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on Risk Management |
| By: | Australian Prudential Regulation Authority (Australian Prudential Regulation Authority) |
| Date: | 2005–07–19 |
| URL: | http://d.repec.org/n?u=RePEc:apr:aprpdp:dp0021&r=rmg |
| By: | Australian Prudential Regulation Authority (Australian Prudential Regulation Authority) |
| Date: | 2005–07–28 |
| URL: | http://d.repec.org/n?u=RePEc:apr:aprpdp:dp0022&r=rmg |
| By: | Australian Prudential Regulation Authority (Australian Prudential Regulation Authority) |
| Date: | 2005–07–28 |
| URL: | http://d.repec.org/n?u=RePEc:apr:aprpdp:dp0023&r=rmg |
| By: | Australian Prudential Regulation Authority (Australian Prudential Regulation Authority) |
| Date: | 2005–10–04 |
| URL: | http://d.repec.org/n?u=RePEc:apr:aprpdp:dp0025&r=rmg |
| By: | Gordy, Michael B.; Lütkebohmert, Eva |
| Abstract: | The credit value-at-risk model underpinning the Basel II Internal Ratings-Based approach assumes that idiosyncratic risk has been diversified away fully in the portfolio, so that economic capital depends only on systematic risk contributions. We develop a simple methodology for approximating the effect of undiversified idiosyncratic risk on VaR. The supervisory review process (Pillar 2) of the new Basel framework offers a potential venue for application of the proposed granularity adjustment (GA). Our GA is a revision and extension of the methodology proposed in the Basel II Second Consultative Paper. The revision incorporates some technical advances as well as modifications to the Basel II rules since the Second Consultative Paper of 2001. Most importantly, we introduce an “upper bound” methodology under which banks would be required to aggregate multiple exposures to the same underlying obligor only for a subset of their obligors. This addresses what appears to be the most significant operational burden associated with any rigorous assessment of residual idiosyncratic risk in the portfolio. For many banks, this approach would permit dramatic reductions in data requirements relative to the full GA. Adressenkonzentration in einem Kreditportfolio entsteht, wenn sehr wenige Kreditnehmer in dem Portfolio sind oder wenn die Kreditvolumina sehr ungleich verteilt sind. Das Kreditrisikomodell, welches dem Internen Ratings-Basierten (IRB) Ansatz von Basel II unterliegt, berücksichtigt die Adressenkonzentration nicht. Es wird vielmehr sogar angenommen, dass das Portfolio einer Bank perfekt granular ist, in dem Sinne, dass jeder einzelne Kredit nur einen sehr kleinen Anteil zum Gesamtportfolio beiträgt. Reale Bankportfolios sind selbstverständlich nicht perfekt granular. In dieser Arbeit stellen wir eine einfache Granularitätsanpassung (GA) als Methode vor, mit der der Beitrag von Adressenkonzentration zum Risiko eines Portfolios quantifiziert werden kann. Das bankenaufsichtliche Überprüfungsverfahren (Säule 2) unter Basel II bietet ein Anwendungsfeld für die vorgeschlagene Methode. Diese Version der GA ist eine Überarbeitung und Erweiterung früherer Methoden und dient insbesondere dazu, die Kosten für eine Umsetzung in der Praxis zu reduzieren. In praktischen Anwendungen stellen meistens die benötigten Daten (und nicht die Formel, die auf diese Daten angewendet wird) das größte Hindernis dar. Wenn eine Bank mehrere Kredite an denselben Kreditnehmer vergeben hat, erfordert die Messung von Adressenkonzentration, dass diese Kredite aggregiert werden. Das ist unabhängig davon, ob die von uns vorgeschlagene Methode oder eine beliebige robuste Alternative verwendet wird. Diese Aggregation von Kreditinformationen stellt momentan eine wesentliche Herausforderung für die Banken dar. Unsere überarbeitete GA bietet den Banken die Möglichkeit, eine obere Schranke für die GA in einem Portfolio zu berechnen, indem sie sich ausschließlich auf Informationen über die größten Kredite stützt. Dadurch entfällt die Notwendigkeit, Daten für jeden einzelnen Kreditnehmer zu aggregieren. Für viele Banken würde dieser Ansatz eine erhebliche Reduktion der Datenanforderungen im Vergleich zu früheren Methoden zur Bestimmung der Granularitätsanpassung darstellen. Wir wenden unsere GA Methode auf mehrere realistische Portfolios an, die auf dem Millionenkreditmeldewesen basieren. Unsere Ergebnisse zeigen, dass der Effekt der Adressenkonzentration bedeutend sein kann und dass die von uns vorgeschlagene GA eine robuste Methode für ihre Messung darstellt. |
| Keywords: | Basel II, granularity adjustment, value-at-risk, idiosyncratic risk |
| JEL: | G28 G31 |
| Date: | 2007 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:bubdp2:5353&r=rmg |
| By: | Rama Cont (Center for Financial Engineering, Columbia University - [Columbia University], CMAP - Centre de Mathématiques Appliquées - [CNRS : UMR7641] - [Université de Versailles-Saint Quentin en Yvelines] - [Polytechnique - X]); Peter Tankov (PMA - Laboratoire de Probabilités et Modèles Aléatoires - [CNRS : UMR7599] - [Université Pierre et Marie Curie - Paris VI][Université Denis Diderot - Paris VII]) |
| Abstract: | Constant proportion portfolio insurance (CPPI) allows an investor to limit downside risk while retaining some upside potential by maintaining an exposure to risky assets equal to a constant multiple m>1 of the 'cushion', the difference between the current portfolio value and the guaranteed amount. In diffusion models with continuous trading, this strategy has no downside risk, whereas in real markets this risk is non-negligible and grows with the multiplier value. We study the behavior of CPPI strategies in models where the price of the underlying portfolio may experience downward jumps. This allows to quantify the 'gap risk' of the portfolio while maintaining the analytical tractability of the continuous--time framework. We establish a direct relation between the value of the multiplier m and the risk of the insured portfolio, which allows to choose the multiplier based on the risk tolerance of the investor, and provide a Fourier transform method for computing the distribution of losses and various risk measures (VaR, expected loss, probability of loss) over a given time horizon. The results are applied to a jump-diffusion model with parameters estimated from market data. |
| Keywords: | Portfolio insurance; CPPI; Lévy process; Value at Risk; expected loss |
| Date: | 2007–02–07 |
| URL: | http://d.repec.org/n?u=RePEc:hal:papers:hal-00129413_v1&r=rmg |
| By: | Wilson Sy (Australian Prudential Regulation Authority) |
| Abstract: | The Value-at-Risk (VaR) risk measure has been widely used in finance and insurance for capital and risk management. However, in recent years it has fallen somewhat out of favour due to a seminal paper by Artzner et al. (1999) who showed that VaR does not in general have all the four coherence properties which are desirable for a risk measure. In particular, the violation of the sub-additive property discourages diversification and is counter-intuitive to risk finance. In this paper, it is proved (Theorem 3.1) that VaR for independent Levy-stable random variates is a coherent risk measure being translational invariant, monotonic, positively homogeneous and sub-additive. That is, Levy-stable distributions are VaR coherent. As Levy-stable distributions are a rich class of probability distributions, the VaR risk measure may still have widespread applications. A brief comparative discussion is also given for L-stable variates for the expected shortfall risk measure.<p> |
| Date: | 2006–05–01 |
| URL: | http://d.repec.org/n?u=RePEc:apr:aprewp:wp2006-02&r=rmg |
| By: | Ralf Becker; Stan Hurn; Vlad Pavlov |
| Abstract: | During periods of market stress, electricity prices can rise dramatically. Electricity retailers cannot pass these extreme prices on to customers because of retail price regulation. Improved prediction of these price spikes, therefore, is important for risk management. This paper builds a time-varying-probability Markov-switching model of Queensland electricity prices, aimed particularly at forecasting price spikes. Variables capturing demand and weather patterns are used to drive the transition probabilities. Unlike traditional Markov-switching models, that assume normality of the prices in each state, the model presented here uses a generalized beta distribution to allow for the skewness in the distribution of electricity prices during high-price episodes. |
| Keywords: | electricity prices, regime switching, time-varying probabilities, beta |
| JEL: | C22 C53 Q49 |
| Date: | 2007–02–27 |
| URL: | http://d.repec.org/n?u=RePEc:qut:auncer:2007-4&r=rmg |
| By: | Australian Prudential Regulation Authority (Australian Prudential Regulation Authority) |
| Date: | 2005–05–03 |
| URL: | http://d.repec.org/n?u=RePEc:apr:aprpdp:dp0016&r=rmg |
| By: | Erick W. Rengifo (Department of Economics. Fordham University, New York); Emanuela Trifan (Institut für Volkswirtschaftslehre (Department of Economics), Technische Universität Darmstadt (Darmstadt University of Technology)) |
| Abstract: | This paper studies the impact of loss aversion on decisions regarding the allocation of wealth between risky and risk-free assets. We use a Value-at-Risk portfolio model with endogenous desired risk levels that are individually determined in an extended prospect theory framework. This framework allows for the distinction between gains and losses with respect to a subjective reference point as in the original prospect theory, but also for the influence of past performance on the current perception of the risky portfolio value. We show how the portfolio evaluation frequency impacts investor decisions and attitudes when facing financial losses and analyze the role of past gains and losses in the current wealth allocation. The perceived portfolio value exhibits distinct evolutions in two frequency segments delimitated by what we consider to be the optimal evaluation horizon of one year. Our empirical results suggest that previous research relying on fixed confidence portfolio risk levels underestimates the aversion of real individual investors to financial losses. |
| Keywords: | prospect theory, loss aversion, capital allocation, Value-at-Risk, portfolio evaluation |
| JEL: | C32 C35 G10 |
| Date: | 2007–02 |
| URL: | http://d.repec.org/n?u=RePEc:tud:ddpiec:180&r=rmg |
| By: | Arnold, Ivo J.M.; Galakis, John (Nyenrode Business Universiteit) |
| Abstract: | Since long the auto industry has been a valued source of leading business cycle indicators. While practitioners continue to use data on new car registrations to forecast economic activity, the predictive performance of auto industry related stock returns has deteriorated in the past decades. For the US this can be traced to the advent of Japanese manufacturers. The increased US market penetration by Japanese automakers coincides with a decline in the predictive ability of domestic auto returns. We are, however, able to recover a role for auto returns in business cycle forecasting by employing Japanese data. No such result can be found for European countries. We do conclude, however, that what’s good for Toyota, is good for the world economy |
| Keywords: | Forecasting, Business Cycle, Financial Markets |
| Date: | 2006 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:nijrep:2006-12&r=rmg |
| By: | Helena Veiga |
| Abstract: | According to the Taylor-Effect the autocorrelations of absolute financial returns are higher than the ones of squared returns. In this work, we analyze this empirical property for three different asymmetric stochastic volatility models, with short and/or long memory. Specially, we investigate how the Taylor-Effect relates to the most important model characteristics: its asymmetry and its capacity to generate volatility persistence and kurtosis. Finally, we realize Monte Carlo experiments to infer about possible biases of the sample Taylor-Effect and fit the models to the return series of the Dow Jones. |
| Date: | 2007–02 |
| URL: | http://d.repec.org/n?u=RePEc:cte:wsrepe:ws070702&r=rmg |
| By: | Yen-Ting Hu |
| Abstract: | This paper conducts a systematic investigation into the correlation between the default rate and three definitions of the recovery rate: price recoveries, settlement recoveries and discounted settlement recoveries. The data suggests a strong linear correlation for price recoveries and a weak one for settlement recoveries, but little or no correlation for discounted settlement recoveries. Using extreme value techniques, I show that the tail dependency for the settlement recoveries is as strong as that for the price recoveries. The probability of high losses (loss given default exceeding 0.9) is consistently higher for the settlement recoveries than for the price recoveries at any level of the quarterly default rate above 0.1%. |
| Date: | 2007–02 |
| URL: | http://d.repec.org/n?u=RePEc:bbk:bbkefp:0705&r=rmg |
| By: | Ian G. Sharpe; Andrei Stadnik (Australian Prudential Regulation Authority) |
| Abstract: | We develop and test a statistical early warning model to identify Australian general insurers experiencing deteriorating financial health over the 1999–2001 period. Using a logit model and two measures of financial distress we are able to predict, with reasonable confidence, the insurers more likely to be distressed. They are generally small and have low return on assets and cession ratios. Relative to holdings of liquid assets they have high levels of property and reinsurance assets, and low levels of equity holdings. They also write more overseas business, and less motor insurance and long-tailed insurance lines, relative to fire and household insurance.<p> |
| Date: | 2006–03–16 |
| URL: | http://d.repec.org/n?u=RePEc:apr:aprewp:wp2006-01&r=rmg |