Makoto Takahashi, Yuta Yamauchi, Toshiaki Watanabe, Yasuhiro Omori
arXiv 24 Jan 2024 · Econometrics
arXiv:2401.13179 · PDF · DOI · OpenAlex · Extracted main text
Accurate forecasting of volatility and return quantiles is essential for evaluating financial tail risks such as value-at-risk and expected shortfall. This study proposes an extension of the traditional stochastic volatility model, termed the realized stochastic volatility model, that incorporates realized volatility as an efficient proxy for latent volatility. To better capture the stylized features of financial return distributions, particularly skewness and heavy tails, we introduce three variants of skewed t-distributions, two of which incorporate skew-normal components to flexibly model asymmetry. The models are estimated using a Bayesian Markov chain Monte Carlo approach and applied to daily returns and realized volatilities from major U.S. and Japanese stock indices. Empirical results demonstrate that incorporating both realized volatility and flexible return distributions substantially improves the accuracy of volatility and tail risk forecasts.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Takahashi, Makoto and Omori, Yasuhiro and Watanabe, Toshiaki (2009) Estimating Stochastic Volatility Models Using Daily Returns and Realized Volatility Simultaneously self | 1.000 | 5 | 3 | 100% |
| 2 | Takahashi, Makoto and Watanabe, Toshiaki and Omori, Yasuhiro (2016) Volatility and Quantile Forecasts by Realized Stochastic Volatility Models with Generalized Hyperbolic Distribution self | 0.941 | 6 | 5 | 83% |
| 3 | Takahashi, Makoto and Omori, Yashuhiro and Watanabe, Toshiaki (2023) Stochastic Volatility and Realized Stochastic Volatility Models self | 0.843 | 4 | 4 | 75% |
| 4 | Barndorff-Nielsen, Ole E. and Hansen, Peter Reinhard and Lunde, Asge… (2008) Designing Realized Kernels to Measure the Ex Post Variation of Equity Prices in the Presence of Noise | 0.843 | 3 | 3 | 100% |
| 5 | Liu, Lily Y. and Patton, Andrew J. and Sheppard, Kevin (2015) Does Anything Beat 5-Minute RV? A Comparison of Realized Measures across Multiple Asset Classes | 0.843 | 3 | 3 | 100% |
| 6 | Hansen, Peter Reinhard and Huang, Zhuo (2016) Exponential GARCH Modeling with Realized Measures of Volatility | 0.811 | 4 | 2 | 100% |
| 7 | Nelson, Daniel B (1991) Conditional Heteroskedasticity in Asset Returns: A New Approach | 0.811 | 4 | 2 | 100% |
| 8 | Aas, Kjersti and Haff, Ingrid Hobaek (2006) The generalized hyperbolic Skew Student's t-Distribution | 0.737 | 3 | 2 | 100% |
| 9 | Fernández, Carmen and Steel, Mark FJ (1995) On Bayesian Modeling of Fat Tails and Skewness | 0.737 | 3 | 2 | 100% |
| 10 | Hansen, Peter R. and Lunde, Asger (2005) A Forecast Comparison of Volatility Models: Does Anything Beat a GARCH(1,1)? | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 68 scored citations.