 ADAPTIVE DRIFT MODELING OF DYNAMIC COINTEGRATED TIME SERIES: APPLICATIONS IN FINANCIAL AND SPORTS GAMBLING MARKETSWilliam Mallios Journal of Prediction Markets, 2010, vol. 4, issue 3, 59-83 Abstract: Cointegrated time processes are viewed graphically in terms of candlestick charts in finance and sports and modeled dynamically in terms of adaptive drift procedures. Forecasts focus on active equity trading, betting against the bookmakers’ lines in sports and assessing trading/betting risks. Modeling premises are that (1) markets fluctuate between periods of efficiency and inefficiency and that (2) during inefficient periods, present and past disequilibria (shocks) have dynamic effects on subsequent price changes/game outcomes. Sports forecasting incorporates the added effects of the lines and lagged gambling shocks. Forecasts are in terms of reduced, higher order, ARMA-type processes that drift to accommodate evolving market conditions. Risk assessment is in terms of adaptive GARCH modeling. Modeling applications are with reference to the Great Depression and NFL playoff games. Keywords: Rational/irrational market behavior; gambling/statistical shocks; higher order ARMA systems (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:buc:jpredm:v:4:y:2010:i:3:p:59-83 Ordering information: This journal article can be ordered from Access Statistics for this article Journal of Prediction Markets is currently edited by Leighton Vaughan Williams, Nottingham Business School More articles in Journal of Prediction Markets from University of Buckingham Press |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.