Knowledge that Transforms

We analyze the risk premia embedded in the S&P 500 spot index and option markets. We use a long time-series of spot prices and a large panel of option prices to jointly estimate the diffusive stock risk premium, the price jump risk premium, the diffusive variance risk premium and the variance jump risk premium. The risk premia are statistically and economically significant and move over time. Investigating the economic drivers of the risk premia, we are able to explain up to 63% of these variations.

The widely-used Baker and Wurgler (2006) sentiment index is strongly correlated with business cycle variables, especially the short interest rate and Lee (2011) liquidity risk factor. The power of the sentiment index to predict cross-sectional stock returns is mainly driven by its information content related to these business cycle variables. About 63% percent of the total variation in the investor sentiment index can be explained by well-known, contemporaneous risk/business cycle variables. We decompose the widely used investor sentiment index into two components: one related to standard risk/business cycle variables and the other unrelated to those variables. We show that the power of the sentiment index to predict cross-sectional stock returns is mainly driven by the risk/business cycle component, while the residual component has little significance in predicting cross-sectional stock returns.

In this study, we forecast the realized volatility of the S&P 500 index using the heterogeneous autoregressive model for realized volatility (HAR-RV) and its various extensions. Our models take into account the time-varying property of the models’ parameters and the volatility of realized volatility. A dynamic model averaging (DMA) approach is used to combine the forecasts of the individual models. Our empirical results suggest that DMA can generate more accurate forecasts than individual model in both statistical and economic senses. Models that use time-varying parameters have greater forecasting accuracy than models that use the constant coefficients. The superiority of time-varying parameter models is also found in volatility density forecasting.

This study investigates whether, how and why industry performance can drive long-term return reversals. Using data from the UK, we find that firms in losing industries significantly outperform those in winning industries over the subsequent five years. These industry reversals remain strong and persistent after controlling for stock momentum, industry momentum, seasonal effects and traditional risk factors. We find a strong influence of past industry performance on stock return reversals. Our results also show that past industry performance is the driving force behind long-term reversals. Specifically, we find that industry components drive stock reversals, while past stock performance does not explain industry reversals. Further analysis suggests that industry reversals are present in both good and bad states of the economy and are stronger in industries with high valuation uncertainty. This implies that industry reversals are more likely to be a result of mispricing.

We estimate the value of the embedded option in U.S. Treasury Inflation-Protected Securities (TIPS). The embedded option value exhibits time variation that is correlated with periods of deflationary expectations. We construct embedded option explanatory variables that are statistically and economically significant for explaining future inflation, even in the presence of traditional inflation variables such as lagged inflation, the gold return, the crude oil return, the VIX return, liquidity, surveys, and the yield spread between nominal Treasuries and TIPS. After conducting robustness tests, we conclude that the TIPS embedded option contains useful information for future inflation.

Although many different aspects of debt structures such as bond covenants and repayment schedules are empirically found to significantly influence values of bonds and equity, many theoretical structural models still oversimplify debt structures and fail to capture phenomena found in financial markets. This paper proposes a carefully designed structural model that faithfully models typical complex debt structures containing multiple bonds with various covenants. For example, the ability for an issuing firm to meet an obligation is modeled to rely on its ability to meet previous repayments, and the default trigger is shaped according to the characteristics of its debt structure such as the amount and schedule of bond repayments. Thus our framework reliably provides theoretical insight and concrete quantitative measurements consistent with extant empirical research such as the shapes of yield spread curves under various firm’s financial statuses, and the impact of payment blockage covenants on newly-issued and other outstanding bonds. We also develop the forest, a novel quantitative method to handle contingent changes in the debt structure due to premature bond redemptions. A forest consists of several trees that capture different debt structures, for instance those before or after a bond redemption. This method can be used to analyze how poison put covenants in the target firm’s bonds influence the bidder’s costs of debt financing for a leveraged buyout, or investigate how the presence of wealth transfer among the remaining claim holders due to a bond redemption influences the firm’s call policy, or further reconcile conflicts among previous empirical studies on call delay phenomena.

This paper examines the determinants and the effects of CEO stock ownership guideline adoption, differentiating Not-meet/Meet adopters – those setting the guideline above/below the CEO’s stock ownership at the time of adoption. While Meet adoption is mainly determined by factors related to stakeholder management, we find that Not-meet adoption is associated with factors related to both incentive alignment and stakeholder management. CEO ownership increases and CEO incentive alignment improves for Not-meet firms. But CEO ownership and incentives are unchanged for Meet firms following guideline adoption. We find no evidence that CEO compensation changes abnormally after adoption. Not-meet firms have larger improvement in operating performance and better stock performance than Meet firms. We provide evidence that the motives and the effects of guideline adoption depend on the level of the ownership restriction relative to the CEO’s ownership at the time of adoption.

This paper identifies the prevailing financial factors that influence credit spread variability and shows how they affected the U.S. business cycle during the 1990–91 and 2001 recessions of the Great Moderation period (1984–2006) and the Great Recession of 2007–09. To do this, we develop and estimate a dynamic general equilibrium model in which financial intermediation and equity assets play a central role. Over the three recession periods, we find that bank market power (sticky rate adjustments and loan rate markups) played a significant role in the credit spread variability that disrupted the U.S. business cycle. Equity prices exacerbate movements in credit spreads through the financial accelerator channel, but cannot be regarded as one of the main driving forces of credit spread variability. Across the three periods, we observe a remarkable decline in the influence of technology and monetary policy shocks. The influence of loan-to-value ratio shocks declined after the 1990–91 recession, while the bank capital requirement shock exacerbated and prolonged credit spread variability over the 2007–09 recession.

European banks became a source of risk to global financial markets during the financial crisis and attention to the European banking sector increased during the sovereign debt crisis. To measure the systemic risk of European banks, we calculate a distress insurance premium (DIP), which integrates the characteristics of bank size, probability of default, and correlation. Based on this measure, the systemic risk of European banks reached its height in late 2011 around €500 billion. We find that this was largely due to sovereign default risk. The DIP methodology is also used to measure the systemic contribution of individual banks. This approach identifies the large systemically important European banks, but Italian and Spanish banks as a group notably increased in systemic importance during the sample period. Bank-specific fundamentals like capital-asset ratios predict the one-year-ahead systemic risk contributions.

A country’s economic growth exhibits a significant response to sovereign rating changes: a one-notch upgrade (downgrade) causes an increase (decline) of about 0.6% (0.3%) in re-rated countries’ five-year average annual growth rates. The results hold after accounting for other determinants of economic growth and potential endogeneity problems, and are robust to the use of quarterly data. Changes in country rating affect economic growth via the interest-rate and capital-flow channels: narrower sovereign bond yield spreads and increased capital inflows are associated with upgrades, which stimulate re-rated countries’ economic performance, and the converse holds for downgrades.