Download or read book A Simple Efficient GMM Estimator of GARCH Models written by Jimmy Skoglund and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper is concerned with efficient GMM estimation and inference in GARCH models. Sufficient conditions for the estimator to be consistent and asymptotically normal are established for the GARCH(1,1) conditional variance process. In addition efficiency results are obtained in the general framework of the GARCH(1,1)-M regression model.

Download or read book Generalized Method of Moments Estimation written by Laszlo Matyas and published by Cambridge University Press. This book was released on 1999-04-13 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: The generalized method of moments (GMM) estimation has emerged as providing a ready to use, flexible tool of application to a large number of econometric and economic models by relying on mild, plausible assumptions. The principal objective of this volume is to offer a complete presentation of the theory of GMM estimation as well as insights into the use of these methods in empirical studies. It is also designed to serve as a unified framework for teaching estimation theory in econometrics. Contributors to the volume include well-known authorities in the field based in North America, the UK/Europe, and Australia. The work is likely to become a standard reference for graduate students and professionals in economics, statistics, financial modeling, and applied mathematics.

Download or read book Efficiency Results of Mle and GMM Estimation with Sampling Weights written by J.S Butler and published by . This book was released on 1998 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper examines the theory of the estimation of econometric models and Hausman tests with sampling weights. The Manski-Lerman weighted conditional MLE is emphasized because of its popularity in econometric estimation with sampling weights. It is an inefficient alternative to full information MLE under choice-based sampling, and weighted conditional MLE can be less efficient than weighted conditional GMM, but not all efficiency results are lost. Weighted conditional MLE is still most efficient in the asymptotically linear class if sampling weights are independent of exogenous variables or linear or nonlinear regressions have homoscedastic additive disturbances. The derivation of the Hausman test and the Cramer-Rao bound are complicated by sampling weights; the covariance of an asymptotically linear estimator (such as a GMM estimator) with the score function is not an identity matrix. When weights are stochastically independent of the regressors in a model, however, of which one example is estimation of a sample mean, the MLE attains the Cramer-Rao bound, which is the standard form multiplied by the design effect from sample design. Simple random samples sometimes do and sometimes do not minimize the variances of econometric models. A simple random sample minimizes the variance of MLE, sample means, and homoscedastic linear and nonlinear regressions, but not of GMM estimators when heteroscedasticity is present. GMM variances are necessarily minimized by simple random samples if GMM is the same as MLE or disturbances are homoscedastic, but not in general. A probit model illustrates conditional GMM variances not minimized by a simple random sample and smaller than weighted conditional MLE variances. The calculation uses the theoretical expectation of the variance matrix, eliminating all sampling error from the estimation of the variances.

Download or read book Efficient and Robust Estimation of GARCH Models written by X. Jiang and published by . This book was released on 2015 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generalized autoregressive conditional heteroscedastic (GARCH) models have been a powerful tool for modeling volatility. In this paper, we propose an efficient and robust method for estimating the parameters of GARCH models. This method involves a sequence of weights and takes a data-driven weighting scheme to maximize the asymptotic efficiency of the estimators. Under regularity conditions, we establish asymptotic distributions of the proposed estimators for a variety of heavy- or light-tailed error distributions. Simulations endorse our theoretical results. Our approach is applied to analyze the S&P 500 Composite index in the U.S. financial market and run some regression diagnostics to validate the fitted model.

Download or read book Financial Risk Management written by Jimmy Skoglund and published by John Wiley & Sons. This book was released on 2015-09-08 with total page 580 pages. Available in PDF, EPUB and Kindle. Book excerpt: A global banking risk management guide geared toward the practitioner Financial Risk Management presents an in-depth look at banking risk on a global scale, including comprehensive examination of the U.S. Comprehensive Capital Analysis and Review, and the European Banking Authority stress tests. Written by the leaders of global banking risk products and management at SAS, this book provides the most up-to-date information and expert insight into real risk management. The discussion begins with an overview of methods for computing and managing a variety of risk, then moves into a review of the economic foundation of modern risk management and the growing importance of model risk management. Market risk, portfolio credit risk, counterparty credit risk, liquidity risk, profitability analysis, stress testing, and others are dissected and examined, arming you with the strategies you need to construct a robust risk management system. The book takes readers through a journey from basic market risk analysis to major recent advances in all financial risk disciplines seen in the banking industry. The quantitative methodologies are developed with ample business case discussions and examples illustrating how they are used in practice. Chapters devoted to firmwide risk and stress testing cross reference the different methodologies developed for the specific risk areas and explain how they work together at firmwide level. Since risk regulations have driven a lot of the recent practices, the book also relates to the current global regulations in the financial risk areas. Risk management is one of the fastest growing segments of the banking industry, fueled by banks' fundamental intermediary role in the global economy and the industry's profit-driven increase in risk-seeking behavior. This book is the product of the authors' experience in developing and implementing risk analytics in banks around the globe, giving you a comprehensive, quantitative-oriented risk management guide specifically for the practitioner. Compute and manage market, credit, asset, and liability risk Perform macroeconomic stress testing and act on the results Get up to date on regulatory practices and model risk management Examine the structure and construction of financial risk systems Delve into funds transfer pricing, profitability analysis, and more Quantitative capability is increasing with lightning speed, both methodologically and technologically. Risk professionals must keep pace with the changes, and exploit every tool at their disposal. Financial Risk Management is the practitioner's guide to anticipating, mitigating, and preventing risk in the modern banking industry.

Download or read book Efficiency Comparison of Maximum Likelihood based Estimators in Garch Models written by Feike C. Drost and published by . This book was released on 1998 with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book A Simple and Efficient Estimation Method for Models with Nonignorable Missing Data written by Chunrong Ai and published by . This book was released on 2018 with total page 51 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper proposes a simple and efficient estimation procedure for the model with non-ignorable missing data studied by Morikawa and Kim (2016). Their semiparametrically efficient estimator requires explicit nonparametric estimation and so suffers from the curse of dimensionality and requires a bandwidth selection. We propose an estimation method based on the Generalized Method of Moments (hereafter GMM). Our method is consistent and asymptotically normal regardless of the number of moments chosen. Furthermore, if the number of moments increases appropriately our estimator can achieve the semiparametric efficiency bound derived in Morikawa and Kim (2016), but under weaker regularity conditions. Moreover, our proposed estimator and its consistent covariance matrix are easily computed with the widely available GMM package. We propose two data-based methods for selection of the number of moments. A small scale simulation study reveals that the proposed estimation indeed out-performs the existing alternatives in finite samples.

Download or read book Efficiency Comparisons of Maximum Likelihood based Estimators in GARCH Models written by Gloria González-Rivera 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 Efficient Estimation in Semiparametric GARCH Models written by Feike C. Drost and published by . This book was released on 1998 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: It is well-knownthat financial data sets exhibit conditional heteroskedasticity. GARCH type models are often used to model this phenomenon. Since the distribution of the rescaled innovations is generally far froma normal distribution, a semiparametric approach is advisable. Several publications observed that adaptive estimation of the Euclidean parameters is not possible in the usual parametrization when the distribution of the rescaled innovations is the unknown nuisance parameter. However, there exists a reparametrization such that the efficient score functions in the parametric model of the autoregression parameters are orthogonal to the tangent space generated by the nuisance parameter, thus suggesting that adaptive estimation of the autoregression parameters is possible. Indeed, we construct adaptive and hence efficient estimators in a general GARCH in mean type context including integrated GARCH models. Our analysis is based on a general LAN Theorem for time-series models, published elsewhere. In contrast to recent literature about ARCH models we do not need any moment condition.

Download or read book Simple Estimators for the GARCH 1 1 Model written by Todd Prono and published by . This book was released on 2014 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt: I propose closed-form estimators for the GARCH(1,1) model that are based on second-order covariances. The ability to obtain closed-form estimates derives from skewness in the sequence being modeled, which permits separate identification and estimation of the ARCH and GARCH effects. I show these estimators to be CAN under weak stationarity using Martingale limit theory. I also demonstrate conditions under which an iterative GLS estimator reliant on these closed-form estimates as starting values shares the same asymptotic distribution with the QMLE. This asymptotic equivalence is achieved given only third moment existence, which substantially relaxes the moment existence criteria generally required for OLS- and TSLS-style estimators of GARCH processes. The proposed estimators are studied in Monte Carlo experiments and applied to a suite of financial data.

Download or read book Improved GMM Estimation of Panel VAR Models written by Kazuhiko Hayakawa and published by . This book was released on 2015 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this study, improved IV/GMM estimators for panel vector autoregressive models (VAR) are proposed by extending Hayakawa (2009b) ("A Simple Efficient Instrumental Variable Estimator in Panel AR(p) Models When Both N and T Are Large,'' Econometric Theory, 25, 873-890) in which an alternative form of instruments is suggested. It is shown that the proposed IV estimator has the same asymptotic distribution as the bias-corrected fixed effects estimator of Hahn and Kuersteiner (2002) ("Asymptotically Unbiased Inference for a Dynamic Panel Model with Fixed Effects When Both n and T Are Large,'' Econometrica, 70, 1639-1657) in the VAR(1) case when both N and T are large where N and T denote the sample sizes of cross section and time series, respectively. Since the proposed estimator is simply to change the form of instruments, it is very easy to implement in practice. As applications of the proposed estimators, we consider a panel Granger causality test and panel impulse response analysis in which the asymptotic distribution of generalized impulse response functions of Pesaran and Shin (1998) ("Generalized Impulse Response Analysis in Linear Multivariate Models,'' Economics Letters, 58, 17-29) is newly derived. Monte Carlo simulation results show that the proposed estimators have comparable or better finite sample properties than the conventional IV/GMM estimators using instruments in levels for moderate or large T.

Download or read book Estimation of the GARCH Model written by Yi-Yi Chen and published by . This book was released on 2000 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Simple Estimators for GARCH Models written by Todd Prono and published by . This book was released on 2017 with total page 13 pages. Available in PDF, EPUB and Kindle. Book excerpt: Contained herein are detailed proofs of all the Lemmas that support the main Theorems discussed in the paper, "Simple Estimators for GARCH models."Original paper can be found at: "https://ssrn.com/abstract=2897867" https://ssrn.com/abstract=2897867.

Download or read book Method of Moments Estimation of GO GARCH Models written by Peter H. Boswijk and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book GEL Estimation for Heavy Tailed GARCH Models with Robust Empirical Likelihood Inference written by Jonathan B. Hill and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We construct a Generalized Empirical Likelihood estimator for a GARCH(1,1) model with a possibly heavy tailed error. The estimator imbeds tail-trimmed estimating equations allowing for over-identifying conditions, asymptotic normality, efficiency and empirical likelihood based confidence regions for very heavy-tailed random volatility data. We show the implied probabilities from the tail-trimmed Continuously Updated Estimator elevate weight for usable large values, assign large but not maximum weight to extreme observations, and give the lowest weight to non-leverage points. Finally, we present robust versions of Generalized Empirical Likelihood Ratio, Wald, and Lagrange Multiplier tests, and an efficient and heavy tail robust moment estimator with an application to the estimation of a conditionally heteroscedastic asset's expected shortfall.

Download or read book Maximum Likelihood and GMM Estimation of Dynamic Panel Data Models with Fixed Effects written by Hugo Kruiniger and published by . This book was released on 2002 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper considers inference procedures for two types of dynamic linear panel data models with fixed effects (FE). First, it shows that the closures of stationary ARMAFE models can be consistently estimated by Conditional Maximum Likelihood Estimators and it derives their asymptotic distributions. Then it presents an asymptotically equivalent Minimum Distance Estimator which permits an analytic comparison between the CMLE for the ARFE (1) model and the GMM estimators that have been considered in the literature. The CMLE is shown to be asymptotically less efficient than the most efficient GMM estimator when N approaches the limit infinity but T is fixed. Under normality some of the moment conditions become asymptotically redundant and the CMLE attains the Cramer-Rao lowerbound when T approaches the limit infinity as well. The paper also presents likelihood based unit root tests. Finally, the properties of CML, GMM, and Modified ML estimators for dynamic panel data models that condition on the initial observations are studied and compared. It is shown that for finite T the MMLE is less efficient than the most efficient GMM estimator.

Download or read book Robust Estimation for the Orthogonal GARCH Model written by Farhat Iqbal and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, we propose a class of robust M-estimators for the orthogonal generalized autoregressive conditional heteroscedastic (GARCH) model. The method involves the estimation of only univariate GARCH models and hence easy to estimate and does not put additional constraints on the model. The forecasting performance of the class of robust estimators in predicting correlation and value-at-risk using various evaluation measures are investigated. We found empirical evidences of the better predictive potential of estimators such as least absolute deviation and B-estimator over the widely used quasi-maximum likelihood estimator when the error distribution is heavy-tailed and asymmetric. Applications to real data sets are also presented.