A first-passage-time model under regime-switching market environment

In this paper, we suggest a first-passage-time model which can explain default probability and default correlation dynamics under stochastic market environment. We add a Markov regime-switching market condition to the first-passage-time model of Zhou [Zhou, C., 2001. An analysis of default correlations and multiple defaults. Review of Financial Studies 14, 555–576]. Using this model, we try to explain various relationship between default probability, default correlation, and market condition. We also suggest a valuation method for credit default swap (CDS) with (or without) counterparty default risk (CDR) and basket default swap under this model.Our numerical results provide us with several meaningful implications. First, default swap spread is higher in economic recession than in economic expansion across default swap maturity. Second, as the difference of asset return volatility between under bear market and under bull market increases, CDS spread increases regardless of maturity. Third, the bigger the intensity shifting from bull market to bear market, the higher the spread for both CDS without CDR and basket default swap.     

Estimating loss-given default through advanced credibility theory

The New Basel Accord allows internationally active banking organizations to calculate their credit risk capital requirements using an internal ratings based approach, subject to supervisory review. One of the modeling components is the loss-given default (LGD): it represents the credit loss for a bank when extreme events occur that influence the obligor ability to repay his debts to the bank. Among researchers and practitioners the use of statistical models such as linear regression, Tobit or decision trees is quite common in order to compute LGDs as a forecasting of historical losses. However, these statistical techniques do not seem to provide robust estimation and show low performance. These results could be driven by some factors that make differences in LGD, such as the presence and quality of collateral, timing of the business cycle, workout process management and M&A activity among banks. This paper evaluates an alternative method of modeling LGD using a technique based on advanced credibility theory typically used in actuarial modeling. This technique provides a statistical component to the credit and workout experts’ opinion embedded in the collateral and workout management process and improve the predictive power of forecasting. The model has been applied to an Italian Bank Retail portfolio represented by Overdrafts; the application of credibility theory provides a higher predictive power of LGD estimation and an out-of-time sample backtesting has shown a stable accuracy of estimates with respect to the traditional LGD model.     

Ex-ante implications of sovereign default

I study how the possibility of default on external debts affects other capital allocation decisions in a small open economy. In the model, default has an option value derived from the randomization over ex-post default regimes, which depends on country-specific productivity shocks. This feature of default reduces incentives for ex-ante diversification, which would reduce exposure to the productivity shock. As a result, if the economys debt to capital ratio is allowed to cross a fixed threshold (identified in the model), the unique equilibrium exhibits an allocation of capital that is less productive in expectation and more volatile than in a benchmark model without default. The model therefore captures a number of salient features of emerging and less developed countries, where low levels of international risk-sharing have gone hand-in-hand with frequent and recurring default events.     

Firm Size and Cyclical Variations in Stock Returns

Recent imperfect capital market theories predict the presence of asymmetries in the variation of small and large firms' risk over the economic cycle. Small firms with little collateral should be more strongly affected by tighter credit market conditions in a recession state than large, better collateralized ones. This paper adopts a flexible econometric model to analyze these mplications empirically. Consistent with theory, small firms display the highest degree of asymmetry in their risk across recession and expansion states, which translates into a higher sensitivity of their expected stock returns with respect to variables that measure credit market conditions.     

Improvements in loss given default forecasts for bank loans

An accurate forecast of the parameter loss given default (LGD) of loans plays a crucial role for risk-based decision making by banks. We theoretically analyze problems arising when forecasting LGDs of bank loans that lead to inconsistent estimates and a low predictive power. We present several improvements for LGD estimates, considering length-biased sampling, different loan characteristics depending on the type of default end, and different information sets according to the default status. We empirically demonstrate the capability of our proposals based on a data set of 69,985 defaulted bank loans. Our results are not only important for banks, but also for regulators, because neglecting these issues leads to a significant underestimation of capital requirements.     

Multi-period credit default prediction with time-varying covariates

In credit default prediction models, the need to deal with time-varying covariates often arises. For instance, in the context of corporate default prediction a typical approach is to estimate a hazard model by regressing the hazard rate on time-varying covariates like balance sheet or stock market variables. If the prediction horizon covers multiple periods, this leads to the problem that the future evolution of these covariates is unknown. Consequently, some authors have proposed a framework that augments the prediction problem by covariate forecasting models. In this paper, we present simple alternatives for multi-period prediction that avoid the burden to specify and estimate a model for the covariate processes. In an application to North American public firms, we show that the proposed models deliver high out-of-sample predictive accuracy.     

An exploration into Pigou's theory of cycles

This paper explores a theory of business cycles in which recessions and booms arise due to difficulties encountered by agents in properly forecasting the economy's future needs in terms of capital. The idea has a long history in the macroeconomic literature, as reflected by the work of Pigou (Industrial Fluctuation, MacMillan, London, 1926). The contribution of this paper is twofold. First, we illustrate the type of general equilibrium structure that can give rise to such phenomena. Second, we examine the extent to which such a model can explain the observed pattern of U.S. recessions (frequency, depth) without relying on technological regress. We argue that such a model offer a framework for understanding elements of both the recent U.S. recession and of the Asia downturns of the late 1990s.     

Banks’ capital buffer,risk and performance in the Canadian banking system: Impact of business cycles and regulatory changes

Using quarterly financial statements and stock market data from 1982 to 2010 for the six largest Canadian chartered banks, this paper documents positive co-movement between Canadian banks’ capital buffer and business cycles. The adoption of Basel Accords and the balance sheet leverage cap imposed by Canadian banking regulations did not change this cyclical behavior of Canadian bank capital. We find Canadian banks to be well-capitalized and that they hold a larger capital buffer in expansion than in recession, which may explain how they weathered the recent subprime financial crisis so well. This evidence that Canadian banks ride the business and regulatory periods underscores the appropriateness of a both micro- and a macro-prudential “through-the-cycle” approach to capital adequacy as advocated in the proposed Basel III framework to strengthen the resilience of the banking sector.     

Optimal clearing margin,capital and price limits for futures clearinghouses

We provide a model for a futures clearinghouse to use for setting optimal levels of clearing margin, capital and price limits, which minimizes the costs to clearing firms and simultaneously protects the clearinghouse from default by clearing firms. We show how to estimate the capital requirement, which supports the clearinghouse’s residual default risk that is not covered by the clearing margin. We apply our model to the Winnipeg Commodity Exchange and demonstrate that price limits reduce the sum of optimal clearing margin and capital to a level that is substantially lower than that required in the absence of price limits.     

Time-varying risk of rare disasters,investment, and asset pricing

We extend an equilibrium business cycle/asset pricing model of production and capital accumulation by introducing a time-varying risk of rare disasters. It predicts that investment is much more volatile than output, which provides theoretical support for the empirical data. Furthermore, the model-generated stationary distribution of the investment-output ratio fits the data remarkably well. Both of them exhibit negative skewness, which means that there is a small probability that this ratio can be very low. Given the observations of the investment-output ratio, we obtain the values of the jump intensity implicit in the historical data and find those recession periods coincide with a rapid increase in the probability of a disaster. Finally, the model shows that the existence of adjustment costs generates a procyclical price of capital and contributes to resolving the equity premium puzzle.