Stochastic Volatility Models with Persistent Latent Factors PDF Download
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Author: Hyoung Il Lee
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
We consider the stochastic volatility model with smooth transition and persistent latent factors. We argue that this model has advantages over the conventional stochastic model for the persistent volatility factor. Though the linear filtering is widely used in the state space model, the simulation result, as well as theory, shows that it does not work in our model. So we apply the density-based filtering method; in particular, we develop two methods to get solutions. One is the conventional approach using the Maximum Likelihood estimation and the other is the Bayesian approach using Gibbs sampling. We do a simulation study to explore their characteristics, and we apply both methods to actual macroeconomic data to extract the volatility generating process and to compare macro fundamentals with them. Next we extend our model into multivariate model extracting common and id- iosyncratic volatility for multivariate processes. We think it is interesting to apply this multivariate model into measuring time-varying uncertainty of macroeconomic variables and studying the links to market returns via a consumption-based asset pric- ing model. Motivated by Bansal and Yaron (2004), we extract a common volatility factor using consumption and dividend growth, and we find that this factor predicts post-war business cycle recessions quite well. Then, we estimate a long-run risk model of asset prices incorporating this macroeconomic uncertainty. We find that both risk aversion and the intertemporal elasticity of substitution are estimated to be around two, and our simulation results show that the model can match the first and second moments of market return and risk-free rate, hence the equity premium.
Author: Hyoung Il Lee
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
We consider the stochastic volatility model with smooth transition and persistent latent factors. We argue that this model has advantages over the conventional stochastic model for the persistent volatility factor. Though the linear filtering is widely used in the state space model, the simulation result, as well as theory, shows that it does not work in our model. So we apply the density-based filtering method; in particular, we develop two methods to get solutions. One is the conventional approach using the Maximum Likelihood estimation and the other is the Bayesian approach using Gibbs sampling. We do a simulation study to explore their characteristics, and we apply both methods to actual macroeconomic data to extract the volatility generating process and to compare macro fundamentals with them. Next we extend our model into multivariate model extracting common and id- iosyncratic volatility for multivariate processes. We think it is interesting to apply this multivariate model into measuring time-varying uncertainty of macroeconomic variables and studying the links to market returns via a consumption-based asset pric- ing model. Motivated by Bansal and Yaron (2004), we extract a common volatility factor using consumption and dividend growth, and we find that this factor predicts post-war business cycle recessions quite well. Then, we estimate a long-run risk model of asset prices incorporating this macroeconomic uncertainty. We find that both risk aversion and the intertemporal elasticity of substitution are estimated to be around two, and our simulation results show that the model can match the first and second moments of market return and risk-free rate, hence the equity premium.
Author: John T. Scruggs
Publisher:
ISBN:
Category :
Languages : en
Pages : 46
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Book Description
We explore high-dimensional linear factor models in which the covariance matrix of excess asset returns follows a multivariate stochastic volatility process. We test crosssectional restrictions suggested by the arbitrage pricing theory, compare competing stochastic volatility specifications for the covariance matrix, test for the number of factors, and analyze possible sources of model misspecification. Estimation and testing of these models is feasible due to recent advances in Bayesian Markov chain Monte Carlo (MCMC) methods. We find that five latent factors with multivariate stochastic volatility best explain excess returns for a sample of seventeen stock and bond portfolios. Analysis of cumulative latent factor shocks suggests that APT pricing restrictions, coupled with constant factor risk premia, do not adequately explain cross-sectional variation in average portfolio excess returns.
Author: Sassan Alizadeh
Publisher:
ISBN:
Category :
Languages : en
Pages : 65
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Book Description
We propose using the price range in the estimation of stochastic volatility models. We show theoretically, numerically, and empirically that the range is not only a highly efficient volatility proxy, but also that it is approximately Gaussian and robust to microstructure noise. The good properties of the range imply that range-based Gaussian quasi-maximum likelihood estimation produces simple and highly efficient estimates of stochastic volatility models and extractions of latent volatility series. We use our method to examine the dynamics of daily exchange rate volatility and discover that traditional one-factor models are inadequate for describing simultaneously the high- and low-frequency dynamics of volatility. Instead, the evidence points strongly toward tw-factor models with one highly persistent factor and one quickly mean-reverting factor.
Author: Mr.Noureddine Krichene
Publisher: International Monetary Fund
ISBN: 1451854846
Category : Business & Economics
Languages : en
Pages : 30
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Book Description
A stochastic volatility model where volatility was driven solely by a latent variable called news was estimated for three stock indices. A Markov chain Monte Carlo algorithm was used for estimating Bayesian parameters and filtering volatilities. Volatility persistence being close to one was consistent with both volatility clustering and mean reversion. Filtering showed highly volatile markets, reflecting frequent pertinent news. Diagnostics showed no model failure, although specification improvements were always possible. The model corroborated stylized findings in volatility modeling and has potential value for market participants in asset pricing and risk management, as well as for policymakers in the design of macroeconomic policies conducive to less volatile financial markets.
Author: Yu-Cheng Ku
Publisher:
ISBN:
Category :
Languages : en
Pages : 87
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Book Description
Author: Torben G. Andersen
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Luc Bauwens
Publisher: John Wiley & Sons
ISBN: 1118272056
Category : Business & Economics
Languages : en
Pages : 566
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Book Description
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.
Author:
Publisher:
ISBN:
Category : Cointegration
Languages : en
Pages :
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Author: Marcio Poletti Laurini
Publisher:
ISBN:
Category :
Languages : en
Pages : 26
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Book Description
We introduce a new multivariate stochastic volatility model, based on the presence of a latent common factor with random jumps. The common factor is parameterized as a permanent component using a compound binomial process. This model can capture common jumps in the latent volatilities between markets, with particular relevance in the presence of crises and contagion in emerging markets.
Author: George J. Jiang
Publisher:
ISBN:
Category :
Languages : en
Pages : 39
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Book Description
This paper proposes a new approach to exploit the information in high frequency data for the statistical inference of continuous-time affine jump diffusion (AJD) models with latent variables. For this purpose, we construct unbiased estimators of the latent variables and their power functions based on the observed state variables over extended horizons. With the estimates of the latent variables, we propose a GMM procedure for the estimation of AJD models with the distinguishing feature that moments of both observed and latent state variables can be used without resorting to path simulation or discretization of the continuous-time process. Using high frequency return observations of the Samp;P 500 index, we implement our estimation approach to various continuous-time asset return models with stochastic volatility and random jumps.
