Jón Daníelsson, 2015, Financial Risk Forecasting: The Theory and Practice of Forecasting Market Risk, with Implementation in R and Matlab, Wiley Finance.
W. Enders, 2014, Applied Econometric Time Series, 4th Edition, Wiley.
Upon completion of the course the student should be able to analyse financial time series and estimate portfolio volatility and Value at risk. He/she should also be alble to read most of the empirical papers in financial econometrics.
Introductory econometrics. Statistcal inference. Calculus and linear algebra.
Additional material provided by the instructor
Type of Assessment
A written test with question similar to those at and of each chapter of the books.
Time-Series Models, Difference Equations and Their Solutions, Lag Operators. Stochastic Difference Equation Models, ARMA Models, Stationarity, Stationarity Restrictions for an ARMA (p, q) Model , The Autocorrelation Function, The Partial Autocorrelation Function, Sample Autocorrelations of Stationary Series, Box–Jenkins Model Selection, Properties of Forecasts, Seasonality, Structural Change, Combining Forecasts. Deterministic and Stochastic Trends, Removing the Trend, Univariate volatility modeling, Modeling volatility, Moving average models, EWMA model, GARCH and conditional volatility, Maximum likelihood estimation of volatility models, Likelihood ratio tests and parameter significance, Analysis of model residuals
Other GARCH-type models, Implied volatility, Realized volatility, Multivariate volatility models, Orthogonal GARCH, CCC and DCC models, Risk measures,
Value-at-risk, Expected shortfall, Backtesting and stress testing. Intradaily data and models.