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published_or_final_version / abstract / toc / Economics and Finance / Master / Master of Philosophy

The U.S. wool industry has long received lower prices for comparable wool types than those of Australia. In order to better understand such price differences, economic evaluations of both the U.S. and Australian wool markets were conducted. This research focused on two primary objectives. The first objective was to determine what price differences existed between the Australian and U.S. wool markets and measure that difference. The second objective was to calculate price differences attributable to wool characteristics, as well as those resulting from regional, seasonal, and yearly differences. In order to accomplish the objectives, the study was set up into three different hedonic pricing models: U.S., Australian, and combined. In the U.S. model, there were significant price differences in season, year, region, level of preparation, and wool description. In addition, average fiber diameter (AFD) had a negative nonlinear relationship with price and lot weight had a positive linear relationship with price. The Australian model was notably different than the U.S. model in that there were only three variables. The yearly variable follows the same general pattern as the U.S. data but with a smaller span of difference. The seasonal price differences were distinctly different than the U.S. because of the difference in seasonal patterns. In addition, the AFD had a similar negative nonlinear relationship with price. The final model combines both the U.S. data and the Australian data. The combined model had only three variables: season, year, AFD and country. As in the case of the previous two models, AFD had the same negative nonlinear relationship and similar price elasticity. Overall, there was a -30.5 percent discount for U.S. wool when compared to Australian wool. This can be attributed to several different factors. One of which is that the Australian wool industry has a more extensive marketing scheme when compared to the U.S wool market as a whole. However, this is only a beginning to future research that needs to be conducted. Continuing this study for future years, having more descriptive categories, and additional countries would further add explanation to wool prices.

We develop and test asset pricing model formulations that are simultaneously conditional and nonlinear. Formulations based upon five popular asset pricing models are tested against the widely studied Fama and French (1993) twenty-five size and book-to-market sorted portfolios. Test results indicate that the conditional nonlinear specification of the Fama and French (1993) three state variable model (FF3) is the only specification not rejected by the data and thus capable of pricing the "size" and "book-to-market" effects simultaneously. The pricing performance of the FF3 conditional nonlinear pricing kernel is corifirmed by robustness tests on out-of-sample data as well as tests with alternative instrumental and conditioning variables. While Bansal and Viswanathan (1993) and Chapman (1997) find unconditional nonlinear pricing kernels sufficient to capture the size effect alone, our results indicate that similar unconditional nonlinear pricing kernels considered here do not price the size and book-to-market effects simultaneously. However, nested model tests indicate that, in isolation, both conditioning information and nonlinearity significantly improve the pricing kernel performance for all five asset pricing models. The success of the conditional nonlinear FF3 model also suggests that the combination of conditioning and nonlinearity is critical to pricing kernel design. Implications for both academic researchers and practitioners are considered.

This thesis consists of two essays which contribute to different but related aspects of the empirical asset pricing literature. The common theme is that incorrect restrictions can lead to inaccurate decisions. The first essay demonstrates that failure to account for the Federal Reserve experiment can lead to incorrect assumptions about the explosiveness of short-term interest rate volatility, while the second essay demonstrates that we need to incorporate skewness to develop models that adequately account for the cross-section of equity returns. Essay 1 empirically compares the Markov-switching and stochastic volatility diffusion models of the short rate. The evidence supports the Markov-switching diffusion model. Estimates of the elasticity of volatility parameter for single-regime models unanimously indicate an explosive volatility process, whereas the Markov-switching models estimates are reasonable. We find that either Markov-switching or stochastic volatility, but not both, is needed to adequately fit the data. A robust conclusion is that volatility depends on the level of the short rate. Finally, the Markov-switching model is the best for forecasting. A technical contribution of this paper is a presentation of quasi-maximum likelihood estimation techniques for the Markov-switching stochastic-volatility model. Essay 2 proposes a new approach to estimating and testing nonlinear pricing models using GMM. The methodology extends the GMM based conditional mean-variance asset pricing tests of Harvey (1989) and He et al (1996) to include preferences over moments higher than variance. In particular we explore the empirical usefulness of the conditional coskewness of an assets return with the market return in explaining the cross-section of equity returns. The methodology is both flexible and parsimonious. We avoid modelling any asset specific parameters and avoid making restrictive assumptions on the dynamics of co-moments. By using GMM to estimate the models' parameters we also avoid making any assumptions about the distribution of the data. The empirical results indicate that coskewness is useful in explaining the cross-section of equity returns, and that both covariance and coskewness are time varying. We also find that the usefulness of coskewness is robust to the inclusion of Fama and French's (1993) SMB and HML factor returns. There is an interesting debate raging in the empirical asset pricing literature comparing the SDF versus beta methodologies. This paper's technique is a conditional version of the beta methodology, which turns out to be directly comparable with the SDF methodology with only minor modifications. Our SDF version imposes the CAPM's restrictions that the coefficients in the pricing kernel are known functions of the moments of market returns, which are modelled using macro-variables. We find that the SDF implied by the three-moment CAPM provides a better fit in this data set than current practice of parameterizing the coefficients on market returns in the SDF. This has an interesting application to the current SDF versus beta methodology debate.

In North America, automobile prices are largely determined through negotiation. Recognizing that some consumers have a strong aversion to negotiation, some manufacturers and dealers are now offering consumers the option of buying cars at a “no-haggle”, or fixed price. This dissertation consists of three essays which address how a fixed price alternative impacts both consumer behavior and firm strategies. The first essay explores the conditions under which a dealer would simultaneously offer a “no-haggle” Internet price and a negotiable price on the lot (which we term a dual-channel), and studies the marketing strategies adopted under this structure. We use consumer haggling cost as a key to understanding a dealer’s choice of pricing strategy. We find that a dual-channel is optimal for the dealer when there is sufficient diversity in consumer haggling cost. We also find that it is optimal for a dealer to specify a higher-than-cost “minimum acceptable price” to the salesperson as a price floor for negotiations. Surprisingly, a dual-channel may serve fewer customers while still being more profitable than a single channel structure. The second essay examines the competitive implications of a no-haggle pricing policy. By using Toyota’s fixed pricing policy in Canada as a natural experiment, we explore the impact of such a strategy on the prices and sales of Toyota and that of its close competitor, Honda. We find that the program has had important competitive consequences. While prices of both Toyota and Honda were higher in provinces with the program, there was an increase in Honda’s sales but with no effect on the sales of Toyota. The third essay determines the impact of a consumer’s bargaining behavior and information she collects on the final price paid. Using an extensive dataset, we find that a consumer’s negotiation skill and attitude toward negotiating significantly influences the negotiation outcome. In particular, consumers that enjoy negotiating have a greater propensity to search for price information which then allows them to obtain a better deal. In addition, we find that, while Internet users pay a lower price, the savings depend on the type of information collected by the buyer.

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In this paper, I empirically identify a new source of price discrimination utilized by airlines, namely, price discrimination based on the day of the week a ticket is purchased. Using unique transaction data, I compare tickets that are identical in every aspect except day of the week purchased (that is, traveling on the same date on the same route on the same airline with the same restrictions on flights with the same load factors and purchased the same number of days in advance), and find that airfares are cheapest when bought on the weekend. The size of this weekend purchase effect varies with distribution channel (online or offline) and how far in advance of departure the ticket is purchased. For transactions occurring more than two weeks before the departure date, offline weekend purchases are 3% cheaper than those made on weekdays, but online purchase prices do not differ significantly throughout the week. Conversely, in the final two weeks before departure, weekend purchases are 4% less expensive online but not significantly cheaper offline. These findings are consistent with price discrimination between high-elasticity leisure customers and low-elasticity business customers. If airlines believe that weekend purchasers are more likely to be price-elastic leisure travelers, then they may offer lower prices or make deals more transparent on the weekend. This conjecture is supported by the finding that the weekend purchase effect is generally larger on routes with a mixture of both business and leisure customers than on routes primarily traveled by leisure customers because price discrimination is both possible and effective on these heterogeneous routes.

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