A pervasive and puzzling feature of banks’ Value-at-Risk (VaR) is its abnormally high level, which leads to excessive regulatory capital. A possible explanation for the tendency of commercial banks to overstate their VaR is that they incompletely account for the diversification effect among broad risk categories (e.g., equity, interest rate, commodity, credit spread, and foreign exchange). By underestimating the diversification effect, bank’s proprietary VaR models produce overly prudent market risk assessments. In this paper, we examine empirically the validity of this hypothesis using actual VaR data from major US commercial banks. In contrast to the VaR diversification hypothesis, we find that US banks show no sign of systematic underestimation of the diversification effect. In particular, diversification effects used by banks is very close to (and quite often larger than) our empirical diversification estimates. A direct implication of this finding is that individual VaRs for each broad risk category, just like aggregate VaRs, are biased risk assessments.

The term structure of interest rates is often summarized using a handful of yield factors that capture shifts in the shape of the yield curve. In this paper, we develop a comprehensive model for volatility dynamics in the level, slope, and curvature of the yield curve that simultaneously includes level and GARCH effects along with regime shifts. We show that the level of the short rate is useful in modeling the volatility of the three yield factors and that there are significant GARCH effects present even after including a level effect. Further, we find that allowing for regime shifts in the factor volatilities dramatically improves the model’s fit and strengthens the level effect. We also show that a regime-switching model with level and GARCH effects provides the best out-of-sample forecasting performance of yield volatility. We argue that the auxiliary models often used to estimate term structure models with simulation-based estimation techniques should be consistent with the main features of the yield curve that are identified by our model.

We use the IPOs of supply-chain partners as precipitating events and test for positive spillovers on private firms (the “IPO spillover hypothesis”). A trading partner’s IPO may benefit its suppliers through increased demand and its customers by reducing an input-related growth constraint. A newly public firm may also transmit additional liquidity to trading partners through trade credit practices. Using Japanese data on important relationships between IPO firms and their private suppliers and customers, we find that suppliers and customers experience significantly higher rates of growth in revenue, cash balances, and PP&E than do other private firms. The paper appears to be the first to document real and financial effects of positive liquidity shocks on supply-chain partners.

In this study, we examine the predictive performance of a wide class of binary classifiers using a large sample of international credit ratings changes from the period 1983–2013. Using a number of financial, market, corporate governance, macro-economic and other indicators as explanatory variables, we compare classifiers ranging from conventional techniques (such as logit/probit and LDA) to fully nonlinear classifiers, including neural networks, support vector machines and more recent statistical learning techniques such as generalised boosting, AdaBoost and random forests. We find that the newer classifiers significantly outperform all other classifiers on both the cross sectional and longitudinal test samples; and prove remarkably robust to different data structures and assumptions. Simple linear classifiers such as logit/probit and LDA are found nonetheless to predict quite accurately on the test samples, in some cases performing comparably well to more flexible model structures. We conclude that simpler classifiers can be viable alternatives to more sophisticated approaches, particularly if interpretability is an important objective of the modelling exercise. We also suggest effective ways to enhance the predictive performance of many of the binary classifiers examined in this study.

In this paper we estimate the effect of particular price incentives on consumer payment patterns using transaction-level data. We find that participation in a loyalty program and access to an interest-free period tend to increase credit card use at the expense of alternative payment methods, such as debit cards and cash. Interestingly though, the pattern of substitution from cash and debit cards differs according to the price incentive. An implication of the findings is that the Reserve Bank reforms of the Australian payments system are likely to have influenced observed payment patterns.

This paper provides a review of developments in the area of risk management at both the firm level and the macro-economy. We review rationales regarding why firms choose to manage risk, as well as new developments in measuring and managing risk in a dynamic setting. We also consider current risk sharing arrangements in light of the theory regarding optimal risk sharing. The paper concludes with some suggestions for additional research that emphasizes the importance of incorporating market incompleteness in an equilibrium setting. We also discuss the role of incompleteness at the macro-level and speculate on how derivatives markets may influence macro-economic stabilization policy.

We examine the role of delegation in predicting CEO successions. Using a novel proxy for delegation in mergers and acquisitions, we find that overall CEO turnover rates are about one third higher following deals where the CEO delegates to a senior manager versus deals with no observable delegation. The delegation-turnover relation is strongest when deals are delegated to heirs apparent, the CEO is older, or the delegation decision is unexpected. Voluntary turnovers are more frequent following delegated deals than non-delegated deals, consistent with delegation signaling an orderly succession. The delegation-turnover relation fades over time while other predictors of turnover such as profitability, CEO age, and the presence of an heir apparent in the corporate hierarchy continue to remain significant up to five years after the deal. Our findings suggest that delegation is unique among our predictors of turnover in the sense that it captures near term orderly successions.