This book directly focuses on finding optimal trading strategies in the real world and supports that with a well-defined theoretical foundation that allows trading strategy problems to be solved. Critically, it also delivers a menu of actual solutions that can be applied by traders with various risk profiles and objectives in markets that exhibit substantial tail risk. It shows how the Markowitz approach leads to excessive risk taking, and trader underperformance, in the real world. It summarizes the key features of Utility Theory, the deficiencies of the Sharpe Ratio as a statistic, and develops an optimal decision theory with fully developed examples for both 'Normal' and leptokurtotic distributions.

Present value based asset pricing models are explored empirically in this thesis. Three contributions are made. First, it is shown that a market timing strategy may be implemented in an excessively volatile market such as the S&P500. The main premise of the strategy is that asset prices may revert to the present value over time. The present value is computed in real-time where the present value variables (future dividends, dividend growth and the discount factor) are forecast from simple models. The strategy works well for monthly data and when dividends are forecast from autoregressive models. The performance of the strategy relies on how discount rates are empirically defined. When discount rates are defined by the rolling and recursive historic average of realized returns, the strategy performs well. The discount rate and dividend growth can also be derived using a structural approach. Using the Campbell and Shiller log-linearized present value equation, and assuming that expected and realized dividend growth are unit related, a state space model is constructed linking the price-dividend ratio to expected returns and expected dividend growth. The model parameters are estimated from the data and, are used to derive the filtered expected returns and expected dividend growth series. The present value is computed using the filtered series. The trading rule tends to perform worse in this case. Discount rates are again found to be the major determinant of its success. Although the structural approach offers a time series of discount rates which is less volatile, it is on average higher than that of the historical mean model. The filtered expected returns is a potential predictor of realized returns. The predictive performance of expected returns is compared to that of the price-dividend ratio. It is found that expected returns is not superior to the price-dividend ratio in forecasting returns both in-sample and out-of-sample. The predictive regression included both simple Ordinary Least Squares and Vector Autoregressions. The second contribution of this thesis is the modeling of expected returns using autoregressive fractionally integrated processes. According to the work of Granger and Joyeux(1980), aggregated series which are derived from utility maximization problems follow a Beta distribution. In the time series literature, it implies that the series may have a fractional order (I(d)). Autoregressive fractionally models may have better appeal than models which explicitly posit unit roots or no unit roots. Two models are presented. The first model, which incorporates an ARFIMA(p,d,q) within the present value through the state equations, is found to be highly unstable. Small sample size may be a reason for this finding. The second model involves predicting dividend growth from simple OLS models, and sequentially netting expected returns from the present value model. Based on the previous finding that expected returns may be a long memory process, the third contribution of this thesis derives a test of long memory based on the asymptotic properties of the variance of aggregated series in the context of the Geweke Porter-Hudak (1982) semiparametric estimator. The test makes use of the fact that pure long memory process will have the same autocorrelation across observations if the observations are drawn at repeated intervals to make a new series. The test is implemented using the Sieve-AR bootstrap which accommodates long range dependence in stochastic processes. The test is relatively powerful against both linear and nonlinear specifications in large samples.

In his new book, Ernest Preeg analyzes international trade and investment in the 1990s and lays out a comprehensive U.S. trade strategy for the uncertain period ahead. He examines the influence of the World Trade Organization (WTO) and argues that economic globalization is beneficial to the U.S. economy in the short- to medium-term while raising important questions about national sovereignty and security over the longer term. Preeg believes regional free trade agreements will soon encompass the majority of world trade, but they can conflict with the WTO's multilateral objectives. The central challenge for U.S. trade strategy, then, is to integrate the now largely separate multilateral and regional tracks of the world trading system. The first essay assesses U.S. interests in economic globalization, the second examines recent steps toward free trade at the multilateral and regional levels, and the next three offer an in-depth critique of U.S. regional free trade objectives in the Americas, across the Pacific, and possibly with Europe. The final essay presents a multilateral/regional synthesis for going from here to free trade over the coming decade.

This dissertation consists of two parts. In the first chapter, we examine the relative performance of four options-based investment strategies versus a buy-and-hold strategy in the underlying stock. Specifically, using ten stocks widely held in 401(k) plans, we examine monthly returns from strategies that include a long stock position as one component. These strategies are long stock, covered call, protective put, collar, and covered combination. Ignoring early exercise for simplicity, we find that the covered combination and covered call strategies generally outperform the long stock strategy, which in turn generally outperforms the collar and protective put strategies regardless of the performance measure considered. Clearly, from the first chapter, strategies that involve writing options, in general, outperform the ones buying options. The second chapter provides a detailed study of the conditions where option writers can maximize returns while minimizing risk. The nonlinear nature of time value decay in options suggests that, theoretically, holding short positions only when the speed of time decay is high might improve the performance of option writing strategies. We examine monthly returns from five option strategies without a position in the underlying asset. These strategies are: short straddle, short strangle, short guts, “crash-neutral” short straddle, and long iron butterfly. The results from two portfolios are compared: a “benchmark” portfolio using standard SPX options that expire the following month and a weekly portfolio using SPXW options that expire at the end of the weekly holding period. The short strangle strategy with weekly options consistently outperforms the other strategies with both standard and weekly options, even after accounting for transaction costs. This finding suggests that short-dated out-of-the-money options can be useful in improving the risk-return characteristics of an option writing strategy. In an effort to improve the performance of the short straddle strategy, this chapter introduces an extremely short holding period portfolio, by stitching together three weekly option expirations into one week. Although the straddle still underperforms relative to the short strangle, the performance of the short straddle is improved by entering the market 15 minutes before the close and by using the extremely short holding period portfolios.

This book provides insights into the true nature of financial and economic data, and is a practical guide on how to analyze a variety of data sources. The focus of the book is on finance and economics, but it also illustrates the use of quantitative analysis and data science in many different areas. Lastly, the book includes practical information on how to store and process data and provides a framework for data driven reasoning about the world.The book begins with entertaining tales from Graham Giller's career in finance, starting with speculating in UK government bonds at the Oxford Post Office, accidentally creating a global instant messaging system that went 'viral' before anybody knew what that meant, on being the person who forgot to hit 'enter' to run a hundred-million dollar statistical arbitrage system, what he decoded from his brief time spent with Jim Simons, and giving Michael Bloomberg a tutorial on Granger Causality.The majority of the content is a narrative of analytic work done on financial, economics, and alternative data, structured around both Dr Giller's professional career and some of the things that just interested him. The goal is to stimulate interest in predictive methods, to give accurate characterizations of the true properties of financial, economic and alternative data, and to share what Richard Feynman described as 'The Pleasure of Finding Things Out.'