OPTION PRICING AND THE ARBITRAGE PRICING THEORY

This paper applies the arbitrage pricing theory to option pricing. Under certain distribution assumptions or the assumption that there is only one common factor, the underlying asset of an option is the sole risky factor that explains its expected return. Based upon this relationship, a new and simple option-pricing formula is derived, and some important existing option-pricing formulae are reproduced. Empirical results show that the new formula performs as well as the Black-Scholes formula.     

THE MAGNITUDE OF PRICING ERRORS IN THE ARBITRAGE PRICING THEORY

In this paper the arbitrage pricing theory (APT) pricing errors for individual securities are estimated employing maximum likelihood factor analysis and Fama-MacBeth style aggregation. Results show that the pricing errors are large and statistically significant and that there is a high degree of variability in pricing errors across securities. This evidence contradicts the prevailing APT intuition that the pricing errors can be ignored as negligible. Pricing errors are also found to be related to residual variance and firm size.     

PRICING CRB FUTURES CONTRACTS

This study provides a valuation model to price Commodity Research Bureau Index futures contracts, now traded at the New York Futures Exchange. An empirical analysis suggests that substantial mispricing was exhibited during the early months of trading in an unseasoned Commodity Research Bureau Index futures market.     

AN EMPIRICAL TEST OF THE ARBITRAGE PRICING THEORY

This paper provides an ex-post analysis of a multifactor return-generating model using the factor scores obtained from a common factor analysis of industry-based portfolios. For the 1975–1980 time period, the correlations among common stock returns can be adequately explained by a three-factor model. Furthermore, ex post, at least three factors are priced in the stock market. A brief economic interpretation of the proposed common factor is also presented.     

THE ARBITRAGE PRICING THEORY AND COST-OF-CAPITAL ESTIMATION: THE CASE OF ELECTRIC UTILITIES

Capital asset pricing model (CAPM) and alternative arbitrage pricing theory (APT) methodologies are used to estimate the cost of capital for a sample of electric utilities. The statistical factors APT method is found to produce significantly different estimates depending on the number of factors specified and the set of firms factor analyzed. The use of macroeconomic factors is explored, and it is shown that this methodology has advantages over the statistical factors APT and the market model.     

BOOTSTRAPPING THE NUMBER OF FACTORS IN THE ARBITRAGE PRICING THEORY

The number of factors in the APT are re-examined through a new methodology called the bootstrap, which provides a nonparametric alternative to the chi-square test used in prior research. Results suggest that the number of statistically significant factors does not increase when the number of firms increases. Moreover, only the first factor is consistently significant across sample sizes of thirty, sixty, and ninety firms.     

AN INVESTIGATION INTO THE ROLE OF THE MARKET PORTFOLIO IN THE ARBITRAGE PRICING THEORY

This paper investigates the role of the market portfolio in the arbitrage pricing theory (APT). We show that if the multifactor return-generating process put forth in the APT is valid, then unexpected deviations in the return on the market portfolio must be completely explained by unexpected deviations in the underlying return-generating factors. As well, market betas are developed as a combination of return-generating factor sensitivity coefficients. These results lead us to conclude that an empirically significant “market factor’ is evidence of omitted return-generating factors, rather than evidence that the market is a factor. Finally, results obtained when market betas are regressed against factor sensitivity coefficients are consistent with these insights. The results suggest that there are at least three return-generating factors. This evidence does not rely on ex post pricing of estimated factors.