Author: Pierre ChausséPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
GMM Estimation of a Stochastic Volatility Model with Realized Volatility: a Monte Carlo Study
Author: Pierre ChausséGMM Estimation of a Stochastic Volatility Model
Author: Torben G. AndersenStochastic Volatility and Realized Stochastic Volatility Models
Author: Makoto TakahashiPublisher: Springer Nature
ISBN: 981990935X
Category : Business & Economics
Languages : en
Pages : 120
Book Description
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.
Stochastic Volatility
Author: Neil ShephardPublisher: Oxford University Press, USA
ISBN: 0199257205
Category : Business & Economics
Languages : en
Pages : 534
Book Description
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 work brings together some of the main papers that have influenced this field, andshows that the development of this subject has been highly multidisciplinary.
Estimation of Stochastic Volatility Models with Markov Chain Monte Carlo Methods
Author: Maximilian RichterPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Markov Chain Monte Carlo (MCMC) methods are a Bayesian approach to tackle one of the main obstacles encountered in the estimation of modern-day stochastic volatility models: the curse of dimensionality induced by the increasing number of latent variables. This thesis strives to study the performance of affine jump-diffusion models in comparison to state-of-the-art Lévy-based return dynamics. Thus MCMC methods are applied to a novel dataset of S & P500 returns that comprises different periods of economic turmoil, such as the subprime crisis. The subordinate research goal is to address difficulties in the implementation of the MCMC methodology. In line with previous studies, the results indicate that jump components are indeed crucial for capturing complex patterns like skewness and excess kurtosis of the return distributions. Moreover, infinite-activity Lévy jumps prove to be superior to discrete compound Poisson jumps.
Markov Chain Monte Carlo Methods for Generalized Stochastic Volatility Models
Author: Siddhartha ChibPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
This paper is concerned with simulation based inference in generalized models of stochastic volatility defined by heavy-tailed student-t distributions (with unknown degrees of freedom) and covariate effects in the observation and volatility equations and a jump component in the observation equation. By building on the work of Kim, Shephard and Chib (1998), we develop efficient Markov chain Monte Carlo algorithms for estimating these models. The paper also discusses how the likelihood function of these models can be computed by appropriate particle filter methods. Computation of the marginal likelihood by the method of Chib (1995) is also considered. The methodology is extensively tested and validated on simulated data and then applied in detail to daily returns data on the S&P 500 index where several stochastic volatility models are formally compared under various priors on the parameters.
Estimating Stochastic Volatility Diffusion Using Conditional Moments of Integrated Volatility
Author: Tim BollerslevPublisher:
ISBN:
Category : Foreign exchange rates
Languages : en
Pages : 56
Book Description
Roughness in Spot Variance?
Author: Anine E. BolkoMacroeconometrics and Time Series Analysis
Author: Steven DurlaufPublisher: Springer
ISBN: 0230280838
Category : Business & Economics
Languages : en
Pages : 417
Book Description
Specially selected from The New Palgrave Dictionary of Economics 2nd edition, each article within this compendium covers the fundamental themes within the discipline and is written by a leading practitioner in the field. A handy reference tool.
The New Palgrave Dictionary of Economics
Author: Publisher: Springer
ISBN: 1349588024
Category : Law
Languages : en
Pages : 7493
Book Description
The award-winning The New Palgrave Dictionary of Economics, 2nd edition is now available as a dynamic online resource. Consisting of over 1,900 articles written by leading figures in the field including Nobel prize winners, this is the definitive scholarly reference work for a new generation of economists. Regularly updated! This product is a subscription based product.