Knowledge that Transforms

Implied volatility (IV) reflects both expected empirical volatility and also risk premia. Stochastic variation in either creates unhedged risk in a delta hedged options position. We develop EGARCH/DCC models for the dynamics of volatilities and correlations among daily IVs from options on twenty-eight large cap stocks. The data strongly support a general correlation structure and also a one-factor model with the VIX index as the common factor. Using IVs from stocks that are either highly correlated with the target stock’s IV or in the same industry together with the VIX can significantly improve hedging of individual IV changes.

We propose a model of limited arbitrage based on financial relationships. Financially constrained arbitrageurs may choose to seek additional financing from banks that have the technology to profit from the strategies themselves. A holdup problem arises because banks cannot commit to providing capital. To minimize competition, arbitrageurs will choose to stay constrained and underinvest in the arbitrage unless banks have sufficient reputational capital. This problem arises when mispricing is largest. More competition among financiers, higher arbitrageur wealth, and allowing for explicit contracts can worsen the holdup problem. When arbitrage is risky, financial relationships are more valuable, mitigating the problem.

We investigate the corporate social responsibility of banks. Lenders offer loans to standard and motivated borrowers who undertake either standard or ethical projects. Standard banks have no restriction on the types of projects for which they can provide a loan. Ethical banks, instead, commit to financing only ethical projects, which have social profitability but lower expected revenues. Motivated borrowers are keen to invest in ethical projects and to deal with ethical banks. When they are active, ethical banks increase social welfare because the matching of ethical lenders with motivated borrowers reduces the frictions caused by the agency issue.

Previous studies have identified several variables that would have predicted future stock returns, though other studies suggest these results may be due to data snooping. To guard against data snooping, researchers have suggested use of Bayesian model averaging (BMA) to account for the uncertainty about prediction models. In common with other researchers, I find evidence of predictability during time periods when a hypothetical investor uses BMA with no restrictions on what variables may be included in the model. However, when the hypothetical investor is limited to using only variables whose predictive ability would have been known at the time of the forecast, predictability disappears. Moreover, predictability also disappears when data are updated through 2010, even without constraints on variable use. The results cast doubt on whether stock returns were ever predictable in real time and also suggest that returns may no longer be predictable even if real-time constraints are removed.

This article evaluates out-of-sample portfolio performance for a real-time investor who can exploit time variation in the conditional mean and volatility of stock returns in optimizing a multiperiod portfolio choice problem. With the presence of parameter uncertainty, our out-of-sample analysis shows that ignoring time variation in the first two return moments leads to significant utility costs of at least 1.97% of annualized certainty equivalent return. Accounting for the time-varying risk premium plays a more important role than considering time-varying volatility in improving portfolio performance. Interestingly, behaving myopically or ignoring the hedge against changes in future investment opportunities can lead to small out-of-sample utility losses or even utility gains.

Insurance contracts are often complex and difficult to verify outside the insurance relation. We show that standard one-period insurance policies with an upper limit and a deductible are the optimal incentive-compatible contracts in a competitive market with repeated interaction. Optimal group insurance policies involve a joint upper limit and individual deductibles; insurance brokers can play a role implementing such contracts for their clients. Our model provides new insights and predictions about the determinants of insurance.

We exploit the exogenous shock to the Brazilian banking system caused by the international turmoil of 2008 and find evidence that the run to systemically important banks is better explained by the perception of a too-big-to-fail policy than by bank fundamentals. We infer that the extra inflow of deposits received by systemically important banks during crises gives them an important competitive advantage. Our analysis also indicates that a bank’s share of funding from institutional investors affects the nonfinancial firms’ and institutional investors’ decision to run.

We develop a new family of estimators of the covariance matrix that relies solely on forward-looking information. It uses only current prices of plain-vanilla options. In an out-of-sample study, we show that a minimum variance strategy based on these fully-implied estimators outperforms several benchmark strategies, including various strategies based on historical estimates, index investing, and 1/N investing. The outperformance originates in crisis periods when information flow and information asymmetry are high. Although the historical benchmark strategies improve when more recent data are used, they never outperform fully-implied strategies. Thus, our results suggest that investors are better off relying on forward-looking information.

We study the joint responses of commodity future prices and positions of various trader groups to changes of the CBOE Volatility Index (VIX) before and after the recent financial crisis. Financial traders reduced their net long positions during the crisis in response to market distress, whereas hedgers facilitated this by reducing their net short positions as prices fell. This “convective risk flow” induced by the greater distress of financial institutions led to a change in the allocation of risk with hedgers holding more risk than they did previously. The presence of such a risk flow confirms the market impact of financial traders conditional on trades they initiate.

We show that after the start of the euro area sovereign debt crisis, lending by non-GIIPS European banks with sizeable holdings of GIIPS sovereign bonds declined relative to nonexposed banks. This effect is not driven by changes in borrower demand or by other shocks to banks’ balance sheets. We also find that affected banks withdrew from all foreign markets with the exception of the USA, suggesting an increase in home bias. The slowdown in lending continued after ECB’s LTRO in December 2011, but it was lower for banks that increased their risky exposures in the early stages of the crisis.