Advanced time-series analysis 2400-M1IiEZASC
1. introduction to R
2. stationarity, random walk, stochastic trends, stationarity testing, spurious regressions, Newbold-Davies experiment.
3. AR and MA processes and their properties.
4. ARIMA and SARIMA models: estimation, diagnostics and forecasting
5. ECM, VAR and VECM models: long-term relationships among time series, error correction model
6. volatility modeling: univariate GARCH models, diagnostics, extensions of GARCH models, practical applications (estimationg Value-at-Risk, option pricing)
7. volatility modeling: multivariate GARCH models (EWMA, DVEC, BEKK, CCC, DCC)
8. creating dynamic documents with Rmarkdown language
Course coordinators
Type of course
Learning outcomes
After the course student
will know:
• what is stationarity of time series, white noise, autocorrelation and partial autocorrelation functions,
• how ARIMA/SARIMA models are constructed
• how ECM, VAR and VECM models are constructed
• how GARCH models are constructed
will understand:
• conception of time-series cointegration and their long-term relationship
• conception of the error correction mechanism
• conception of conditional variance and univariate and multivariate models from GARCH family.
will be able to:
• estimate models from ARIMA/SARIMA famili, do diagnostic analysis and produce forecasts
• assess ex-post quality of the forecast
• estimate models from ECM/VAR/VECM family and interprete their results
• estimate models from GARCH family, produce forecasts of conditional variance and apply the model to particular problems
• create dynamic dokument with RMarkdown language.
Assessment criteria
Home taken project and class activity
Bibliography
1. Tsay (2013) An Introduction to Analysis of Financial Data with R
2. Biecek (2016) Przewodnik po pakiecie R
3. Tsay (2010) Analysis of Financial Times Series, Wiley
4. Brooks (2014) Introductory Econometrics for Finance, CUP
5. Gągolewski (2014) Programowanie w języku R
6. Suchwałko, Zagdański (2019) Analiza i prognozowanie szeregów czasowych