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Forecasting realised volatility: a Markov switching approach with time‐varying transition probabilities

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  • Xunxiao Wang
  • Keshab Shrestha
  • Qi Sun

Abstract

This paper introduces a markov‐switching heterogeneous autoregressive (MS‐HAR) model with time‐varying transition probabilities (TVTP) for the realised volatility of Shanghai securities composite index returns. Its various extensions have been obtained by including negative returns outside trading hours in addition to the leverage effects and trading volume. The findings show asymmetries in the impact of explanatory variables on the realised volatility. Moreover, the out‐of‐sample results show that the benchmark MS‐HAR with TVTP model and its extensions consistently outperform the simple HAR model, MS‐HAR model with constant transition probabilities (CTP) and their extensions. These results are robust to alternative realised measurements, and have economic implications.

Suggested Citation

  • Xunxiao Wang & Keshab Shrestha & Qi Sun, 2019. "Forecasting realised volatility: a Markov switching approach with time‐varying transition probabilities," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 59(S2), pages 1947-1975, November.
    • Handle: RePEc:bla:acctfi:v:59:y:2019:i:s2:p:1947-1975
    DOI: 10.1111/acfi.12503
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    Cited by:

    1. Qin Zhang & He Ni & Hao Xu, 2023. "Forecasting models for the Chinese macroeconomy in a data‐rich environment: Evidence from large dimensional approximate factor models with mixed‐frequency data," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(1), pages 719-767, March.
    2. Wei Zhang & Kai Yan & Dehua Shen, 2021. "Can the Baidu Index predict realized volatility in the Chinese stock market?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-31, December.
    3. Wang, Lu & Ma, Feng & Hao, Jianyang & Gao, Xinxin, 2021. "Forecasting crude oil volatility with geopolitical risk: Do time-varying switching probabilities play a role?," International Review of Financial Analysis, Elsevier, vol. 76(C).
    4. Chen, Zhonglu & Liang, Chao & Umar, Muhammad, 2021. "Is investor sentiment stronger than VIX and uncertainty indices in predicting energy volatility?," Resources Policy, Elsevier, vol. 74(C).
    5. Zhao, Yixiu & Upreti, Vineet & Cai, Yuzhi, 2021. "Stock returns, quantile autocorrelation, and volatility forecasting," International Review of Financial Analysis, Elsevier, vol. 73(C).
    6. Jiqian Wang & Feng Ma & Chao Liang & Zhonglu Chen, 2022. "Volatility forecasting revisited using Markov‐switching with time‐varying probability transition," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 1387-1400, January.
    7. Chen, Juan & Xiao, Zuoping & Bai, Jiancheng & Guo, Hongling, 2023. "Predicting volatility in natural gas under a cloud of uncertainties," Resources Policy, Elsevier, vol. 82(C).

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