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Risks
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Model Risk and Risk Measures
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Model Risk and Risk Measures

A special issue of Risks (ISSN 2227-9091).

Deadline for manuscript submissions: closed (30 May 2020) | Viewed by 21868

Special Issue Editors

Prof. Dr. Radu Tunaru

E-Mail Website
Guest Editor
Kent Business School, University of Kent, Canterbury, UK
Interests: structured finance; credit risk; financial markets and risk management; real estate finance and model risk
Prof. Dr. Arturo Leccadito

E-Mail Website
Guest Editor
Department of Economics, Statistics and Finance, University of Calabria, Ponte Bucci, 87030 Rende, Italy
Interests: financial econometrics; credit risk; financial markets and risk management; derivatives pricing

Special Issue Information

Dear Colleagues,

Risk management is a fundamental activity in finance, insurance, and economics. Over the years, a myriad of risk measures have been proposed in the literature and are now widely used in practice. Yet, the risk associated with estimating those measures using samples of data is not fully understood. In this volume, we explore new facets of model risk related to the computation of risk measures. This covers different estimation procedures, impact of data frequency and length of sample data, and sensitivity due to model selection. Special attention is devoted to the role played by risk measures in satisfying conditions imposed by regulators on economic agents and institutions. To this end, we invite you to contribute theoretical, applied, computational, and comparative analysis articles that can help to advance knowledge on model risk-selection, estimation, and pitfalls for risk measures such as value-at-risk, expected shortfall, median shortfall, betas, skewness, the Aumann–Serrano measure, and the Foster–Hart measure. Research related to model risk involving credit default swaps, liquidity measures or risk parity is also encouraged. Another important facet of risk measures is the evolution of statistical features of risk measures over time and geographically across countries/economies.

Prof. Dr. Radu Tunaru
Prof. Arturo Leccadito
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. Risks 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 1800 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.

Keywords

  • Risk measures in finance, insurance, and economics
  • Estimation risk
  • Model identification risk
  • Monte Carlo simulation
  • Financial data
  • Market risk
  • Credit risk
  • Liquidity risk
  • Computational pitfalls

Published Papers (7 papers)

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Research

13 pages, 933 KiB  
Article
A New Approach to Risk Attribution and Its Application in Credit Risk Analysis
by Christoph Frei
Risks 2020, 8(2), 65; https://doi.org/10.3390/risks8020065 - 16 Jun 2020
Cited by 2 | Viewed by 3174
Abstract
How can risk of a company be allocated to its divisions and attributed to risk factors? The Euler principle allows for an economically justified allocation of risk to different divisions. We introduce a method that generalizes the Euler principle to attribute risk to [...] Read more.
How can risk of a company be allocated to its divisions and attributed to risk factors? The Euler principle allows for an economically justified allocation of risk to different divisions. We introduce a method that generalizes the Euler principle to attribute risk to its driving factors when these factors affect losses in a nonlinear way. The method splits loss contributions over time and is straightforward to implement. We show in an example how this risk decomposition can be applied in the context of credit risk. Full article
(This article belongs to the Special Issue Model Risk and Risk Measures)
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13 pages, 503 KiB  
Article
Testing the Least-Squares Monte Carlo Method for the Evaluation of Capital Requirements in Life Insurance
by Massimo Costabile and Fabio Viviano
Risks 2020, 8(2), 48; https://doi.org/10.3390/risks8020048 - 18 May 2020
Cited by 2 | Viewed by 2621
Abstract
In this paper, we test the efficiency of least-squares Monte Carlo method to estimate capital requirements in life insurance. We choose a simplified Gaussian evaluation framework where closed-form formulas are available and allow us to obtain solid benchmarks. Extensive numerical experiments were conducted [...] Read more.
In this paper, we test the efficiency of least-squares Monte Carlo method to estimate capital requirements in life insurance. We choose a simplified Gaussian evaluation framework where closed-form formulas are available and allow us to obtain solid benchmarks. Extensive numerical experiments were conducted by considering different combinations of simulation runs and basis functions, and the corresponding results are illustrated. Full article
(This article belongs to the Special Issue Model Risk and Risk Measures)
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15 pages, 672 KiB  
Article
CARL and His POT: Measuring Risks in Commodity Markets
by Bernardina Algieri and Arturo Leccadito
Risks 2020, 8(1), 27; https://doi.org/10.3390/risks8010027 - 13 Mar 2020
Cited by 1 | Viewed by 2934
Abstract
The present study aims at modelling market risk for four commodities, namely West Texas Intermediate (WTI) crude oil, natural gas, gold and corn for the period 2007–2017. To this purpose, we use Extreme Value Theory (EVT) together with a set of Conditional Auto-Regressive [...] Read more.
The present study aims at modelling market risk for four commodities, namely West Texas Intermediate (WTI) crude oil, natural gas, gold and corn for the period 2007–2017. To this purpose, we use Extreme Value Theory (EVT) together with a set of Conditional Auto-Regressive Logit (CARL) models to predict risk measures for the futures return series of the considered commodities. In particular, the Peaks-Over-Threshold (POT) method has been combined with the Indicator and Absolute Value CARL models in order to predict the probability of tail events and the Value-at-Risk and the Expected Shortfall risk measures for the selected commodities. Backtesting procedures indicate that generally CARL models augmented with specific implied volatility outperform the benchmark model and thus they represent a valuable tool to anticipate and manage risks in the markets. Full article
(This article belongs to the Special Issue Model Risk and Risk Measures)
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22 pages, 495 KiB  
Article
A Discrete-Time Approach to Evaluate Path-Dependent Derivatives in a Regime-Switching Risk Model
by Emilio Russo
Risks 2020, 8(1), 9; https://doi.org/10.3390/risks8010009 - 29 Jan 2020
Cited by 3 | Viewed by 2887
Abstract
This paper provides a discrete-time approach for evaluating financial and actuarial products characterized by path-dependent features in a regime-switching risk model. In each regime, a binomial discretization of the asset value is obtained by modifying the parameters used to generate the lattice in [...] Read more.
This paper provides a discrete-time approach for evaluating financial and actuarial products characterized by path-dependent features in a regime-switching risk model. In each regime, a binomial discretization of the asset value is obtained by modifying the parameters used to generate the lattice in the highest-volatility regime, thus allowing a simultaneous asset description in all the regimes. The path-dependent feature is treated by computing representative values of the path-dependent function on a fixed number of effective trajectories reaching each lattice node. The prices of the analyzed products are calculated as the expected values of their payoffs registered over the lattice branches, invoking a quadratic interpolation technique if the regime changes, and capturing the switches among regimes by using a transition probability matrix. Some numerical applications are provided to support the model, which is also useful to accurately capture the market risk concerning path-dependent financial and actuarial instruments. Full article
(This article belongs to the Special Issue Model Risk and Risk Measures)
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20 pages, 379 KiB  
Article
Measuring Financial Contagion and Spillover Effects with a State-Dependent Sensitivity Value-at-Risk Model
by Alin Marius Andries and Elena Galasan
Risks 2020, 8(1), 5; https://doi.org/10.3390/risks8010005 - 10 Jan 2020
Cited by 17 | Viewed by 4413
Abstract
In this paper, we measure the size and the direction of the spillover effects among European commercial banks, with respect to their size, geographical position, income sources, and systemic importance for the period from 2006 to 2016, using a state-dependent sensitivity value-at-risk model, [...] Read more.
In this paper, we measure the size and the direction of the spillover effects among European commercial banks, with respect to their size, geographical position, income sources, and systemic importance for the period from 2006 to 2016, using a state-dependent sensitivity value-at-risk model, conditioning on the state of the financial market. Low during normal times, the same shocks cause notable spillover effects during the volatile period. The results suggest a high level of interconnectedness across all the European regions, highlighting the importance of large and systemic important banks that create considerable systemic risk during the entire period. Regarding the non-interest income banks, the outcomes reveals an alert signal concerning the spillovers spread to interest income banks. Full article
(This article belongs to the Special Issue Model Risk and Risk Measures)
14 pages, 1429 KiB  
Article
Tail Dependence in Financial Markets: A Dynamic Copula Approach
by Federico Pasquale Cortese
Risks 2019, 7(4), 116; https://doi.org/10.3390/risks7040116 - 11 Nov 2019
Cited by 6 | Viewed by 3063
Abstract
This article is concerned with the study of the tail correlation among equity indices by means of dynamic copula functions. The main idea is to consider the impact of the use of copula functions in the accuracy of the model’s parameters and in [...] Read more.
This article is concerned with the study of the tail correlation among equity indices by means of dynamic copula functions. The main idea is to consider the impact of the use of copula functions in the accuracy of the model’s parameters and in the computation of Value-at-Risk (VaR). Results show that copulas provide more sophisticated results in terms of the accuracy of the forecasted VaR, in particular, if they are compared with the results obtained from Dynamic Conditional Correlation (DCC) model. Full article
(This article belongs to the Special Issue Model Risk and Risk Measures)
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9 pages, 319 KiB  
Article
Generalized Multiplicative Risk Apportionment
by Hongxia Wang
Risks 2019, 7(2), 65; https://doi.org/10.3390/risks7020065 - 12 Jun 2019
Cited by 1 | Viewed by 2221
Abstract
This work examines apportionment of multiplicative risks by considering three dominance orderings: first-degree stochastic dominance, Rothschild and Stiglitz’s increase in risk and downside risk increase. We use the relative nth-degree risk aversion measure and decreasing relative nth-degree risk aversion to provide [...] Read more.
This work examines apportionment of multiplicative risks by considering three dominance orderings: first-degree stochastic dominance, Rothschild and Stiglitz’s increase in risk and downside risk increase. We use the relative nth-degree risk aversion measure and decreasing relative nth-degree risk aversion to provide conditions guaranteeing the preference for “harm disaggregation” of multiplicative risks. Further, we relate our conclusions to the preference toward bivariate lotteries, which interpret correlation-aversion, cross-prudence and cross-temperance. Full article
(This article belongs to the Special Issue Model Risk and Risk Measures)
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