Research

32 pages, 529 KiB

Open AccessArticle

Firm’s Risk-Return Association Facets and Prospect Theory Findings—An Emerging versus Developed Country Context

by Ranjan Das Gupta and Rajesh Pathak

Risks 2018, 6(4), 143; https://doi.org/10.3390/risks6040143 - 08 Dec 2018

Cited by 8 | Viewed by 2849

A risk-return association under normal market conditions can be conventional positive (risk-averse) or “paradoxical” negative (risk seeking). This study has the objective to investigate whether such an association is stable across market trends (i.e., bull and bear) and for overall, industry-classified and partitions [...] Read more.

A risk-return association under normal market conditions can be conventional positive (risk-averse) or “paradoxical” negative (risk seeking). This study has the objective to investigate whether such an association is stable across market trends (i.e., bull and bear) and for overall, industry-classified and partitions sub-samples after controlling for a firm’s age, size, leverage and liquidity using operating performance risk-return measures. In total, this study analyses 2666 firms (1199 firms from 15 developed countries and 1467 firms from 12 emerging countries) for the period of 1999–2015. Results show that in the overall and bull sub-periods, firms across countries are showing conventional positive (superior firms) and “paradoxical” negative (poor firms) in most cases. However, in the bear sub-periods all firms from emerging countries are risk seeking in order to maintain their position in the pecking order. Full article

(This article belongs to the Special Issue Risk vs Performance Measures: Robustness, Elicitability and Time-Dependency)

28 pages, 571 KiB

Open AccessArticle

A Discussion on Recent Risk Measures with Application to Credit Risk: Calculating Risk Contributions and Identifying Risk Concentrations

by Matthias Fischer, Thorsten Moser and Marius Pfeuffer

Risks 2018, 6(4), 142; https://doi.org/10.3390/risks6040142 - 07 Dec 2018

Cited by 10 | Viewed by 5148

Abstract

In both financial theory and practice, Value-at-risk (VaR) has become the predominant risk measure in the last two decades. Nevertheless, there is a lively and controverse on-going discussion about possible alternatives. Against this background, our first objective is to provide a current overview [...] Read more.

In both financial theory and practice, Value-at-risk (VaR) has become the predominant risk measure in the last two decades. Nevertheless, there is a lively and controverse on-going discussion about possible alternatives. Against this background, our first objective is to provide a current overview of related competitors with the focus on credit risk management which includes definition, references, striking properties and classification. The second part is dedicated to the measurement of risk concentrations of credit portfolios. Typically, credit portfolio models are used to calculate the overall risk (measure) of a portfolio. Subsequently, Euler’s allocation scheme is applied to break the portfolio risk down to single counterparties (or different subportfolios) in order to identify risk concentrations. We first carry together the Euler formulae for the risk measures under consideration. In two cases (Median Shortfall and Range-VaR), explicit formulae are presented for the first time. Afterwards, we present a comprehensive study for a benchmark portfolio according to Duellmann and Masschelein (2007) and nine different risk measures in conjunction with the Euler allocation. It is empirically shown that—in principle—all risk measures are capable of identifying both sectoral and single-name concentration. However, both complexity of IT implementation and sensitivity of the risk figures w.r.t. changes of portfolio quality vary across the specific risk measures. Full article

(This article belongs to the Special Issue Risk vs Performance Measures: Robustness, Elicitability and Time-Dependency)

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13 pages, 1195 KiB

Open AccessArticle

An Intersection–Union Test for the Sharpe Ratio

by Gabriel Frahm

Risks 2018, 6(2), 40; https://doi.org/10.3390/risks6020040 - 19 Apr 2018

Cited by 1 | Viewed by 4305

Abstract

An intersection–union test for supporting the hypothesis that a given investment strategy is optimal among a set of alternatives is presented. It compares the Sharpe ratio of the benchmark with that of each other strategy. The intersection–union test takes serial dependence into account [...] Read more.

An intersection–union test for supporting the hypothesis that a given investment strategy is optimal among a set of alternatives is presented. It compares the Sharpe ratio of the benchmark with that of each other strategy. The intersection–union test takes serial dependence into account and does not presume that asset returns are multivariate normally distributed. An empirical study based on the G–7 countries demonstrates that it is hard to find significant results due to the lack of data, which confirms a general observation in empirical finance. Full article

(This article belongs to the Special Issue Risk vs Performance Measures: Robustness, Elicitability and Time-Dependency)

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18 pages, 731 KiB

Open AccessArticle

Lambda Value at Risk and Regulatory Capital: A Dynamic Approach to Tail Risk

by Asmerilda Hitaj, Cesario Mateus and Ilaria Peri

Risks 2018, 6(1), 17; https://doi.org/10.3390/risks6010017 - 06 Mar 2018

Cited by 6 | Viewed by 4625

Abstract

This paper presents the first methodological proposal of estimation of the

Λ V a R

. Our approach is dynamic and calibrated to market extreme scenarios, incorporating the need of regulators and financial institutions in more sensitive risk measures. We also propose a [...] Read more.

This paper presents the first methodological proposal of estimation of the

Λ V a R

. Our approach is dynamic and calibrated to market extreme scenarios, incorporating the need of regulators and financial institutions in more sensitive risk measures. We also propose a simple backtesting methodology by extending the

V a R

hypothesis-testing framework. Hence, we test our

Λ V a R

proposals under extreme downward scenarios of the financial crisis and different assumptions on the profit and loss distribution. The findings show that our

Λ V a R

estimations are able to capture the tail risk and react to market fluctuations significantly faster than the

V a R

and expected shortfall. The backtesting exercise displays a higher level of accuracy for our

Λ V a R

estimations. Full article

(This article belongs to the Special Issue Risk vs Performance Measures: Robustness, Elicitability and Time-Dependency)

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