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Abstract:
Using a large sample of hedge fund manager characteristics, we provide one of the first comprehensive studies on the impact of manager characteristics, such as education and career concern, on hedge fund performances. We document differential ability among hedge fund managers in generating risk-adjusted returns and flow-chasing-return behaviors among hedge fund investors. In particular, we find that managers from higher-SAT undergraduate institutes tend to have higher raw and risk-adjusted returns, more inflows, and take less risks. Our results provide supporting evidence to some of the assumptions and implications of the rational theory of active portfolio management of Berk and Green (2004). However, in contrast to the results for mutual funds, we find a rather symmetric relation between hedge fund flows and past performance, and that hedge fund flows do not have a significant negative impact on future performance.

hedge fund performance, manager characteristics, hedge fund flows

Abstract:
We develop a simply theory on interest rate swaps based on the difference between bank loans and public debts. While restrictive covenants of bank loans help reduce agency costs, banks also have natural disadvantages in bearing interest rate risk due to their floating liabilities. A firm that wants a fixed-rate loan can borrow a floating-rate loan from a bank and enter an interest rate swap to hedge the interest rate risk. Consistent with our theory, we find empirically that fixed-rate swap payers generally have lower credit ratings, higher leverage ratios, higher percentages of long-term floating-rate loans, and are more likely to use bank loans than floating-rate swap payers.

Interest rate swap, Agency costs, Information Asymmetry, Bank loan, Comparative advantage

Abstract:
We develop a simply theory on interest rate swaps based on the difference between bank loans and public debts. While restrictive covenants of bank loans help reduce agency costs, banks also have natural disadvantages in bearing interest rate risk due to their floating liabilities. A firm that wants a fixed-rate loan can borrow a floating-rate loan from a bank and enter an interest rate swap to hedge the interest rate risk. Consistent with our theory, we find empirically that fixed-rate swap payers generally have lower credit ratings, higher leverage ratios, higher percentages of long-term floating-rate loans, and are more likely to use bank loans than floating-rate swap payers.

Interest rate swap, Agency costs, Information Asymmetry, Bank loan, Comparative advantage

Abstract:
With the adoption of Regulation Fair Disclosure (Reg FD), market behavior around earnings releases displays no significant change in return volatility (after controlling for decimalization of stock trading) but significant increases in trading volume due to difference in opinion. Analyst forecast dispersion increases, and increases in other measures of disagreement and difference of opinion suggest greater difficulty in forming forecasts beyond the current quarter. Corporations increase the quantity of voluntary disclosures, but only for current quarter earnings. Thus, Reg FD seems to increase the quantity of information available to the public while demanding more effort and struggle from investment professionals.

Regulation Fair Disclosure, Reg FD, earnings announcements, market reaction, event study

Abstract:
With the adoption of Regulation Fair Disclosure (Reg FD), market behavior around earnings releases displays no significant change in return volatility (after controlling for decimalization of stock trading) but significant increases in trading volume due to difference in opinion. Analyst forecast dispersion increases, and increases in other measures of disagreement and difference of opinion suggest greater difficulty in forming forecasts beyond the current quarter. Corporations increase the quantity of voluntary disclosures, but only for current quarter earnings. Thus, Reg FD seems to increase the quantity of information available to the public while demanding more effort and struggle from investment professionals.

Regulation Fair Disclosure, Reg FD, earnings announcements, market reaction, event study

Abstract:
We examine the effects of liquidity and information risks on expected returns of U.S. government bonds. Information risk is measured by probability of information-based trading (PIN) derived from the market microstructure model of Easley, Hvidkjaer, and O'Hara (2002). Liquidity risk is captured by sensitivity of individual bond returns to a market-wide liquidity measure along the line of Pastor and Stambaugh (2003). Controlling for systematic risks and bond characteristics, we find that both liquidity and information risks have a significantly positive effect on expected bond returns. Our findings suggest that incorporating microstructure factors into existing term structure models is a promising avenue for improving our understanding of bond price behavior.

Information risk, Liquidity risk, PIN, asset pricing, order imbalance

Abstract:
We show that the well-known model of market survival of Brown, Goetzmann and Ross (1995) fails to explain the "equity premium puzzle." The reasons are (1) the survival bias implied by the model is too small; (2) the model predicts rapidly declining of survival bias in the equity premium over the history of the survived market. We also demonstrate that other survival models are unlikely to succeed either, since to constantly generate high survival bias, the ex ante probability of long-term market survival has to be extremely small which contradicts the history of the world financial markets. Given that no theory in the existing literature predicts high survival bias in the U.S. equity premium, the current concerns for such bias are probably without grounds.

Abstract:
We assess the impact of Regulation Fair Disclosure (Reg FD) by examining market and analyst forecast behavior around earnings releases. After the implementation of Reg FD, stocks experience declines in event period return volatility, increases in event period trading volume due to differential informed judgment or difference in opinions, and increases in pre announcement forecast dispersion. Additional tests suggest increases in disagreement and differences of opinion among analysts after implementation of Reg FD. Thus, Reg FD is significant, though not necessarily beneficial. In particular, the regulation appears to impair the ability of the market to reach consensus.

Regulation FD, Fair Disclosure, earnings announcements, market reaction, event study

Abstract:
We propose two nonparametric specification tests for continuous-time models based on transition density, which unlike the marginal density used in the literature, can capture the full dynamics of a continuous-time process. To improve the finite sample performance of nonparametric methods, we introduce a data transformation and correct the boundary bias of kernel estimators. As a result, our tests are robust to persistent dependence in data and provide reliable inferences for sample sizes often encountered in empirical finance. Simulation studies show that even for data with highly persistent dependence, our tests have reasonable size and good power against a variety of alternatives in finite samples. Besides one-factor diffusion models, our tests can also be applied to a broad class of dynamic models, including discrete-time dynamic models, time-inhomogeneous diffusion models, stochastic volatility models, jump-diffusion models,and multi-factor diffusion models. When applied to Eurodollar interest rates, our tests overwhelmingly reject a variety of existing popular one-factor diffusion models. We find that introducing a nonlinear drift does not significantly improve the goodness of fit, and the main reason for the rejection of one-factor diffusion models is the violation of the Markov assumption. Some existing popular non-Markovian models with GARCH, regime switching and jumps significantly outperform one-factor diffusion models, but they are still far from being adequate to fully capture the interest rate dynamics. Our study shows that contrary to the general perception in the literature, nonparametric methods can be a reliable and powerful tool for analyzing financial data.

Boundary bias, Continuous-time model, Generalized residual, Hellinger metric, Kernel method, Parameter estimation uncertainty, Probability integral transform, Quadratic form, Short-term interest rate, Transition density

Abstract:
The current large empirical literature on interest rate modeling typically focuses on the in-sample performance and ignores the out-of-sample performance of existing models. We fill the gap in this literature by providing probably the first comprehensive empirical study (to our knowledge) of the out-of-sample performance of a wide variety of popular interest rate models in forecasting the probability density of future interest rates. Out-of-sample density forecasts are important for at least two reasons: (i) out-of-sample analysis helps minimize the data snooping bias due to excessive searching for more complicated models using the same or similar data sets; (ii) the conditional probability density, which completely characterizes the dynamics of an interest rate model, is an essential input to many important financial applications such as pricing fixed-income securities and interest rate risk management. Using a rigorous econometric procedure developed in Hong (2000) for density forecast evaluation, we examine the out-of-sample performance of single-factor diffusion, GARCH, regime-switching and jump-diffusion models. Among other things we focus on the relative importance of (i) linear versus nonlinear drift specification in modeling conditional mean, (ii) level versus GARCH effect in modeling conditional variance, and (iii) regime-switching versus jumps in capturing the tail distribution of interest rate data. Consistent with the in-sample findings in the literature, we find that for out-of-sample density forecasts, it is important to model mean-reversion, conditional heteroskedasticity, and excess kurtosis or heavy-tails of interest rates. Contrary to the in-sample findings, we find that models that perform well in out-of-sample forecasts are those with simpler specifications for all the above three important features. Our results point out the potential risk of overparameterization in the existing interest rate models and show that simplicity is indeed a virtue in out-of-sample applications.

Density forecasts, Diffusion model, GARCH, Generalized spectrum, Jumps, Out-of-sample forecasts, Nonlinear time series, Parameter estimation uncertainty, Regime-Switching, Short-term interest rate, Transition density

Abstract:
Numerous studies have shown that the simple random walk model outperforms all structural and time series models in forecasting the conditional mean of exchange rate changes. However, in many important applications, such as risk management, forecasts of the probability distribution of exchange rate changes are often needed. In this paper, we develop a nonparametric portmanteau evaluation procedure for out-of-sample density forecast and provide a comprehensive empirical study on the out-of-sample performance of a wide variety of time series models in forecasting the intraday probability density of two major exchange rates - Euro/Dollar and Yen/Dollar. We find that some nonlinear time series models provide better density forecast than the simple random walk model, although they underperform in forecasting the conditional mean. For Euro/Dollar, it is important to model heavy tails through a Student-t innovation and asymmetric timevarying conditional volatility through a regime-switching GARCH model for both insample and out-of-sample performance; modeling conditional mean and serial dependence in higher order moments (e.g., conditional skewness), although important for in-sample performance, does not help out-of-sample density forecast. For Yen/Dollar, it is also important to model heavy tails and volatility clustering, and the best density forecast model is a RiskMetrics model with a Student-t innovation. As a simple application, we find that the models that provide good density forecast generally provide good forecast of Value-at-Risk.

density forecasts, eurodollar, GARCH, intraday exchange rate, jumps, maximum likelihood estimation, nonlinear time series, out-of-sample forecasts, regime-switching

Abstract:
We develop a reduced-form approach for valuing callable corporate bonds by characterizing the call probability via an intensity process. Asymmetric information and market frictions justify the existence of a call-arrival intensity from the market's perspective. Our approach extends the reduced-form model of Duffie and Singleton (1999) for defaultable bonds to callable bonds and can capture some important differences between call and default decisions.We also provide one of the first comprehensive empirical analyses of callable bonds using both our approach and the traditional approach of valuing callable bonds as American options. Empirical results show that the reduced-form model fits callable bond price data well and outperforms the traditional approach in both in-sample and out-of-sample applications.

Callable bond, reduced-form

Abstract:
The numerous empirical studies on affine term structure models have primarily focused on the in-sample fit of historical bond yields and ignored the out-of-sample forecast of future bond yields. Based on an omnibus nonparametric procedure for density forecast evaluation developed in this paper, we provide probably the first comprehensive empirical analysis of the out-of-sample performance of affine models in forecasting the joint conditional probability density of bond yields. We show that although it is difficult to forecast the conditional mean of bond yields, some affine models have good forecasts of the joint conditional density of bond yields and they significantly outperform the simple random walk models in density forecast. Our analysis demonstrates the great potential of affine models for financial risk management in fixed-income markets.

Density forecast, Affine term structure models, Probability integral transform, Financial risk management, Value-at-Risk, Fixed-income portfolio management

Abstract:
We characterize the dynamics of the U.S. short-term interest rate using a Markov regime switching model. Using a test developed by Garcia (1998), we show that there are two regimes in the data: In one regime, the short rate behaves like a random walk with low volatility; in another regime, it exhibits strong mean reversion and high volatility. In our model, the sensitivity of interest rate volatility to the level of interest rate is much lower than what is commonly found in the literature. We also show that the findings of nonlinear drift in Ait-Sahalia (1996b) and Stanton (1997), using nonparametric methods, are consistent with our regime switching model.

Regime Switching, Likelihood Ratio Test, Nonlinear Drift

Abstract:
Previous authors have raised the concern that there could be serious survival bias in the observed U.S. equity premium. Contrary to conventional wisdom, we argue that the survival bias in the U.S. data is unlikely to be significant. To reach this conclusion, we introduce a general framework for modeling survival and derive a mathematical relationship between the ex ante survival probability and the average survival bias. This relationship reveals the fundamental difficulty facing the survival argument: High survival bias requires an ex ante probability of market failure, which seems unrealistically high given the history of world financial markets.

survival bias, equity premium

Abstract:
Previous authors have raised the concern that there could be serious survival bias in the observed U.S. equity premium. Contrary to conventional wisdom, we argue that the survival bias in the U.S. data is unlikely to be significant. To reach this conclusion, we introduce a general framework for modeling survival and derive a mathematical relationship between the ex ante survival probability and the average survival bias. This relationship reveals the fundamental difficulty facing the survival argument: High survival bias requires an ex ante probability of market failure, which seems unrealistically high given the history of world financial markets.

survival bias, equity premium

Abstract:
We develop a reduced-form approach for valuing callable corporate bonds by characterizing the call probability via an intensity process. Asymmetric information and market frictions justify the existence of a call-arrival intensity from the market's perspective. Our approach both extends the reduced-form model of Duffie and Singleton (1999) for defaultable bonds to callable bonds and captures some important differences between call and default decisions. We also provide one of the first comprehensive empirical analyses of callable bonds using both our model and the more traditional American option approach for valuing callable bonds. Our empirical results show that the reduced-form model fits callable bond prices well and that it outperforms the traditional approach both in- and out-of-sample.

Abstract:
We extend the maximum likelihood estimation method of Ait-Sahalia (2002) for time-homogeneous diffusions to time-inhomogeneous ones. We derive a closed-form approximation of the likelihood function for discretely sampled time-inhomogeneous diffusions, and prove that this approximation converges to the true likelihood function and yields consistent parameter estimates. Monte Carlo simulations for several financial models reveal that our method largely outperforms other widely used numerical procedures in approximating the likelihood function. Furthermore, parameter estimates produced by our method are very close to the parameter estimates obtained by maximizing the true likelihood function, and superior to estimates obtained from the Euler approximation.

Maximum likelihood estimation, time-inhomogeneous diffusion, transition density, Hermite expansion

Abstract:
We extend the maximum likelihood estimation method of Ait-Sahalia (2002) for time-homogeneous diffusions to time-inhomogeneous ones. We derive a closed-form approximation of the likelihood function for discretely sampled time-inhomogeneous diffusions, and prove that this approximation converges to the true likelihood function and yields consistent parameter estimates. Monte Carlo simulations for several financial models reveal that our method largely outperforms other widely used numerical procedures in approximating the likelihood function. Furthermore, parameter estimates produced by our method are very close to the parameter estimates obtained by maximizing the true likelihood function, and superior to estimates obtained from the Euler approximation.

Maximum likelihood estimation, time-inhomogeneous diffusion, transition density, Hermite expansion

Abstract:
Using a large sample of hedge fund manager characteristics, we provide one of the first comprehensive studies on the impact of manager characteristics, such as education and career concern, on hedge fund performances. We document differential ability among hedge fund managers in generating risk-adjusted returns and flow-chasing-return behaviors among hedge fund investors. In particular, we find that managers from higher-SAT undergraduate institutes tend to have higher raw and risk-adjusted returns, more inflows, and take less risks. Our results provide supporting evidence to some of the assumptions and implications of the rational theory of active portfolio management of Berk and Green (2004). However, in contrast to the results for mutual funds, we find a rather symmetric relation between hedge fund flows and past performance, and that hedge fund flows do not have a significant negative impact on future performance.

hedge fund performance, manager characteristics, hedge fund flows

Abstract:
Modeling the joint term structure of interest rates in the United States and the European Union, the two largest economies in the world, is extremely important in international finance. In this paper, we provide both theoretical and empirical analysis of multi-factor joint affine term structure models for dollar and euro interest rates. In particular, we provide a systematic classification of multi-factor joint affine term structure models similar to that of Dai and Singleton (2000). A principal component analysis of daily dollar and euro interest rates reveals four factors in the data. We estimate four-factor joint affine term structure models using the approximate maximum likelihood method of Ait-Sahalia (2002, 2007) and compare the in-sample and out-of-sample performances of these models using some of the latest nonparametric methods. We find that a new four-factor model with two common and two local factors captures the joint term structure dynamics in the U.S. and the E.U. reasonably well.

Affine term structure models, International term structure models, Approximate Maximum Likelihood, LIBOR, Euribor, Specification analysis of term structure of interest rates, Out-of-sample model evaluation.

Abstract:
We develop a systematic approach for evaluating asset pricing models based on the second Hansen-Jagannathan distance (HJD), which requires a good asset pricing model to not only have small pricing errors but also be arbitrage free. Our approach includes a specification test and a sequence of model selection procedures for non-nested, overlapping, and nested models. While the former can test whether a given model is correctly specified, the latter can compare relative performances of potentially misspecified models. Our methods are more powerful than existing ones in (i) detecting misspecified models that have small pricing errors but are not arbitrage free; and (ii) differentiating models that have similar pricing errors of a given set of test assets. Simulation studies show that our tests have reasonably good finite sample performances for typical sample sizes considered in the literature. Using the Fama-French size and book-to-market portfolios or hedge fund portfolios that exhibit option-like returns, we reach dramatically different conclusions on model performances based on our approach and existing methods.

Stochastic Discount Factor, Specification Tests, Model Selection, HJ distance, Arbitrage

Abstract:
We characterize the dynamics of the US short-term interest rate using a Markov regime-switching model. Using a test developed by Garcia, we show that there are two regimes in the data: In one regime, the short rate behaves like a random walk with low volatility; in another regime, it exhibits strong mean reversion and high volatility. In our model, the sensitivity of interest rate volatility to the level of interest rate is much lower than what is commonly found in the literature. We also show that the findings of nonlinear drift in Aït-Sahalia and Stanton, using nonparametric methods, are consistent with our regime-switching model.

Abstract:
We have developed Bayesian Markov chain Monte Carlo (MCMC) methods for inferences of continuous-time models with stochastic volatility and infinite-activity Lévy jumps using discretely sampled data. Simulation studies show that (i) our methods provide accurate joint identification of diffusion, stochastic volatility, and Lévy jumps, and (ii) the affine jump-diffusion (AJD) models fail to adequately approximate the behavior of infinite-activity jumps. In particular, the AJD models fail to capture the “infinitely many” small Lévy jumps, which are too big for Brownian motion to model and too small for compound Poisson process to capture. Empirical studies show that infinite-activity Lévy jumps are essential for modeling the S&P 500 index returns.

G12, C11, C15, C32

Abstract:
In this paper, I study the valuation of interest rate and currency swaps with default risk under the contingent claim analysis framework. I demonstrate that the traditional approach of pricing swap contracts as exchanges of loans underestimates the value of such contracts to the counterparty with higher credit rating and exaggerates the credit spread required to guard against default risk. Numerical simulations show that the swap rate is not sensitive to counterparty credit rating: for a ten year interest rate swap, a one hundred basis point increase in counterparty bond yield spread results in only about one basis point increase in the swap rate.

credit risk, interest rate swaps, currency swaps, contingent claim analysis

Abstract:
Several recent studies present evidence of investor misreaction in the options market. While the interpretation of their results is still controversial, we note that the important question of economic significance has not been fully addressed. We fill in this gap by formulating regression-based tests to identify misreaction and its duration and constructing trading strategies to exploit the empirical patterns of misreaction. Using regular S&P 500 index options and long-dated S&P 500 LEAPS, we find an underreaction that on average dissipates over the course of three trading days and an increasing misreaction that peaks after four consecutive daily variance shocks of the same sign. Option trading strategies based on these findings produce economically significant abnormal returns in the range of one to three percent per day. However, they are not profitable in the presence of transaction costs.