Cyber risks are an important point on the business agenda in every company, but they are difficult to assess due to the absence of reliable data and profound analyses. To improve this situation, we identify cyber losses from an operational risk database and analyze these with methods from the field of actuarial science. Specifically, we apply operational risk models in order to yield consistent risk estimates, depending on country, industry, size, and other variables. Our results show that human behavior is the main source of cyber risk and that cyber risks are very different compared to other operational risk. The results of the paper are useful for practitioners, policymakers and regulators in order to provide a better understanding of this new and important type of risk.
Das Ziel dieser Arbeit besteht darin, verschiedene Rückversicherungsverträge in die dynamische Finanzanalyse zu integrieren und Auswirkungen auf Risiko und Rendite eines Schadenversicherers zu analysieren. Je nachdem, welchen Rückversicherungsvertrag und welche Intensität der Abhängigkeit wir dabei betrachten, finden wir sehr unterschiedliche Ergebnisse für Risikomasse wie die Ruinwahrscheinlichkeit oder das Expected Policyholder Deficit. Unser Untersuchungsergebnis hat eine hohe Relevanz für Regulierungsbehörden und Ratingagenturen, welche diese Masse als Fundament für die Aufsicht und für die Herleitung von Ratings nutzen. In unserer Arbeit erweitern wir die Ergebnisse aus Eling u. Toplek (2008) um eine detaillierte Analyse normaler Abhängigkeitsbeziehungen für verschiedene Rückversicherungsverträge und stellen diese Varianten im Rahmen einer neuen Beispielsimulation dar.
The aim of this paper is to study the influence of nonlinear dependencies on a nonlife
insurers risk and return profile. To achieve this, we integrate several copula
models in a dynamic financial analysis (DFA) framework and conduct numerical
tests within a simulation study. We also test several risk management strategies in
response to adverse outcomes generated by nonlinear dependencies. We find that
nonlinear dependencies have a crucial influence on the insurers risk profile that can
hardly be affected by the analyzed management strategies. Depending on the copula
concept employed, we find large differences in risk assessment for the ruin
probability and for the expected policyholder deficit. This has important implications
for regulators and rating agencies that use these risk measures as a foundation
for capital standards and ratings.
Whether the Sharpe ratio is an appropriate performance index for ranking hedge funds remains a controversial question among both academics and practitioners. Eling and Schuhmacher compared the Sharpe ratio with other performance measures and found virtually identical rank ordering using hedge fund data. They conclude that the choice of performance measure has no critical influence on fund evaluation. Their analysis does not include the new class of tailor-made performance ratios capable of being personalized to investment style as developed by Sortino and Satchell, Biglova et al and Farinelli et al. Specifically, we deal with the Sortino-Satchell, Farinelli-Tibiletti and Rachev ratios. Considering a large international hedge fund data set, we illustrate that if the ratios are tailored to a moderate investment style, they lead to rankings not too dissimilar to those found with the Sharpe ratio. But when the performance ratios are used to describe aggressive investment styles, rank correlations with the Sharpe ratio shrink drastically.
This article extends the classic Samuelson (1970) and Merton (1973) model of optimal portfolio allocation with one risky asset and a riskless one to include the effect of the skewness. Using an extended version of Stein's Lemma, we provide the explicit solution for optimal demand that holds for all expected utility maximizing investors when the risky asset is skew-normally and normally distributed. A closed expression is achieved for investors with constant absolute risk aversion.
Main academic criticism on the Sharpe ratio concerns its lack in incorporating skewness in performance evaluation. In this note we rewrite the classical Sharpe ratio for skew normal distributions. This new skew-normal Shape ratio consistently moves with skewness and no distorted information on performance is provided. An empirical investigation illustrates skew-normality of mutual and hedge fund returns. When investors are concerned about skewness, the use of the skewnormal
Sharpe ratio thus seems a proper choice for making performance rankings.
