ADAPTIVE MESH MODELING AND BARRIER OPTION PRICING UNDER A JUMP‐DIFFUSION PROCESS

The computational burden of numerical barrier option pricing is significant, even prohibitive, for some parameterizations—especially for more realistic models of underlying asset behavior, such as jump diffusions. We extend a binomial jump diffusion pricing algorithm into a trinomial setting and demonstrate how an adaptive mesh may fit into the model. Our result is a barrier option pricing method that employs fewer computational resources, reducing run times substantially. We demonstrate that this extension allows the pricing of options that were previously computationally infeasible and examine the parameterizations in which use of the adaptive mesh is most beneficial.     

RISK PREMIUM EFFECTS ON IMPLIED VOLATILITY REGRESSIONS

This article provides new insights into the sources of bias of option implied volatility to forecast its physical counterpart. We argue that this bias can be attributed to volatility risk premium effects. The latter are found to depend on high‐order cumulants of the risk‐neutral density. These cumulants capture the risk‐averse behavior of investors in the stock and option markets for bearing the investment risk that is reflected in the deviations of the implied risk‐neutral distribution from the normal distribution. We show that the bias of implied volatility to forecast its corresponding physical measure can be eliminated when the implied volatility regressions are adjusted for risk premium effects. The latter are captured mainly by the third‐order risk‐neutral cumulant. We also show that a substantial reduction of higher order risk‐neutral cumulants biases to predict their corresponding physical cumulants is supported when adjustments for risk premium effects are made.     

INFORMATION AND NOISE IN FINANCIAL MARKETS: EVIDENCE FROM THE E‐MINI INDEX FUTURES

I examine the informational contributions and effects on transitory volatility of trades initiated by different types of traders in three actively traded index futures markets. The results show that trades initiated by exchange member firms account for more than 60% of price discovery during the trading day. These institutional trades appear to be more informative than trades of individual exchange members or off‐exchange traders. I also find that off‐exchange traders introduce more noise into the prices than do exchange members. My findings provide new evidence on the role of different types of traders in the price formation process.     

END‐OF‐DAY PRICING IN THE U.S. TREASURY MARKET: A COMPARISON OF GovPX AND THE FEDERAL RESERVE BANK OF NEW YORK

We compare end‐of‐day indicative U.S. Treasury prices from GovPX and the Federal Reserve Bank of New York (FRBNY). We find that the two sources rarely quote identical prices, and differences are not simply due to noise or rounding. The average bid price differential is 2 cents for notes and bonds, but it is only ¹/₁₀ of 1 cent for bills. Bid‐ask spreads in both sources appear to be largely artificial and contain limited information. Finally, we find that the end‐of‐day indicative FRBNY bid prices are closer to true intraday GovPX market quotes than end‐of‐day indicative quotes provided by GovPX itself.     

RISK AND HEDGING BEHAVIOR: THE ROLE AND DETERMINANTS OF LATENT HETEROGENEITY

The notion of heterogeneous behavior is well grounded in economic theory. Recently it has been shown in a hedging context that the influence of risk attitudes and risk perceptions varies for different segments using a generalized mixture regression model. Here, using recently developed individual risk attitude measurement techniques and experimental and accounting data from investors with differing decision environments, we examine the determinants of heterogeneity in hedging behavior in a concomitant mixture regression framework. Allowing for latent heterogeneity, we find that risk attitudes and risk perceptions do not influence behavior uniformly and that the heterogeneity is influenced by manager's focus on shareholder value and the firm's capital structure.     

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.     

VOLATILITY FORECASTS,TRADING VOLUME,AND THE ARCH VERSUS OPTION‐IMPLIED VOLATILITY TRADE‐OFF

We investigate empirically the role of trading volume (1) in predicting the relative informativeness of volatility forecasts produced by autoregressive conditional heteroskedasticity (ARCH) models versus the volatility forecasts derived from option prices, and (2) in improving volatility forecasts produced by ARCH and option models and combinations of models. Daily and monthly data are explored. We find that if trading volume was low during period t?1 relative to the recent past, ARCH is at least as important as options for forecasting future stock market volatility. Conversely, if volume was high during period t?1 relative to the recent past, option‐implied volatility is much more important than ARCH for forecasting future volatility. Considering relative trading volume as a proxy for changes in the set of information available to investors, our findings reveal an important switching role for trading volume between a volatility forecast that reflects relatively stale information (the historical ARCH estimate) and the option‐implied forward‐looking estimate.     

EXACT FORMULAS FOR PRICING BONDS AND OPTIONS WHEN INTEREST RATE DIFFUSIONS CONTAIN JUMPS

I develop Heath‐Jarrow‐Morton extensions of the Vasicek and Jamshidian pure‐diffusion models, extend these models to incorporate Poisson‐Gaussian interest rate jumps, and obtain closed‐form models for valuing default‐free, zero‐coupon bonds and European call and put options on default‐free, zero‐coupon bonds in a market where interest rates can experience discontinuous information shocks. The jump‐diffusion pricing models value the instrument as the probability‐weighted average of the pure‐diffusion model prices, each conditional on a specific number of jumps occurring during the life of the instrument. I extend the models to coupon‐bearing instruments by applying Jamshidian's serial‐decomposition technique.     

EVIDENCE ON THE MARKET FOR PROFESSIONAL DIRECTORS

Using the temporary increase in the supply of directors associated with successful tender offers, we examine characteristics of directors who are valuable in the market for board seats. We find that after a takeover, professional directors are three times more likely to receive new appointments than other types of directors released from their respective boards. We find that the proportion of professional directors in above‐average performing targets is significantly greater than that in underperforming targets. Our results indicate that professional directors generally have valuable general human capital that more than offsets the costs of multiple directorships.     

DIRECT EVIDENCE ON THE MARKET‐DRIVEN ACQUISITION THEORY

We provide direct empirical evidence that share overvaluation is an important motive for firms to make stock acquisitions. We find that more overvalued firms are more likely to acquire with stock, and acquirers are more overvalued in successful stock mergers than in withdrawn mergers. Acquirers' overvaluation, on average, exceeds the targets' premium‐adjusted overvaluation. Shareholders of stock acquirers, whose overvaluation is greater than their targets' premium‐adjusted overvaluation, realize sustained wealth gains from one day before the merger announcement up to three years after the merger completion, as compared with a matching sample of similarly overvalued but nonacquiring firms.     

INSTITUTIONAL TRADING AND OPENING PRICE BEHAVIOR: EVIDENCE FROM A FAST‐EMERGING MARKET

Using daily and intraday data, we investigate the cross‐sectional relation between stock prices and institutional trading in the Taiwan stock market. Consistent with the investigative herding hypothesis, we find that institutional herding exists because of institutional positive feedback trading behavior rather than following trades made by other institutions, as suggested by the information cascade hypothesis. Moreover, the positive correlation between institutional trade imbalance and stock returns mainly comes from institutional positive feedback trading. The institutional trading decisions rely on returns measured not only over the lagged trading day but also over the opening session during the same day.     

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.     

ON THE ROBUSTNESS OF RANGE‐BASED VOLATILITY ESTIMATORS

We empirically examine Parkinson's range‐based volatility estimate in the federal funds market, which is unique because institutional regulations create a predictable pattern in interday volatility. We find that range‐based volatility estimates and standard deviations produce the expected volatility pattern. We also find that at trading pressure points where microstructure noise should be greatest, range‐based estimates are less than the standard deviations. Thus, we support the argument that range‐based volatility estimates remove the upward bias created by microstructure noise. We find that the Parkinson method is the most efficient range‐based volatility measure among a set of alternates in this market.     

ESTIMATING EXPECTED EXCESS RETURNS USING HISTORICAL AND OPTION‐IMPLIED VOLATILITY

We test the relation between expected and realized excess returns for the S&P 500 index from January 1994 through December 2003 using the proportional reward‐to‐risk measure to estimate expected returns. When risk is measured by historical volatility, we find no relation between expected and realized excess returns. In contrast, when risk is measured by option‐implied volatility, we find a positive and significant relation between expected and realized excess returns in the 1994–1998 subperiod. In the 1999–2003 subperiod, the option‐implied volatility risk measure yields a positive, but statistically insignificant, risk‐return relation. We attribute this performance difference to the fact that, in the 1994–1998 subperiod, return volatility was lower and the average return was much higher than in the 1999–2003 subperiod, thereby increasing the signal‐to‐noise ratio in the latter subperiod.     

THE FUNDAMENTAL DETERMINANTS OF TRADING VOLUME REACTION TO FINANCIAL INFORMATION: EVIDENCE AND IMPLICATIONS FOR EMPIRICAL CAPITAL MARKET RESEARCH

Prior empirical research indicates that trading volume reaction to new information increases with the heterogeneity of investors’ prior beliefs. We examine three potential factors that theoretical models of financial economists show determine trading volume reaction to new information: heterogeneous prior beliefs, differential interpretation, and the consensus effect—the extent to which the information causes their beliefs to converge or diverge. We find that these three factors have a distinct and significant incremental effect on trading volume, thereby suggesting that empirical trading volume models that exclude or fail to control for any of these determinants are misspecified with biased estimated coefficients.     

THE PRICING OF EQUITY CARVE‐OUTS DURING THE 1990s

We examine the level of underpricing and characteristics of equity carveouts (ECOs) from 1990 to 1998 (the 1990s) and from 1999 to 2000 (the bubble period). For a sample of 458 ECOs, we find a mean initial return of 8.75% for the 1990s and 47.76% for the bubble period. The results suggest that, similar to other initial public offerings (IPOs), ECOs have been more willing to accept underpricing through time because of an increased importance in analyst coverage and the increased use of spinning, the practice where investment bankers allocate IPOs to high‐profile customers to garner potential future business.     

THE EFFECT OF THE TRADING SYSTEM ON IPO UNDERPRICING: EVIDENCE FROM THE 1997 ORDER‐HANDLING RULES

We use a natural experiment resulting from the 1997 Securities and Exchange Commission rule mandating a change in the order‐handling rules (OHR) for all NASDAQ stocks to test whether secondary market structure affects initial public offering (IPO) underpricing. We find that the increase in liquidity that the OHR represent led to a decrease in underpricing for cold NASDAQ IPOs, suggesting that when liquidity is lowest, changes in market liquidity display a negative relation to initial returns.     

ON THE INFORMATIONAL EFFECT OF SHORT‐SALES CONSTRAINTS: EVIDENCE FROM THE TOKYO STOCK EXCHANGE

Using a database of stock lending fees for Japanese centralized margin transactions, I show that short‐sales constraints reduce the adjustment speed of stock prices to negative information before the announcements of revised earnings forecasts disclosed by firms in the Tokyo Stock Exchange from July 1998 to December 2001. I find that the cumulative abnormal returns (CARs) of the stocks with high short‐sales costs are insensitive to negative information on pre‐announcement days, but the CARs of these stocks become significantly lower than the CARs of the stocks with low short‐sales costs when the announcements reveal negative information to the public.     

FURTHER EVIDENCE ON INSIDER TRADING AND THE MERITS OF SECURITIES CLASS ACTIONS

To provide further evidence on the merits of securities class actions, we examine insider transactions immediately before and during the class period, using a larger and newer data set. We show that insiders reduce their stock sales by an abnormal amount immediately before the class period. Alternative measures of insider transactions and analysis of data before the enactment of the Private Securities Litigation Reform Act of 1995 provide consistent results. These new findings indicate that class actions, on average, have merit. Our data also reestablish a previous empirical result that there is no abnormal selling during the class period.