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Asymmetric Stable Stochastic Volatility Models
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Book Synopsis Asymmetric Stable Stochastic Volatility Models by : Francisco Blasques
Download or read book Asymmetric Stable Stochastic Volatility Models written by Francisco Blasques and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper considers a stochastic volatility model featuring an asymmetric stable error distribution and a novel way of accounting for the leverage effect. We adopt simulation-based methods to address key challenges in parameter estimation, the filtering of time-varying volatility, and volatility forecasting. Specifically, we make use of the indirect inference method to estimate the static parameters, and the extremum Monte Carlo method to extract latent volatility. Both methods can be easily adapted to modifications of the model, such as having other distributions for the errors and other dynamic specifications for the volatility process. Illustrations are presented for a simulated dataset and for an empirical application to a time series of Bitcoin returns.
Book Synopsis Alternative Asymmetric Stochastic Volatility Models by : Manabu Asai
Download or read book Alternative Asymmetric Stochastic Volatility Models written by Manabu Asai and published by . This book was released on 2010 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Handbook of Volatility Models and Their Applications by : Luc Bauwens
Download or read book Handbook of Volatility Models and Their Applications written by Luc Bauwens and published by John Wiley & Sons. This book was released on 2012-03-22 with total page 566 pages. Available in PDF, EPUB and Kindle. Book excerpt: A complete guide to the theory and practice of volatility models in financial engineering Volatility has become a hot topic in this era of instant communications, spawning a great deal of research in empirical finance and time series econometrics. Providing an overview of the most recent advances, Handbook of Volatility Models and Their Applications explores key concepts and topics essential for modeling the volatility of financial time series, both univariate and multivariate, parametric and non-parametric, high-frequency and low-frequency. Featuring contributions from international experts in the field, the book features numerous examples and applications from real-world projects and cutting-edge research, showing step by step how to use various methods accurately and efficiently when assessing volatility rates. Following a comprehensive introduction to the topic, readers are provided with three distinct sections that unify the statistical and practical aspects of volatility: Autoregressive Conditional Heteroskedasticity and Stochastic Volatility presents ARCH and stochastic volatility models, with a focus on recent research topics including mean, volatility, and skewness spillovers in equity markets Other Models and Methods presents alternative approaches, such as multiplicative error models, nonparametric and semi-parametric models, and copula-based models of (co)volatilities Realized Volatility explores issues of the measurement of volatility by realized variances and covariances, guiding readers on how to successfully model and forecast these measures Handbook of Volatility Models and Their Applications is an essential reference for academics and practitioners in finance, business, and econometrics who work with volatility models in their everyday work. The book also serves as a supplement for courses on risk management and volatility at the upper-undergraduate and graduate levels.
Book Synopsis Asymmetry in Stochastic Volatility Models by : Daniel R. Smith
Download or read book Asymmetry in Stochastic Volatility Models written by Daniel R. Smith and published by . This book was released on 2008 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt: We compare the ability of correlation and threshold effects in a stochastic volatility model to capture the asymmetric relationship between stock returns and volatility. The parameters are estimated using Maximum Likelihood based on the extended Kalman filter and uses numerical integration over the latent volatility process. The stochastic volatility model with only correlation does a better job of capturing asymmetry than a threshold stochastic volatility model even though it has fewer parameters. We develop a stochastic volatility model that includes both threshold effects and correlated innovations. We find that the general model with both threshold effects and correlated innovations dominates purely threshold and correlated models. In this augmented model volatility and returns are negatively correlated, and volatility is more persistent, less volatile and higher following negative returns even after accounting for the negative correlation.
Book Synopsis Asymmetric Stochastic Volatility Models by : Xiuping Mao
Download or read book Asymmetric Stochastic Volatility Models written by Xiuping Mao and published by . This book was released on 2016 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, we derive the statistical properties of a general family of Stochastic Volatility (SV) models with leverage effect which capture the dynamic evolution of asymmetric volatility in financial returns. We provide analytical expressions of moments and autocorrelations of power-transformed absolute returns. Moreover, we use an Approximate Bayesian Computation (ABC) filter-based Maximum Likelihood (ML) method to estimate the parameters of the SV models. In Monte Carlo simulations we show that the ABC filter-based ML accurately estimates the parameters of a very general specification of the log-volatility with standardized returns following the Generalized Error Distribution (GED). The results are illustrated by analyzing series of daily S&P 500 and MSCI World returns.
Book Synopsis Linear Filtering for Asymmetric Stochastic Volatility Models by : Chris Kirby
Download or read book Linear Filtering for Asymmetric Stochastic Volatility Models written by Chris Kirby and published by . This book was released on 2006 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Linear filtering techniques are used to develop a quasi maximum likelihood estimator for asymmetric stochastic volatility models. The estimator is straightforward to implement and performs well in Monte Carlo experiments.
Book Synopsis Predictive Ability of Asymmetric Volatility Models At Medium-Term Horizons by : Turgut Kisinbay
Download or read book Predictive Ability of Asymmetric Volatility Models At Medium-Term Horizons written by Turgut Kisinbay and published by International Monetary Fund. This book was released on 2003-06-01 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using realized volatility to estimate conditional variance of financial returns, we compare forecasts of volatility from linear GARCH models with asymmetric ones. We consider horizons extending to 30 days. Forecasts are compared using three different evaluation tests. With data from an equity index and two foreign exchange returns, we show that asymmetric models provide statistically significant forecast improvements upon the GARCH model for two of the datasets and improve forecasts for all datasets by means of forecasts combinations. These results extend to about 10 days in the future, beyond which the forecasts are statistically inseparable from each other.
Book Synopsis Estimating a Semiparametric Asymmetric Stochastic Volatility Model with a Dirichlet Process Mixture by : Mark J. Jensen
Download or read book Estimating a Semiparametric Asymmetric Stochastic Volatility Model with a Dirichlet Process Mixture written by Mark J. Jensen and published by . This book was released on 2014 with total page 39 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, we extend the parametric, asymmetric, stochastic volatility model (ASV), where returns are correlated with volatility, by flexibly modeling the bivariate distribution of the return and volatility innovations nonparametrically. Its novelty is in modeling the joint, conditional, return-volatility distribution with an infinite mixture of bivariate Normal distributions with mean zero vectors, but having unknown mixture weights and covariance matrices. This semiparametric ASV model nests stochastic volatility models whose innovations are distributed as either Normal or Student-t distributions, plus the response in volatility to unexpected return shocks is more general than the fixed asymmetric response with the ASV model. The unknown mixture parameters are modeled with a Dirichlet process prior. This prior ensures a parsimonious, finite, posterior mixture that best represents the distribution of the innovations and a straightforward sampler of the conditional posteriors. We develop a Bayesian Markov chain Monte Carlo sampler to fully characterize the parametric and distributional uncertainty. Nested model comparisons and out-of-sample predictions with the cumulative marginal-likelihoods, and one-day-ahead, predictive log-Bayes factors between the semiparametric and parametric versions of the ASV model shows the semiparametric model projecting more accurate empirical market returns. A major reason is how volatility responds to an unexpected market movement. When the market is tranquil, expected volatility reacts to a negative (positive) price shock by rising (initially declining, but then rising when the positive shock is large). However, when the market is volatile, the degree of asymmetry and the size of the response in expected volatility is muted. In other words, when times are good, no news is good news, but when times are bad, neither good nor bad news matters with regards to volatility.
Book Synopsis Estimation of Asymmetric Box-Cox Stochastic Volatility Models Using MCMC Simulation by : Xibin Zhang
Download or read book Estimation of Asymmetric Box-Cox Stochastic Volatility Models Using MCMC Simulation written by Xibin Zhang and published by . This book was released on 2003 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Stochastic Volatility and Realized Stochastic Volatility Models by : Makoto Takahashi
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.
Book Synopsis Asymmetric Response of Volatility by : Jun Yu
Download or read book Asymmetric Response of Volatility written by Jun Yu and published by . This book was released on 2004 with total page 29 pages. Available in PDF, EPUB and Kindle. Book excerpt: Examines the news impact function (NIF) in the context of stochastic volatility models using daily index data on S & P500 and non-parametrically using realized daily volatility based on the high frequency data on the same index.
Book Synopsis A Stochastic Volatility Model with Conditional Skewness by : Bruno Feunou
Download or read book A Stochastic Volatility Model with Conditional Skewness written by Bruno Feunou and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Multiple Time Scales in Volatility and Leverage Correlations by : Josep Perelló
Download or read book Multiple Time Scales in Volatility and Leverage Correlations written by Josep Perelló and published by . This book was released on 2013 with total page 19 pages. Available in PDF, EPUB and Kindle. Book excerpt: Financial time series exhibit two different type of non linear correlations: (i) volatility autocorrelations that have a very long range memory, on the order of years, and (ii) asymmetric return-volatility (or 'leverage') correlations that are much shorter ranged. Different stochastic volatility models have been proposed in the past to account for both these correlations. However, in these models, the decay of the correlations is exponential, with a single time scale for both the volatility and the leverage correlations, at variance with observations. We extend the linear Ornstein-Uhlenbeck stochastic volatility model by assuming that the mean reverting level is itself random. We find that the resulting three-dimensional diffusion process can account for different correlation time scales. We show that the results are in good agreement with a century of the Dow Jones index daily returns (1900-2000), with the exception of crash days.
Book Synopsis Realized Stochastic Volatility with General Asymmetry and Long Memory by : Manabu Asai
Download or read book Realized Stochastic Volatility with General Asymmetry and Long Memory written by Manabu Asai and published by . This book was released on 2017 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt: The paper develops a novel realized stochastic volatility model of asset returns and realized volatility that incorporates general asymmetry and long memory (hereafter the RSV-GALM model). The contribution of the paper ties in with Robert Basmann's seminal work in terms of the estimation of highly non-linear model specifications (“Causality tests and observationally equivalent representations of econometric models”, Journal of Econometrics, 1988), especially for specifying causal effects from returns to future volatility. This paper discusses asymptotic results of a Whittle likelihood estimator for the RSV-GALM model and a test for general asymmetry, and analyses the finite sample properties. The paper also develops an approach to obtain volatility estimates and out-of-sample forecasts. Using high frequency data for three US financial assets, the new model is estimated and evaluated. The paper compares the forecasting performance of the new model with a realized conditional volatility model.
Book Synopsis A Note on the Wang Transform for Stochastic Volatility Pricing Models by : Alex Badescu
Download or read book A Note on the Wang Transform for Stochastic Volatility Pricing Models written by Alex Badescu and published by . This book was released on 2016 with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper we study a conditional version of the Wang transform in the context of discrete GARCH models and their diffusion limits. Our first contribution shows that the conditional Wang transform and Duan's generalized local risk-neutral valuation relationship based on equilibrium considerations, lead to the same GARCH option pricing model. We derive the weak limit of an asymmetric GARCH model risk-neutralized via Wang's transform. The connection with stochastic volatility limits constructed using other standard pricing kernels, such as the conditional Esscher transform or the extended Girsanov principle, is further investigated by comparing the corresponding market prices of variance risk.
Book Synopsis Asymmetry and Long Memory in Volatility Modelling by : Manabu Asai
Download or read book Asymmetry and Long Memory in Volatility Modelling written by Manabu Asai and published by . This book was released on 2010 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Exotic Derivatives And Risk: Theory, Extensions And Applications by : Bellalah Mondher
Download or read book Exotic Derivatives And Risk: Theory, Extensions And Applications written by Bellalah Mondher and published by World Scientific Publishing Company. This book was released on 2008-11-07 with total page 616 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses in detail the workings of financial markets and over-the-counter (OTC) markets, focusing specifically on standard and complex derivatives. The subjects covered range from the fundamental products in OTC markets, standard and exotic options, the concepts of value at risk, credit derivatives and risk management, to the applications of option pricing theory to real assets.To further elucidate these complex concepts and formulas, this book also explains in each chapter how theory and practice go hand-in-hand. This volume, a culmination of the author's 12 years of professional experience in the field of finance, derivative analysis and risk management, is a valuable guide for postgraduate students, academics and practitioners in the field of finance.
