Browsing by Author "Päivinen, Ville"

Efficient estimation and forecasting of the cash flow is an interest of pension insurance companies. At the turn of the year 2019 Finnish national Incomes Register was introduced and the payment cycle of TyEL (Employees Pensions Act) changed substantially. TyEL payments are calculated and paid monthly by all of the employers insured under TyEL after January 1st 2019. Vector autoregressive (VAR) models are one of the most used and successful multivariate time series models. They are widely used with economic and financial data due to the good forecasting abilities and the possibility of analysing dynamic structures between the variables of the model. The aim of this thesis is to determine whether a VAR model offers a good fit for predicting the incoming TyEL cash flow of a pension insurance company. With the monthly payment cycle arises a question of seasonality of the incoming TyEL cash flow, and thus the focus is on forecasting with seasonally varying data. The essential theory of VAR models is given. The forecast abilities are tested by building a VAR model for monthly, seasonally varying time series similar than the pension insurance companies would have and could use for the particular prediction problem.