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Volatility Analysis of Returns of Financial Assets Using a Bayesian Time-Varying Realized GARCH-Itô Model

Author

Listed:
  • Pathairat Pastpipatkul

    (Faculty of Economics, Chiang Mai University, Chiang Mai 50200, Thailand)

  • Htwe Ko

    (Faculty of Economics, Chiang Mai University, Chiang Mai 50200, Thailand)

Abstract

In a stage of more and more complex and high-frequency financial markets, the volatility analysis is a cornerstone of modern financial econometrics with practical applications in portfolio optimization, derivative pricing, and systematic risk assessment. This paper introduces a novel Bayesian Time-varying Generalized Autoregressive Conditional Heteroskedasticity (BtvGARCH-Itô) model designed to improve the precision and flexibility of volatility modeling in financial markets. Original GARCH-Itô models, while effective in capturing realized volatility and intraday patterns, rely on fixed or constant parameters; thus, it is limited to studying structural changes. Our proposed model addresses this restraint by integrating the continuous-time Ito process with a time-varying Bayesian inference to allow parameters to vary over time based on prior beliefs to quantify uncertainty and minimize overfitting, especially in small-sample or high-dimensional settings. Through simulation studies, using sample sizes of N = 100 and N = 200, we find that BtvGARCH-Itô outperformed original GARCH-Itô in-sample fit and out-of-sample forecast accuracy based on posterior estimates comparison with true parameter values and forecasting error metrics. For the empirical validation, this model is applied to analyze the volatility of S&P 500 and Bitcoin (BTC) using one-minute length data for S&P 500 (from 3 January 2023 to 31 December 2024) and BTC (from 1 January 2023 to 1 January 2025). This model has potential as a robust tool and a new direction in volatility modeling for financial risk management.

Suggested Citation

  • Pathairat Pastpipatkul & Htwe Ko, 2025. "Volatility Analysis of Returns of Financial Assets Using a Bayesian Time-Varying Realized GARCH-Itô Model," Econometrics, MDPI, vol. 13(3), pages 1-21, September.
    • Handle: RePEc:gam:jecnmx:v:13:y:2025:i:3:p:34-:d:1745120
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    References listed on IDEAS

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    1. Xin Huang & George Tauchen, 2005. "The Relative Contribution of Jumps to Total Price Variance," Journal of Financial Econometrics, Oxford University Press, vol. 3(4), pages 456-499.
    2. Gao, Jiti & Peng, Bin & Wu, Wei Biao & Yan, Yayi, 2024. "Time-varying multivariate causal processes," Journal of Econometrics, Elsevier, vol. 240(1).
    3. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    Cited by:

    1. Tashreef Muhammad, 2026. "Temporal Coverage Bias in Financial Panel Data: A Coverage-Aware Structuring Framework with Evidence from the Dhaka Stock Exchange," Papers 2603.20237, arXiv.org, revised Mar 2026.

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