Financial time series modeling, dealing with outliers and censoring, water quality modeling and process improvement

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The proposed research is part of an ongoing research program in the areas of Time Series Modeling and Statistics in Business, Finance and Industry. Modeling and predicting volatility plays an important role in assessing risk and uncertainty in financial markets. One of the objectives in this project is to develop new stochastic volatility models, devising efficient methods of estimating them, checking for their adequacy and generating predictions and prediction intervals. Recently we have introduced a new class of models referred to as a gamma stochastic volatility (GSV) process. We plan to develop prediction and inference procedures for this class of models as well as for traditional volatility models. Policy decisions and the implementation of various environmental regulations require accurate information from appropriately collected and analyzed data. A second objective of the project is to develop new methods and enhance existing ones for the analysis of time series data to assess trends in water quality. We plan to devise new procedures to deal with outliers, censoring and conditional heteroscedasticity which are often encountered in water quality time series. Quality Improvement efforts are very important for the success of Canadian Business and Industrial organizations. A third objective of the project is to develop new statistical methods and enhance existing ones which can be applied to Canadian Business and Industry. We plan to develop empirical likelihood procedures for industrial modeling, bootstrap analysis of performance measures in designs and dimension reduction methods for multivariate prediction.