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Advances in Modeling Value at Risk and Expected Shortfall
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Advances in Modeling Value at Risk and Expected Shortfall

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Risk".

Deadline for manuscript submissions: closed (30 July 2016) | Viewed by 29359

Special Issue Editors

Prof. Dr. Stefan Mittnik

E-Mail Website
Guest Editor
Department of Statistics, Ludwig-Maximilians-Universität München, Akademiestrasse 1/I, 80799 Munich, Germany
Interests: statistics; econometrics; financial econometrics; quantitative finance; empirical finance; risk management; time series; volatility; forecasting
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Marc S. Paolella

E-Mail Website
Guest Editor
Department of Banking and Finance, University of Zurich, Zurich, Switzerland
Interests: computational statistics; volatility modeling; large-scale multivariate density prediction of financial asset returns; portfolio optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Since the adoption of Value at Risk (VaR) as a measure of financial market risk two decades ago, we have seen an explosion of research and a proliferation of methods for VaR computation. More recently, in light of its attractive properties and explicit considerations in the forthcoming Basel III regulations, the use of Expected Shortfall (ES) has gained relevance. A thorough understanding of these measures, and methods for their reliable and efficient computation, have direct implications for the design of regulatory frameworks and  the stability of financial institutions and financial systems as a whole. The aim of this special issue is to present latest theoretical and empirical advances in quantifying financial market risk using these or related concepts.

General topics of interest include, but are not limited to:
  • Measuring financial market risk: VaR, Expected Shortfall, density, coherence
  • Risk dynamics: volatility clustering, GARCH, stochastic and realized volatility, prediction
  • quantile regression
  • extreme value theory
  • Risk aggregation: tail correlation, copula approaches
  • model validation
  • stress-testing
  • scenario analysis

Prof. Dr. Stefan Mittnik
Prof. Dr. Marc S. Paolella
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Risk and Financial Management is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (5 papers)

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Research

774 KiB  
Article
Accurate Evaluation of Expected Shortfall for Linear Portfolios with Elliptically Distributed Risk Factors
by Dobrislav Dobrev∗, Travis D. Nesmith and Dong Hwan Oh
J. Risk Financial Manag. 2017, 10(1), 5; https://doi.org/10.3390/jrfm10010005 - 2 Feb 2017
Cited by 6 | Viewed by 5222
Abstract
We provide an accurate closed-form expression for the expected shortfall of linear portfolios with elliptically distributed risk factors. Our results aim to correct inaccuracies that originate in Kamdem (2005) and are present also in at least thirty other papers referencing it, including the [...] Read more.
We provide an accurate closed-form expression for the expected shortfall of linear portfolios with elliptically distributed risk factors. Our results aim to correct inaccuracies that originate in Kamdem (2005) and are present also in at least thirty other papers referencing it, including the recent survey by Nadarajah et al. (2014) on estimation methods for expected shortfall. In particular, we show that the correction we provide in the popular multivariate Student t setting eliminates understatement of expected shortfall by a factor varying from at least four to more than 100 across different tail quantiles and degrees of freedom. As such, the resulting economic impact in financial risk management applications could be significant. We further correct such errors encountered also in closely related results in Kamdem (2007 and 2009) for mixtures of elliptical distributions. More generally, our findings point to the extra scrutiny required when deploying new methods for expected shortfall estimation in practice. Full article
(This article belongs to the Special Issue Advances in Modeling Value at Risk and Expected Shortfall)
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3965 KiB  
Article
Portfolios Dominating Indices: Optimization with Second-Order Stochastic Dominance Constraints vs. Minimum and Mean Variance Portfolios
by Neslihan Fidan Keçeci, Viktor Kuzmenko and Stan Uryasev
J. Risk Financial Manag. 2016, 9(4), 11; https://doi.org/10.3390/jrfm9040011 - 4 Oct 2016
Cited by 7 | Viewed by 6612
Abstract
The paper compares portfolio optimization with the Second-Order Stochastic Dominance (SSD) constraints with mean-variance and minimum variance portfolio optimization. As a distribution-free decision rule, stochastic dominance takes into account the entire distribution of return rather than some specific characteristic, such as variance. The [...] Read more.
The paper compares portfolio optimization with the Second-Order Stochastic Dominance (SSD) constraints with mean-variance and minimum variance portfolio optimization. As a distribution-free decision rule, stochastic dominance takes into account the entire distribution of return rather than some specific characteristic, such as variance. The paper is focused on practical applications of the portfolio optimization and uses the Portfolio Safeguard (PSG) package, which has precoded modules for optimization with SSD constraints, mean-variance and minimum variance portfolio optimization. We have done in-sample and out-of-sample simulations for portfolios of stocks from the Dow Jones, S&P 100 and DAX indices. The considered portfolios’ SSD dominate the Dow Jones, S&P 100 and DAX indices. Simulation demonstrated a superior performance of portfolios with SD constraints, versus mean-variance and minimum variance portfolios. Full article
(This article belongs to the Special Issue Advances in Modeling Value at Risk and Expected Shortfall)
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421 KiB  
Article
On Setting Day-Ahead Equity Trading Risk Limits: VaR Prediction at Market Close or Open?
by Ana-Maria Fuertes and Jose Olmo
J. Risk Financial Manag. 2016, 9(3), 10; https://doi.org/10.3390/jrfm9030010 - 9 Sep 2016
Cited by 3 | Viewed by 4664
Abstract
This paper investigates the information content of the ex post overnight return for one-day-ahead equity Value-at-Risk (VaR) forecasting. To do so, we deploy a univariate VaR modeling approach that constructs the forecast at market open and, accordingly, exploits the available overnight close-to-open price [...] Read more.
This paper investigates the information content of the ex post overnight return for one-day-ahead equity Value-at-Risk (VaR) forecasting. To do so, we deploy a univariate VaR modeling approach that constructs the forecast at market open and, accordingly, exploits the available overnight close-to-open price variation. The benchmark is the bivariate VaR modeling approach proposed by Ahoniemi et al. that constructs the forecast at the market close instead and, accordingly, it models separately the daytime and overnight return processes and their covariance. For a small cap portfolio, the bivariate VaR approach affords superior predictive ability than the ex post overnight VaR approach whereas for a large cap portfolio the results are reversed. The contrast indicates that price discovery at the market open is less efficient for small capitalization, thinly traded stocks. Full article
(This article belongs to the Special Issue Advances in Modeling Value at Risk and Expected Shortfall)
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189 KiB  
Article
VaR and CVaR Implied in Option Prices
by Giovanni Barone Adesi
J. Risk Financial Manag. 2016, 9(1), 2; https://doi.org/10.3390/jrfm9010002 - 29 Feb 2016
Cited by 20 | Viewed by 6680
Abstract
VaR (Value at Risk) and CVaR (Conditional Value at Risk) are implied by option prices. Their relationships to option prices are derived initially under the pricing measure. It does not require assumptions about the distribution of portfolio returns. The effects of changes of [...] Read more.
VaR (Value at Risk) and CVaR (Conditional Value at Risk) are implied by option prices. Their relationships to option prices are derived initially under the pricing measure. It does not require assumptions about the distribution of portfolio returns. The effects of changes of measure are modest at the short horizons typically used in applications. The computation of CVaR from option price is very convenient, because this measure is not elicitable, making direct comparisons of statistical inferences from market data problematic. Full article
(This article belongs to the Special Issue Advances in Modeling Value at Risk and Expected Shortfall)
278 KiB  
Article
The Two Defaults Scenario for Stressing Credit Portfolio Loss Distributions
by Dirk Tasche
J. Risk Financial Manag. 2016, 9(1), 1; https://doi.org/10.3390/jrfm9010001 - 31 Dec 2015
Cited by 2 | Viewed by 5151
Abstract
The impact of a stress scenario of default events on the loss distribution of a credit portfolio can be assessed by determining the loss distribution conditional on these events. While it is conceptually easy to estimate loss distributions conditional on default events by [...] Read more.
The impact of a stress scenario of default events on the loss distribution of a credit portfolio can be assessed by determining the loss distribution conditional on these events. While it is conceptually easy to estimate loss distributions conditional on default events by means of Monte Carlo simulation, it becomes impractical for two or more simultaneous defaults as then the conditioning event is extremely rare. We provide an analytical approach to the calculation of the conditional loss distribution for the CreditRisk + portfolio model with independent random loss given default distributions. The analytical solution for this case can be used to check the accuracy of an approximation to the conditional loss distribution whereby the unconditional model is run with stressed input probabilities of default (PDs). It turns out that this approximation is unbiased. Numerical examples, however, suggest that the approximation may be seriously inaccurate but that the inaccuracy leads to overestimation of tail losses and, hence, the approach errs on the conservative side. Full article
(This article belongs to the Special Issue Advances in Modeling Value at Risk and Expected Shortfall)
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