Dimitrios P. Louzis

A financial systemic stress index for Greece

The paper develops a financial systemic stress index (FSSI) for Greece. We present a methodology for constructing and evaluating a systemic stress index which: i) adopts the suggestion of Hollo et al. (2012) [Hollo, Kremer, and Duca (2012) “CISS – A ‘Composite Indicator of Systemic Stress’ in the Financial System” ECB Working Paper 1426] to incorporate time-varying correlations between different market segments, and uses a multivariate GARCH approach which is able to capture abrupt changes in correlations; ii) utilizes both market and balance sheet data; and iii) evaluates the FSSI utilizing the results of a survey, conducted among financial experts, in order to construct a benchmark chronology of financial crises for Greece, which in turn is used to investigate whether changes in the FSSI are good indicators for financial crises. The results show that the FSSI is able to provide a precise periodization of crises.

Measuring return and volatility spillovers in euro area financial markets

This study examines the return (price) and volatility spillovers among the money, stock, foreign exchange and bond markets of the euro area, utilizing the forecast-error variance decomposition framework of a generalized VAR model proposed by Diebold and Yilmaz (2012) [Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 23, 57-66]. Our empirical results, based on a data set covering a twelve-year period (2000-2012), suggest a high level of total return and volatility spillover effects throughout the sample, indicating that, on average, more than the 50% of the forecast-error variance of the respective VAR model is explained by spillover effects. Moreover, the stock market is identified as the main transmitter of both return and volatility spillovers even during the current sovereign debt crisis. With the exception of the period 2011-2012, bonds of the periphery countries under financial support mechanisms are receivers of return spillovers, whereas, they transmit volatility spillovers to other markets diachronically. Finally, we identify the key role of money market in volatility transmission in the euro area during the outbreak of the global financial crisis.

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.