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Author : Toshiaki Watanabe
Publisher :
ISBN 13 :
Total Pages : 64 pages
Book Rating : 4.:/5 (318 download)
Download or read book Stochastic Volatility Models with Heavy-tailed Distributions written by Toshiaki Watanabe and published by . This book was released on 2001 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Roman Liesenfeld
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (312 download)
Download or read book Stochastic volatility models written by Roman Liesenfeld and published by . This book was released on 1997 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : C. A. Abanto-Valle
Publisher :
ISBN 13 :
Total Pages : 42 pages
Book Rating : 4.3/5 (91 download)
Download or read book Robust Bayesian Analysis of Heavy-tailed Stochastic Volatility Models Using Scale Mixtures of Normal Distributions written by C. A. Abanto-Valle and published by . This book was released on 2008 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Roman Liesenfeld
Publisher :
ISBN 13 :
Total Pages : 38 pages
Book Rating : 4.:/5 (758 download)
Download or read book Stochastic Volatility Models written by Roman Liesenfeld and published by . This book was released on 1997 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Verda Davasligil Atmaca
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (139 download)
Download or read book Bayesian Analysis of Additive Factor Volatility Models with Heavy-Tailed Distributions with Specific Reference to S&P 500 and SSEC Indices written by Verda Davasligil Atmaca and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The distribution of the financial return series is unsuitable for normal distribution. The distribution of financial series is heavier than the normal distribution. In addition, parameter estimates obtained in the presence of outliers are unreliable. Therefore, models that allow heavy-tailed distribution should be preferred for modelling high kurtosis. Accordingly, univariate and multivariate stochastic volatility models, which allow heavy-tailed distribution, have been proposed to model time-varying volatility. One of the multivariate stochastic volatility (MSVOL) model structures is factor-MSVOL model. The aim of this study is to investigate the convenience of Bayesian estimation of additive factor-MSVOL (AFactor-MSVOL) models with normal, heavy-tailed Student-t and Slash distributions via financial return series. In this study, AFactor-MSVOL models that allow normal, Student-t, and Slash heavy-tailed distributions were estimated in the analysis of return series of S&P 500 and SSEC indices. The normal, Student-t, and Slash distributions were assigned to the error distributions as the prior distributions and full conditional distributions were obtained by using Gibbs sampling. Model comparisons were made by using DIC. Student-t and Slash distributions were shown as alternatives of normal AFactor-MSVOL model.
Author : Jaehyuk Choi
Publisher :
ISBN 13 :
Total Pages : 26 pages
Book Rating : 4.:/5 (13 download)
Download or read book Hyperbolic Normal Stochastic Volatility Model written by Jaehyuk Choi and published by . This book was released on 2019 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt: For option pricing models and heavy-tailed distributions, this study proposes a continuous-time stochastic volatility model based on an arithmetic Brownian motion: a one-parameter extension of the normal stochastic alpha-beta-rho (SABR) model. Using two generalized Bougerol's identities in the literature, the study shows that our model has a closed-form Monte-Carlo simulation scheme and that the transition probability for one special case follows Johnson's SU distribution -- a popular heavy-tailed distribution originally proposed without stochastic process. It is argued that the SU distribution serves as an analytically superior alternative to the normal SABR model because the two distributions are empirically similar.
Author : S.T Rachev
Publisher : Elsevier
ISBN 13 : 0080557732
Total Pages : 707 pages
Book Rating : 4.0/5 (85 download)
Download or read book Handbook of Heavy Tailed Distributions in Finance written by S.T Rachev and published by Elsevier. This book was released on 2003-03-05 with total page 707 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Handbooks in Finance are intended to be a definitive source for comprehensive and accessible information in the field of finance. Each individual volume in the series should present an accurate self-contained survey of a sub-field of finance, suitable for use by finance and economics professors and lecturers, professional researchers, graduate students and as a teaching supplement. The goal is to have a broad group of outstanding volumes in various areas of finance. The Handbook of Heavy Tailed Distributions in Finance is the first handbook to be published in this series. This volume presents current research focusing on heavy tailed distributions in finance. The contributions cover methodological issues, i.e., probabilistic, statistical and econometric modelling under non- Gaussian assumptions, as well as the applications of the stable and other non -Gaussian models in finance and risk management.
Author : Andreas Berg
Publisher :
ISBN 13 :
Total Pages : 40 pages
Book Rating : 4.:/5 (129 download)
Download or read book Deviance Information Criterion for Comparing Stochastic Volatility Models written by Andreas Berg and published by . This book was released on 2013 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian methods have been efficient in estimating parameters of stochastic volatility models for analyzing financial time series. Recent advances made it possible to fit stochastic volatility models of increasing complexity, including covariates, leverage effects, jump components and heavy-tailed distributions. However, a formal model comparison via Bayes factors remains difficult. The main objective of this paper is to demonstrate that model selection is more easily performed using the deviance information criterion (DIC). It combines a Bayesian measure-of-fit with a measure of model complexity. We illustrate the performance of DIC in discriminating between various different stochastic volatility models using simulated data and daily returns data on the Samp;P100 index.
Author : Michele Leonardo Bianchi
Publisher : World Scientific
ISBN 13 : 9813276215
Total Pages : 598 pages
Book Rating : 4.8/5 (132 download)
Download or read book Handbook Of Heavy-tailed Distributions In Asset Management And Risk Management written by Michele Leonardo Bianchi and published by World Scientific. This book was released on 2019-03-08 with total page 598 pages. Available in PDF, EPUB and Kindle. Book excerpt: The study of heavy-tailed distributions allows researchers to represent phenomena that occasionally exhibit very large deviations from the mean. The dynamics underlying these phenomena is an interesting theoretical subject, but the study of their statistical properties is in itself a very useful endeavor from the point of view of managing assets and controlling risk. In this book, the authors are primarily concerned with the statistical properties of heavy-tailed distributions and with the processes that exhibit jumps. A detailed overview with a Matlab implementation of heavy-tailed models applied in asset management and risk managements is presented. The book is not intended as a theoretical treatise on probability or statistics, but as a tool to understand the main concepts regarding heavy-tailed random variables and processes as applied to real-world applications in finance. Accordingly, the authors review approaches and methodologies whose realization will be useful for developing new methods for forecasting of financial variables where extreme events are not treated as anomalies, but as intrinsic parts of the economic process.
Download or read book On a Topic of Generalized Linear Mixed Models and Stochastic Volatility Model written by and published by . This book was released on 2003 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: (Uncorrected OCR) Abstract of the thesis entitled On a Topic of Generalized Linear Mixed Models and Stochastic Volatility Model submitted by Yam, Ho Kwan for the degree of Master of Philosophy at The University of Hong Kong in October 2002 Generalized Linear Mixed Models (GLMMs) are extensions to the Generalized Linear Models (GLMs). The inclusion of random effects into the models widens the scope of applicability of GLMMs considerably. However, it also increases the computational effort in estimation. There are various methodologies in making inference on the GLMMs nowadays. We attempt to investigate the use of Gibbs output within the Bayesian framework to carry out the Monte Carlo Approximation of the complicated likelihood function involving random effects by a classical likelihood approach. This methodology is a combination of classical likelihood and Bayesian approaches and it serves as a bridge between them. We will demonstrate this methodology using a famous Salamander Mating data reported by McCullagh and Nelder (1989). Moreover, although the normal distribution plays an important role in statistics, it is not suitable for modeling GLMMs with outlying random effects. The use of a general class of random effects models, such as heavy-tailed distributions should be considered. We will further investi- i gate the use of the Student-i distribution on the random effects in the Salamander Mating data. Furthermore, we suggest to adopt a scale mixture of normal form on the Student-i distribution for simplification of calculation and at the same time, locating the outliers directly through the mixing parameters. Nevertheless, since most financial and economic data exhibits a thick tail behavior, we further investigate the use of heavy tail distributions instead of normal distribution on these kinds of data. A two stage hierarchical scale mixture form on the Student-^ distribution will be demonstrated on the Stochastic Volatility (SV) model in a Bayesian aspect. it.
Author : Philippe J. Deschamps
Publisher :
ISBN 13 :
Total Pages : 41 pages
Book Rating : 4.:/5 (13 download)
Download or read book Alternative Formulations of the Leverage Effect in a Stochastic Volatility Model with Asymmetric Heavy-Tailed Errors written by Philippe J. Deschamps and published by . This book was released on 2016 with total page 41 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper investigates three formulations of the leverage effect in a stochastic volatility model with a skewed and heavy-tailed observation distribution. The first formulation is the conventional one, where the observation and evolution errors are correlated. The second is a hierarchical one, where log-volatility depends on the past log-return multiplied by a time-varying latent coefficient. In the third formulation, this coefficient is replaced by a constant. The three models are compared with each other and with a GARCH formulation, using Bayes factors. MCMC estimation relies on a parametric proposal density estimated from the output of a particle smoother. The results, obtained with recent S&P500 and Swiss Market Index data, suggest that the last two leverage formulations strongly dominate the conventional one. The performance of the MCMC method is consistent across models and sample sizes, and its implementation only requires a very modest (and constant) number of filter and smoother particles.
Author : Jayakrishnan Nair
Publisher : Cambridge University Press
ISBN 13 : 1009062964
Total Pages : 266 pages
Book Rating : 4.0/5 (9 download)
Download or read book The Fundamentals of Heavy Tails written by Jayakrishnan Nair and published by Cambridge University Press. This book was released on 2022-06-09 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: Heavy tails –extreme events or values more common than expected –emerge everywhere: the economy, natural events, and social and information networks are just a few examples. Yet after decades of progress, they are still treated as mysterious, surprising, and even controversial, primarily because the necessary mathematical models and statistical methods are not widely known. This book, for the first time, provides a rigorous introduction to heavy-tailed distributions accessible to anyone who knows elementary probability. It tackles and tames the zoo of terminology for models and properties, demystifying topics such as the generalized central limit theorem and regular variation. It tracks the natural emergence of heavy-tailed distributions from a wide variety of general processes, building intuition. And it reveals the controversy surrounding heavy tails to be the result of flawed statistics, then equips readers to identify and estimate with confidence. Over 100 exercises complete this engaging package.
Author : Tony S. Wirjanto
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (13 download)
Download or read book Multiscale Stochastic Volatility Model with Heavy Tails and Leverage Effects written by Tony S. Wirjanto and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper extends the multiscale stochastic volatility (MSSV) models to allow for heavy tails of the marginal distribution of the asset returns and correlation between the innovation of the mean equation and the innovations of the latent factor processes. Novel algorithms of Markov Chain Monte Carlo (MCMC) are developed to estimate parameters of these models. Results of simulation studies suggest that our proposed models and corresponding estimation methodology perform quite well. We also apply an auxiliary particle filter technique to construct one-step-ahead in-sample and out-of-sample volatility forecasts of the fitted models. In addition the models and MCMC methods are applied to data sets of asset returns from both foreign currency and equity markets.
Author : Ling Luo
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (13 download)
Download or read book High Quantile Estimation for Some Stochastic Volatility Models written by Ling Luo and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Jouchi Nakajima
Publisher :
ISBN 13 :
Total Pages : 28 pages
Book Rating : 4.:/5 (318 download)
Download or read book EGARCH and Stochastic Volatility written by Jouchi Nakajima and published by . This book was released on 2008 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This paper proposes the EGARCH [Exponential Generalized Autoregressive Conditional Heteroskedasticity] model with jumps and heavy-tailed errors, and studies the empirical performance of different models including the stochastic volatility models with leverage, jumps and heavy-tailed errors for daily stock returns. In the framework of a Bayesian inference, the Markov chain Monte Carlo estimation methods for these models are illustrated with a simulation study. The model comparison based on the marginal likelihood estimation is provided with data on the U.S. stock index."--Author's abstract.
Download or read book Stochastic Volatility Models for Ordinal Valued Time Series with Application to Finance written by and published by . This book was released on 2006 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper we introduce two stochastic volatility models where the response variable takes on only finite many ordered values. Corresponding time series occur in high-frequency finance when the stocks are traded on a coarse grid. For parameter estimation we develop an efficient Grouped Move Multigrid Monte Carlo (GM-MGMC) sampler. We apply both models to price changes of the IBM stock in January, 2001 at the NYSE. Dependencies of the price change process on covariates are quantified and compared with theoretical considerations on such processes. we also investigate whether this data set requires modeling with a heavy-tailed Student-t distribution. -- Grouped move ; High-frequency finance ; Markov chain Monte Carlo ; Multigrid Monte Carlo ; Price process
Author : Makoto Takahashi
Publisher : Springer Nature
ISBN 13 : 981990935X
Total Pages : 120 pages
Book Rating : 4.8/5 (199 download)
Download or read book Stochastic Volatility and Realized Stochastic Volatility Models written by Makoto Takahashi and published by Springer Nature. This book was released on 2023-04-18 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
