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SEMIPARAMETRIC ESTIMATION AND INFERENCE FOR CONDITIONAL VALUE-AT-RISK AND EXPECTED SHORTFALL.
Author: Chuan-Sheng Wang
Publisher:
Published: 2018
Total Pages:
ISBN-13:
DOWNLOAD EBOOK →Conditional Value-at-Risk (hereafter, CVaR) and Expected Shortfall (CES) play an important role in financial risk management. Parametric CVaR and CES enjoy both nice interpretation and capability of multi-dimensional modeling, however they are subject to errors from mis-specification of the noise distribution. On the other hand, nonparametric estimations are robust but suffer from the ''curse of dimensionality'' and slow convergence rate. To overcome these issues, we study semiparametric CVaR and CES estimation and inference for parametric model with nonparametric noise distribution. In this dissertation, under a general framework that allows for many widely used time series models, we propose a semiparametric CVaR estimator and a semiparametric CES estimator that both achieve the parametric convergence rate. Asymptotic properties of the estimators are provided to support the inference. Furthermore, to draw simultaneous inference for CVaR at multiple confidence levels, we establish a functional central limit theorem for CVaR process indexed by the confidence level and use it to study the conditional expected shortfall. A user-friendly bootstrap approach is introduced to facilitate non-expert practitioners to perform confidence interval construction for CVaR and CES. The methodology is illustrated through both Monte Carlo studies and an application to S&P 500 index.
Measuring Market Risk with Value at Risk
Author: Pietro Penza
Publisher: John Wiley & Sons
Published: 2001
Total Pages: 324
ISBN-13: 9780471393139
DOWNLOAD EBOOK →"This book, Measuring Market Risk with Value at Risk by Vipul Bansal and Pietro Penza, has three advantages over earlier works on the subject. First, it takes a decidedly global approach-an essential ingredient for any comprehensive work on market risk. Second, it ties the scientifically grounded, yet intuitively appealing, VaR measure to earlier, more idiosyncratic measures of market risk that are used in specific market environs (e.g., duration in fixed income). Finally, it encompasses all of the accepted approaches to calculating a VaR measure and presents them in a clearly explained fashion with supporting illustrations and completely worked-out examples." -from the Foreword by John F. Marshall, PhD, Principal, Marshall, Tucker & Associates, LLC "Measuring Market Risk with Value at Risk offers a much-needed intellectual bridge, a translation from the esoteric realm of mathematical finance to the domain of financial managers who seek guidance in applying developments from this important field of research as well as that of MBA-level graduate instruction. I believe the authors have done a commendable job of providing a carefully crafted, highly readable, and most useful work, and intend to recommend it to all those involved in business risk management applications." -Anthony F. Herbst, PhD, Professor of Finance and C.R. and D.S. Carter Chair, The University of Texas, El Paso and Founding editor of The Journal of Financial Engineering (1991-1998) "Finally there's a book that strikes a balance between rigor and application in the area of risk management in the banking industry. This innovative book is a MUST for both novices and professionals alike." -Robert P. Yuyuenyongwatana, PhD, Associate Professor of Finance, Cameron University "Measuring Market Risk with Value at Risk is one of the most complete discussions of this emerging topic in finance that I have seen. The authors develop a logical and rigorous framework for using VaR models, providing both historical references and analytical applications." -Kevin Wynne, PhD, Associate Professor of Finance, Lubin School of Business, Pace University
Semi-Parametric Estimation of Risk-Return Relationships
Author: Juan Carlos Escanciano
Publisher:
Published: 2013
Total Pages: 30
ISBN-13:
DOWNLOAD EBOOK →This article proposes semi-parametric least squares estimation of parametric risk-return relationships, i.e. parametric restrictions between the conditional mean and the conditional variance of excess returns given a set of unobservable parametric factors. A distinctive feature of our estimator is that it does not require a parametric model for the conditional mean and variance. We establish consistency and asymptotic normality of the estimates. The theory is non-standard due to the presence of estimated factors. We provide simple sufficient conditions for the estimated factors not to have an impact in the asymptotic standard error of estimators. A simulation study investigates the nite sample performance of the estimates. Finally, an application to the CRSP value-weighted excess returns highlights the merits of our approach. In contrast to most previous studies using non-parametric estimates, we find a positive and significant price of risk in our semi-parametric setting.
Evaluating Portfolio Value-at-Risk Using Semi-Parametric GARCH Models
Author: J. V. K. Rombouts
Publisher:
Published: 2009
Total Pages: 32
ISBN-13:
DOWNLOAD EBOOK →In this paper we examine the usefulness of multivariate semi-parametric GARCH models for evaluating the Value-at-Risk (VaR) of a portfolio with arbitrary weights. We specify and estimate several alternative multivariate GARCH models for daily returns on the Samp;P 500 and Nasdaq indexes. Examining the within sample VaRs of a set of given portfolios shows that the semi-parametric model performs uniformly well, while parametric models in several cases have unacceptable failure rates. Interestingly, distributional assumptions appear to have a much larger impact on the performance of the VaR estimates than the particular parametric specification chosen for the GARCH equations.
Semiparametric Estimation of Duration and Competing Risk Models
Extremes and Related Properties of Random Sequences and Processes
Author: M. R. Leadbetter
Publisher: Springer Science & Business Media
Published: 2012-12-06
Total Pages: 344
ISBN-13: 1461254493
DOWNLOAD EBOOK →Classical Extreme Value Theory-the asymptotic distributional theory for maxima of independent, identically distributed random variables-may be regarded as roughly half a century old, even though its roots reach further back into mathematical antiquity. During this period of time it has found significant application-exemplified best perhaps by the book Statistics of Extremes by E. J. Gumbel-as well as a rather complete theoretical development. More recently, beginning with the work of G. S. Watson, S. M. Berman, R. M. Loynes, and H. Cramer, there has been a developing interest in the extension of the theory to include, first, dependent sequences and then continuous parameter stationary processes. The early activity proceeded in two directions-the extension of general theory to certain dependent sequences (e.g., Watson and Loynes), and the beginning of a detailed theory for stationary sequences (Berman) and continuous parameter processes (Cramer) in the normal case. In recent years both lines of development have been actively pursued.
Econometric Modeling of Value-at-risk
Author: Timotheos Angelidis
Publisher:
Published: 2009
Total Pages: 0
ISBN-13: 9781607410409
DOWNLOAD EBOOK →Recently risk management has become a standard prerequisite for all financial institutions. Value-at-Risk is the main tool of reporting to the bank regulators the risk that the financial institutions face. This book provides a selective survey of the risk management techniques.
Introduction to Empirical Processes and Semiparametric Inference
Author: Michael R. Kosorok
Publisher: Springer Science & Business Media
Published: 2007-12-29
Total Pages: 482
ISBN-13: 0387749780
DOWNLOAD EBOOK →Kosorok’s brilliant text provides a self-contained introduction to empirical processes and semiparametric inference. These powerful research techniques are surprisingly useful for developing methods of statistical inference for complex models and in understanding the properties of such methods. This is an authoritative text that covers all the bases, and also a friendly and gradual introduction to the area. The book can be used as research reference and textbook.
The Effect of Mis-Estimating Correlation on Value-at-Risk
Author: Vasiliki D. Skintzi
Publisher:
Published: 2005
Total Pages:
ISBN-13:
DOWNLOAD EBOOK →This paper examines the systematic relationship between correlation mis-estimation and the corresponding Value-at-Risk (VaR) mis-calculation. To this end, first a semi-parametric approach, and then a parametric approach is developed. Both approaches are based on a simulation setup. Various linear and non-linear portfolios are considered, as well as variance-covariance and Monte-Carlo simulation methods are employed. We find that the VaR error increases significantly as the correlation error increases, particularly in the case of well-diversified linear portfolios. In the case of option portfolios, this effect is more pronounced for short-maturity, in-the-money options. The use of MC simulation to calculate VaR magnifies the correlation bias effect. Our results have important implications for measuring market risk accurately.
