This paper introduces and analyzes a new form of contingent convertible: a call option enhanced reverse convertible (COERC). If an issuing bank’s market value of capital breaches a trigger, COERCs convert to many new equity shares that would heavily dilute existing shareholders, except that shareholders have the option to purchase these shares at the bond’s par value. COERCs have low risk: They are almost always fully repaid in cash. Yet, they reduce government bailouts by replenishing a bank’s capital. COERCs’ design also avoids problems with market-value triggers, such as manipulation or panic, while reducing moral hazard and debt overhang.

In this paper, we develop a state-dependent sensitivity value-at-risk (SDSVaR) approach that enables us to quantify the direction, size, and duration of risk spillovers among financial institutions as a function of the state of financial markets (tranquil, normal, and volatile). For four sets of major financial institutions (commercial banks, investment banks, hedge funds, and insurance companies), we show that while small during normal times, equivalent shocks lead to considerable spillover effects in volatile market periods. Commercial banks and, especially, hedge funds appear to play a major role in the transmission of shocks to other financial institutions.

This paper examines the relationship between a firm’s exposure to industry downturns that we call industry risk and bank loan pricing. We measure industry risk based on the relationship between a firm’s stock returns and industry returns conditional on an industry downturn. We find industry risk is significantly related to the recovery rates in bankruptcy and the likelihood of the firm experiencing financial distress when its peers are also in distress. More importantly, we find that the spreads on unsecured bank loans are positively related to industry risk measures. These relationships are stronger for firms with more industry-specific assets.

Recent empirical evidence suggests that the variance risk premium predicts aggregate stock market returns. We demonstrate that statistical finite sample biases cannot “explain” this apparent predictability. Further corroborating the existing evidence of the United States, we show that country-specific regressions for France, Germany, Japan, Switzerland, the Netherlands, Belgium, and the United Kingdom result in quite similar patterns. Defining a “global” variance risk premium, we uncover even stronger predictability and almost identical cross-country patterns through the use of panel regressions.

Many studies have documented that daily realized volatility estimates based on intraday returns provide volatility forecasts that are superior to forecasts constructed from daily returns only. We investigate whether these forecasting improvements translate into economic value added. To do so, we develop a new class of affine discrete-time option valuation models that use daily returns as well as realized volatility. We derive convenient closed-form option valuation formulas, and we assess the option valuation properties using Standard & Poor’s (S&P) 500 return and option data. We find that realized volatility reduces the pricing errors of the benchmark model significantly across moneyness, maturity, and volatility levels.

We study whether asset-class risk dynamics can help explain the predominantly negative stock-bond return relation and movements in the term structure’s slope over 1997–2011. Using option-derived implied volatilities to measure risk, we find i) the negative stock-bond return relation largely disappears when controlling for risk movements, at both monthly and weekly horizons; ii) the partial relation between equity-risk changes and 10-year T-bond excess returns (term-slope movements) is reliably positive (negative); and iii) a stronger link between equity risk and stock returns implies a more negative stock-bond return correlation. Our results suggest a flight-to-quality influence between equity-risk dynamics and longer-term Treasury pricing.

This article investigates the options market around a revision in the financial analysts’ consensus recommendation. The results demonstrate that options investors trade in the correct direction of the upcoming revision approximately 3 days prior to the announcement. We find this behavior in option-implied prices, implied volatilities, and options trading volume. Tests confirm that the options market leads the stock market before the financial analysts’ revision. Moreover, using all firms with outstanding options, an out-of-sample analysis produces a profitable zero-cost trading strategy net of transaction costs based on the relative valuations between the synthetic and the underlying equity security.

We extend the Fama-French (1992) model by considering growth option (as well as distress/leverage) variables in explaining the cross section of stock returns. We find that growth option variables, namely growth in capital investment and yet-unexercised growth options (GO), are significantly and negatively related to stock returns. Investors may be willing to accept lower average returns from growth stocks in exchange for a more favorable (positively skewed) risk-return profile. Book-to-market (BM) ratio seems to proxy for omitted distress/leverage variables. When these are explicitly accounted for, BM is not that significant. Our growth options variables have added explanatory power.

The voluntary nature of hedge fund database reporting creates strategic listing opportunities for hedge funds. However, little is known about how managers list funds across multiple databases or whether investors are fooled by funds’ listing decisions. In this paper, we find that hedge funds strategically list their small, best-performing funds in multiple outlets immediately while preserving the option to list their other funds in additional databases later. We generally find that investors react rationally to these fund listings based on the predictability of performance. Finally, our results lead to specific guidelines on handling backfilled returns to minimize biases.

Numerous hedge funds stop reporting each year to commercial databases, wreaking havoc with analyzing investment strategies that incur the unobserved delisting return. We use estimated portfolio holdings for funds-of-funds to back out estimated hedge-fund delisting returns. For all exiting funds, the estimated mean delisting return is insignificantly different from the average monthly return for live hedge funds. However, funds with poor prior performance and no clearly stated delisting reason had a significantly negative estimated mean delisting return of -5.97%, suggesting that a shock to their returns “tips them over the edge” and leads to delisting.