Hayette Gatfaoui

Credit Default Swap Spreads and U.S. Financial Market: Investigating Some Dependence Structure

Under Basel II framework, credit risk assessment is of high significance in the light of correlation risk. Correlation risk is often envisioned along with business conditions and financial markets impact. We employ copula methodology to identify the dependence structures that may exist between market risk fundamentals and credit risk fundamentals. Considering credit derivative spreads as credit risk fundamentals and market data as market risk determinants, we describe and quantify the asymmetric link prevailing between credit risk and market risk. Credit risk is negatively linked with market price risk whereas it becomes positively linked with market volatility risk. Such patterns give rise to interesting asymmetric dependence structures between both risk sources. We are then able to balance reliably market price risk with market volatility feedback, the market trend supporting a common correlation between securities. In the light of the previous trade-off, we propose also a simple credit Risk management rule.

Idiosyncratic Risk, Systematic Risk and Stochastic Volatility: An Implementation of Merton's Credit Risk Valuation

We extend the credit risk valuation framework introduced by Gatfaoui (2003) to stochastic volatility models. We state a general setting for valuing risky debt in the light of systematic risk and idiosyncratic risk, which are known to affect each risky asset in the financial market. The option nature of corporate debt allows then to account for the well-known volatility smile along with two documented determinants, namely stochastic volatility and market risk. Under some regularity conditions, we specify diffusion functionals leading to an asymptotically (relative to time) mean reverting volatility process. The behavior of such a specification is studied along with simulation techniques since debt is valued via a call on the firm assets value. Specifically, our examination resorts to Monte Carlo accelerators to realize related simulations. First, we consider the evolution of stochastic volatility for given parameter values. Then, we assess its impact on both risky debt and the related credit spread.

How Does Systematic Risk Impact Stocks? A Study on the French Financial Market

Systematic risk is known to affect the market prices of traded financial assets (Stulz, 1999a, 1999b, 1999c). Indeed, the capital asset pricing model (CAPM) theory argues that each financial asset bears an undiversifiable risk known as systematic or market risk, as introduced by Sharpe (1963, 1964, 1970) and Treynor (1961) among others.1 Such a risk can be estimated through a well-diversified portfolio so far as this portfolio presents as low as possible an idiosyncratic risk (French and Poterba, 1991). Recent literature focuses mainly on a sound assessment of the influence of systematic risk on financial assets, along with the beta coefficient in a CAPM framework. Koutmos and Knif (2002) estimate the influence of systematic risk while employing time-varying distributions (for example, conditional distributions depending on past innovations). Using market stock indices of the financial markets under consideration, they find that financial assets’ betas are stationary mean-reverting processes with an average degree of persistence equal to four days. Gencay, Selcuk and Whitcher (2003) use wavelet techniques to assess the influence of systematic risk on any asset, or equivalently to compute its beta in a CAPM model. These authors use the S&P 500 index as a systematic risk benchmark. Therefore, common practice resorts to available stock indices as proxies for a well-diversified market portfolio, and pays little attention to the sound assessment of systematic risk itself.2

Capturing Long-Term Coupling and Short-Term Decoupling Crude Oil and Natural Gas Prices

Recent research has focused on the prevailing relationship between crude oil and natural gas prices. Nowadays, it is well acknowledged that crude oil prices often drive natural gas prices. However, their relationship is unstable over time so that it exhibits regime changes. As a result, crude oil and natural gas prices tend to decouple in the short term while coupling together in the long term. Our present work captures the long-term coupling and short-term decoupling behaviors of crude oil and natural gas prices. Using Kalman filter methodology in linear regressions, we describe such time-varying relationship while allowing for stochastic coefficients (e.g. capturing local linear trend, non-stationarity). As a result, our findings open the door to possible short- and/or long-term arbitrage strategies between crude oil and natural gas prices.

Are critical slowing down indicators useful to detect financial crises

In this article, we consider financial markets as complex dynamical systems, and check whether the critical slowing down indicators can be used as early warning signals to detect a phase transition. Using various rolling windows, we analyze the evolution of three indicators: i) First-order autocorrelation, ii) Variance, and iii) Skewness. Using daily data for ten European stock exchanges plus the United States, and focusing on the Global Financial Crisis, our results are mitigated and depend both on the series used and the indicator. Using the main log-indices, critical slowing down indicators seem weak to predict the Global Financial Crisis. Using cumulative returns, for almost all countries, an increase in variance and skewness does precede the crisis. However, first-order autocorrelations of both log-indices and cumulative returns do not provide any useful information about the Global Financial Crisis. Thus, only some of the reported critical slowing down indicators may have informational content, and could be used as early warnings.

Estimating Fundamental Sharpe Ratios: A Kalman Filter Approach

A wide community of practitioners still focuses on classic Sharpe ratio as a risk adjusted performance measure due to its simplicity and easiness of implementation. Performance is computed as the excess return relative to the risk free rate whereas risk adjustment is provided by the asset return’s volatility as a denominator. However, such risk/return representation is only relevant under a Gaussian world. Moreover, Sharpe ratio exhibits time variation and can also be biased by market trend and idiosyncratic risk. As an implementation, we propose to filter out classic Sharpe ratios (SR) so as to extract their fundamental component on a time series basis. Time-varying filtered Sharpe ratios are obtained while employing the Kalman filter methodology. In this light, fundamental/filtered Sharpe ratios (FSR) are free of previous reported biases, and reflect the pure performance of assets. A brief analysis shows that SR is strongly correlated with other well-known comparable risk-adjusted performance measures while FSR exhibits a low correlation. Moreover, FSR is a more efficient performance estimator than previous comparable risk adjusted performance measures because it exhibits a lower standard deviation. Finally, a comparative analysis combines GARCH modeling, extreme value theory, multivariate copula representation and Monte Carlo simulations. Based on 10 000 trials and building equally weighted portfolios with the 30 best performing stocks according to each considered performance measure, the top-30 FSR portfolio offers generally higher perspectives of expected gains as well as reduced Value-at-Risk forecasts (i.e. worst loss scenario) over one week and one-month horizons as compared to other performing portfolios.