THE FAMA‐FRENCH MODEL,LEVERAGE, AND THE MODIGLIANI‐MILLER PROPOSITIONS

For a cost‐of‐equity model to conform to the Modigliani‐Miller cost‐of‐capital propositions, any sensitivity coefficients in the model must be related to the firm's leverage. In this paper I apply these principles to the Fama‐French model for the cost of equity and develop the relation between its sensitivity coefficients and firm leverage. I then examine an empirical process developed by Fama and French (1997) to model the evolution through time of their sensitivity coefficients and show that this empirical process is inconsistent with the Modigliani‐Miller propositions. Separable functions are proposed for these sensitivity coefficients that are consistent with the Modigliani‐Miller propositions.     

ROBUSTNESS OF THE HEADQUARTERS‐CITY EFFECT ON STOCK RETURNS

Recent studies report that U.S. firms headquartered near each other experience positive comovement in their stock returns, a finding suggestive of local biases in equity trading activity. We investigate the robustness of these findings and find that including additional pricing factors in models for monthly stock returns materially reduces the magnitude of the headquarters‐city effect in stock returns. Additionally, we find that an implicit null hypothesis of zero local return comovement is inappropriate as there is positive comovement between a stock's return and returns on portfolios of stocks from nonheadquarters cities, on average. Nevertheless, results benchmarked against estimates based on resampling methods indicate a significant and robust headquarters‐city effect in stock returns.     

OPTIMAL PORTFOLIO SELECTION WITH A SHORTFALL PROBABILITY CONSTRAINT: EVIDENCE FROM ALTERNATIVE DISTRIBUTION FUNCTIONS

We propose a new approach to optimal portfolio selection in a downside risk framework that allocates assets by maximizing expected return subject to a shortfall probability constraint, reflecting the typical desire of a risk-averse investor to limit the maximum likely loss. Our empirical results indicate that the loss-averse portfolio outperforms the widely used mean-variance approach based on the cumulative cash values, geometric mean returns, and average risk-adjusted returns. We also evaluate the relative performance of the loss-averse portfolio with normal, symmetric thin-tailed, symmetric fat-tailed, and skewed fat-tailed return distributions in terms of average return, risk, and average risk-adjusted return.     

FORECASTING STOCK INDEX VOLATILITY: COMPARING IMPLIED VOLATILITY AND THE INTRADAY HIGH–LOW PRICE RANGE

The intraday high–low price range offers volatility forecasts similarly efficient to high‐quality implied volatility indexes published by the Chicago Board Options Exchange (CBOE) for four stock market indexes: S&P 500, S&P 100, NASDAQ 100, and Dow Jones Industrials. Examination of in‐sample and out‐of‐sample volatility forecasts reveals that neither implied volatility nor intraday high–low range volatility consistently outperforms the other.     

LIQUIDITY AND ASSET PRICING UNDER THE THREE‐MOMENT CAPM PARADIGM

We examine whether the use of the three‐moment capital asset pricing model can account for liquidity risk. We also make a comparative analysis of a four‐factor model based on Fama–French and Pástor–Stambaugh factors versus a model based solely on stock characteristics. Our findings suggest that neither of the models captures the liquidity premium nor do stock characteristics serve as proxies for liquidity. We also find that sensitivities of stock return to fluctuations in market liquidity do not subsume the effect of characteristic liquidity. Furthermore, our empirical findings are robust to differences in market microstructure or trading protocols between NYSE/AMEX and NASDAQ.     

EXPECTED VOLATILITY,UNEXPECTED VOLATILITY,AND THE CROSS‐SECTION OF STOCK RETURNS

The existing literature finds conflicting results on the cross‐sectional relation between expected returns and idiosyncratic volatility. We contend that at the firm level, the sample correlation between unexpected returns and expected idiosyncratic volatility can cloud the true relation between the expected return and expected idiosyncratic volatility. We show strong evidence that unexpected idiosyncratic volatility is positively related to unexpected returns. Using unexpected idiosyncratic volatility to control for unexpected returns, we find expected idiosyncratic volatility to be significantly and positively related to expected returns. This result holds after controlling for various firm characteristics, and it is robust across different sample periods.     

VALUATION OF EVENT‐CONTINGENT OPTIONS

I study a new class of investment options, event‐contingent options. These are options to invest and divest in projects that are dependent on other projects of the same firm or that are conditioned by projects of other firms in its value chain. I construct payoff functions and derive closed‐form solutions for the value of options to invest contingent on investment (OICI), options to invest contingent on divestment (OICD), options to divest contingent on divestment (ODCD), and options to divest contingent on investment (ODCI). I also derive analytical comparative statics for these option valuation equations and examine their implications on the firm's wealth. I offer examples of event‐contingent options in a global context.     

THE IMPORTANCE OF LIQUIDITY AS A FACTOR IN ASSET PRICING

We employ the Fama‐French time‐series regression approach to examine liquidity as a risk factor affecting stock returns. Prior studies establish liquidity as an important consideration in investment decisions. Here, liquidity is found to be an important factor affecting portfolio returns, even after the effects of market, size, book‐to‐market equity, and momentum are considered. Nonzero intercepts remain, however, indicating continued missing risk factors.     

DETERMINANTS OF CAPITAL STRUCTURE IN BUSINESS START‐UPS: THE ROLE OF NONFINANCIAL STAKEHOLDER RELATIONSHIP COSTS

According to the finance literature, nonfinancial stakeholders (NFS), such as customers, suppliers, and employees, take into account their expected liquidation costs when dealing with a firm. In this framework, firms can influence their probability of liquidation by choosing an appropriate capital structure. Also, the literature suggests NFS bargaining power may affect firm financing decisions. In the current article we investigate these ideas for initial financing decisions by business start‐ups, where ex ante failure risk is high and NFS must decide whether to make relationship‐specific investments. We find that start‐ups imposing larger costs on their NFS following liquidation significantly reduce leverage. This effect is strengthened when suppliers have greater bargaining power. We also document a marginally negative effect of NFS liquidation costs on the proportion of bank loans. Finally, business start‐ups rely less on bank loans when customers and suppliers are in a powerful bargaining position.     

DYNAMIC HEDGE FUND STYLE ANALYSIS WITH ERRORS‐IN‐VARIABLES

We revisit the traditional return‐based style analysis in the presence of time‐varying exposures and errors‐in‐variables (EIV). We apply a benchmark selection algorithm using the Kalman filter and compute the estimated EIV of the selected benchmarks. We adjust them by subtracting their EIV from the initial return series to obtain an estimate of the true uncontaminated benchmarks. Finally, we run the Kalman filter on these adjusted regressors. Analyzing EDHEC alternative index styles, we show that this technique improves the factor loadings and allows more precise identification of the return sources of the considered hedge fund strategy.     

PRESIDENTIAL ELECTION UNCERTAINTY AND COMMON STOCK RETURNS IN THE UNITED STATES

There is substantial evidence on the influence of political outcomes on the business cycle and stock market. We further hypothesize that uncertainty about the outcome of a U.S. presidential election should be reflected in pre‐election common stock returns. Prior research pools returns based on the party of the winning candidate, assuming that the outcome of the election is known a priori. We use candidate preference (i.e., polling) data to construct a measure of election uncertainty. We find that if the election does not have a candidate with a dominant lead, stock market volatility (risk) and average returns rise.     

On the robustness of higher-moment factors in explaining average expected returns: Evidence from Australia

This study tests the importance of systematic skewness and systematic kurtosis of Australian stock returns in the spirit of the higher-moment asset pricing model. We apply the Dagenais and Dagenais (1997) higher-moment estimators to correct for the errors-in-variables (EIVs) problems commonly found in the Fama and MacBeth (1973) two-pass regression methodology. After correcting for the EIVs problems, the two higher-moment factors, especially systematic skewness, are important in pricing Australian stocks. Systematic kurtosis appears to replace beta which plays a diminished role in the heavy-tailed return distribution.     

THE ECONOMIC SIGNIFICANCE OF CONDITIONING INFORMATION ON PORTFOLIO EFFICIENCY IN THE PRESENCE OF COSTLY SHORT‐SELLING

The economic significance of conditioning information in the presence of costly short‐selling is investigated. Using a compact testing framework, results demonstrate that fixed‐weight stock‐bond portfolios appear inefficient with respect to stock‐bond portfolios with weights determined by extant predictors. However, this result is highly dependent on ex ante knowledge of the predictor set and the ability to short‐sell at low cost. In the absence of such conditions, fixed‐weight stock‐bond portfolios appear efficient with respect to conditioning information.     

A Generalized Extreme Value Approach to Financial Risk Measurement

This paper develops an unconditional and conditional extreme value approach to calculating value at risk (VaR), and shows that the maximum likely loss of financial institutions can be more accurately estimated using the statistical theory of extremes. The new approach is based on the distribution of extreme returns instead of the distribution of all returns and provides good predictions of catastrophic market risks. Both the in-sample and out-of-sample performance results indicate that the Box–Cox generalized extreme value distribution introduced in the paper performs surprisingly well in capturing both the rate of occurrence and the extent of extreme events in financial markets. The new approach yields more precise VaR estimates than the normal and skewed t distributions.     

INTANGIBLE ASSETS,BOOK‐TO‐MARKET,AND COMMON STOCK RETURNS

I examine two anomalies where the Fama and French three‐factor model fails to adequately explain monthly industry and index returns. Both anomalies are consistent with a bad model problem where the book‐to‐market factor introduces a negative bias in the intercepts. I propose the intangibles model as an alternative where the three‐factor model is known to have difficulty. This alternative model, which replaces the book‐to‐market factor with zero investment portfolio returns based on prior investments in intangible assets, is well specified in random samples, has comparable power, and fully explains both anomalies.     

THE TIME‐VARYING EQUITY PREMIUM AND THE S&P 500 IN THE TWENTIETH CENTURY

I present a new hindcast stock market index for the United States over the twentieth century. This is constructed by calibrating a rational asset pricing model that allows for a time‐varying equity premium driven by heteroskedasticity in consumption growth. By incorporating this variation in risk, the mean square error of the generated index series, when compared to the observed levels of the S&P 500, is significantly reduced. The model also explains the broad magnitudes and timings of the major bull and bear markets of the twentieth century, particularly before 1973, and the excess volatility puzzle is largely resolved.     

What drives volatility persistence in the foreign exchange market?

We propose a new empirical specification of volatility that links volatility to the information flow, measured as the order flow in the market, and to the price sensitivity to that information. The time-varying market sensitivity to information is estimated from high-frequency data, and movements in volatility can therefore be directly related to movements in order flow and market sensitivity. Empirically, the model explains a large share of the long-run variation in volatility. Importantly, the time variation in the market's sensitivity to information is at least as relevant in explaining the persistence of volatility as the rate of information arrival itself. This may be evidence of a link between changes over time in the aggregate behavior of market participants and the time-series properties of realized volatility.     

TRADING‐VOLUME SHOCKS AND STOCK RETURNS: AN EMPIRICAL ANALYSIS

We examine high‐volume premiums based on weekly risk‐adjusted returns. Significant average weekly abnormal high‐volume premiums up to 0.50% per week are documented for 1962–2005. Most premiums are generated in the first two weeks and monotonically decline as holding periods are extended. Evidence of reversal is found as the holding periods are extended. Premiums depend on realized turnover in the holding period. The last finding supports the theories of Miller and Merton. Finally, we test whether premiums are compensation for taking additional risk. Negative skewness, idiosyncratic risk, and liquidity risk do not explain the high‐volume premiums.     

THE GRAMM‐LEACH‐BLILEY ACT OF 1999: RISK IMPLICATIONS FOR THE FINANCIAL SERVICES INDUSTRY

We document significant risk changes in the financial services industry following the passage of the Gramm‐Leach‐Bliley Act of 1999. Banks experience an increase in risk regardless of whether they have taken steps to participate actively in the investment banking business. Insurance companies also experience an increase in risk, whereas securities firms experience a decrease in risk. We attribute the increase in risk for banks and insurance companies to the fact that the securities business is relatively more risky, and the decline in risk for securities firms to the fact that they can now diversify into relatively less risky banking and insurance businesses.     

Stochastic skew in currency options

We analyze the behavior of over-the-counter currency option prices across moneyness, maturity, and calendar time on two of the most actively traded currency pairs over the past eight years. We find that, on any given date, the conditional risk-neutral distribution of currency returns can show strong asymmetry. This asymmetry varies greatly over time and often switches signs. We develop and estimate a class of models that captures this stochastic skew behavior. Model estimation shows that our stochastic skew models significantly outperform traditional jump-diffusion stochastic volatility models both in sample and out of sample.