"Modeling, forecasting and diagnostic testing for financial and econometric time series"

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The proposed research is in the area of Time Series Analysis and Statistics in Business, Economics and Finance. Its main objectives are (i) developing new models for the volatility of high frequency data; (ii) designing effective methods for measuring and forecasting volatility; (iii) constructing diagnostic tests for the detection of changes in the market dynamics. The context of this research is today's financial markets where transactions and price changes are recorded on the millisecond time scale. The classical quantitative models developed by statisticians and econometricians in the 1980s fail to explain characteristic features of such high frequency data. More suitable models have been introduced since the late 1990s, however, there is still a lack of comprehensive models explaining market behaviour both on the intra- and inter-daily level. The proposed research aims to close this gap and lead to a better understanding of how market dynamics manifests itself on different time scales. Its main focus is on the study of volatility which measures the variation of commodity prices over time and has important application in risk management. A special focus will be on efficient methods for forecasting volatility. Another main line of research is the development of models for the durations between transaction events. Empirical studies have shown strong correlations among the durations, so there is a need for models which can capture this long-range dependence in the data. Finally, the proposed research aims to look at models in which the market dynamics is governed by a set of latent variables. Highly relevant questions in this context are how regime switches in the latent structure can be detected, and to what extent it is possible to forecast market dynamics. The proposed research will yield methods which help researchers and practitioners in the Canadian financial industry to better understand the dynamics of financial markets, obtain more accurate predictions and better assess risk-return tradeoffs. It involves the training of five undergraduate, eight Master's and one PhD students who will acquire solid knowledge in financial time series analysis, strong programming and problem solving skills, and gain experience in frontier interdisciplinary research.