Abstract
This chapter reviews the basic concepts in probability and statistics and discusses the bivariate linear regression model. It provides an introduction to time series analysis with a focus on the models most relevant for financial risk management. The probability distribution of a discrete random variable describes the probability of each possible outcome. Even if an asset price in reality can only take on discrete values and not a continuum of values, one uses continuous densities rather than discrete distributions to describe probability of various outcomes. Continuous probability densities are more analytically tractable and they approximate well the discrete probability distributions relevant for risk management. Along with this, multivariate time series model analysis is provided for risk management because one often considers risk models with multiple related risk factors or models with many assets. This chapter briefly discusses the time series regressions, spurious relationships, co-integration, cross correlations, vector autoregressions, and spurious causality.
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