Comment on Jacquier, Polson and Rossi's "Bayesian Analysis of Stochastic Volatility Models PDF Download
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Author: Daniel B. Nelson
Publisher:
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Languages : en
Pages :
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Author: Daniel B. Nelson
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Languages : en
Pages :
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Author: Eric Jacquier
Publisher:
ISBN:
Category : Bayesian statistical decision theory
Languages : en
Pages : 41
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Author: Asma Graja
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Languages : en
Pages :
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Time varying volatility is a characteristic of many financial series. An alternative to the popular ARCH framework is a Stochastic Volatility model which is harder to estimate than the ARCH family. In this paper we estimate and compare two classes of Stochastic Volatility models proposed in financial literature: the Log normal autoregressive model with some extensions and the Heston model. The basic univariate Stochastic Volatility model is extended to allow for the quot;leverage effectquot; via correlation between the volatility and the mean innovations and for fat tails in the mean equation innovation.A Bayesian Markov Chain Monte Carlo algorithm developed in Jacquier, Polson and Rossi 2004 is analyzed and applied to a large data base of the French financial market. Moreover, explicit expression for the parameter's estimators is found via Monte Carlo technique.
Author: Joanne Jia Jia Wang
Publisher:
ISBN:
Category : Bayesian statistical decision theory
Languages : en
Pages : 468
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Author: Neil Shephard
Publisher: OUP Oxford
ISBN: 0191531421
Category : Business & Economics
Languages : en
Pages : 536
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Stochastic volatility is the main concept used in the fields of financial economics and mathematical finance to deal with time-varying volatility in financial markets. This book brings together some of the main papers that have influenced the field of the econometrics of stochastic volatility, and shows that the development of this subject has been highly multidisciplinary, with results drawn from financial economics, probability theory, and econometrics, blending to produce methods and models that have aided our understanding of the realistic pricing of options, efficient asset allocation, and accurate risk assessment. A lengthy introduction by the editor connects the papers with the literature.
Author: Eric Jacquier
Publisher:
ISBN:
Category :
Languages : en
Pages : 31
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The basic univariate stochastic volatility model specifies that conditional volatility follows a log-normal auto-regressive model with innovations assumed to be independent of the innovations in the conditional mean equation. Since the introduction of practical methods for inference in the basic volatility model (JPR-(1994)), it has been observed that the basic model is too restrictive for many financial series. We extend the basic SVOL to allow for a so-called quot;Leverage effectquot; via correlation between the volatility and mean innovations, and for fat-tails in the mean equation innovation. A Bayesian Markov Chain Monte Carlo algorithm is developed for the extended volatility model. Thus far, likelihood-based inference for the correlated SVOL model has not appeared in the literature. We develop Bayes Factors to assess the importance of the leverage and fat-tail extensions. Sampling experiments reveal little loss in precision from adding the model extensions but a large loss from using the basic model in the presence of mis-specification. For both equity and exchange rate data, there is overwhelming evidence in favor of models with fat-tailed volatility innovations, and for a leverage effect in the case of equity indices. We also find that volatility estimates from the extended model are markedly different from those produced by the basic SVOL.
Author: Jack C. Lee
Publisher: Springer
ISBN: 1461224144
Category : Mathematics
Languages : en
Pages : 458
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Modelling and Prediction Honoring Seymour Geisser contains the refereed proceedings of the Conference on Forecasting, Prediction, and Modelling held at National Chiao Tung University, Taiwan in 1994. The papers discuss general methodological issues; prediction; design of experiments and classification; prior distributions and estimation; posterior odds, testing, and model selection; modelling and prediction in finance; and time series modelling and applications. Specific topics include very interesting and topical statistical issues related to DNA fingerprinting and spatial image reconstruction, foundational issues for applied statistics and testing hypotheses, forecasting tax revenues and bond prices, and assessing oxone depletion.
Author: Renate Meyer
Publisher:
ISBN:
Category : Bayesian statistical decision theory
Languages : en
Pages : 20
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Author: Toshiaki Watanabe
Publisher:
ISBN:
Category : Bayesian statistical decision theory
Languages : en
Pages : 64
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Author: Pawel J. Szerszen
Publisher:
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Category :
Languages : en
Pages :
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