Volume 16, Issue 5

In this paper we investigate asymmetries in time-varying means, volatilities, correlations, and betas of equity returns in a multivariate threshold framework. We consider alternative specifications in which the threshold variable is based on well established equity pricing factors and predictors. We find strong threshold effects with respect to market excess return, value premium, and term spread. Our results indicate that the threshold model based on the market excess return provides a flexible and computationally inexpensive specification for modeling asymmetries. We test significance of specific forms of asymmetries using subsampling methods. We compare performance of the proposed threshold model with a variety of alternatives in an out-of-sample setup and find that the threshold model performs remarkably well, especially for investors with relatively high risk aversion.

This paper extends the work in Orphanides (2003) by re-examining the empirical evidence for a Taylor rule in a nonlinear framework. In doing so, it updates the Greenbook dataset used by the afore mentioned author to the most recent available period. A three-regime threshold regression model is utilized to capture the possibly asymmetric policy reaction function used by the U.S. Federal Reserve. The theoretical foundations for such an approach to monetary policy are discussed in Orphanides and Wilcox (2002). Our results indicate that the estimated Taylor rule for the U.S., based on real-time Greenbook data for the period 1982:3-2003:4, is probably nonlinear.

The weak form of the Efficient Market Hypothesis (EMH) states that the current market price fully reflects the information of past prices and rules out predictions based on price data alone. In an efficient market, consistent prediction of the next outcome of a financial time series is problematic because there are no reoccurring patterns that can be used for a reliable prediction. This research offers an alternative test of the weak form of the EMH. It uses a universal prediction algorithm based on the Variable Order Markov tree model to identify re-occurring patterns in the data, constructs explanatory models, and predicts the next time-series outcome. Based on these predictions, it rejects the EMH for certain stock markets while accepting it for other markets. The weak form of the EMH is tested for four international stock exchanges: the German DAX index; the American Dow-Jones30 index; the Austrian ATX index and the Danish KFX index. The universal prediction algorithm is used with sliding windows of 50, 75, and 100 consecutive daily returns for periods of up to 12 trading years. Statistically significant predictions are detected for 17% to 81% of the ATX, KFX and DJ30 stock series for about 3% to 30% of the trading days. A summary prediction analysis indicates that for a confidence level of 99% the more volatile German (DAX) and American (DJ30) markets are indeed efficient. The algorithm detects periods of potential market inefficiency in the ATX and KFX markets that may be exploited for obtaining excess returns.

Empirical Mode Decomposition (EMD) was developed late last century, but has still to be introduced to the vast majority of economists. EMD was originally one of the components of Hilbert Huang Transform (HHT) which was a process of extracting the frequency mode features of cycles embedded in any time series using an adaptive data method which can be applied without making any assumption about stationarity or linear data-generating properties of time series. This paper introduces economists to the two constituent parts of the HHT transform, namely EMD and the Hilbert Spectrum, and also a new variant of this methodology, Ensemble EMD (EEMD). Several illustrative applications using the methodology are also included.

This paper devises a class of endogenous growth models with physical capital, human capital and product varieties. Unlike previous literature, we introduce a general specification of production that allows for disentangling the gains from specialization from the monopolistic markup. The consequences of separating both parameters on the equilibrium dynamics of the model are analyzed. We find that the long-run growth rate ceases to depend on the markup when it is disentangled from the returns to specialization, and the same happens with the conditions for saddle-path stability. Numerical simulations show that the steady-state equilibria are more prone to be stable in the model that disentangles parameters. Furthermore, the model is able to generate a rich variety of development scenarios, accounting for a number of stylized facts related to the development process of industrialized countries.

This paper generalizes the univariate unit root test proposed by Sollis (2009) by adding correlated covariates for a power boost. The asymptotic distribution of the proposed test is derived, and the asymptotic critical values are tabulated. Simulation experiments are conducted to demonstrate that for both small and moderate sample sizes found in applications, the new test can exhibit an excellent control over the empirical sizes, and most importantly, it delivers larger power gains from inclusion of correlated covariates, as compared to the counterpart without covariates. An application to real exchange rates for 18 Asian countries suggests that our proposed test is able to unveil stronger evidence in favour of mean reversion in real exchange rates than the counterpart without covariates does. Moreover, several real exchange rates are tested to display asymmetries in the adjustment process towards their equilibrium values.