دانلود مقاله ISI انگلیسی شماره 787
عنوان فارسی مقاله

استراتژی های مدیریت ریسک مقاوم GFC تحت پیمان بازل

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
787 2012 15 صفحه PDF سفارش دهید محاسبه نشده
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عنوان انگلیسی
GFC-robust risk management strategies under the Basel Accord
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : International Review of Economics & Finance, Available online 18 September 2012

کلمات کلیدی
ارزش ریسک () - عوارض سرمایه روزانه - استراتژی مقاوم - پیمان بازل - بحران مالی جهانی -
پیش نمایش مقاله
پیش نمایش مقاله استراتژی های مدیریت ریسک مقاوم GFC تحت پیمان بازل

چکیده انگلیسی

A risk management strategy is proposed as being robust to the Global Financial Crisis (GFC) by selecting a Value-at-Risk (VaR) forecast that combines the forecasts of different VaR models. The robust forecast is based on the median of the point VaR forecasts of a set of conditional volatility models. This risk management strategy is GFC-robust in the sense that maintaining the same risk management strategies before, during and after a financial crisis would lead to comparatively low daily capital charges and violation penalties. The new method is illustrated by using the S&P500 index before, during and after the 2008–09 global financial crisis. We investigate the performance of a variety of single and combined VaR forecasts in terms of daily capital requirements and violation penalties under the Basel II Accord, as well as other criteria. The median VaR risk management strategy is GFC-robust as it provides stable results across different periods relative to other VaR forecasting models. The new strategy based on combined forecasts of single models is straightforward to incorporate into existing computer software packages that are used by banks and other financial institutions.

مقدمه انگلیسی

The Global Financial Crisis (GFC) of 2008–09 has left an indelible mark on economic and financial structures worldwide, and caused a generation of investors to wonder how things could have become so bad (see, for example, Borio, 2008). There have been many questions asked about whether appropriate regulations were in place, especially in the USA, to ensure the appropriate monitoring and encouragement of (possibly excessive) risk taking. The Basel II Accord1 was designed to monitor and encourage sensible risk taking, using appropriate models of risk to calculate Value-at-Risk (VaR) and subsequent daily capital charges. VaR is defined as an estimate of the probability and size of the potential loss to be expected over a given period, and is now a standard tool in risk management. It has become especially important following the 1995 amendment to the Basel Accord, whereby banks and other Authorized Deposit-taking Institutions (ADIs) were permitted (and encouraged) to use internal models to forecast daily VaR (see Jorion, 2000 for a detailed discussion). The last decade has witnessed a growing academic and professional literature comparing alternative modeling approaches to determine how to measure VaR, especially for large portfolios of financial assets. The amendment to the initial Basel Accord was designed to encourage and reward institutions with superior risk management systems. A back-testing procedure, whereby actual returns are compared with the corresponding VaR forecasts, was introduced to assess the quality of the internal models used by ADIs. In cases where internal models led to a greater number of violations than could reasonably be expected, given the confidence level, the ADI is required to hold a higher level of capital (see Table 1 for the penalties imposed under the Basel II Accord). Penalties imposed on ADIs affect profitability directly through higher capital charges, and indirectly through the imposition of a more stringent external model to forecast VaR.2 This is one reason why financial managers may prefer risk management strategies that are passive and conservative rather than active and aggressive (for more on this, see below).

نتیجه گیری انگلیسی

In this paper we proposed robust risk forecasts that use combinations of several conditional volatility models for forecasting VaR. Different strategies for combining models were compared over three different time periods, using S&P500 to investigate whether we can determine a GFC-robust risk management strategy. Backtesting provided evidence that a risk management strategy based on VaR forecast corresponding to the 50th percentile (median) of the VaR forecasts of a set of univariate conditional volatility models is robust, in that it yields reasonable daily capital charges, number of violations that do not jeopardize institutions that might use it, and more importantly, is invariant before, during and after the 2008–09 GFC. It is worth noting that, as in McAleer et al. (2010b), the VaR model minimizing DCC before, during and after the GFC changed frequently. Although the median model is not derived as the best model for minimizing DCC and the number of violation penalties, it is nevertheless a model that balances daily capital charges and violation penalties in minimizing DCC. Faced with the question of whether the outcomes of the different strategies are truly statistically different from each other, we turned to a novel statistical tool, the model confidence set (MCS) of Hansen et al. (2005). This technique allows comparing the outcomes of different statistical models and discriminates whether they are significantly different from each other. Another reason for using the MCS is to allow for multiple model comparisons. The conclusion of this analysis of the different models with respect to daily capital charges (DCC) is that the models are significantly different from each other. This result supports the idea of using the MEDIAN as a GFC-robust strategy under a variety of circumstances, including the periods before, during and after the Global Financial Crisis. The idea of combining different VaR forecasting models is entirely within the spirit of the Basel II Accord, although its use would require approval by the regulatory authorities, as for any forecasting model. This approach is not computationally demanding, even though several models have to be specified and estimated over time. Further research is needed to compute the standard errors of the forecasts of the combination models, including the median forecast.

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