Are realized volatility models good candidates for alternative Value at Risk prediction strategies?

In this paper, we assess the Value at Risk (VaR) prediction accuracy and efficiency of six ARCH-type models, six realized volatility models and two GARCH models augmented with realized volatility regressors. The α-th quantile of the innovation’s distribution is estimated with the fully parametric method using either the normal or the skewed student distributions and also with the Filtered Historical Simulation (FHS), or the Extreme Value Theory (EVT) methods. Our analysis is based on two S&P 500 cash index out-of-sample forecasting periods, one of which covers exclusively the recent 2007-2009 financial crisis. Using an extensive array of statistical and regulatory risk management loss functions, we find that the realized volatility and the augmented GARCH models with the FHS or the EVT quantile estimation methods produce superior VaR forecasts and allow for more efficient regulatory capital allocations. The skewed student distribution is also an attractive alternative, especially during periods of high market volatility.High frequency intraday data; Filtered Historical Simulation; Extreme Value Theory; Value-at-Risk forecasting; Financial crisis.

Isolement de Chlamydia psittaci, agent de l’omithose, chez la Perdrix grise d’élevage (Perdix perdix)

Chlamydia psittaci a été isolé sur des Perdrix grises d'élevage atteintes par ailleurs de Mycoplasmose à Mycoplasma gallisepticum. L’agent étiologique de l'Ornithose a été mis en évidence après coloration de calques de rate, cultures sur œuf embryonné et système cellulaire et inoculation à la souris de laboratoire

An investigation of systemic stress and interdependencies within the Eurozone and Euro area countries

One of themost challenging issues that economists are dealingwith is the investigation of the financial turmoil in Eurozone economies. Particularly, the issue of exposing the potential crisis transmission channels has attracted considerable interest. Aiming to contribute to this literature, we construct financial stress indices on a country level and explore further the potential inter-reactions between the root causes of systemic risk. The countryspecific index consists of a wide number of series drawn from the money, equity and bond markets, as well as from the banking sector of each Eurozone country. A Euro Area stress index is also provided, exploring the evolution of financial conditions for this group of countries. The investigation of the potential transmission channels is implemented through a multivariate analysis and the corresponding impulse responses' dynamics. The empirical findings suggest that countries aremostly responsive to their own financial shocks, while a degree of regionalism is also evident. That is, the peripheral countries are more susceptible to their financial stress,while the same holds for the core Eurozone countries. Additionally, in contrast to common wisdom, financial conditions in Greece and Portugal do not seem to affect the rest of the Euro Area, at least in the degree that Italy and Ireland do. These results are consistent under alternative model and sample specifications

The role of high frequency intra-daily data, daily range and implied volatility in multi-period Value-at-Risk forecasting

In this paper, we assess the informational content of daily range, realized variance, realized bipower variation, two time scale realized variance, realized range and implied volatility in daily, weekly, biweekly and monthly out-of-sample Value-at-Risk (VaR) predictions. We use the recently proposed Realized GARCH model combined with the skewed student distribution for the innovations process and a Monte Carlo simulation approach in order to produce the multi-period VaR estimates. The VaR forecasts are evaluated in terms of statistical and regulatory accuracy as well as capital efficiency. Our empirical findings, based on the S&P 500 stock index, indicate that almost all realized and implied volatility measures can produce statistically and regulatory precise VaR forecasts across forecasting horizons, with the implied volatility being especially accurate in monthly VaR forecasts. The daily range produces inferior forecasting results in terms of regulatory accuracy and Basel II compliance. However, robust realized volatility measures such as the adjusted realized range and the realized bipower variation, which are immune against microstructure noise bias and price jumps respectively, generate superior VaR estimates in terms of capital efficiency, as they minimize the opportunity cost of capital and the Basel II regulatory capital. Our results highlight the importance of robust high frequency intra-daily data based volatility estimators in a multi-step VaR forecasting context as they balance between statistical or regulatory accuracy and capital efficiency

Are realized volatility models good candidates for alternative Value at Risk prediction strategies?

In this paper, we assess the Value at Risk (VaR) prediction accuracy and efficiency of six ARCH-type models, six realized volatility models and two GARCH models augmented with realized volatility regressors. The α-th quantile of the innovation’s distribution is estimated with the fully parametric method using either the normal or the skewed student distributions and also with the Filtered Historical Simulation (FHS), or the Extreme Value Theory (EVT) methods. Our analysis is based on two S&P 500 cash index out-of-sample forecasting periods, one of which covers exclusively the recent 2007-2009 financial crisis. Using an extensive array of statistical and regulatory risk management loss functions, we find that the realized volatility and the augmented GARCH models with the FHS or the EVT quantile estimation methods produce superior VaR forecasts and allow for more efficient regulatory capital allocations. The skewed student distribution is also an attractive alternative, especially during periods of high market volatility

Are realized volatility models good candidates for alternative Value at Risk prediction strategies?

In this paper, we assess the Value at Risk (VaR) prediction accuracy and efficiency of six ARCH-type models, six realized volatility models and two GARCH models augmented with realized volatility regressors. The α-th quantile of the innovation’s distribution is estimated with the fully parametric method using either the normal or the skewed student distributions and also with the Filtered Historical Simulation (FHS), or the Extreme Value Theory (EVT) methods. Our analysis is based on two S&P 500 cash index out-of-sample forecasting periods, one of which covers exclusively the recent 2007-2009 financial crisis. Using an extensive array of statistical and regulatory risk management loss functions, we find that the realized volatility and the augmented GARCH models with the FHS or the EVT quantile estimation methods produce superior VaR forecasts and allow for more efficient regulatory capital allocations. The skewed student distribution is also an attractive alternative, especially during periods of high market volatility

Credit risk in European banks: The bright side of the internal ratings based approach

This paper investigates the accuracy of internal rating based (IRB) models in measuring credit risk. We contribute to the growing debate on the current prudential regulatory framework by investigating the use of validated IRB models in promoting efficient risk management practices. Our empirical analysis is based on a novel panel data set of 177 Western European banks observed from 2008 to 2015, in the aftermath of the financial and economic crisis. We find that IRB banks were able to curb the increase in credit risk driven by the macroeconomic slowdown better than banks under the standardized approach. This suggests that the introduction of the internal ratings based approach by Basel II has promoted the adoption of stronger risk management practices among banks, as meant by the regulators

Market risk of BRIC Eurobonds in the financial crisis period

The market risk of returns for BRIC Eurobonds has not been thoroughly analyzed via nonparametric estimation methods. The significance of risk and jumps is examined in a monthly sampling frequency. A detailed comparison upon significance of risk and jumps between BRIC Eurobonds is provided. Comparison concerns risk and jumps during the international financial crisis period: February 2007 up to February 2010. Among the BRIC countries, Chinese Eurobonds are the most significant in terms of both risk and jumps. The most significant estimator is the monthly Yang & Zhang range across the set of BRIC Eurobonds. The shorter the expiry period, the higher is the significance of risk and jumps. This is evident in all BRIC Eurobonds. Risk and jumps estimates are higher for theoretical prices rather than for actual prices according to all risk and jump significance measures