Fathi Abid

The determinants of credit default swap rates: an explanatory study

The aim of this paper is to explain empirically the determinants of credit default swap rates using a linear regression. We document that the majority of variables, detected from the credit risk pricing theories, explain more than 60% of the total level of credit default swap. These theoretical variables are credit rating, maturity, riskless interest rate, slope of the yield curve and volatility of equities. The estimated coefficients for the majority of these variables are consistent with theory and they are significant both statistically and economically. We conclude that credit rating is the most determinant of credit default swap rates.

Credit‐default swap rates and equity volatility: a nonlinear relationship

Purpose – The aim of this paper is to study the impact of equity returns volatility of reference entities on credit-default swap rates using a new dataset from the Japanese market. Design/methodology/approach – Using a copula approach, the paper models the different relationships that can exist in different ranges of behavior. It studies the bivariate distributions of credit-default swap rates and equity return volatility estimated with GARCH (1,1) and focus on one parameter Archimedean copula. Findings – First, the paper emphasizes the finding that pairs with higher rating present a weaker dependence coefficient and then, the impact of equity returns volatility on credit-default swap rates is higher for the lowest rating class. Second, the dependence structure is positive and asymmetric indicating that protection sellers ask for higher credit-default swap returns to compensate the higher credit risk incurred by low rating class. Practical implications – The paper has several practical implications that are of value for financial hedgers and engineers, loan market participants, financial regulators, government regulators, central banks, and risk managers. Originality/value – The paper also illustrates the potential benefits of equity returns volatility of reference entities as a proxy of default risk. These simplifications could be lifted in future research on this theme.

The Impact of Option Strategies in Financial Portfolios Performance: Mean-Variance and Stochastic Dominance Approaches

This study employs the mean-variance (MV) criterion, Capital Asset Pricing Model (CAPM) statistics and stochastic dominance (SD) analysis to investigate the performance of option strategies, including writing out-of-the-money (OTM) covered call and buying in-the-money (ITM) protective put, with that of the pure-stock investment by analysing the French data in the entire 1999 year. Our results from MV criterion show that none of these three strategies dominate one another but our CAPM statistics show that in general buying ITM protective-put strategy obtains the highest performance, followed by the writing OTM covered-call strategy while the naked stock obtains the smallest values. This confirms the superiority of ITM protective-put strategy, followed by OTM covered-call strategy by using the Beta coefficient, Sharpe ratio, Treynor and Jensen indices. As the return distributions of these strategies are non-normal, the MV criterion and the CAPM statistics may not be appropriate to assess the relative performance measurement of the portfolios. We further investigate the performance by employing SD approach. Our SD findings reveal that most of the buying covered-call and writing protective - put strategies are superior to their corresponding pure-stock strategy, as in general the former stochastically dominates the latter in the sense of first order SD. This infers that there may exist an anomaly of the existence of an arbitrage opportunity in option trading that all types of non-satiated investors will increase their wealth and utility by switching from the pure unhedged stock strategy to their corresponding buying protective - put or writing covered-call strategies. In addition, we find the dominance relationship between the two hedged positions is not as clear as the comparison with their unhedged positions, but on average more buying ITM protective put outperforms writing OTM covered call in the sense of the first-order SD. In short, our results confirm that option introduction improve significantly the performance of unhedged portfolios, especially buying ITM protective put.

A Combined Analytic Hierarchy Process and Goal Programming Approach to International Portfolio Selection in the Presence of Investment Barriers

The aim of this paper is to develop an integrated multiple criteria decision making approach combining the analytic hierarchy process (AHP) and the goal programming (GP) model to study the impact of a mixture of investment barriers on international portfolio selection, and therefore the home bias puzzle from the viewpoint of G-7 investors over the period 2001-2009. The AHP is used to determine the suitable international equity portfolios with respect to seven barriers to international investment. The GP model, incorporating the market weights of the maximum return, minimal variance, and AHP portfolios is formulated to determine the optimal international equity portfolios. The main results show that the AHP-GP optimal international portfolio weights are different from those predicted by the I-CAPM. Also, except for French and US investors, home bias values determined according to the AHP-GP portfolios are lower than those calculated on the basis of the value-weighted world market portfolio.

Sequential Investment and Delay: An Agribusiness Firm Case Study

Purpose - Investment decisions by agribusiness firms are costly and subject to high volatility and uncertainty. In many cases, the project value is not only determined by its cash-flows stream and financial side effects but also by the presence of substantial future uncertainty such as project implementation delay and growth opportunities. The purpose of this paper is to evaluate an agribusiness project taking into account these two options and to illustrate the how risks that evolve over time can affect sequential investment decisions in the oleic oil industry in Tunisia. Design/methodology/approach - The methodology used to capture the investment project value and analyze the impact of lags between the initial investment decision and its implementation on project value is based on a decision tree method and binomial lattice method (which adds growth option). The project valuation is based, first on actual data at the time of the initial decision and second the authors use the full information to report on the true value of the investment opportunity as real time evolved. Findings - Findings show that time to build is a very important factor in valuing an agribusiness especially when efficiency is strongly governed by climatic conditions and international market uncertainty. Our real options approach shows that production delays can deteriorate the follow-on project value by as much as 53 percent. The implicit growth option falls to only 27 percent of the total project value while it was about 58 percent according to the standard forecast. The delay in project implementation not only affects the firm project financing costs and the loss of revenue, but also it contributes to modify the initial marketing strategy. Originality/value - The paper is a first application of real option approach to the oleic oil industry. The methodology used in the paper can be adapted by practitioners and investors to adequately value oleic projects.

Financial Distress Prediction Using Neural Networks

This exploratory research examines and models the financial distress prediction using neural network approach. The study is based on financial ratios. Nine different neural network models are constructed to test the predictive capability of the models by considering: (1) the impact of time varying information structure prior the distressed situation using first, independent annual financial ratios (four models)and second, different panel data sets (three models) and, (2) the influence of time varying probability estimates of financial distress in panel data sets (two models). Results support that it is not necessary to have complex architecture in neural models to predict firms financial distress. Besides more the predictability horizon is shorter and the input information structure is most recent, more the predictive capability of the neural model is better.

Regime-Switching Behaviour in the Conditional Volatility of MENA Stock Market Returns

The objective of this article is to investigate the behaviour of the time-varying volatility in 11 Middle East and North African (MENA) countries’ stock market using a three-state Markov regime switching model over the period from 30 October 2006 to 21 October 2011. We find that MENA stock market volatility can be characterised by three regimes: tranquil period with low volatility of volatility, turmoil regime with high volatility of volatility and crisis regime with extremely high volatility of volatility. Besides, the Granger causation effects from the MSCI World index to MENA stock markets are stronger and statistically significant especially in crisis regime. JEL Classification: F30, G01, G15

Should Americans invest internationally? mean–variance portfolios optimization and stochastic dominance approaches

Using both mean–variance portfolio optimization (MVPO) and stochastic dominance (SD) approaches, this paper investigates whether international diversification and home bias inertia are substitutes or complements for Americans. More specifically, we compare daily closing prices of 30 US stocks and the stock indices from American, Latin American, and Asian financial markets, including both emerging and major markets. Results from the MVPO show that a domestic diversification strategy performs better for any risk level up to 0.5%, whereas international diversification performs better for any risk level higher than 0.5%. Some results from the SD test support international diversification, some promote home bias, and still others conclude that there is no difference between investing domestically and internationally. However, our findings show that one could not find any single internationally diversified portfolio that dominates all domestically diversified portfolios and, similarly, one could not find any single domestically diversified portfolio that dominates all internationally diversified portfolios. At last, our SD findings also recommends that the US investors have a “home bias” if they prefer less risk and to be “internationally diversified” if they prefer higher risk.

The Determinants of Domestic and Foreign Biases: An Empirical Study

The international capital asset pricing model (ICAPM), based on traditional portfolio theory developed by Sharpe (1964) and Lintner (1965), suggests that, to maximize risk-adjusted returns, investors should hold a world market portfolio of risky assets. However, domestic assets are heavily weighted in investors’ portfolios even after the relaxing of capital control after 1980. For example, in 1997, 89.9 percent of US investors’ equity portfolios were domestic equities, while the size of the USA in world market capitalization was about 48.3 percent (Ahearne et al., 2004). The wide disparity between actual and recommended international equity portfolio weights constitutes the equity home bias, one of the unresolved puzzles in international finance literature.1

A Methodology to Estimate the Interest Rate Yield Curve in Illiquid Market

The aim of this article is to develop a methodology to estimate the interest rate yield curve and its dynamics in the Tunisian bond market, which is considered as an illiquid market with a low trading volume. To achieve this, first, we apply the cubic spline interpolation method to deal with the missing observation problem. Second, we focus on the work of Vasicek (1977) and Cox-Ingersoll and Ross (CIR) (1985), we estimate each model and discuss its performance in predicting the dynamics of the interest rate yield curve using ordinary least squares (OLS) and maximum likelihood estimation (MLE) methods. The data sample consists of Treasury bond prices for the period from 14 July 2004 to 10 September 2012 collected from over the counter market. This assessment is done by Matlab software using the specified algorithms. The results suggest that the cubic spline method is accurate for construction of the average interest rate yield curve. Then, the estimation of Vasicek (1977) and CIR (1985) models generate an upward sloping yield curves and the Vasicek (1977) model shows more ability to replicate the stylised facts of the short-term interest rates in the Tunisian bond market. Finally, the forecasting of the yield curve predicts an economic growth in the future characterised by a higher inflation. JEL Classification: G12