This paper examines the impact of imposing capital requirements on systemic risk. We use a static model on financial institutions’ risk-taking behavior to quantify the systemic risk in the cross-sectional dimension in both regulated and unregulated systems. Although imposing a capital requirement can lower individual risk, it simultaneously enhances systemic linkage within the system. By using a proper systemic risk measure combining both individual risk and systemic linkage, we show that systemic risk in a regulated system can be higher than that in an unregulated system. In addition, we analyze a sufficient condition under which the systemic risk in a regulated system is always lower.

This paper studies the effect of banking deregulation on credit risk. Its theoretical model shows that a bank is willing to invest more resources in screening borrowers when there is an entry threat, even though loan rates are driven lower. Thus, deregulation may result in improved loan quality and lower credit risk. This result is tested using bank-level balance sheet data and macroeconomic data for the European Union. The data reveal that competition intensified after the completion of the Second Banking Directive, while loan quality improved in most markets. Evidence is found that the loan quality improvement is associated with lower interest margin.

[We decompose aggregate market variance into an average correlation component and an average variance component. Only the latter commands a negative price of risk in the cross section of portfolios sorted by idiosyncratic volatility. Portfolios with high (low) idiosyncratic volatility relative to the Fama-French (1993) model have positive (negative) exposures to innovations in average stock variance and therefore lower (higher) expected returns. These two findings explain the idiosyncratic volatility puzzle of Ang et al. (2006, 2009). The factor related to innovations in average variance also reduces the pricing errors of book-to-market and momentum portfolios relative to the Fama-French (1993) model.]

[Nonlinearity is an important consideration in many problems of finance and economics, such as pricing securities and solving equilibrium models. This article provides analytical treatment of a general class of nonlinear transforms for processes with tractable conditional characteristic functions. We extend existing results on characteristic function-based transforms to a substantially wider class of nonlinear functions while maintaining low dimensionality by avoiding the need to compute the density function. We illustrate the applications of the generalized transform in pricing defaultable bonds with stochastic recovery. We also use the method to analytically solve a class of general equilibrium models with multiple goods and apply this model to study the effects of time-varying labor income risk on the equity premium.]

[We introduce a new class of parametric models applicable to a mixture of high and low frequency returns and revisit the concept of news impact curves introduced by Engle and Ng (1993). Overall, we find that moderately good (intra-daily) news reduces volatility (the next day), while both very good news (unusual high intra-daily positive returns) and bad news (negative returns) increase volatility, with the latter having a more severe impact. The asymmetries disappear over longer horizons. Models featuring asymmetries dominate in terms of out-of-sample forecasting performance, especially during the 2007-2008 financial crisis.]

[A crucial issue in asset pricing is to understand the relative importance of discount rate (DR) news and cash flow (CF) news in driving the time-series and cross-sectional variations of stock returns. Many studies directly estimate the DR news but back out the CF news as the residual. We argue that this approach has a serious limitation because the DR news cannot be accurately measured due to the small predictive power, and the CF news, as the residual, inherits the large misspecification error of the DR news. We apply this residualbased decomposition approach to Treasury bonds and equities and find results that are either counterintuitive or unrobust. Potential solutions, including modeling both DR news and CF news directly, the Bayesian model averaging approach, and the principal component analysis, are explored.]

Using both a linear regression method and a probability-based method, we find that on average, analysts place larger than efficient weights on (i.e., they overweight) their private information when they forecast corporate earnings. We also find that analysts overweight more when issuing forecasts more favorable than the consensus, and overweight less, and may even underweight, private information when issuing forecasts less favorable than the consensus. Further, the deviation from efficient weighting increases when the benefits from doing so are high or when the costs of doing so are low. These results suggest that analysts' incentives play a larger role in misweighting than their behavioral biases.

Reviews the book "Accounting for Data Processing Costs," by Robert W. McGee.