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＜電子ブック＞
Stochastic Volatility and Realized Stochastic Volatility Models / by Makoto Takahashi, Yasuhiro Omori, Toshiaki Watanabe
(JSS Research Series in Statistics. ISSN:23640065)

版	1st ed. 2023.
出版者	(Singapore : Springer Nature Singapore : Imprint: Springer)
出版年	2023
本文言語	英語
大きさ	VIII, 113 p. 41 illus., 26 illus. in color : online resource
著者標目	*Takahashi, Makoto author Omori, Yasuhiro author Watanabe, Toshiaki author SpringerLink (Online service)
件　名	LCSH:Statistics  LCSH:Social sciences -- Statistical methods  全ての件名で検索 LCSH:Econometrics FREE:Statistics in Business, Management, Economics, Finance, Insurance FREE:Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy FREE:Statistical Theory and Methods FREE:Quantitative Economics
一般注記	1 Introduction -- 2 Stochastic Volatility Model -- 3 Asymmetric Stochastic Volatility Model -- 4 Stochastic Volatility Model with Generalized Hyperbolic Skew Student’s t Error -- 5 Realized Stochastic Volatility Model This treatise delves into the latest advancements in stochastic volatility models, highlighting the utilization of Markov chain Monte Carlo simulations for estimating model parameters and forecasting the volatility and quantiles of financial asset returns. The modeling of financial time series volatility constitutes a crucial aspect of finance, as it plays a vital role in predicting return distributions and managing risks. Among the various econometric models available, the stochastic volatility model has been a popular choice, particularly in comparison to other models, such as GARCH models, as it has demonstrated superior performance in previous empirical studies in terms of fit, forecasting volatility, and evaluating tail risk measures such as Value-at-Risk and Expected Shortfall. The book also explores an extension of the basic stochastic volatility model, incorporating a skewed return error distribution and a realized volatility measurement equation. The concept of realized volatility, a newly established estimator of volatility using intraday returns data, is introduced, and a comprehensive description of the resulting realized stochastic volatility model is provided. The text contains a thorough explanation of several efficient sampling algorithms for latent log volatilities, as well as an illustration of parameter estimation and volatility prediction through empirical studies utilizing various asset return data, including the yen/US dollar exchange rate, the Dow Jones Industrial Average, and the Nikkei 225 stock index. This publication is highly recommended for readers with an interest in the latest developments in stochastic volatility models and realized stochastic volatility models, particularly in regards to financial risk management HTTP:URL=https://doi.org/10.1007/978-981-99-0935-3

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