This paper presents theoretical models and their empirical results for the return and variance dynamics of German stocks. A factor structure is used in order to allow for a parsimonious modeling of the first two moments of returns. Dynamic factor models with GARCH dynamics (GARCH(1,1)-M,...

This double-issue contains 11 papers invited for the first special issue on “Computational methods for Russian economic and financial modelling”. It was an attempt to explore and bring together practical, state-of-the-art applications of computational techniques with a particular focus on...

In this paper, we study main problems and practical issues of modeling and forecasting of macroeconomic variables in the national economy. For that, we employ astructural VAR models and estimate interdependencies among different economic variables. Initial data analysis of macroeconomic...

In this study, we investigate forecasting performance of various univariate and multivariate models in predicting inflation for different horizons. We design our forecast experiment for the post-oil boom years of 2010-2014 and compare forecasting ability of the different models with that of...

This paper exploits the fact that implied volatilities calculated from identical call and put options have often been empirically found to differ, although they should be equal in theory. We propose a new bivariate mixture multiplicative error model and show that it is a good fit to Nikkei 225...

This paper provides an extensive analysis of the predictive ability of financial volatility measures for economic activity. We construct monthly measures of aggregated and industry-level stock volatility, and bond market volatility from daily returns. We model log financial volatility as...

This paper surveys the recent literature on inflation forecasting and conducts an extensive empirical analysis on forecasting inflation in Singapore, Japan, South Korea and Hong Kong paying particular attention to whether the inflation-markup theory can help to forecast inflation. We first...

This paper develops methods for automatic selection of variables in forecasting Bayesian vector autoregressions (VARs) using the Gibbs sampler. In particular, I provide computationally efficient algorithms for stochastic variable selection in generic (linear and nonlinear) VARs. The performance...

Using sample data from 1960 to 2006, GDP forecasts upto 2050 has been prepared for the Australian economy using the VAR methodology.

Bayesian inference requires an analyst to set priors. Setting the right prior is crucial for precise forecasts. This paper analyzes how optimal prior changes when an economy is hit by a recession. For this task, an autoregressive distributed lag (ADL) model is chosen. The results show that a...