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Application Of Nonparametric Quantile Regression To Estimating Value At Risk
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Book Synopsis Application of Nonparametric Quantile Regression to Estimating Value at Risk by : Wanying Li
Download or read book Application of Nonparametric Quantile Regression to Estimating Value at Risk written by Wanying Li and published by . This book was released on 2011 with total page 81 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Economic Applications of Quantile Regression by : Bernd Fitzenberger
Download or read book Economic Applications of Quantile Regression written by Bernd Fitzenberger and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 325 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantile regression has emerged as an essential statistical tool of contemporary empirical economics and biostatistics. Complementing classical least squares regression methods which are designed to estimate conditional mean models, quantile regression provides an ensemble of techniques for estimating families of conditional quantile models, thus offering a more complete view of the stochastic relationship among variables. This volume collects 12 outstanding empirical contributions in economics and offers an indispensable introduction to interpretation, implementation, and inference aspects of quantile regression.
Book Synopsis Handbook of Financial Econometrics and Statistics by : Cheng-Few Lee
Download or read book Handbook of Financial Econometrics and Statistics written by Cheng-Few Lee and published by Springer. This book was released on 2014-09-28 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Handbook of Financial Econometrics and Statistics provides, in four volumes and over 100 chapters, a comprehensive overview of the primary methodologies in econometrics and statistics as applied to financial research. Including overviews of key concepts by the editors and in-depth contributions from leading scholars around the world, the Handbook is the definitive resource for both classic and cutting-edge theories, policies, and analytical techniques in the field. Volume 1 (Parts I and II) covers all of the essential theoretical and empirical approaches. Volumes 2, 3, and 4 feature contributed entries that showcase the application of financial econometrics and statistics to such topics as asset pricing, investment and portfolio research, option pricing, mutual funds, and financial accounting research. Throughout, the Handbook offers illustrative case examples and applications, worked equations, and extensive references, and includes both subject and author indices.
Book Synopsis Predicting Extreme VaR by : Julia Schaumburg
Download or read book Predicting Extreme VaR written by Julia Schaumburg and published by . This book was released on 2010 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper studies the performance of nonparametric quantile regression as a tool to predict Value at Risk (VaR). The approach is flexible as it requires no assumptions on the form of return distributions. A monotonized double kernel local linear estimator is applied to estimate moderate (1%) conditional quantiles of index return distributions. For extreme (0.1%) quantiles, where particularly few data points are available, we propose to combine nonparametric quantile regression with extreme value theory. The out-of-sample forecasting performance of our methods turns out to be clearly superior to different specifications of the Conditionally Autoregressive VaR (CAViaR) models. -- Value at Risk ; nonparametric quantile regression ; risk management ; extreme value theory ; monotonization ; CAViaR
Download or read book CAViaR written by Robert F. Engle and published by . This book was released on 1999 with total page 51 pages. Available in PDF, EPUB and Kindle. Book excerpt: Value at Risk has become the standard measure of market risk employed by financial institutions for both internal and regulatory purposes. Despite its conceptual simplicity, its measurement is a very challenging statistical problem and none of the methodologies developed so far give satisfactory solutions. Interpreting Value at Risk as a quantile of future portfolio values conditional on current information, we propose a new approach to quantile estimation which does not require any of the extreme assumptions invoked by existing methodologies (such as normality or i.i.d. returns). The Conditional Value at Risk or CAViaR model moves the focus of attention from the distribution of returns directly to the behavior of the quantile. We postulate a variety of dynamic processes for updating the quantile and use regression quantile estimation to determine the parameters of the updating process. Tests of model adequacy utilize the criterion that each period the probability of exceeding the VaR must be independent of all the past information. We use a differential evolutionary genetic algorithm to optimize an objective function which is non-differentiable and hence cannot be optimized using traditional algorithms. Applications to simulated and real data provide empirical support to our methodology and illustrate the ability of these algorithms to adapt to new risk environments
Book Synopsis Handbook of Quantile Regression by : Roger Koenker
Download or read book Handbook of Quantile Regression written by Roger Koenker and published by CRC Press. This book was released on 2017-10-12 with total page 739 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantile regression constitutes an ensemble of statistical techniques intended to estimate and draw inferences about conditional quantile functions. Median regression, as introduced in the 18th century by Boscovich and Laplace, is a special case. In contrast to conventional mean regression that minimizes sums of squared residuals, median regression minimizes sums of absolute residuals; quantile regression simply replaces symmetric absolute loss by asymmetric linear loss. Since its introduction in the 1970's by Koenker and Bassett, quantile regression has been gradually extended to a wide variety of data analytic settings including time series, survival analysis, and longitudinal data. By focusing attention on local slices of the conditional distribution of response variables it is capable of providing a more complete, more nuanced view of heterogeneous covariate effects. Applications of quantile regression can now be found throughout the sciences, including astrophysics, chemistry, ecology, economics, finance, genomics, medicine, and meteorology. Software for quantile regression is now widely available in all the major statistical computing environments. The objective of this volume is to provide a comprehensive review of recent developments of quantile regression methodology illustrating its applicability in a wide range of scientific settings. The intended audience of the volume is researchers and graduate students across a diverse set of disciplines.
Book Synopsis Predicting Extreme VaR by : Julia Schaumburg
Download or read book Predicting Extreme VaR written by Julia Schaumburg and published by . This book was released on 2010 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper studies the performance of nonparametric quantile regression as a tool to predict Value at Risk (VaR). The approach is flexible as it requires no assumptions on the form of return distributions. A monotonized double kernel local linear estimator is applied to estimate moderate (1%) conditional quantiles of index return distributions. For extreme (0.1%) quantiles, where particularly few data points are available, we propose to combine nonparametric quantile regression with extreme value theory. The out-of-sample forecasting performance of our methods turns out to be clearly superior to different specifications of the Conditionally Autoregressive VaR (CAViaR) models. -- Value at Risk ; nonparametric quantile regression ; risk management ; extreme value theory ; monotonization ; CAViaR
Book Synopsis Robust Inference with Quantile Regression in Stochastic Volatility Models with Application to Value at Risk Calculation by :
Download or read book Robust Inference with Quantile Regression in Stochastic Volatility Models with Application to Value at Risk Calculation written by and published by . This book was released on 2004 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic Volatility (SV) models play an integral role in modeling time varying volatility, with widespread application in finance. Due to the absence of a closed form likelihood function, estimation is a challenging problem. In the presence of outliers, and the high kurtosis prevalent in financial data, robust estimation techniques are desirable. Also, in the context of risk assessment when the underlying model is SV, computing the one step ahead predictive return densities for Value at Risk (VaR) calculation entails a numerically indirect procedure. The Quantile Regression (QR) estimation is an increasingly important tool for analysis, which helps in fitting parsimonious models in lieu of full conditional distributions. We propose two methods (i) Regression Quantile Method of Moments (RQMM) and (ii) Regression Quantile - Kalman Filtering method (RQ-KF) based on the QR approach that can be used to obtain robust SV model parameter estimates as well as VaR estimates. The RQMM is a simulation based indirect inference procedure where auxiliary recursive quantile models are used, with gradients of the RQ objective function providing the moment conditions. This was motivated by the Efficient Method of Moments (EMM) approach used in SV model estimation and the Conditional Autoregressive Value at Risk (CAViaR) method. An optimal linear quantile model based on the underlying SV assumption is derived. This is used along with other CAViaR specifications for the auxiliary models. The RQ-KF is a computationally simplified procedure combining the QML and QR methodologies. Based on a recursive model under the SV framework, quantile estimates are produced by the Kalman filtering scheme and are further refined using the RQ objective function, yielding robust estimates. For illustration purposes, comparison of the RQMM method with EMM under different data scenarios show that RQMM is stable under model misspecification, presence of outliers and heavy-tailedness. Comparison of the RQ.
Book Synopsis Recursive Quantile Estimation with Application to Value at Risk by : Chen Ruan
Download or read book Recursive Quantile Estimation with Application to Value at Risk written by Chen Ruan and published by . This book was released on 2007 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: Keywords: regression quantile, stochastic approximation.
Book Synopsis Risk Margin Quantile Function Via Parametric and Non-Parametric Bayesian Quantile Regression by : Alice Dong
Download or read book Risk Margin Quantile Function Via Parametric and Non-Parametric Bayesian Quantile Regression written by Alice Dong and published by . This book was released on 2014 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt: We develop quantile regression models in order to derive risk margin and to evaluate capital in non-life insurance applications. By utilizing the entire range of conditional quantile functions, especially higher quantile levels, we detail how quantile regression is capable of providing an accurate estimation of risk margin and an overview of implied capital based on the historical volatility of a general insurers loss portfolio. Two modelling frameworks are considered based around parametric and nonparametric quantile regression models which we develop specifically in this insurance setting.In the parametric quantile regression framework, several models including the flexible generalized beta distribution family, asymmetric Laplace (AL) distribution and power Pareto distribution are considered under a Bayesian regression framework. The Bayesian posterior quantile regression models in each case are studied via Markov chain Monte Carlo (MCMC) sampling strategies.In the nonparametric quantile regression framework, that we contrast to the parametric Bayesian models, we adopted an AL distribution as a proxy and together with the parametric AL model, we expressed the solution as a scale mixture of uniform distributions to facilitate implementation. The models are extended to adopt dynamic mean, variance and skewness and applied to analyze two real loss reserve data sets to perform inference and discuss interesting features of quantile regression for risk margin calculations.
Book Synopsis Extreme Value Modeling and Risk Analysis by : Dipak K. Dey
Download or read book Extreme Value Modeling and Risk Analysis written by Dipak K. Dey and published by CRC Press. This book was released on 2016-01-06 with total page 538 pages. Available in PDF, EPUB and Kindle. Book excerpt: Extreme Value Modeling and Risk Analysis: Methods and Applications presents a broad overview of statistical modeling of extreme events along with the most recent methodologies and various applications. The book brings together background material and advanced topics, eliminating the need to sort through the massive amount of literature on the subje
Book Synopsis Journal of the American Statistical Association by :
Download or read book Journal of the American Statistical Association written by and published by . This book was released on 2009 with total page 896 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book CA ViaR written by Robert F. Engle and published by . This book was released on 1999 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Quantile Regression by : Cristina Davino
Download or read book Quantile Regression written by Cristina Davino and published by John Wiley & Sons. This book was released on 2013-12-31 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: A guide to the implementation and interpretation of Quantile Regression models This book explores the theory and numerous applications of quantile regression, offering empirical data analysis as well as the software tools to implement the methods. The main focus of this book is to provide the reader with a comprehensive description of the main issues concerning quantile regression; these include basic modeling, geometrical interpretation, estimation and inference for quantile regression, as well as issues on validity of the model, diagnostic tools. Each methodological aspect is explored and followed by applications using real data. Quantile Regression: Presents a complete treatment of quantile regression methods, including, estimation, inference issues and application of methods. Delivers a balance between methodolgy and application Offers an overview of the recent developments in the quantile regression framework and why to use quantile regression in a variety of areas such as economics, finance and computing. Features a supporting website (www.wiley.com/go/quantile_regression) hosting datasets along with R, Stata and SAS software code. Researchers and PhD students in the field of statistics, economics, econometrics, social and environmental science and chemistry will benefit from this book.
Book Synopsis International Financial Markets by : Julien Chevallier
Download or read book International Financial Markets written by Julien Chevallier and published by Routledge. This book was released on 2019-06-28 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an up-to-date series of advanced chapters on applied financial econometric techniques pertaining the various fields of commodities finance, mathematics & stochastics, international macroeconomics and financial econometrics. International Financial Markets: Volume I provides a key repository on the current state of knowledge, the latest debates and recent literature on international financial markets. Against the background of the "financialization of commodities" since the 2008 sub-primes crisis, section one contains recent contributions on commodity and financial markets, pushing the frontiers of applied econometrics techniques. The second section is devoted to exchange rate and current account dynamics in an environment characterized by large global imbalances. Part three examines the latest research in the field of meta-analysis in economics and finance. This book will be useful to students and researchers in applied econometrics; academics and students seeking convenient access to an unfamiliar area. It will also be of great interest established researchers seeking a single repository on the current state of knowledge, current debates and relevant literature.
Book Synopsis Financial Econometrics Modeling: Market Microstructure, Factor Models and Financial Risk Measures by : G. Gregoriou
Download or read book Financial Econometrics Modeling: Market Microstructure, Factor Models and Financial Risk Measures written by G. Gregoriou and published by Springer. This book was released on 2010-12-13 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book proposes new methods to build optimal portfolios and to analyze market liquidity and volatility under market microstructure effects, as well as new financial risk measures using parametric and non-parametric techniques. In particular, it investigates the market microstructure of foreign exchange and futures markets.
Book Synopsis Selected topics on nonparametric conditional quantiles and risk theory by : Yebin Cheng
Download or read book Selected topics on nonparametric conditional quantiles and risk theory written by Yebin Cheng and published by Rozenberg Publishers. This book was released on 2007 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt: