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Understanding Time Series Analysis for Financial Forecasting

This outline explores the essentials of time series analysis in the context of financial forecasting. It covers stationary processes and general linear processes, including ARMA models, and delves into non-linear and multivariate ARMA models. Key topics also include unit root processes, the Dickey-Fuller test, cointegration, and procedures such as Engle-Granger and Johansen. The focus is on forecasting critical financial indicators, including returns, prices, dividends, volatility, defaults, and liquidity, to inform investment decisions effectively.

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Understanding Time Series Analysis for Financial Forecasting

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  1. TIME SERIESCLASS 2

  2. OUTLINE • Stationary Processes • General Linear Processes - ARMA • Non-linear Processes • Multivariate ARMA • Unit Root Processes • Distribution of the Dickey Fuller Test • Cointegration • Engle Granger and Johansen Procedures

  3. FORECASTING IN FINANCIAL MARKETS WHAT DO WE WANT TO FORECAST? • Returns or Prices • Dividends or Earnings or Volume • Volatility • Defaults or Rating Changes • Value at Risk • Liquidity

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