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on Risk Management |
| By: | Albert H. De Wet (First Rand Bank); Reneé Van Eyden (Department of Economics, University of Pretoria) |
| Abstract: | Driven by intense competition for market share banks across the globe have increasingly allowed credit portfolios to become less diversified (across all dimensions - country, industry, sector and size) and were willing to accept lesser quality assets on their books. As a result, even well capitalised banks could come under severe solvency pressure when global economic conditions turn. The banking industry have realised the need for more sophisticated loan origination, credit and capital management practices. To this end, the reforms introduced by the Bank of International Settlement through the New Basel Accord (Basel II) aim to include exposure specific credit risk characteristics within the regulatory capital requirement framework. The new regulatory capital framework still does not allow diversification and concentration risk to be fully recognised within the credit portfolio because it does not account for systematic and idiosyncratic risk in a multifactor framework. The core principle for addressing practical questions in credit portfolio management is enclosed in the ability to link the cyclical or systematic components of firm credit risk with the firm’s own idiosyncratic credit risk as well as the systematic credit risk component of every other exposure in the portfolio. Simple structural credit portfolio management approaches have opted to represent the general economy or systematic risk by a single risk factor. The systematic component of all exposures, the process generating asset values and therefore the default thresholds are homogeneous across all firms. Indeed, this Asymptotic Single Risk Factor (ASRF) model has been the foundation for Basel II. While the ASRF framework is appealing due to its analytical closed-form properties for regulatory and generally universal application in large portfolios, the single risk factor characteristic is also its major drawback. Essentially it does not allow for enough flexibility in answering real life questions. Commercially available credit portfolio models make an effort to address this by introducing more systematic factors in the asset value generating process but from a practitioner’s point of view, these models are often a “black-box” allowing little economic meaning or inference to be attributed to systematic factors. The methodology proposed by Pesaran, Schuermann, and Weiner (2004) and supplemented by Pesaran, Schuermann, Treutler and Weiner (2006) has made a significant advance in credit risk modelling in that it avoids the use of proprietary balance sheet and distance to default data, focussing on credit ratings which are more freely available. Linking an adjusted structural default model to a structural global econometric model (GVAR) credit risk analysis and portfolio management can be done through the use of a conditional loss distribution estimation and simulation process. The GVAR model used in Pesaran et al. (2004) comprises a total of 25 countries which is grouped into 11 regions and accounts for 80 per cent of world production. In the case of South Africa the GVAR model lacks applicability since it does not include an African component. In this paper we construct a country specific macro-econometric risk driver engine which is compatible with and could feed into the GVAR model and framework of PSW (2004) using vector error correcting (VECM) techniques. This will allow conditional loss estimation of a South African specific credit portfolio but also opens the door for credit portfolio modelling on a global scale as such a model can easily be linked into the GVAR model. We extend the set of domestic factors beyond those used in PSW (2004) in such a way that the risk driver model is applicable for both retail and corporate credit risk. As such, the model can be applied to a total bank balance sheet, incorporating the correlation and diversification between both retail and corporate credit exposures. Assuming statistical over-identification restrictions, our results indicate that it is possible to construct a South African component for the GVAR model and that such a component could easily be integrated into a global content. From a practical application perspective the framework and model is particularly appealing since it could be used as a theoretically consistent correlation model within a South African specific credit portfolio management tool. |
| Keywords: | Credit portfolio management, multifactor model, vector error correction model (VECM), credit correlations |
| JEL: | C32 C51 E44 |
| Date: | 2007–09 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:200719&r=rmg |
| By: | Charles Goodhart |
| Abstract: | |
| Date: | 2007–10 |
| URL: | http://d.repec.org/n?u=RePEc:fmg:fmgsps:sp175&r=rmg |
| By: | Timotheos Angelidis |
| Abstract: | This paper analyses the properties of idiosyncratic risk in the Greek Stock Market by disaggregating the total volatility of stocks at market, industry, and firm level. Idiosyncratic risk is much larger and represents a smaller component of total volatility in Greece compared with other developed markets, is persistent, shows no trend over time but tends to increase more during upward than downward movements of the market. Average firm specific risk in Greece is best described by a two-state Markov process and during periods of high volatility (in 1987, in 1989-1990, in 1994 and in 1998-2000) the average idiosyncratic variance is twice than that of the low variance regime. The implications for portfolio and risk management of changing idiosyncratic volatility are also discussed. |
| Keywords: | Idiosyncratic Risk, Risk Management, Stock Market Volatility |
| Date: | 2007 |
| URL: | http://d.repec.org/n?u=RePEc:uop:wpaper:0001&r=rmg |
| By: | Joseph H. Golec; John A. Vernon |
| Abstract: | The biotechnology industry has been an engine of innovation for the U.S. healthcare system and, more generally, the U.S. economy. It is by far the most research intensive industry in the U.S. In our analyses in the current paper, for example, we find that, over the past 25 years, average R&D intensity (R&D spending to total firm assets) for this industry was 38 percent. Consider that over this same period average R&D intensity for all industries was only about 3 percent. In the current paper we examine this industry along a number of dimensions and estimate its average financial risk. Specifically, we use Compustat and Center for Research in Securities Prices (CRSP) data from 1982 to 2005 for firms defined by the North American Industry Classification System (NAICS) as biotechnology firms to estimate several Fama-French three factor return models. The finance literature has established this model as the gold standard. Single factor models like the Capital Asset Pricing Model (CAPM) do not capture all of the types of systematic risk that influence firm cost of capital. In particular, the CAPM does not reflect the empirical evidence that supports both a size-related and a book-to-market related systematic risk factor . Both of these factors, based on biotech industry characteristics, will exert a greater influence on biotech firms, on average. Another implication is, of course, that cost of capital estimates for the industry will be underestimated when a single factor model, like the CAPM, is used. This also implies that the cost estimates of bringing a new drug and/or biologic to market will be understated if financial risk and cost of capital are measured using a single-factor model. In the current study we find that biotechnology firms are exposed to greater financial risk than other industries and are also more sensitive to policy shocks that affect, or could affect, industry profitability. Average nominal costs of capital over the 1982-2005 time period were 16.25 percent for biotechnology firms. Of course, these average estimates obscure significant variation in financial risk at the firm level, but nonetheless shed light on some interesting aggregate differences in risk. In the current paper we discuss the theoretical links between financial risk, stock prices and returns, and R&D spending. Several caveats are also discussed. |
| JEL: | G18 G32 I0 I18 K23 L0 L2 L21 L5 L65 |
| Date: | 2007–11 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:13604&r=rmg |
| By: | Georges Gallais-Hamonno (Centre Emile Bernheim, Solvay Business School, Université Libre de Bruxelles, Brussels and LEO, University of Orleans, France.); Huyen Nguyen-Thi-Thanh (La Rochelle Business School - CEREGE and LEO, France.) |
| Abstract: | We study two principal mechanisms suggested in the literature to correct the serial correlation in hedge fund returns and the impact of this correction on financial characteristics of their returns as well as on their risk level and on their performances. The methods of Geltner (1993), its extension by Okunev & White (2003) and that of Getmansky, Lo & Makarov (2004) are applied on a sample of 54 hedge fund indexes. The results show that the unsmoothing leaves the mean unchanged but increases significantly the risk level of hedge funds, whether the risk is measured in terms of the return standard-deviation or the modified VaR. Funds' absolute performances, measured by traditional Sharpe ratio and Omega index, decline considerably. By contrast, funds' rankings after the unsmoothing unexpectedly change slightly. However, some notable modifications in ranks of several funds are observed. The necessary transparency of the management practice requires that such a correction must be systematically done. |
| Keywords: | hedge funds, smoothed returns, performance evaluation, Sharpe ratio, Omega index. |
| JEL: | G2 G11 G15 |
| Date: | 2007–11 |
| URL: | http://d.repec.org/n?u=RePEc:sol:wpaper:07-034&r=rmg |