Forecasting Financial Processes by Using Diffusion Models
Journal Title: Dynamic Econometric Models - Year 2010, Vol 10, Issue 1
Abstract
Time series forecasting is one of the most important issues in the financial econometrics. In the face of growing interest in models with continuous time, as well as rapid development of methods of their estimation, we try to use the diffusion models to modeling and forecasting time series from various financial markets. We use Monte-Carlo-based method, introduced by Cziraky and Kucherenko (2008). Received forecasts are confronted with those determined with the commonly applied parametrical time series models.
Authors and Affiliations
Piotr Płuciennik
Keywords
Related Articles
- EP ID EP145333
- DOI -
- Views 153
- Downloads 0
Risk Modeling of Commodities using CAViaR Models, the Encompassing Method and the Combined Forecasts
The aim of the research is to compare VaR methods/models for commodities. For risk measurement Conditional Autoregressive Value at Risk models (CAViaR), implied quantile model and encompassing method are used. The aim is...
Density Forecasts Based on Disaggregate Data: Nowcasting Polish Inflation
The paper investigates gains in performance of density forecasts from models using disaggregate data when forecasting aggregate series. The problem is considered within a restricted VAR framework with alternative sets of...
Distribution Choice for the Asymmetric ACD Models
In the paper, I generalize the Asymmetric Autoregressive Conditional Duration (AACD) model proposed by Bauwens and Giot (2003) with respect to the generalized gamma and the Burr distribution for an error term. I derive...
Modelling and Forecasting Business Cycle in CEE Countries using a Threshold Approach
We propose to apply a time-series-based nonlinear mechanism in the threshold autoregression (TAR) form in order to examine business cycles in Central and Eastern European economies and compare them to the entire EU busin...
The Sign RCA Models: Comparing Predictive Accuracy of VaR Measures
Evaluating Value at Risk (VaR) methods of predictive accuracy in an objective and effective framework is important for both efficient capital allocation and loss prediction. From this reasons, finding an adequate method...