A flexible statistical approach for the analysis of time-varying dynamics of transaction data on financial markets is here applied to intra-day trading strategies. A local adaptive technique is used to successfully predict financial time series, i.e., the buyer and the seller-initiated trading...

Many companies depend on weather conditions, so they require reliable weather forecasts for production planning or risk hedging. In this article, we propose a new way of gaining weather forecasts by exploiting the forward-looking information included in the market prices of weather derivatives...

We use administrative microdata and statistical learning methods to analyze how personal characteristics and the consumption of healthcare services help predict the first-time receipt of "long-term care allowance" (LTCA), a needs-tested cash-for-care benefit in Austria. Our findings suggest that...

The increasing share of wind energy in the portfolio of energy sources highlights its uncertainties due to changing weather conditions. To account for the uncertainty in predicting wind power production, this article examines the volatility forecasting abilities of different GARCH-type models...

We analyse the short-term spot price of European Union Allowances (EUAs), which is of particular importance in the transition of energy markets and for the development of new risk management strategies. Due to the characteristics of the price process, such as volatility persistence, breaks in...

We model the dynamics of ask and bid curves in a limit order book market using a dynamic semiparametric factor model. The shape of the curves is captured by a factor structure which is estimated nonparametrically. Corresponding factor loadings are assumed to follow multivariate dynamics and are...