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on Risk Management |
| By: | Stéphane Loisel (SAF - EA2429 - Laboratoire de Science Actuarielle et Financière - Université Claude Bernard - Lyon I); Christian Mazza (Département de Mathématiques - Université de Fribourg); Didier Rullière (SAF - EA2429 - Laboratoire de Science Actuarielle et Financière - Université Claude Bernard - Lyon I) |
| Abstract: | We consider the classical risk model and carry out a sensitivity and robustness analysis of finite-time ruin probabilities. We provide algorithms to compute the related influence functions. We also prove the weak convergence of a sequence of empirical finite-time ruin probabilities starting from zero initial reserve toward a Gaussian random variable. We define the concepts of reliable finite-time ruin probability as a Value-at-Risk of the estimator of the finite-time ruin probability. To control this robust risk measure, an additional initial reserve is needed and called Estimation Risk Solvency Margin (ERSM). We apply our results to show how portfolio experience could be rewarded by cut-offs in solvency capital requirements. An application to catastrophe contamination and numerical examples are also developed. |
| Keywords: | Finite-time ruin probability; robustness; Solvency II; reliable ruin probability; asymptotic Normality; influence function; Estimation Risk Solvency Margin (ERSM) |
| Date: | 2008–04 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-00168714_v1&r=rmg |
| By: | Shamiri, Ahmed; Shaari, Abu Hassan; Isa, Zaidi |
| Abstract: | Being able to choose most suitable volatility model and distribution specification is a more demanding task. This paper introduce an analyzing procedure using the Kullback-Leibler information criteria (KLIC) as a statistical tool to evaluate and compare the predictive abilities of possibly misspecified density forecast models. The main advantage of this statistical tool is that we use the censored likelihood functions to compute the tail minimum of the KLIC, to compare the performance of a density forecast models in the tails. We include an illustrative simulation and an empirical application to compare a set of distributions, including symmetric/asymmetric distribution, and a family of GARCH volatility models. We highlight the use of our approach to a daily index, the Kuala Lumpur Composite index (KLCI). Our results shows that the choice of the conditional distribution appear to be a more dominant factor in determining the adequacy of density forecasts than the choice of volatility model. Furthermore, the results support the Skewed for KLCI return distribution. |
| Keywords: | Density forecast; Conditional distribution; Forecast accuracy; KLIC; GARCH models |
| JEL: | D53 C32 C16 C52 |
| Date: | 2007–08–20 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:9790&r=rmg |
| By: | Francesco Lisi; Edoardo Otranto |
| Abstract: | Mutual funds classifications, often made by rating agencies, are very common and sometimes criticized. In this work, a three-step statistical procedure for mutual funds classification is proposed. In the first step time series funds are characterized in terms of returns. In the second step, a clustering analysis is performed in order to obtain classes of homogeneous funds with respect to the risk levels. In particular, the risk is defined starting from an Asymmetric Threshold-GARCH model aimed to describe minimum, normal and turmoil risk. The third step merges the previous two. An application to 75 European funds belonging to 5 different categories is given. |
| Keywords: | Cluster, distance, GARCH models, risk |
| JEL: | C22 G11 G23 |
| Date: | 2008 |
| URL: | http://d.repec.org/n?u=RePEc:cns:cnscwp:200813&r=rmg |
| By: | Wayne Fisher (Enterprise Risk Management Institute, International - ERM-II); Stéphane Loisel (SAF - EA2429 - Laboratoire de Science Actuarielle et Financière - Université Claude Bernard - Lyon I); Shaun Wang (Department of Risk Management and Insurance - Georgia State University) |
| Abstract: | The goal of this short communication is to give an overview of the key research issues in Enterprise Risk Management that arose during the talks and the brainstorming session of the first ERMII research workshop, which was held at ISFA, University of Lyon in June 2007. To define and compute economic capital at group level, fundamental problems related for example to value creation, correlation and capital allocation are stated. The ideas gathered in this paper are not directly ours, we just collected and summarized the ones that arose during the workshop. |
| Date: | 2008 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-00268841_v1&r=rmg |
| By: | Mototsugu Shintani (Department of Economics, Vanderbilt University, and Economist, Institute for Monetary and Economic Studies, Bank of Japan (E-mail: mototsugu.shintani@vanderbilt.edu, mototsugu.shintani@boj.or.jp)); Tomoyoshi Yabu (Assistant Professor, Graduate School of Systems and Information Engineering, University of Tsukuba (E-mail: tyabu@sk.tsukuba.ac.jp)); and Daisuke Nagakura (Economist, Institute for Monetary and Economic Studies, Bank of Japan (E-mail: daisuke.nagakura@boj.or.jp)) |
| Abstract: | This paper investigates the spurious effect in forecasting asset returns when signals from technical trading rules are used as predictors. Against economic intuition, the simulation result shows that, even if past information has non predictive power, buy or sell signals based on the difference between the short-period and long-period moving averages of past asset prices can be statistically significant when the forecast horizon is relatively long. The theory implies that both e momentumf and econtrarianf strategies can be falsely supported, while the probability of obtaining each result depends on the type of the test statistics employed. Several modifications to these test statistics are considered for the purpose of avoiding spurious regressions. They are applied to the stock market index and the foreign exchange rate in order to reconsider the predictive power of technical trading rules. |
| Keywords: | Efficient market hypothesis, Nonstationary time series, Random walk, Technical analysis |
| JEL: | C12 C22 C25 G11 G15 |
| Date: | 2008–06 |
| URL: | http://d.repec.org/n?u=RePEc:ime:imedps:08-e-9&r=rmg |