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This book discusses cointegrated time series associated with financial and sports gambling markets are analyzed in terms of time-varying parameter models. Modeling premises are that present and past disequilibria-shocks both within and between time series-may affect subsequent changes and rates of these changes within individual series and sufficiently large shocks may disrupt/alter model structure such that resulting forecasts may be temporarily unreliable. Reduced forecasting equations are in terms of higher ...
This book discusses cointegrated time series associated with financial and sports gambling markets are analyzed in terms of time-varying parameter models. Modeling premises are that present and past disequilibria-shocks both within and between time series-may affect subsequent changes and rates of these changes within individual series and sufficiently large shocks may disrupt/alter model structure such that resulting forecasts may be temporarily unreliable. Reduced forecasting equations are in terms of higher order ARMA models that are not limited to bilinear processes. Sports forecasting models based on public information are usually more effective-in terms of profitable trading/wagering strategies-than those for the financial sector for two reasons: insider information is less prevalent, and modeling is simplified since lagged shocks associated with the gambling lines/spreads are known-in contrast with financial modeling where there are no comparable gambling shocks, only unknown, lagged statistical shocks in terms of MA variables. Forecasting is illustrated for NFL and NBA playoff games. In financial markets, cointegration is discussed in terms of candlestick chart variants with modeling illustrations given in terms of recent Google price changes. Chapter coverage includes candlestick charts, higher order ARMA processes in financial markets, the effects of gambling shocks in sports gambling markets, cointegrated time series with model drift, modeling volatility, and the promotion of financial and mathematical literacy.
1.1 Favorable Betting Scenarios.
1.2 Gambling Shocks.
1.3 The Dark Side of Sports: The Fixes.
2. Market Perspectives: Through a Glass Darkly.
2.1 Changing Paradigms.
2.2 Modeling Commentaries.
2.3 Sports Hedge Funds.
2.4 Gambling Markets: Prohibition, Repeal and Taxation.
2.5 Quantifying the Madness of Crowds in Sports Gambling Markets.
2.6 Statistical Shocks: Alias Variables.
3. Opacity and Present Day Variables.
3.1 Dilemmas between Social and Economic Efficiency.
3.2 Towards a More Visible Hidden Hand.
3.3 Hedge Funds and Galapagos.
3.4 Lotteries: Market for Losers.
4. Adaptive Modeling Concepts in Dynamic Markets.
4.1 Quant Funds and Algorithmic Trading.
4.2 Market Volatility and Fat-Trailed Distributions.
4.3 Adaptive ARMA(1,1) Drift Processes.
4.4 Time Varying Volatility.
5. Studies in Japanese Candlestick Charts.
5.1 Bullish and Bearish Patterns from Chartist Perspectives.
5.2 Black Monday.
5.3 A Matter of Alleged Insider Trading.
5.4 Commodity Bubbles and Volatility.
5.5 Short Selling.
5.6 Terrorist Attacks and the Markets.
5.7 A Hollywood Romance: Spiderman and Tinkerbell.
5.8 Copenhagen and Climate Change: Exxon Mobile Buys XTO Energy.
6. Pseudo Candlestick Graphics for Major League Baseball.
6.1 The 2008 World Series: Philadelphia Versus Tampa Bay.
6.2 The 2008 Chicago Cubs: Visions of 1908 Heroics.
6.3 A Strange Set of Coincidences: A Plate Umpire’s Affinity for a Pitcher.
7. Single Equation Adaptive Drift Modeling.
7.1 Adaptive ARMA Processes.
7.2 Variable Selection: Identifying the Reduced Model.
7.3 Reduced Model Estimation: Single Equations.
7.4 Reduced Model Empirical Bayesian Estimation: Single Equations.
7.5 Single Equation Volatility Modeling: Adaptive GARCH Processes.
7.6 Modeling Monetary Growth Data.
7.7 Modeling GNP Deflator Growth.
8. Single Equation Modeling: Sports Gambling Markets.
8.1 Effects of Interactive Gambling Shocks.
8.2 End of an Era: Modeling Profile of the 1988-89 Los Angeles Lakers.
8.3 Spread Betting.
8.4 Modeling Profile of a Dream Team: The 1989-90 San Francisco 49ers.
8.5 Major League Baseball: A Data Intensive Game.
8.6 While Still Under the Curse: Modeling Profile of the 1990 Red Sox.
8.7 Portrait of Controversy: Modeling Profile of Roger Clemens with the 1990 Red Sox.
8.8 Pitcher of the Year in 1990: Modeling Profile of the Oakland’s Bob Gibson.
9. Simultaneous Financial Time Series.
9.1 The Cruse of Higher Dimensionality.
9.2 From Candlesticks to Cointegration.
9.3 Cointegration in Terms of Autoregressive Processes.
9.4 Estimating Disequilibria through Factor Analysis.
9.5 Simultaneous Time Series: Adaptive Drift Modeling.
9.6 Simultaneous Time Series: Adaptive Volatility Modeling.
9.7 Exploratory Modeling: Marathon Oil Company.
9.8 The High Tech Bubble of 2000.
9.9 Twenty-Five Standard Deviation Moves.
9.10 The March 2009 Nadir.
10. Modeling Cointegrated Time Series Associated with NBA and NFL Games.
10.1 Modeling Transitions.
10.2 The 2007-08 NY Giants: As Unexpected as Katrina.
10.3 Misery for the Patriot Faithful.
10.4 The Pittsburgh Steelers in Super Bowl 2005.
10.6 Miami’s First NBA Title: 2005-06.
10.7 The 2006-07 San Antonio Spurs: Unexpected Titlists.
10.8 Monitoring MBA Referee Performances.
11. Categorical Forecasting.
11.1 Fisher’s Discriminant Function.
11.2 Bayesian Discriminant Analysis.
11.3 Logistic Regression Analysis.
11.4 Allocating Betting Monies in the Sports Gambling Markets.
12. Financial/Mathematical Illiteracy and Adolescent Problem Gambling: Epidemics More Damaging Than the 1918 Influenza Pandemic.
12.1 The Call for Financial/Mathematical Literacy in 21st Century America.
12.2 Data, Information and the Information Age.
12.3 The Companion Epidemic of Adolescent Problem Gambling.
12.4 Results of a Pilot Study on Adolescent Problem Gambling and Financial /Mathematical Literacy.
13. The Influenza Futures Markets.
13.1 Markets for Expert Information Retrieval.
13.2 Adaptive Seasonal Time Series Modeling.
13.3 Forecasting Weekly Influenza/Pneumonia Deaths.
List of Figures.