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on Risk Management |
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Issue of 2010‒11‒06
six papers chosen by |
| By: | Georg Mainik; Ludger R\"uschendorf |
| Abstract: | A new notion of stochastic ordering is introduced to compare multivariate stochastic risk models with respect to extreme portfolio losses. In the framework of multivariate regular variation comparison criteria are derived in terms of ordering conditions on the spectral measures, which allows for analytical or numerical verification in practical applications. Additional comparison criteria in terms of further stochastic orderings are derived. The application examples include worst case and best case scenarios, elliptically contoured distributions, and multivariate regularly varying models with Gumbel, Archimedean, and Galambos copulas. |
| Date: | 2010–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1010.5171&r=rmg |
| By: | Balakrishna, B S |
| Abstract: | Subordinators are Levy processes with non-decreasing sample paths. They are natural processes to model default dependency. They help ensure that the loss process is non-decreasing leading to a promising class of dynamic models. The simplest subordinator is the Levy subordinator, a maximally skewed stable process with index of stability 1/2. Interestingly, this simplest subordinator turns out to be the appropriate choice as the basic process in modeling default dependency. It involves just two parameters to assess dependency risk, a measure of correlation and that of the likelihood of a catastrophe. Its attractive feature is that it admits a closed form expression for its distribution function. This helps in automatic calibration to individual hazard rate curves and efficient pricing with Fast Fourier Transform techniques. It is structured similar to the one-factor Gaussian copula model and can easily be implemented within the framework of the existing computational infrastructure. As it turns out, the Gaussian copula model can itself be recast into this framework highlighting its limitations. The model can also be investigated numerically with a Monte Carlo simulation algorithm. As is now well appreciated, random recovery is helpful in better pricing of the senior tranches and the model admits a tractable framework of random recovery. The model is investigated numerically and the implied base correlations are presented over a wide range of its parameters. The investigation also demonstrates its ability to generate reasonable hedge ratios. |
| Keywords: | default risk; correlation smile; CDO; Levy process; subordinator; semi-analytical; FFT; copula; catastrophe |
| JEL: | G13 |
| Date: | 2010–10–28 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:26274&r=rmg |
| By: | Artmann, Sabine; Finter, Philipp; Kempf, Alexander |
| Abstract: | This paper conducts a comprehensive asset pricing study based on a unique dataset for the German stock market. For the period 1963 to 2006 we show that two value characteristics (book-to-market equity, earnings-to-price) and momentum explain the cross-section of stock returns. Corresponding factor portfolios have significant premiums across various doublesorted characteristic-based test assets. In a horse race of competing asset pricing models the Fama-French 3-factor model does a poor job in explaining average stock returns, whereas the Carhart 4-factor model performs well. However, both models are inferior to a 4-factor model containing an earnings-to-price factor instead of a size factor. -- |
| Keywords: | asset pricing,characteristics,risk factors,multifactor models,Germany |
| JEL: | G12 |
| Date: | 2010 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:cfrwps:1001&r=rmg |
| By: | Rui Castro (Department of Economics and CIREQ, Université de Montréal); Gian Luca Clementi (Department of Economics, Stern School of Business, New York University and RCEA); Yoonsoo Lee (Department of Economics, Sogang University and Federal Reserve Bank of Cleveland) |
| Abstract: | We estimate firm–level idiosyncratic risk in the U.S. manufacturing sector. Our proxy for risk is the volatility of the portion of growth in sales or TFP which is not explained by either industry– or economy–wide factors, or firm characteristics systematically associated with growth itself. We find that idiosyncratic risk accounts for about 90% of the overall uncertainty faced by firms. The extent of cross–sectoral variation in idiosyncratic risk is remarkable. Firms in the most volatile sector are subject to at least three times as much uncertainty as firms in the least volatile. Our evidence indicates that idiosyncratic risk is higher in industries where the extent of creative destruction is likely to be greater. |
| Keywords: | Schumpeterian Competition, Creative Destruction, Product Turnover, R&D Intensity, Investment–Specific Technological Change |
| JEL: | D24 L16 L60 O30 O31 |
| Date: | 2010–01 |
| URL: | http://d.repec.org/n?u=RePEc:rim:rimwps:28_10&r=rmg |
| By: | Delphine Lautier; Franck Raynaud |
| Abstract: | This article presents an empirical study of thirteen derivative markets for commodity and financial assets. It compares the statistical properties of futures contracts's daily returns at different maturities, from 1998 to 2010 and for delivery dates up to 120 months. The analysis of the fourth first moments of the distribution shows that the mean and variance of the commodities follow a scaling behavior in the maturity dimension. The comparison of the tails of the probability distribution according to the expiration dates also shows that there is a segmentation in the fat tails exponent term structure above the L'evy stable region. Finally, the test of the robustness of the inverse cubic law in the maturity dimension shows that there are two regimes of extreme events for derivative markets, reminding of a phase diagram with a transition value at the 18th delivery month. |
| Date: | 2010–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1010.6026&r=rmg |
| By: | Hatemi-J, Abdulnasser; El-Khatib, Youssef |
| Abstract: | The minimum variance hedge ratio is widely used by investors to immunize against the price risk. This hedge ratio is usually assumed to be constant across time by practitioners, which might be too restrictive assumption because the optimal hedge ratio might vary across time. In this paper we put forward a proposition that a stochastic hedge ratio performs differently than a hedge ratio with constant structure even in the situations in which the mean value of the stochastic hedge ratio is equal to the constant hedge ratio. A mathematical proof is provided for this proposition combined with some simulation results and an application to the US stock market during 1999-2009 using weekly data. |
| Keywords: | Optimal Hedge Ratio; Stochastic Hedge Ratio; the US |
| JEL: | C32 G10 |
| Date: | 2010 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:26153&r=rmg |