Forecasting exchange rates in transition economies: A comparison of multivariate time series models

This article compares the accuracy of vector autoregressive (VAR), restricted vector autoregressive (RVAR), Bayesian vector autoregressive (BVAR), vector error correction (VEC) and Bayesian vector error correction (BVEC) models in forecasting the exchange rates for five Central and Eastern European currencies (Czech Koruna, Hungarian Forint, Polish Zloty, Slovak Koruna and Slovenian Tolar) against the Euro and the US dollar. Among the specifications composing this battery of multivariate time series models, those with the smallest prediction error still fail to reject the test of equality of forecasting accuracy against the random walk model in short-term predictions, with the exception of the Slovenian Tolar/Euro exchange rate.First version received: October 2002/Final version received: September 2003The authors are grateful to two anonymous referees and the participants in the workshop Monetary and Exchange Rate Strategies Related to the Current European Unions Enlargement Processes, held in Leuven in September 2000, for very helpful comments.     

Forecasting the Euro exchange rate using vector error correction models

Forecasting the Euro Exchange Rate Using Vector Error Correction Models. — This paper presents an exchange rate model for the Euro exchange rates of four major currencies, namely the US dollar, the British pound, the Japanese yen and the Swiss franc. The model is based on the monetary approach of exchange rate theory which uses fundamental macroeconomic variables to explain the exchange rate. A crucial point when using such a model is its proper estimation through cointegration analysis. The euro exchange rate model is therefore estimated in the form of a Vector Autoregressive (VAR) model with cointegrating vectors (VECM). We find that when cointegration analysis is undertaken properly, the naive random walk prediction can be out-performed for the US dollar, the British pound and the Japanese yen, but not for the Swiss franc.     

Finite Sample Correction Factors for Panel Cointegration Tests*

In this paper we present finite T mean and variance correction factors and corresponding response surface regressions for the panel cointegration tests presented in Pedroni (1999, 2004) , Westerlund (2005) , Larsson et al. (2001) and Breitung (2005) . For the single equation tests, we consider up to 12 regressors and for the system tests vector autoregression dimensions up to 12 variables. All commonly used specifications for the deterministic components are considered. The sample sizes considered are T ∈ {10,20,30,40,50,60,70,80,90,100,200,500}.     

Capital income taxation and risk taking under prospect theory

This research examines capital income taxation for a prospect theory investor under some acceptable in the literature reference levels relative to which are the changes in the level of wealth valued. Depending on the reference level, some results indicate that it is possible for a capital income tax increase not to stimulate risk taking even if the tax code provides attractive full loss offset provisions. However, risk taking can be stimulated when investors compare their reference level with others. Risk taking can increase also if the investor interprets part of the tax as a loss instead as a reduced gain. Then the investor becomes risk seeking and moves away from the discomfort zone of relative losses. This later response to taxation causes private risk taking to increase.     

Optimal asset allocation under linear loss aversion

We study the asset allocation of a linear loss-averse (LA) investor and compare it to the more traditional mean-variance (MV) and conditional value-at-risk (CVaR) investors. First we derive conditions under which the LA problem is equivalent to the MV and CVaR problems and solve analytically the two-asset problem of the LA investor for a risk-free and a risky asset. Then we run simulation experiments to study properties of the optimal LA and MV portfolios under more realistic assumptions. We find that under asymmetric dependence LA portfolios outperform MV portfolios, provided investors are sufficiently loss-averse and dependence is large. Finally, using 13 EU and US assets, we implement the trading strategy of a linear LA investor who reallocates his/her portfolio on a monthly basis. We find that LA portfolios clearly outperform MV and CVaR portfolios and that incorporating a dynamic update of the LA parameters significantly improves the performance of LA portfolios.     

Multistep predictions for multivariate GARCH models: Closed form solution and the value for portfolio management

This paper derives the closed form solution for multistep predictions of the conditional means and covariances for multivariate ARMA-GARCH models. These predictions are useful e.g. in mean-variance portfolio analysis when the rebalancing frequency is lower than the data frequency. In this situation the conditional mean and the conditional covariance matrix of the cumulated higher frequency returns are required as inputs in the mean-variance portfolio problem. The empirical value of the result is evaluated by comparing the performance of quarterly and monthly rebalanced portfolios using monthly MSCI index data across a large set of GARCH models. Using correct multistep predictions generally results in lower risk and higher returns.     

CEEC growth projections: Certainly necessary and necessarily uncertain*

In this paper we discuss the necessity for an indirect approach to assess the growth and convergence prospects of ten Central and Eastern European countries (CEEC10). Ongoing structural changes in these countries and the recent European Union membership of eight countries in the sample have to be taken into account in growth projections. Our indirect approach consists of basing growth projections for the CEEC10 on growth equations estimated for the incumbent EU member states. The study improves upon current practice in two ways. First, growth equations are estimated for the EU14 and not on a large heterogeneous panel that includes many countries unrelated to the CEEC10. Second, by means of a variety of equations and scenarios we assess the uncertainty inherent in such projections. We present growth‐rate and convergence time distributions. The mean convergence times are in line with previous findings. The growth‐rate and convergence time distributions are bi‐modal, reflecting the possibility of two distinct growth paths, depending upon economic policy choices.