Corporate defaults exhibit two key characteristics that have profound implications for default risk management. First, default risk is correlated through time. Bankruptcies are normally the end of a process that begins with adverse economic shock and end with financial distress. Although some bankruptcies are unexpected and, therefore, are point events, like Enron and Worldcom, investors become aware of the company’s difficulties some years prior to the bankruptcy event. Second, financial wealth of companies in the same industry, or within the same economic area, is a function of managers’ skills and common factors that introduce correlations.
Companies’ default risk is linked through sector-specific and/or macroeconomic factors. Whilst a great deal of effort has been made by practitioners to measure and explain companies’ default correlations, academics have only recently began to devote attention to this issue. The existing literature on default correlations can be divided into two approaches: the structural approach that models default correlations through companies’ assets values; and the reduced-form approach that models default correlations through default intensities. While financial institutions, namely banks, are aware of these relationships, their ability to model such correlations is still not fully developed. Basle Committee on Banking and Supervision (BCBS 1999, p. 31) states “… the factors affecting the credit worthiness of obligors sometimes behave in a related manner…” which “… requires consideration of the dependencies between the factors determining credit related losses”. Whilst there are many different models and approaches to compute default probabilities, there is no consensus on the importance of different factors that drive default correlations. BCBS (1999) report points out that whilst practitioners have been managing and studying this dependence, there is a lack of theoretical and empirical work on this issue that tests the robustness of the frameworks.
Recent empirical evidence has documented that some financial and macroeconomic variables can be used to predict the excess returns of the U.S. Treasury bonds. For instance, financial variables found to have such predictive power include forward rates or spreads (Fama and Bliss, 1987; Stambaugh, 1988; Cochrane and Piazzesi, 2005) and yield spreads (Campbell and Shiller, 1991). In particular, Cochrane and Piazzesi show that a tent-shaped linear combination of five forward rates can explain between 30 and 35 percent of the variation in 1-year excess returns on bonds with 2-5 years to maturity. On the other hand, Ludvigson and Ng (2008) and Duffee (2008) present empirical evidence indicating the presence of hidden factors closely related to economic activity. More specifically, Ludvigson and Ng obtain a factor (extracted from a monthly panel of 132 macroeconomic variables using dynamic factor analysis) that can explain 21-26 percent of the 1-year excess returns. Furthermore, they document that this factor plus the Cochrane-Piazzesi factor can explain 42-45 percent of the 1-year excess returns. These findings have generated a large and fast growing literature on the determinants of bond risk premia. Nonetheless, some recent studies raise concerns about the robustness of the documented power of those financial and macro variables for predicting bond risk premia (see, e.g. Duffee (2007)).
Arbitrage ensures that covered interest parity holds. The condition is central to price foreign exchange forwards and interbank lending rates, and reflects the efficient functioning of markets. Normally, deviations from arbitrage, if any, last seconds and reach a few basis points. But after the Lehman bankruptcy, arbitrage broke down. By replicating exactly two major arbitrage strategies and using high frequency prices from novel datasets, this paper shows that arbitrage profits were large, persisted for months and involved borrowing in dollars. Empirical analysis suggests that insufficient funding liquidity in dollars kept traders from arbitraging away excess profits.
