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.

When a firm writes incomplete debt contracts, its limited ability to commit to not strategically default and renegotiate its debt requires the firm to pay higher yields to its creditors. Hedged by credit derivatives, creditors have stronger bargaining power in the case of debt renegotiation, which ex-ante demotivates the firm to default strategically. In this paper, I aim to investigate theoretically and empirically whether credit derivatives could help reduce the cost of debt contracting stemming from the possibility of strategic default. I find that firms with a priori high strategic default incentives experience a relatively large reduction in their corporate bond spreads after the introduction of credit default swaps (CDS) written on their debt. This result is robust to controlling for the endogeneity of CDS introduction. My finding is consistent with the presence of CDS reducing the strategic default-related cost of corporate debt, suggesting the beneficial role of credit derivatives as a commitment device for the borrower to repay the lender.

We employ Amortizing Participation Mortgage (APM) to offer a novel ex post renegotiation method of a foreclosure. APM belongs to the family of home loan credit facilities advocated in the Dodd–Frank Wall Street Reform and Consumer Protection Act 2010. In our framework, APMs reduce the endemic agency costs of debt by improving affordability. These benefits increase the demand for real estate in bust times and reduce fragility of the financial system thereby preventing foreclosures. We evaluate APMs in a stochastic control framework and provide solutions for an optimal amortization schedule. We generalize our approach to partially amortizing and commercial mortgages which encompass balloon payments. Finally, we provide concrete numerical examples of home loan modifications. We also offer detailed sensitivity analysis to market parameters such as house price volatility and interest rates.

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 paper investigates the time-series predictability of commodity futures excess returns from factor models that exploit two risk factors – the equally weighted average excess return on long positions in a universe of futures contracts and the return difference between the high- and low-basis portfolios. Adopting a standard set of statistical evaluation metrics, we find weak evidence that the factor models provide out-of-sample forecasts of monthly excess returns significantly better than the benchmark of random walk with drift model. We also show, in a dynamic asset allocation environment, that the information contained in the commodity-based risk factors does not generate systematic economic value to risk-averse investors pursuing a commodity stand-alone strategy or a diversification strategy.

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.

Within the new Basel regulatory framework for market risks, non-securitization credit positions in the trading book are subject to a separate default risk charge (formally incremental default risk charge). Banks using the internal model approach are required to use a two-factor model and a 99.9% VaR capital charge. This model prescription is intended to reduce risk-weighted asset variability, a known feature of internal models, and improve their comparability among financial institutions. In this paper, we analyze the theoretical foundations and relevance of these proposals. We investigate the practical implications of the two-factor and correlation calibration constraints through numerical applications. We introduce the Hoeffding decomposition of the aggregate unconditional loss to provide a systematic-idiosyncratic representation. In particular, we examine the impacts of a J-factor correlation structure on risk measures and risk factor contributions for long-only and long-short credit-sensitive portfolios.

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.