, Xiuping & Czellar, Veronika & Ruiz, Esther & Veiga, Helena, 2020. "Asymmetric stochastic volatility models: Properties and particle filter-based simulated maximum likelihood estimation," Econometrics and Statistics, Elsevier, vol. 13(C), pages 84-105.
    12. Tim Bollerslev & Julia Litvinova & George Tauchen, 2006. "Leverage and Volatility Feedback Effects in High-Frequency Data," Journal of Financial Econometrics, Oxford University Press, vol. 4(3), pages 353-384.
    13. Lele, Subhash R. & Nadeem, Khurram & Schmuland, Byron, 2010. "Estimability and Likelihood Inference for Generalized Linear Mixed Models Using Data Cloning," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1617-1625.
    14. Wu, Xinyu & Wang, Xiaona, 2020. "Forecasting volatility using realized stochastic volatility model with time-varying leverage effect," Finance Research Letters, Elsevier, vol. 34(C).
    15. So, Mike K P & Li, W K & Lam, K, 2002. "A Threshold Stochastic Volatility Model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(7), pages 473-500, November.
    16. Jia Li & Zhipeng Liao & Rogier Quaedvlieg, 2022. "Conditional Superior Predictive Ability," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 89(2), pages 843-875.
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    Keywords

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    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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