This paper documents a new channel for rating-based bond market segmentation, which, in contrast to prior research, is based on nonregulatory investment management practices. A 2005 Lehman Brothers index redefinition provides a quasinatural experiment in which a number of previously high-yield split-rated bonds were mechanically relabeled as investment grade. Although their regulatory standing was unaffected, these bonds had abnormal yield declines of 21 basis points. These valuation changes can be traced to buying by asset-class-sensitive institutional investors for whom these bonds became investable. Reputation, regulation, indexation, and liquidity cannot explain the observed price and trading patterns. (JEL G12, G14)

Monetary policy became more difficult to characterize during and after the mortgage foreclose and financial crises because of a shift to a new credit policy focused on private sector credit and that relies on traditional commercial banking strategies. The new credit policy broke the tight link that had existed between Fed credit and its effective monetary base, the monetary base that affects monetary aggregates. The Fed has adopted an exit strategy, but the discretionary powers that it followed remain in place as does a mistaken policy on the payment of interest on excess reserves.

We investigate an asset pricing model with preferences cycling between high risk aversion and low EIS in fall/winter and the reverse in spring/summer. Calibrating to consumption data and allowing plausible preference parameter values, we produce returns that match observed equity and Treasury returns across the seasons: risky returns are higher and risk-free returns are lower or stable in fall/winter, and they reverse in spring/summer. Further, risky returns vary more than risk-free returns. A novel finding is that both EIS and risk aversion must vary seasonally to match observed returns. Further, the degree of necessary seasonal change in EIS is small. (JEL E44, G11, G12)

This paper contributes to the empirical literature on risk shifting. It proposes a method to find out whether risk shifting is present in the banking industry and, if so, what type. The type of risk shifting depends on the group of debt holders to whom risk is shifted. We apply this method to the US banking sector in 1998–2011. To study the relationship between risk shifting and the 2008 crisis, the sample is also split into pre-crisis, crisis, and post-crisis periods. Our results suggest that the same type of risk shifting is present in the entire sample and in the pre-crisis and crisis subsamples. We find no evidence of risk shifting after the crisis. Furthermore, holding capital buffers seems to disincentivize risk shifting. This finding appears to provide support for the conservative buffer included in Basel III.

Controlling for unobserved heterogeneity (or "common errors"), such as industry-specific shocks, is a fundamental challenge in empirical research. This paper discusses the limitations of two approaches widely used in corporate finance and asset pricing research: demeaning the dependent variable with respect to the group (e.g., "industry-adjusting") and adding the mean of the group's dependent variable as a control. We show that these methods produce inconsistent estimates and can distort inference. In contrast, the fixed effects estimator is consistent and should be used instead. We also explain how to estimate the fixed effects model when traditional methods are computationally infeasible.

We model the expected support of banks with credit ratings from Moody's and Fitch, taking explicitly into account the capacity and willingness of governments to provide support in case of need, as well as their concerns about moral hazard (i.e., that the expected support may induce banks to assume bigger risks). Our results suggest that moral hazard concerns are relatively weak. In addition, a substantial part of the expected support can be attributed to the quality of a country's institutions. These findings have important implications for the dynamics of banking crises, the value of the ‘fair’ insurance premium banks might be called upon to pay for the expected support, as well as for ways to reduce the resulting negative externalities.

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Prior studies attribute the turn-of-the-year effect whereby small capitalization stocks earn unusually high returns in early January to tax-loss-selling by individual investors and window-dressing by institutional investors. My results suggest that a significant portion of the effect on turn-of-the-year returns that prior studies attribute to window-dressing is actually attributable to tax-loss-selling by institutional investors. Among small capitalization stocks, I find that institutional investors with strong tax incentives and weak window-dressing incentives realize significantly more losses in the fourth quarter than in the first three quarters of the calendar year, and that their fourth quarter realized losses have a significant impact on turn-of-the-year returns. A one percentage point change in these institutional investors' fourth quarter realized losses scaled by a firm's market capitalization results in an increase of 47 basis points in the firm's average daily return over the first three trading days of January, which represents a 46 percent change for the mean firm.

This paper analyses the network structure of the credit default swap (CDS) market and its determinants, using a unique dataset of bilateral notional exposures on 642 financial and sovereign reference entities. We find that the CDS network is centred around 14 major dealers, exhibits a “small world” structure and a scale-free degree distribution. A large share of investors are net CDS buyers, implying that total credit risk exposure is fairly concentrated. Consistent with the theoretical literature on the use of CDS, the debt volume outstanding and its structure (maturity and collateralization), the CDS spread volatility and market beta, as well as the type (sovereign/financial) of the underlying bond are statistically significantly related—with expected signs—to structural characteristics of the CDS market.