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Quasi Maximum Likelihood Inference For Stochastic Volatility Models
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Book Synopsis Quasi Maximum Likelihood Inference for Stochastic Volatility Models by : Maddalena Cavicchioli
Download or read book Quasi Maximum Likelihood Inference for Stochastic Volatility Models written by Maddalena Cavicchioli and published by . This book was released on 2015 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the present paper we consider the Quasi Maximum Likelihood (QML) procedure for the estimation of stationary Stochastic Volatility models. We prove the consistency of the QML estimators and compute explicitly their asymptotic variances. This allows us to obtain also consistent estimators of the asymptotic variances in explicit forms. The knowledge of the asymptotic variance-covariance matrix of the QML estimators gives a concrete possibility for the use of the classical testing procedures. Our results are related to those obtained in Ruiz (1994) and Bartolucci and De Luca (2001) (2003).
Book Synopsis Analytical Quasi Maximum Likelihood Inference in Multivariate Volatility Models by : Christian Hafner
Download or read book Analytical Quasi Maximum Likelihood Inference in Multivariate Volatility Models written by Christian Hafner and published by . This book was released on 2003 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Quasi-Maximum Likelihood Estimation of Volatility with High Frequency Data by : Dacheng Xiu
Download or read book Quasi-Maximum Likelihood Estimation of Volatility with High Frequency Data written by Dacheng Xiu and published by . This book was released on 2010 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper investigates the properties of the well-known maximum likelihood estimator in the presence of stochastic volatility and market microstructure noise, by extending the classic asymptotic results of quasi-maximum likelihood estimation. When trying to estimate the integrated volatility and the variance of noise, this parametric approach remains consistent, efficient and robust as a quasi-estimator under misspecified assumptions. Moreover, it shares the model-free feature with nonparametric alternatives, for instance realized kernels, while being advantageous over them in terms of finite sample performance. In light of quadratic representation, this estimator behaves like an iterative exponential realized kernel asymptotically. Comparisons with a variety of implementations of the Tukey-Hanning 2 kernel are provided using Monte Carlo simulations, and an empirical study with the Euro/US Dollar future illustrates its application in practice.
Book Synopsis Bayesian Inference for Stochastic Volatility Models by : Zhongxian Men
Download or read book Bayesian Inference for Stochastic Volatility Models written by Zhongxian Men and published by . This book was released on 2012 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic volatility (SV) models provide a natural framework for a representation of time series for financial asset returns. As a result, they have become increasingly popular in the finance literature, although they have also been applied in other fields such as signal processing, telecommunications, engineering, biology, and other areas. In working with the SV models, an important issue arises as how to estimate their parameters efficiently and to assess how well they fit real data. In the literature, commonly used estimation methods for the SV models include general methods of moments, simulated maximum likelihood methods, quasi Maximum likelihood method, and Markov Chain Monte Carlo (MCMC) methods. Among these approaches, MCMC methods are most flexible in dealing with complicated structure of the models. However, due to the difficulty in the selection of the proposal distribution for Metropolis-Hastings methods, in general they are not easy to implement and in some cases we may also encounter convergence problems in the implementation stage. In the light of these concerns, we propose in this thesis new estimation methods for univariate and multivariate SV models.
Book Synopsis Range-Based Estimation of Stochastic Volatility Models by : Sassan Alizadeh
Download or read book Range-Based Estimation of Stochastic Volatility Models written by Sassan Alizadeh and published by . This book was released on 2003 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 two-factor models with one highly persistent factor and one quickly mean-reverting factor.
Book Synopsis Stochastic Volatility Models by : Jian Yang
Download or read book Stochastic Volatility Models written by Jian Yang and published by . This book was released on 2006 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 Quasi-maximum Likelihood Estimation of Stochastic Variance Models by : Esther Ruiz
Download or read book Quasi-maximum Likelihood Estimation of Stochastic Variance Models written by Esther Ruiz and published by . This book was released on 1992 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Comparing Estimation Procedures for Stochastic Volatility Models of Short-Term Interest Rates by : Ramaprasad Bhar
Download or read book Comparing Estimation Procedures for Stochastic Volatility Models of Short-Term Interest Rates written by Ramaprasad Bhar and published by . This book was released on 2009 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper compares the performance of three maximum likelihood estimation procedures -quasi-maximum likelihood, Monte Carlo likelihood and the particle filter to estimate stochastic volatility models of short term interest rates. The procedures are compared in an empirical study of interest rate volatility where a number of diagnostic tests in- and out-of-sample are utilized to evaluate both model specification and estimation procedure. Empirically, the results suggest interest rates follow the Cox-Ingersoll-Ross model with stochastic volatility and that volatility increases after Federal Open Market Committee meetings. Overall, the Monte Carlo likelihood procedure provided the best results.
Book Synopsis Parameter Estimation in Stochastic Volatility Models by : Jaya P. N. Bishwal
Download or read book Parameter Estimation in Stochastic Volatility Models written by Jaya P. N. Bishwal and published by Springer Nature. This book was released on 2022-08-06 with total page 634 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book develops alternative methods to estimate the unknown parameters in stochastic volatility models, offering a new approach to test model accuracy. While there is ample research to document stochastic differential equation models driven by Brownian motion based on discrete observations of the underlying diffusion process, these traditional methods often fail to estimate the unknown parameters in the unobserved volatility processes. This text studies the second order rate of weak convergence to normality to obtain refined inference results like confidence interval, as well as nontraditional continuous time stochastic volatility models driven by fractional Levy processes. By incorporating jumps and long memory into the volatility process, these new methods will help better predict option pricing and stock market crash risk. Some simulation algorithms for numerical experiments are provided.
Book Synopsis Maximum Likelihood Approach for Stochastic Volatility Models by : Jordi Camprodon Masnou
Download or read book Maximum Likelihood Approach for Stochastic Volatility Models written by Jordi Camprodon Masnou and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Maximum Likelihood Estimation of Stochastic Volatility Models by : Yacine Ait-Sahalia
Download or read book Maximum Likelihood Estimation of Stochastic Volatility Models written by Yacine Ait-Sahalia and published by . This book was released on 2009 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt: We develop and implement a new method for maximum likelihood estimation in closed-form of stochastic volatility models. Using Monte Carlo simulations, we compare a full likelihood procedure, where an option price is inverted into the unobservable volatility state, to an approximate likelihood procedure where the volatility state is replaced by the implied volatility of a short dated at-the-money option. We find that the approximation results in a negligible loss of accuracy. We apply this method to market prices of index options for several stochastic volatility models, and compare the characteristics of the estimated models. The evidence for a general CEV model, which nests both the affine model of Heston (1993) and a GARCH model, suggests that the elasticity of variance of volatility lies between that assumed by the two nested models.
Download or read book GARCH Models written by Christian Francq and published by John Wiley & Sons. This book was released on 2019-06-10 with total page 517 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides a comprehensive and updated study of GARCH models and their applications in finance, covering new developments in the discipline This book provides a comprehensive and systematic approach to understanding GARCH time series models and their applications whilst presenting the most advanced results concerning the theory and practical aspects of GARCH. The probability structure of standard GARCH models is studied in detail as well as statistical inference such as identification, estimation, and tests. The book also provides new coverage of several extensions such as multivariate models, looks at financial applications, and explores the very validation of the models used. GARCH Models: Structure, Statistical Inference and Financial Applications, 2nd Edition features a new chapter on Parameter-Driven Volatility Models, which covers Stochastic Volatility Models and Markov Switching Volatility Models. A second new chapter titled Alternative Models for the Conditional Variance contains a section on Stochastic Recurrence Equations and additional material on EGARCH, Log-GARCH, GAS, MIDAS, and intraday volatility models, among others. The book is also updated with a more complete discussion of multivariate GARCH; a new section on Cholesky GARCH; a larger emphasis on the inference of multivariate GARCH models; a new set of corrected problems available online; and an up-to-date list of references. Features up-to-date coverage of the current research in the probability, statistics, and econometric theory of GARCH models Covers significant developments in the field, especially in multivariate models Contains completely renewed chapters with new topics and results Handles both theoretical and applied aspects Applies to researchers in different fields (time series, econometrics, finance) Includes numerous illustrations and applications to real financial series Presents a large collection of exercises with corrections Supplemented by a supporting website featuring R codes, Fortran programs, data sets and Problems with corrections GARCH Models, 2nd Edition is an authoritative, state-of-the-art reference that is ideal for graduate students, researchers, and practitioners in business and finance seeking to broaden their skills of understanding of econometric time series models.
Book Synopsis Maximum Likelihood Estimation of Stochastic Volatility Models by : Yacine Aït-Sahalia
Download or read book Maximum Likelihood Estimation of Stochastic Volatility Models written by Yacine Aït-Sahalia and published by . This book was released on 2004 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: We develop and implement a new method for maximum likelihood estimation in closed-form of stochastic volatility models. Using Monte Carlo simulations, we compare a full likelihood procedure, where an option price is inverted into the unobservable volatility state, to an approximate likelihood procedure where the volatility state is replaced by the implied volatility of a short dated at-the-money option. We find that the approximation results in a negligible loss of accuracy. We apply this method to market prices of index options for several stochastic volatility models, and compare the characteristics of the estimated models. The evidence for a general CEV model, which nests both the affine model of Heston (1993) and a GARCH model, suggests that the elasticity of variance of volatility lies between that assumed by the two nested models.
Book Synopsis Range-based Estimation of Stochastic Volatility Models Or Exchange Rate Dynamics are More Interesting Than You Think by : Sassan Alizadeh
Download or read book Range-based Estimation of Stochastic Volatility Models Or Exchange Rate Dynamics are More Interesting Than You Think written by Sassan Alizadeh and published by . This book was released on 2000 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Maximum Likelihood Estimation of Stochastic Volatility Models by : Gleb Sandmann
Download or read book Maximum Likelihood Estimation of Stochastic Volatility Models written by Gleb Sandmann and published by . This book was released on 1996 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Stochastic Volatility by : Neil Shephard
Download or read book Stochastic Volatility written by Neil Shephard and published by Oxford University Press, USA. This book was released on 2005 with total page 534 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
