|Examples of time series. Objectives of time series analysis. Time series plot. Additive and multiplicative
models. Decomposition. Moving Averages and Filtering. Exponential smoothing and Holt-Winters
forecasting. Autocovariance function. White Noise. Stationarity. MA processes. AR processes. Random
walk models. Discrete and continuous. Random walk with drift. Univariate time series models with the
Markov property. Multivariate Markov model. Introduction to Box-Jenkins methods. Simple ARIMA
processes. Model specification, estimation & diagnostics. Applications of time series analysis using R.
Introduction to multivariate autoregressive models. Simple VAR model. Cointegration.
8 © University of Mauritius 2015, 2018, 2019
Statistical distributions for modelling individual and aggregate losses. Deductibles and Retention limits.
Proportional and excess of loss reinsurance. Estimation of parameters for loss distributions. Calculation of
goodness of fit measures. Compound distributions: the derivation of mean, variance and coefficient of
skewness, compound binomial, compound Poisson and compound negative binomial random variables.
Introduction to copulas: characterisation as a multivariate distribution function, tail dependence, Gaussian
copula and the Archimedean family of copulas. Introduction to extreme value theory: extreme value
distributions, modelling severity of loss, measures of tail weight.
Administrative assistant: DILMAHOMED BOCUS Bibi Swaleha