A BIAS-CORRECTING PROCEDURE FOR BETA ESTIMATION IN THE PRESENCE OF THIN TRADING

In this paper, an alternative technique is developed for obtaining consistent estimates of beta in the presence of thin trading. The new estimator is tested on simulated data and the results are compared with those obtained from the Dimson [ 4 ] Scholes and Williams [ 9 ] techniques. The new estimator is found to have approximately the same bias as the others, but it has a considerably lower variance.     

Friction in the trading process and the estimation of systematic risk

This paper considers how estimates of the market model beta parameter can be biased by friction in the trading process (information, decision, and transaction costs) that (a) leads to a distinction between observed and ‘true’ returns; (b) causes observed returns to be generated asynchronously for a set of interdependent securities; and (c) thereby introduces serial cross-correlation into security returns. Several propositions are derived from which consistent estimators of beta are obtained, and the effect of differencing interval length on beta estimates is specified. The formulation is contrasted with the related analyses of Scholes-Williams (1977) and Dimson (1979).     

THIN TRADING AND THE ESTIMATION OF BETAS: THE EFFICACY OF ALTERNATIVE TECHNIQUES

Infrequent trading induces biased estimates of the beta risk coefficient. This paper reports on the efficacy of approaches that seek to correct for this bias and documents the extent of thin trading among New Zealand securities. Parameter estimates free of the thin-trading bias are obtained. These are compared with estimates obtained using ordinary least squares (OLS) applied in the conventional manner to nonsynchronous data, with and without bias-correcting procedures. OLS beta estimates are found to be less biased, more efficient, and as consistent when compared with Dimson or Scholes-Williams estimators. Lower beta estimates are associated with lower trading frequencies.     

Adjusting for Beta Bias: An Assessment of Alternate Techniques: A Note

This paper tests the effectiveness of techniques proposed by: Scholes-Williams; Dimson; Fowler, Rorke, and Jog; and Cohen, Hawawini, Maier, Schwartz, and Whitcomb to control for bias in beta estimates from thin trading and price adjustment delays. Each technique produces beta estimates that reduce the amount of this bias, but the amount of reduction in the best case is only 29%.     

The Cost of Employee Stock Option Grants: An Empirical Analysis

This study presents empirical evidence on the ex post costs of employee stock option (ESO) grants to issuing firms and examines whether the Black–Scholes [1973] model provides reasonable estimates of these values. Because there are no market prices for ESOs, the traditional avenues for testing option–pricing models are unavailable. This research relies instead on techniques from the economic forecasting literature, viewing model values as forecasts of the options' payoff. The theoretically appropriate rate at which to discount ESO payoffs is derived under the maintained hypothesis that the Black–Scholes model is valid. This rate is used in estimating ex post ESO costs at the time of grant, which are then compared with Black–Scholes estimates using Theil's [1966] tests of forecast rationality. Based on a sample of 966 ESO grants over 1963–1984, the results suggest that the Black–Scholes model, adjusted for concavity in the time to exercise using the Hemmer, Matsunaga, and Shevlin [1994] procedure, appears to provide reasonable estimates of ex post ESO costs for the average ESO grant. However, there is significant variability in the amount of model error on an individual grant basis.     

An Adjustment Procedure for Predicting Betas When Thin Trading is Present: Canadian Evidence

We have incorporated effects of the process that generates true betas for TSE stocks, as well as thin trading effects, into the beta adjustment model. We note the Blume and Dimson and Marsh beta adjustment techniques aim at eliminating beta forecast error through regression tendency bias. Effects of other sources of forecast error have been ignored. We show the process generating security betas affects both cross-sectional correlation coefficient and order bias, while thin trading affects only cross-sectional correlation coefficient. We demonstrate that when OLS beta estimates are used to forecast their future risk levels, order bias accounts for 86% of forecast error, while thin trading effects account for 14% of forecast error. A beta regression tendency model which properly accounts for effects of cross-sectional correlation (which is a function of thin trading) and order bias completely abates forecast error. Our results have implications for the use of correlation coefficient to measure stability of betas across time, for beta adjustment models proposed in the literature, and for event study methodologies that rely on prediction errors.     

Loss aversion and the price of risk

This paper derives a simple theoretical relationship between the degree of loss aversion, the concavity/convexity of the value function, and the equilibrium market price of risk. We show that while the degree of loss aversion is key in determining the market price of risk, the convexity/concavity of the value function is much less important in this respect. The theoretical relationship obtained is tested empirically by using international data from 16 different countries during over 100 years, as documented by Dimson et al. [Triumph of the Optimists: 101 Years of Global Investment Returns, 2002 (Princeton University Press)]. The empirical data yield an estimate of λ=2.3 for the loss aversion index. This value is in striking agreement with estimates obtained in the very different methodology of laboratory experiments of individual decision-making.     

Adjusted accounting beta,operating leverage and financial leverage as determinants of market beta: A synthesis and empirical evaluation

This article reexamines and synthesizes two streams of research dealing with the relationship between market beta and accounting risk measures. It is shown that, with some minor rearrangement the Mandelker and Rhee (1984) model can be shown to be as a decomposition of the familiar accounting beta (Beaver, Kettler and Scholes 1970) into operating leverage, financial leverage, and an adjusted accounting beta. The adjusted accounting beta can be further decomposed into productivity gains and the relative cyclical sensitivity of the accounting flows of the firm. Empirical estimates of this extension made using three accounting flow measures in addition to earnings show that the intrinsic business risk factor not identified in the original Mandelker and Rhee model is the most significant explanatory factor related to market beta.     

Alternative beta risk estimators and asset pricing tests in emerging markets: The case of Pakistan

This paper tests and compares the applicability of two asset pricing models specifically, the CAPM and the Fama–French three factor models for an emerging stock market namely, Pakistan. The paper analyses a number of beta risk estimators, including OLS, the Dimson thin trading estimator, a trade-to-trade estimator and a sample selectivity estimator. To uncover any possible influence of the return interval and the type of the market index, the analysis is carried out on three data frequencies namely daily, weekly and monthly as well as for a value and an equally weighted market index. The alternative beta estimators appear to correct thin trading bias but their effects on asset pricing tests are not visible. Moreover contrary to the expectations the test results for monthly and weekly frequencies are not promising. Instead for daily data the cross-section of returns are explained by a number of risk factors and trading volume.     

Unbiased estimation of the Black/Scholes formula

The Black/Scholes model gives the price of an option as a function of the true variance rate of the underlying stock and other parameters. Because the true variance rate is unobservable, an estimate of the variance rate is used in empirical tests. But, because the Black/Scholes formula is non-linear in the variance, option price estimates using an estimated variance are biased, even if the variance estimate itself is unbiased. This paper develops an unbiased estimator of the Black/Scholes formula from a Taylor series expansion of the formula and the properties of the pdf of the estimated variance.     

A test of the beta model on Eurodollar futures options

The paper reports empirical tests of the beta model for pricing fixed-income options. The beta model resembles the Black–Scholes model with the lognormal probability distribution replaced by a beta probability distribution. The test is based on 32 817 daily prices of Eurodollar futures options and concludes that the beta model is more accurate than alternative option pricing models.     

Alternative methods to derive option pricing models: review and comparison

The main purposes of this paper are: (1) to review three alternative methods for deriving option pricing models (OPMs), (2) to discuss the relationship between binomial OPM and Black–Scholes OPM, (3) to compare Cox et al. method and Rendleman and Bartter method for deriving Black–Scholes OPM, (4) to discuss lognormal distribution method to derive Black–Scholes OPM, and (5) to show how the Black–Scholes model can be derived by stochastic calculus. This paper shows that the main methodologies used to derive the Black–Scholes model are: binomial distribution, lognormal distribution, and differential and integral calculus. If we assume risk neutrality, then we don’t need stochastic calculus to derive the Black–Scholes model. However, the stochastic calculus approach for deriving the Black–Scholes model is still presented in Sect. 6. In sum, this paper can help statisticians and mathematicians understand how alternative methods can be used to derive the Black–Scholes option model.     

Return,Risk, and Yield: Evidence from Ex Ante Data

The purpose of this study is to investigate the relationship between return and yield in the context of ex ante data from The Value Line Investment Survey and by examining the role of dividends as a proxy for risk. The use of ex ante data should substantially reduce the confounding of tax and information effects that has affected earlier studies. Heteroscedasticity is detected in the after-tax CAPM and found to be negatively related to yield and positively related to beta. Maximum likelihood methods are used to correct for heteroscedasticity and generate efficient coefficient estimates. Using data for each of the years 1973 through 1983, there is an overall positive relationship between expected return and yield. However, coefficient estimates of yield are highly variable from year to year.     

The Pricing of Risk and Sentiment: A Study of Executive Stock Options

Option pricing models accounting for illiquidity generally imply the options are valued at a discount to the Black‐Scholes value. Our model considers the role of sentiment, which offsets illiquidity. Using executive stock options and compensation data from 1992 to 2004 for S&P 1500 firms, we find that executives value employee stock options (ESOs) at a 48% premium to the Black‐Scholes value. These premia are explained by a sentiment level of 12% in risk‐adjusted, annualized return, suggesting a high level of executive overconfidence. Subjective value relates negatively to illiquidity and idiosyncratic risk, and positively to sentiment in all specifications, consistent with the offsetting roles of sentiment and risk aversion.     

A General Stationary Stochastic Regression Model for Estimating and Predicting Beta

A beta regression model is proposed where the coefficients follow a general class of stationary stochastic processes. The procedure identifies the process and estimates the parameters of the model simultaneously from the information contained in the return series. The returns of each of the Dow Jones 30 securities are examined. Betas of 5 of the securities are nonstationary and do not appear to follow a particular form of nonstationarity. Conclusions of many earlier studies may be suspect since they are based on procedures tailored to adoption of a specific form of beta nonstationarity and, thereby, based on an erroneous a priori assumption regarding such form. The ordinary least squares model is also found to be quite robust, providing reliable beta and intercept estimates not materially different from the more complex procedure with 25 of the return series.     

Direct evidence on the marginal rate of taxation on dividend income

Miller and Scholes (1978) hypothesize that the marginal tax rate on dividend income may be less than the marginal rate of tax on capital gains. Their hypothesis is dependent upon individuals utilizing existing provisions of the Code which serve to reduce the taxation of dividends. In this study, estimates of the marginal and effective rates of tax on dividend income for the year 1979 are presented using the Statistics of Income sample of returns. The average marginal rate of tax on dividend income is estimated to be 40%, while the average effective rate of tax is estimated to be 30%.     

Black–Scholes versus artificial neural networks in pricing FTSE 100 options

This paper compares the performance of Black–Scholes with an artificial neural network (ANN) in pricing European‐style call options on the FTSE 100 index. It is the first extensive study of the performance of ANNs in pricing UK options, and the first to allow for dividends in the closed‐form model. For out‐of‐the‐money options, the ANN is clearly superior to Black–Scholes. For in‐the‐money options, if the sample space is restricted by excluding deep in‐the‐money and long maturity options (3.4% of total volume), then the performance of the ANN is comparable to that of Black–Scholes. The superiority of the ANN is a surprising result, given that European‐style equity options are the home ground of Black–Scholes, and suggests that ANNs may have an important role to play in pricing other options for which there is either no closed‐form model, or the closed‐form model is less successful than is Black–Scholes for equity options. Copyright © 2005 John Wiley & Sons, Ltd.     

A linearly implicit predictor–corrector scheme for pricing American options using a penalty method approach

Pricing of an American option is complicated since at each time we have to determine not only the option value but also whether or not it should be exercised (early exercise constraint). This makes the valuation of an American option a free boundary problem. Typically at each time there is a particular value of the asset, which marks the boundary between two regions: to one side one should hold the option and to other side one should exercise it. Assuming that investors act optimally, the value of an American option cannot fall below the value that would be obtained if it were exercised early. Effectively, this means that the American option early exercise feature transforms the original linear pricing partial differential equation into a nonlinear one. We consider a penalty method approach in which the free and moving boundary is removed by adding a small and continuous penalty term to the Black–Scholes equation; consequently,the problem can be solved on a fixed domain. Analytical solutions of the Black–Scholes model of American option problems are seldom available and hence such derivatives must be priced by stable and efficient numerical techniques. Standard numerical methods involve the need to solve a system of nonlinear equations, evolving from the finite difference discretization of the nonlinear Black–Scholes model, at each time step by a Newton-type iterative procedure. We implement a novel linearly implicit scheme by treating the nonlinear penalty term explicitly, while maintaining superior accuracy and stability properties compared to the well-known θ-methods.     

Bias in estimating the systematic risk of extreme performers: Implications for financial analysis, the leverage effect, and long-run reversals

We show how bias can arise systematically in the beta estimates of extreme performers when long-run return reversals are present and partly, or wholly, due to sign changes in unanticipated factor realizations. Our evidence is consistent with this bias being responsible for the large shifts in the beta estimates of extreme performers, more so than the leverage effect, which has been the predominant explanation in prior literature. Bias in these contemporaneous realized betas, estimated with the same returns that are to be risk adjusted, arises due to the general problem of “overconditioning,” where betas are estimated conditional on information that is not yet known. Several methods for conditioning betas on out-of-sample returns are evaluated and found to be lacking, although some offer improvement under certain circumstances. We also show evidence of this bias in the Fama-French Three-factor loadings of extreme performers. Our findings indicate not only that previous studies of long-run reversals understate contrarian profits but that bias is prevalent in the OLS beta estimates of extreme performers, and this has implications for estimating the cost of capital and measuring long-run performance. We offer recommendations for identifying when this bias is likely present, as well as general methods to correct for it.     

Pricing exotic options in a regime switching economy: a Fourier transform method

This article considers the valuation of digital, barrier, and lookback options in a Markovian, regime-switching, Black–Scholes model. In Fourier space, integral representations for the option prices are derived via the theory on matrix Wiener–Hopf factorizations. Our main focus is on the 2-state case where the matrix Wiener–Hopf factorization is available analytically. A comparison to several numerical alternatives (analytical approximations, the Brownian bridge algorithm and a finite element scheme) demonstrates that the pricing formulas are easy to implement and lead to accurate price estimates.