Class Of Risk Measures Research Articles

Distortion risk measures: Prudence, coherence, and the expected shortfall

AbstractDistortion risk measures (DRM) are risk measures that are law invariant and comonotonic additive. The present paper is an extensive inquiry into this class of risk measures in light of new ideas such as qualitative robustness, prudence and no reward for concentration, and tail relevance. Results include several characterizations of prudent DRMs, a novel representation of coherent DRMs as well as an axiomatization of the Expected Shortfall alternative to the one recently provided by Wang and Zitikis. By linking the two axiomatizations, the paper provides a new perspective on the idea of no reward for concentration. The paper also contains results of independent interest such as the lower semicontinuity with respect to convergence in distribution of the Haezendonck–Goovaerts risk measures, the extension of non‐necessarily convex risk measures as well as the structure of the core of a general submodular distortion.

New risk measures for variance distortion and catastrophic financial risk measures

In recent years, expectation distortion risk measures have been widely used in financial and insurance applications due to their attractive properties. The author introduced two new classes of financial risk measures “VaR raised to the power of t” and “ES raised to the power of t” in his works and also investigated the issue of the belonging of these risk measures to the class of risk measures of expectation distortion, and described the corresponding distortion functions. The aim of this study is to introduce a new concept of variance distortion risk measures, which opens up a significant area for investigating the properties of these risk measures that may be useful in applications. The paper proposes a method of finding new variance distortion risk measures that can be used to acquire risk measures with special properties. As a result of the study, it was found that the class of risk measures of variance distortion includes risk measures that are in a certain way related to “VaR raised to the power of t” and “ES raised to the power of t” measures. The article describes the composite method for constructing new variance distortion functions and corresponding distortion risk measures. This method is used to build a large set of examples of variance distortion risk measures that can be used in assessing certain financial risks of a catastrophic nature. The author concludes that the study of the variance distortion risk measures introduced in this paper can be used both for the development of theoretical risk management methods and in the practice of business risk management in assessing unlikely risks of high catastrophe.

Robustness in the Optimization of Risk Measures

We study issues of robustness in the context of Quantitative Risk Management and Optimization. We develop a general methodology for determining whether a given risk-measurement-related optimization problem is robust, which we call “robustness against optimization.” The new notion is studied for various classes of risk measures and expected utility and loss functions. Motivated by practical issues from !nancial regulation, special attention is given to the twomost widely used riskmeasures in the industry, Valueat-Risk (VaR) and Expected Shortfall (ES).We establish that for a class of general optimization problems, VaR leads to nonrobust optimizers, whereas convex riskmeasures generally lead to robust ones. Our results offer extra insight on the ongoing discussion about the comparative advantages of VaR and ES in banking and insurance regulation. Our notion of robustness is conceptually different from the !eld of robust optimization, to which some interesting links are derived.

New Risk Measures “<i>VaR</i> to the Power of <i>t</i>” and “<i>ES</i> to the Power of <i>t</i>” and Distortion Risk Measures

Distortion risk measures have been popular in financial and insurance applications in recent years due to their attractive properties. The aim of the article is to investigate whether risk measures “VaR in the power of t”, introduced by the author, belong to the class of distortion risk measures, as well as to describe the corresponding distortion functions. The author introduces a new class of risk measures “ES to the power of t” and investigates whether it belongs to distortion risk measures, and also describes the corresponding distortion functions. The author used the composite method to design new distortion functions and corresponding distortion risk measures, to prove that risk measures “VaR to the power of t” and “ES to the power of t” belong to the class of distortion risk measures. The paper presents examples to illustrate the relevant concepts and results that show the importance of risk measures “VaR to the power of t” and “ES to the power of t” as subsets of distortion risk measures that allow identifying various financial catastrophic risks. The author concludes that risk measures “VaR to the power of t” and “ES to the power of t” can be used in risk management of companies when assessing remote, highly catastrophic risks.

A Critical Comparison of Three Notions of Fractional Stochastic Dominance

Two notions of fractional stochastic dominance are recently proposed by Muller et al. (2017) and Huang et al. (2020), respectively. Our main objective is to understand the comparative advantages of the two notions, as well as their suitability in different contexts, by establishing several new technical results. For a more comprehensive comparison, we further include a third natural notion of fractional stochastic dominance based on the coefficient of relative risk aversion. Among the three notions, it turns out that one can be seen as a logarithmic version of second-order stochastic dominance (SSD), another one can be seen as a power version of SSD, whereas there does not exist a transformation to associate the last one with SSD. We find that these notions of fractional stochastic dominance are naturally connected to five classes of risk measures, including Value-at-Risk, Expected Shortfall, expectiles, entropic risk measures, and loss certainty equivalents. The three notions are further characterized in the contexts of the rank-dependent utility model and the cumulative prospect theory. We make some recommendations on which notion to use in specific situations, as they all have their own merits.

A cost-effective approach to portfolio construction with range-based risk measures

In this paper, we introduce a new class of risk measures and the corresponding risk minimizing portfolio optimization problem. Instead of measuring the expected deviation of a daily return from a single target value, we propose to measure its deviation from a range of values centered on the single target value. By relaxing the definition of deviation, the proposed risk measure is robust to the variation of data input and thus the resulting risk-minimizing portfolio has a lower turnover rate and is resilient to outliers. To construct a practical portfolio, we propose to impose an -norm constraint on the portfolio weights to stabilize the portfolio's out-of-sample performance. We show that for some cases of our proposed range-based risk measures, the corresponding portfolio optimization can be recast as a support vector regression problem. This allows us to tap into the machine learning literature on support vector regression and effectively solve the portfolio optimization problem even in high dimensions. Moreover, we present theoretical results on the robustness of our range-based risk minimizing portfolios. Simulation and empirical studies are conducted to examine the out-of-sample performance of the proposed portfolios.

SET-VALUED DYNAMIC RISK MEASURES FOR BOUNDED DISCRETE-TIME PROCESSES

In this paper, we study how to evaluate the risk of a financial portfolio, whose components may be dependent and come from different markets or involve more than one kind of currencies, while we also take into consideration the uncertainty about the time value of money. Namely, we introduce a new class of risk measures, named set-valued dynamic risk measures for bounded discrete-time processes that are adapted to a given filtration. The time horizon can be finite or infinite. We investigate the representation results for them by making full use of Legendre–Fenchel conjugation theory for set-valued functions. Finally, some examples such as the set-valued dynamic average value at risk and the entropic risk measure for bounded discrete-time processes are also given.

A Cost-effective Approach to Portfolio Construction with Range-based Risk Measures

In this paper, we introduce a new class of risk measures and the corresponding risk minimizing portfolio optimization problem. Instead of measuring the expected deviation of a daily return from a single target value, we propose to measure its deviation from a range of values centered on the single target value. By relaxing the definition of deviation, the proposed risk measure is robust to the variation of data input and thus the resulting risk-minimizing portfolio has a lower turnover rate and is resilient to outliers. To construct a practical portfolio, we propose to impose an l2-norm constraint on the portfolio weights to stabilize the portfolio's out-of-sample performance. We show that for some cases of our proposed range-based risk measures, the corresponding portfolio optimization can be recast as a support vector regression problem. This allows us to tap into the machine learning literature on support vector regression and effectively solve the portfolio optimization problem even in high dimensions. Moreover, we present theoretical results on the robustness of our range-based risk minimizing portfolios. Simulation and empirical studies are conducted to examine the out-of-sample performance of the proposed portfolios.

Robustness in the Optimization of Risk Measures

We study issues of robustness in the context of Quantitative Risk Management and Optimization. We develop a general methodology for determining whether a given risk measurement related optimization problem is robust, which we call "robustness against optimization". The new notion is studied for various classes of risk measures and expected utility and loss functions. Motivated by practical issues from financial regulation, special attention is given to the two most widely used risk measures in the industry, Value-at-Risk (VaR) and Expected Shortfall (ES). We establish that for a class of general optimization problems, VaR leads to non-robust optimizers whereas convex risk measures generally lead to robust ones. Our results offer extra insight on the ongoing discussion about the comparative advantages of VaR and ES in banking and insurance regulation. Our notion of robustness is conceptually different from the field of robust optimization, to which some interesting links are derived.

Weighted risk capital allocations in the presence of systematic risk

Determining aggregate risk capital is a fundamental problem of modern Enterprise Risk Management, and the determination process has been fairly well studied. The allocation problem, on the other hand, is generally much more involved even when a specific risk measure inducing the allocation rule is assumed, let alone the case when a class of risk measures is considered. In this paper we put forward arguments showing that the problems of determining and allocating the aggregate risk capital can often be viewed as being of similar complexity. In particular, we show that this is the case for the entire class of weighted risk capital allocations, as well as for risk portfolios that are exposed to systematic and specific risk factors. We provide detailed analyses of the Weighted Insurance Pricing Model (WIPM) under multiplicative and additive systematic-risk frameworks. Also, a Gini-type WIPM, which is related to the WIPM in a similar way as the dual (i.e., rank dependent) utility theory is related to the classical utility theory, is proposed.

Risk-Averse Approximate Dynamic Programming with Quantile-Based Risk Measures

In this paper, we consider a finite-horizon Markov decision process (MDP) for which the objective at each stage is to minimize a quantile-based risk measure (QBRM) of the sequence of future costs; we call the overall objective a dynamic quantile-based risk measure (DQBRM). In particular, we consider optimizing dynamic risk measures where the one-step risk measures are QBRMs, a class of risk measures that includes the popular value at risk (VaR) and the conditional value at risk (CVaR). Although there is considerable theoretical development of risk-averse MDPs in the literature, the computational challenges have not been explored as thoroughly. We propose data-driven and simulation-based approximate dynamic programming (ADP) algorithms to solve the risk-averse sequential decision problem. We address the issue of inefficient sampling for risk applications in simulated settings and present a procedure, based on importance sampling, to direct samples toward the “risky region” as the ADP algorithm progresses. Finally, we show numerical results of our algorithms in the context of an application involving risk-averse bidding for energy storage.The online appendix is available at https://doi.org/10.1287/moor.2017.0872 .

Weighted Risk Capital Allocations in the Presence of Systematic Risk

Determining aggregate risk capital has become a fundamental problem in modern Enterprise Risk Management, and the determination process has been fairly well studied. The consequent exercise of allocating the aggregate risk capital to constituents has also been given high priority in, e.g., both life and general insurance in such contexts as pricing, performance management, and profitability testing. In fact, the allocation exercise has been often called the primary driver for calculating the aggregate risk capital. Unfortunately, the allocation problem is, in general, noticeably more involved than the problem of the aggregate risk capital determination. In fact, allocating risk capital is not easy even when a specific risk measure that induces the allocation rule has been assumed, let alone when a class of risk measures is considered. In this paper we demonstrate that, quite often, the problems of determining and allocating the aggregate risk capital are of a similar complexity. Remarkably, this turns out to be the case for the entire class of weighted risk capital allocations, as well as for risk portfolios that are exposed to systematic and specific risk factors.

Risk management with weighted VaR

AbstractThis article studies the optimal portfolio selection of expected utility‐maximizing investors who must also manage their market‐risk exposures. The risk is measured by a so‐called weighted value‐at‐risk (WVaR) risk measure, which is a generalization of both value‐at‐risk (VaR) and expected shortfall (ES). The feasibility, well‐posedness, and existence of the optimal solution are examined. We obtain the optimal solution (when it exists) and show how risk measures change asset allocation patterns. In particular, we characterize three classes of risk measures: the first class will lead to models that do not admit an optimal solution, the second class can give rise to endogenous portfolio insurance, and the third class, which includes VaR and ES, two popular regulatory risk measures, will allow economic agents to engage in “regulatory capital arbitrage,” incurring larger losses when losses occur.

Coherent and convex loss-based risk measures for portfolio vectors

In this paper, we introduce two new classes of risk measures, named coherent and convex loss-based risk measures for portfolio vectors. These new risk measures can be considered as a multivariate extension of univariate loss-based risk measures introduced by Cont et al. (Stat Risk Model 30:133–167, 2013). Representation results for these new introduced risk measures are provided. The links between convex loss-based risk measures for portfolios and convex risk measures for portfolios introduced by Burgert and Ruschendorf (Insur Math Econ 38:289–297, 2006) or Wei and Hu (Stat Probab Lett 90:114–120, 2014) are stated. Finally, applications to the multi-period coherent and convex loss-based risk measures are addressed.

Risk Management with Weighted VaR

This article studies the optimal portfolio selection of expected utility maximizing investors who must also manage their market-risk exposures. The risk is measured by a so-called weighted Value-at-Risk (WVaR) risk measure, which is a generalization of both Value-at-Risk (VaR) and Expected Shortfall (ES). The feasibility, well-posedness, and existence of the optimal solution are examined. We obtain the optimal solution (when it exists) and show how risk measures change asset allocation patterns. In particular, we characterize three classes of risk measures: the first class will lead to models that do not admit an optimal solution; the second class can give rise to endogenous portfolio insurance; and the third class, which includes VaR and ES, two popular regulatory risk measures, will allow economic agents to engage in “regulatory capital arbitrage,” incurring larger losses when losses occur.

Stochastic dominance with respect to a capacity and risk measures

Abstract In our previous work, we have extended the classical notion of increasing convex stochastic dominance relation with respect to a probability to the more general case of a normalized monotone (but not necessarily additive) set function, also called a capacity. In the present paper, we pursue that work by studying the set of monetary risk measures (defined on the space of bounded real-valued measurable functions) satisfying the properties of comonotonic additivity and consistency with respect to the generalized stochastic dominance relation. Under suitable assumptions on the underlying capacity space, we characterize that class of risk measures in terms of Choquet integrals with respect to a distorted capacity whose distortion function is concave. Kusuoka-type characterizations are also established. A generalization to the case of a capacity of the Tail Value at Risk is provided as an example. It is also shown that some well-known results about Choquet integrals with respect to a distorted probability do not necessarily hold true in the more general case of a distorted capacity.

From the Editors

From the Editors

Tail nonlinearly transformed risk measure and its application

This paper discusses risk measurement and portfolio selection under the mean-risk framework. Through introducing a nonlinear convex transformation to large losses, we propose a new class of risk measures which are convex and monotone. We demonstrate the new risk measure's favorable financial and mathematical properties, and consider its estimation in practice and relevant consistent and asymptotic issues. A realistic portfolio selection model based on the new risk measure is then established with typical market frictions taken into account simultaneously. Based on trade data from Chinese stock markets and American stock markets over stable and volatile periods, respectively, both in-sample and out-of-sample empirical studies are carried out. Theoretical and empirical results show that the new risk measure and the corresponding portfolio selection model can not only reflect different investors' risk averse attitudes and control the fat-tail phenomenon of the return distribution, but also find a realistic optimal portfolio with superior performance and robustness than the corresponding optimal portfolio obtained under the expected shortfall risk measure.

Parametric multi-attribute utility functions for optimal profit under risk constraints

We provide an economic interpretation of the practice consisting in incorporating risk measures as constraints in an expected prospect maximization problem. For what we call the infimum of expectations class of risk measures, we show that if the decision maker (DM) maximizes the expectation of a random prospect under constraint that the risk measure is bounded above, he then behaves as a generalized expected utility maximizer in the following sense. The DM exhibits ambiguity with respect to a family of utility functions defined on a larger set of decisions than the original one; he adopts pessimism and performs first a minimization of expected utility over this family, then performs a maximization over a new decisions set. This economic behaviour is called maxmin under risk and studied by Maccheroni (Econ Theory 19:823-831, 2002). As an application, we make the link between an expected prospect maximization problem, subject to conditional value-at-risk being less than a threshold value, and a non-expected utility economic formulation involving loss aversion -type utility functions.

Nonlinearly weighted convex risk measure and its application

We propose a new class of risk measures which satisfy convexity and monotonicity, two well-accepted axioms a reasonable and realistic risk measure should satisfy. Through a nonlinear weight function, the new measure can flexibly reflect the investor’s degree of risk aversion, and can control the fat-tail phenomenon of the loss distribution. A realistic portfolio selection model with typical market frictions taken into account is established based on the new measure. Real data from the Chinese stock markets and American stock markets are used for empirical comparison of the new risk measure with the expected shortfall risk measure. The in-sample and out-of-sample empirical results show that the new risk measure and the corresponding portfolio selection model can not only reflect the investor’s risk-averse attitude and the impact of different trading constraints, but can find robust optimal portfolios, which are superior to the corresponding optimal portfolios obtained under the expected shortfall risk measure.