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Aggregations And Marginalization Of Garch And Stochastic Volatility Models
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Author :Meddahi, Nour Publisher :Montréal : Université de Montréal, Centre de recherche et développement en économique ISBN 13 :9782893823737 Total Pages :73 pages Book Rating :4.8/5 (237 download)
Book Synopsis Aggregations and Marginalization of GARCH and Stochastic Volatility Models by : Meddahi, Nour
Download or read book Aggregations and Marginalization of GARCH and Stochastic Volatility Models written by Meddahi, Nour and published by Montréal : Université de Montréal, Centre de recherche et développement en économique. This book was released on 1997 with total page 73 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Aggregations and marginalization of GARCH and stochastic volatility models by : Nour Meddahi
Download or read book Aggregations and marginalization of GARCH and stochastic volatility models written by Nour Meddahi and published by . This book was released on 1998 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Contemporaneous Aggregation of GARCH Processes by : Paolo Zaffaroni
Download or read book Contemporaneous Aggregation of GARCH Processes written by Paolo Zaffaroni and published by . This book was released on 2000 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Handbook of Financial Econometrics by : Yacine Ait-Sahalia
Download or read book Handbook of Financial Econometrics written by Yacine Ait-Sahalia and published by Elsevier. This book was released on 2009-10-19 with total page 809 pages. Available in PDF, EPUB and Kindle. Book excerpt: This collection of original articles—8 years in the making—shines a bright light on recent advances in financial econometrics. From a survey of mathematical and statistical tools for understanding nonlinear Markov processes to an exploration of the time-series evolution of the risk-return tradeoff for stock market investment, noted scholars Yacine Aït-Sahalia and Lars Peter Hansen benchmark the current state of knowledge while contributors build a framework for its growth. Whether in the presence of statistical uncertainty or the proven advantages and limitations of value at risk models, readers will discover that they can set few constraints on the value of this long-awaited volume. Presents a broad survey of current research—from local characterizations of the Markov process dynamics to financial market trading activity Contributors include Nobel Laureate Robert Engle and leading econometricians Offers a clarity of method and explanation unavailable in other financial econometrics collections
Book Synopsis Nonlinear and Nonstationary Signal Processing by : W. J. Fitzgerald
Download or read book Nonlinear and Nonstationary Signal Processing written by W. J. Fitzgerald and published by Cambridge University Press. This book was released on 2000 with total page 510 pages. Available in PDF, EPUB and Kindle. Book excerpt: Signal processing, nonlinear data analysis, nonlinear time series, nonstationary processes.
Book Synopsis Option Pricing, Interest Rates and Risk Management by : Elyès Jouini
Download or read book Option Pricing, Interest Rates and Risk Management written by Elyès Jouini and published by Cambridge University Press. This book was released on 2001 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: This 2001 handbook surveys the state of practice, method and understanding in the field of mathematical finance. Every chapter has been written by leading researchers and each starts by briefly surveying the existing results for a given topic, then discusses more recent results and, finally, points out open problems with an indication of what needs to be done in order to solve them. The primary audiences for the book are doctoral students, researchers and practitioners who already have some basic knowledge of mathematical finance. In sum, this is a comprehensive reference work for mathematical finance and will be indispensable to readers who need to find a quick introduction or reference to a specific topic, leading all the way to cutting edge material.
Book Synopsis Modeling Stochastic Volatility with Application to Stock Returns by : Mr.Noureddine Krichene
Download or read book Modeling Stochastic Volatility with Application to Stock Returns written by Mr.Noureddine Krichene and published by International Monetary Fund. This book was released on 2003-06-01 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis A Closer Look at the Relation between GARCH and Stochastic Autoregressive Volatility by : Jeff Fleming
Download or read book A Closer Look at the Relation between GARCH and Stochastic Autoregressive Volatility written by Jeff Fleming and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We show that, for three common SARV models, fitting a minimum mean square linear filter is equivalent to fitting a GARCH model. This suggests that GARCH models may be useful for filtering, forecasting, and parameter estimation in stochastic volatility settings. To investigate, we use simulations to evaluate how the three SARV models and their associated GARCH filters perform under controlled conditions and then we use daily currency and equity index returns to evaluate how the models perform in a risk management application. Although the GARCH models produce less precise forecasts than the SARV models in the simulations, it is not clear that the performance differences are large enough to be economically meaningful. Consistent with this view, we find that the GARCH and SARV models perform comparably in tests of conditional value-at-risk estimates using the actual data.
Book Synopsis Parametric and Nonparametric Volatility Measurement by : Torben Gustav Andersen
Download or read book Parametric and Nonparametric Volatility Measurement written by Torben Gustav Andersen and published by . This book was released on 2002 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt: Volatility has been one of the most active areas of research in empirical finance and time series econometrics during the past decade. This chapter provides a unified continuous-time, frictionless, no-arbitrage framework for systematically categorizing the various volatility concepts, measurement procedures, and modeling procedures. We define three different volatility concepts: (i) the notional volatility corresponding to the ex-post sample-path return variability over a fixed time interval, (ii) the ex-ante expected volatility over a fixed time interval, and (iii) the instantaneous volatility corresponding to the strength of the volatility process at a point in time. The parametric procedures rely on explicit functional form assumptions regarding the expected and/or instantaneous volatility. In the discrete-time ARCH class of models, the expectations are formulated in terms of directly observable variables, while the discrete- and continuous-time stochastic volatility models involve latent state variable(s). The nonparametric procedures are generally free from such functional form assumptions and hence afford estimates of notional volatility that are flexible yet consistent (as the sampling frequency of the underlying returns increases). The nonparametric procedures include ARCH filters and smoothers designed to measure the volatility over infinitesimally short horizons, as well as the recently-popularized realized volatility measures for (non-trivial) fixed-length time intervals.
Download or read book Journal of Empirical Finance written by and published by . This book was released on 1997 with total page 1096 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Persistence and Kurtosis in GARCH and Stochastic Volatility Models by : M. Angeles Carnero
Download or read book Persistence and Kurtosis in GARCH and Stochastic Volatility Models written by M. Angeles Carnero and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This article shows that the relationship between kurtosis, persistence of shocks to volatility, and first-order autocorrelation of squares is different in GARCH and ARSV models. This difference can explain why, when these models are fitted to the same series, the persistence estimated is usually higher in GARCH than in ARSV models, and, why gaussian ARSV models seem to be adequate, whereas GARCH models often require leptokurtic conditional distributions. We also show that introducing the asymmetric response of volatility to positive and negative returns does not change the conclusions. These results are illustrated with the analysis of daily financial returns.
Book Synopsis Deciding between GARCH and Stochastic Volatility Via Strong Decision Rules by : Arie Preminger
Download or read book Deciding between GARCH and Stochastic Volatility Via Strong Decision Rules written by Arie Preminger and published by . This book was released on 2008 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt: The GARCH and stochastic volatility (SV) models are two competing, well-known and often used models to explain the volatility of financial series. In this paper, we consider a closed form estimator for a stochastic volatility model and derive its asymptotic properties. We confirm our theoretical results by a simulation study. In addition, we propose a set of simple, strongly consistent decision rules to compare the ability of the GARCH and the SV model to fit the characteristic features observed in high frequency financial data such as high kurtosis and slowly decaying autocorrelation function of the squared observations. These rules are based on a number of moment conditions that is allowed to increase with sample size. We show that our selection procedure leads to choosing the best and simple model with probability one as the sample size increases. The finite sample size behaviour of our procedure is analyzed via simulations. Finally, we provide an application to stocks in the Dow Jones industrial average index.
Book Synopsis Aggregation and Model Construction for Volatility Models by : Ole E. Barndorff-Nielsen
Download or read book Aggregation and Model Construction for Volatility Models written by Ole E. Barndorff-Nielsen and published by . This book was released on 1998 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book GARCH Models written by Christian Francq and published by John Wiley & Sons. This book was released on 2019-03-19 with total page 504 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 A Simple Test for GARCH Against a Stochastic Volatility Model by : Philip Hans Franses
Download or read book A Simple Test for GARCH Against a Stochastic Volatility Model written by Philip Hans Franses and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: GARCH models and Stochastic Volatility (SV) models can both be used to describe unobserved volatility in asset returns. We consider the issue of testing a GARCH model against an SV model. For that purpose, we propose a new and parsimonious GARCH-t model with an additional restricted moving average term, which can capture SV model properties. We discuss model representation, parameter estimation, and our simple test for model selection. Furthermore, we derive the theoretical moments and the autocorrelation function of our new model. We illustrate our model and test for nine daily stock-return series.
Book Synopsis Multivariate Stochastic Volatility Models with Correlated Errors by : David X. Chan
Download or read book Multivariate Stochastic Volatility Models with Correlated Errors written by David X. Chan and published by . This book was released on 2008 with total page 31 pages. Available in PDF, EPUB and Kindle. Book excerpt: We develop a Bayesian approach for parsimoniously estimating the correlation structure of the errors in a multivariate stochastic volatility model. Since the number of parameters in the joint correlation matrix of the return and volatility errors is potentially very large, we impose a prior that allows the off-diagonal elements of the inverse of the correlation matrix to be identically zero. The model is estimated using a Markov chain simulation method that samples from the posterior distribution of the volatilities and parameters. We illustrate the approach using both simulated and real examples. In the real examples, the method is applied to equities at three levels of aggregation: returns for firms within the same industry, returns for different industries and returns aggregated at the index level. We find pronounced correlation effects only at the highest level of aggregation.
Book Synopsis An Empirical Analysis of Stochastic Volatility Models by : Adrien-Paul Lambillon
Download or read book An Empirical Analysis of Stochastic Volatility Models written by Adrien-Paul Lambillon and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this paper is to explain and apply the stochastic volatility models of Heston and GARCH to model the S&P 500 index volatility. The maximum likelihood estimate of the CIR process in the volatility equation of the Heston model and GARCH(1,1) with different underlying distributions are compared. It is shown that the model with strongest mean reversion, the CIR model, is the best volatility estimation for the overall period. For periods of volatility clustering, however, GARCH models capture the behaviour more accurately.
