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Author : Hassan Malongo
Publisher :
ISBN 13 :
Total Pages : 34 pages
Book Rating : 4.:/5 (931 download)
Download or read book On the Stationarity of Dynamic Conditional Correlation Models written by Hassan Malongo and published by . This book was released on 2013 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Jean-David Fermanian
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (931 download)
Download or read book On the Stationarity of Dynamic Conditional Correlation Models written by Jean-David Fermanian and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Michael McAleer
Publisher :
ISBN 13 :
Total Pages : 21 pages
Book Rating : 4.:/5 (13 download)
Download or read book Stationarity and Invertibility of a Dynamic Correlation Matrix written by Michael McAleer and published by . This book was released on 2017 with total page 21 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the most widely-used multivariate conditional volatility models is the dynamic conditional correlation (or DCC) specification. However, the underlying stochastic process to derive DCC has not yet been established, which has made problematic the derivation of asymptotic properties of the Quasi-Maximum Likelihood Estimators (QMLE). To date, the statistical properties of the QMLE of the DCC parameters have purportedly been derived under highly restrictive and unverifiable regularity conditions. The paper shows that the DCC model can be obtained from a vector random coefficient moving average process, and derives the stationarity and invertibility conditions of the DCC model. The derivation of DCC from a vector random coefficient moving average process raises three important issues, as follows: (i) demonstrates that DCC is, in fact, a dynamic conditional covariance model of the returns shocks rather than a dynamic conditional correlation model; (ii) provides the motivation, which is presently missing, for standardization of the conditional covariance model to obtain the conditional correlation model; and (iii) shows that the appropriate ARCH or GARCH model for DCC is based on the standardized shocks rather than the returns shocks. The derivation of the regularity conditions, especially stationarity and invertibility, should subsequently lead to a solid statistical foundation for the estimates of the DCC parameters. Several new results are also derived for univariate models, including a novel conditional volatility model expressed in terms of standardized shocks rather than returns shocks, as well as the associated stationarity and invertibility conditions.
Author : Luc Bauwens
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)
Download or read book Estimation and Empirical Performance of Non-Scalar Dynamic Conditional Correlation Models written by Luc Bauwens and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A method capable of estimating richly parametrized versions of the dynamic conditional correlation (DCC) model that go beyond the standard scalar case is presented. The algorithm is based on the maximization of a Gaussian quasi-likelihood using a Bregman-proximal trust-region method that handles the various non-linear stationarity and positivity constraints that arise in this context. The general matrix Hadamard DCC model with full rank, rank equal to two and, additionally, two different rank one matrix specifications are considered. In the last mentioned case, the elements of the vectors that determine the rank one parameter matrices are either arbitrary or parsimoniously defined using the Almon lag function. Actual stock returns data in dimensions up to thirty are used in order to carry out performance comparisons according to several in- and out-of-sample criteria. Empirical results show that the use of richly parametrized models adds value with respect to the conventional scalar case.
Author : Jon Danielsson
Publisher : John Wiley & Sons
ISBN 13 : 1119977118
Total Pages : 307 pages
Book Rating : 4.1/5 (199 download)
Download or read book Financial Risk Forecasting written by Jon Danielsson and published by John Wiley & Sons. This book was released on 2011-04-20 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: Financial Risk Forecasting is a complete introduction to practical quantitative risk management, with a focus on market risk. Derived from the authors teaching notes and years spent training practitioners in risk management techniques, it brings together the three key disciplines of finance, statistics and modeling (programming), to provide a thorough grounding in risk management techniques. Written by renowned risk expert Jon Danielsson, the book begins with an introduction to financial markets and market prices, volatility clusters, fat tails and nonlinear dependence. It then goes on to present volatility forecasting with both univatiate and multivatiate methods, discussing the various methods used by industry, with a special focus on the GARCH family of models. The evaluation of the quality of forecasts is discussed in detail. Next, the main concepts in risk and models to forecast risk are discussed, especially volatility, value-at-risk and expected shortfall. The focus is both on risk in basic assets such as stocks and foreign exchange, but also calculations of risk in bonds and options, with analytical methods such as delta-normal VaR and duration-normal VaR and Monte Carlo simulation. The book then moves on to the evaluation of risk models with methods like backtesting, followed by a discussion on stress testing. The book concludes by focussing on the forecasting of risk in very large and uncommon events with extreme value theory and considering the underlying assumptions behind almost every risk model in practical use – that risk is exogenous – and what happens when those assumptions are violated. Every method presented brings together theoretical discussion and derivation of key equations and a discussion of issues in practical implementation. Each method is implemented in both MATLAB and R, two of the most commonly used mathematical programming languages for risk forecasting with which the reader can implement the models illustrated in the book. The book includes four appendices. The first introduces basic concepts in statistics and financial time series referred to throughout the book. The second and third introduce R and MATLAB, providing a discussion of the basic implementation of the software packages. And the final looks at the concept of maximum likelihood, especially issues in implementation and testing. The book is accompanied by a website - www.financialriskforecasting.com – which features downloadable code as used in the book.
Author : Bram van Os
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (128 download)
Download or read book Pooling Dynamic Conditional Correlation Models written by Bram van Os and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The Dynamic Conditional Correlation (DCC) model by Engle (2002) has become an extremely popular tool for modeling the time-varying dependence of asset returns. However, applications to large cross-sections have been found to be problematic, due to the curse of dimensionality. We propose a novel DCC model with Conditional LInear Pooling (CLIP-DCC) which endogenously determines an optimal degree of commonality in the correlation innovations, allowing a part of the update to be of reduced dimension. In contrast to existing approaches such as the Dynamic EquiCOrrelation (DECO) model, the CLIP-DCC model does not restrict long-run behavior, thereby naturally complementing target correlation matrix shrinkage approaches. Empirical findings suggest substantial benefits for a minimum-variance investor in real-time. Combining the CLIP-DCC model with target shrinkage yields the largest improvements, confirming that they address distinct parts of uncertainty of the conditional correlation matrix.
Author : Robert F. Engle
Publisher :
ISBN 13 :
Total Pages : 34 pages
Book Rating : 4.:/5 (129 download)
Download or read book Dynamic Conditional Correlation written by Robert F. Engle and published by . This book was released on 2008 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt: Time varying correlations are often estimated with Multivariate Garch models that are linear in squares and cross products of the data. A new class of multivariate models called dynamic conditional correlation (DCC) models is proposed. These have the flexibility of univariate GARCH models coupled with parsimonious parametric models for the correlations. They are not linear but can often be estimated very simply with univariate or two step methods based on the likelihood function. It is shown that they perform well in a variety of situations andprovide sensible empirical results.
Author : Christian Francq
Publisher : John Wiley & Sons
ISBN 13 : 1119313562
Total Pages : 504 pages
Book Rating : 4.1/5 (193 download)
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.
Author : Denise R. Osborn
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (129 download)
Download or read book Periodic Dynamic Conditional Correlations between Stock Markets in Europe and the Us written by Denise R. Osborn and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This study extends the dynamic conditional correlation model of Engle (2002, Journal of Business and Economic Statistics 20, 339-350) to allow periodic (day-specific) conditional correlations of shocks across international stock markets. The properties of the resulting periodic dynamic conditional correlation (PDCC) model are examined, focusing particularly on stationarity and the implications for unconditional shock correlations. When applied to the intraweek interactions between six developed European stock markets and the United States over 1993-2005, we find very strong evidence of periodic conditional correlations for the shocks. The highest correlations are generally observed on Thursdays, with these sometimes being twice those on Monday or Tuesday. In addition to these PDCC effects, strong day-of-the-week effects are found in mean returns for the French, Italian, and Spanish stock markets, while periodic effects are also present in volatility for all stock markets except Italy.
Author : Christian Francq
Publisher : John Wiley & Sons
ISBN 13 : 1119957397
Total Pages : 469 pages
Book Rating : 4.1/5 (199 download)
Download or read book GARCH Models written by Christian Francq and published by John Wiley & Sons. This book was released on 2011-06-24 with total page 469 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 coverage of several extensions such as asymmetric and multivariate models and looks at financial applications. Key features: Provides up-to-date coverage of the current research in the probability, statistics and econometric theory of GARCH models. Numerous illustrations and applications to real financial series are provided. Supporting website featuring R codes, Fortran programs and data sets. Presents a large collection of problems and exercises. This authoritative, state-of-the-art reference is ideal for graduate students, researchers and practitioners in business and finance seeking to broaden their skills of understanding of econometric time series models.
Author : Giovanni Petris
Publisher : Springer Science & Business Media
ISBN 13 : 0387772383
Total Pages : 258 pages
Book Rating : 4.3/5 (877 download)
Download or read book Dynamic Linear Models with R written by Giovanni Petris and published by Springer Science & Business Media. This book was released on 2009-06-12 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: State space models have gained tremendous popularity in recent years in as disparate fields as engineering, economics, genetics and ecology. After a detailed introduction to general state space models, this book focuses on dynamic linear models, emphasizing their Bayesian analysis. Whenever possible it is shown how to compute estimates and forecasts in closed form; for more complex models, simulation techniques are used. A final chapter covers modern sequential Monte Carlo algorithms. The book illustrates all the fundamental steps needed to use dynamic linear models in practice, using R. Many detailed examples based on real data sets are provided to show how to set up a specific model, estimate its parameters, and use it for forecasting. All the code used in the book is available online. No prior knowledge of Bayesian statistics or time series analysis is required, although familiarity with basic statistics and R is assumed.
Author : Robert F. Engle
Publisher :
ISBN 13 :
Total Pages : 43 pages
Book Rating : 4.:/5 (129 download)
Download or read book Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH. written by Robert F. Engle and published by . This book was released on 2008 with total page 43 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, we develop the theoretical and empirical properties of a new class of multivariate GARCH models capable of estimating large time-varying covariance matrices, Dynamic Conditional Correlation Multivariate GARCH. We show that the problem of multivariate conditional variance estimation can be simplified by estimating univariate GARCH models for each asset, and then, using transformed residuals resulting from the first stage, estimating a conditional correlation estimator. The standard errors for the first stage parameters remain consistent, and only the standard errors for the correlation parameters need to be modified. We use the model to estimate the conditional covariance of up to 100 assets using Samp;P 500 Sector Indices and Dow Jones Industrial Average stocks, and conduct specification tests of the estimator using an industry standard benchmark for volatility models. This new estimator demonstrates very strong performance especially considering ease of implementation of the estimator.
Author : Simona Boffelli
Publisher :
ISBN 13 : 9781597182140
Total Pages : 0 pages
Book Rating : 4.1/5 (821 download)
Download or read book Financial Econometrics Using Stata written by Simona Boffelli and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Financial Econometrics Using Stata is an essential reference for graduate students, researchers, and practitioners who use Stata to perform intermediate or advanced methods. After discussing the characteristics of financial time series, the authors provide introductions to ARMA models, univariate GARCH models, multivariate GARCH models, and applications of these models to financial time series. The last two chapters cover risk management and contagion measures. After a rigorous but intuitive overview, the authors illustrate each method by interpreting easily replicable Stata examples.
Author : Enzo D'Innocenzo
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (134 download)
Download or read book Dynamic Partial Correlation Models written by Enzo D'Innocenzo and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We introduce a new, easily scalable model for dynamic conditional correlation matrices based on a recursion of dynamic bivariate partial correlation models. By exploiting the model's recursive structure and the theory of perturbed stochastic recurrence equations, we establish stationarity, ergodicity, and filter invertibility in the multivariate setting using conditions for bivariate slices of the data only. From this, we establish consistency and asymptotic normality of the maximum likelihood estimator for the model's static parameters. The new model outperforms benchmarks like the t-cDCC and the multivariate t-GAS, both in simulations and in an in-sample and outof-sample asset pricing application to 1980-2021 US stock returns across twelve industries.
Author : Zhongfang He
Publisher :
ISBN 13 :
Total Pages : 20 pages
Book Rating : 4.:/5 (13 download)
Download or read book A Class of Generalized Dynamic Correlation Models written by Zhongfang He and published by . This book was released on 2018 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper proposes a class of parametric correlation models that apply a two-layer autoregressive-moving-average structure to the dynamics of correlation matrices. The proposed model contains the Dynamic Conditional Correlation model of Engle (2002) and the Varying Correlation model of Tse and Tsui (2002) as special cases and offers greater flexibility in a parsimonious way. Performance of the proposed model is illustrated in a simulation exercise and an application to the U.S. stock indices.
Author : Marc S. Paolella
Publisher : John Wiley & Sons
ISBN 13 : 1119431859
Total Pages : 900 pages
Book Rating : 4.1/5 (194 download)
Download or read book Linear Models and Time-Series Analysis written by Marc S. Paolella and published by John Wiley & Sons. This book was released on 2018-10-10 with total page 900 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive and timely edition on an emerging new trend in time series Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH sets a strong foundation, in terms of distribution theory, for the linear model (regression and ANOVA), univariate time series analysis (ARMAX and GARCH), and some multivariate models associated primarily with modeling financial asset returns (copula-based structures and the discrete mixed normal and Laplace). It builds on the author's previous book, Fundamental Statistical Inference: A Computational Approach, which introduced the major concepts of statistical inference. Attention is explicitly paid to application and numeric computation, with examples of Matlab code throughout. The code offers a framework for discussion and illustration of numerics, and shows the mapping from theory to computation. The topic of time series analysis is on firm footing, with numerous textbooks and research journals dedicated to it. With respect to the subject/technology, many chapters in Linear Models and Time-Series Analysis cover firmly entrenched topics (regression and ARMA). Several others are dedicated to very modern methods, as used in empirical finance, asset pricing, risk management, and portfolio optimization, in order to address the severe change in performance of many pension funds, and changes in how fund managers work. Covers traditional time series analysis with new guidelines Provides access to cutting edge topics that are at the forefront of financial econometrics and industry Includes latest developments and topics such as financial returns data, notably also in a multivariate context Written by a leading expert in time series analysis Extensively classroom tested Includes a tutorial on SAS Supplemented with a companion website containing numerous Matlab programs Solutions to most exercises are provided in the book Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH is suitable for advanced masters students in statistics and quantitative finance, as well as doctoral students in economics and finance. It is also useful for quantitative financial practitioners in large financial institutions and smaller finance outlets.
Author : Dirk G. Baur
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)
Download or read book A Flexible Dynamic Correlation Model written by Dirk G. Baur and published by . This book was released on 2009 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Existing multivariate GARCH models either impose strong restrictions on the parameters or do not guarantee a well-defined (positive definite) covariance matrix. I discuss all main Multivariate GARCH models and focus on the BEKK model for which it is shown that the covariance and correlation is not adequately specified under certain conditions. This implies that any analysis of the persistence and the asymmetry of the correlation is difficult and potentially inaccurate. I therefore propose a new Flexible Dynamic Correlation (FDC) model that parameterizes the conditional correlation directly and eliminates various shortcomings of the existing Multivariate GARCH models. Empirical results of daily and monthly returns of four international stock market indices reveal that correlations exhibit different degrees of persistence and different asymmetric reactions to shocks than variances. In addition, I find that correlations do not always increase with jointly negative shocks implying a justification for international portfolio diversification. Time-varying Correlations.
