In this article we provide a review of the literature with respect to the e$cient markets hypothesis and chaos. In doing so, we contrast the martingale behavior of asset prices to nonlinear chaotic dynamics, discuss some recent techniques used in distinguishing between probabilistic and deterministic behavior in asset prices, and report some evidence. Moreover, we look at the controversies that...

Most econometric methods for testing the proposition of long-run monetary neutrality rely on the assumption that money and real output do not cointegrate. This paper argues that these results can be attributed in part to the low power of univariate tests, and that a violation of the noncointegration assumption is likely to result in a nonrejection of the neutrality proposition. To alleviate thi...

This paper establishes analytically what the asymptotic behavior of the DickeyFuller coefficient tests and the Dickey-Fuller t-statistic tests will be when the true data-generating process is a trigonometric function of an integrated process. Using some recently established limit theorems, it is shown that for such a data generating process, the asymptotic behavior of these unit root tests is r...

In this paper, we consider the role of “leads” of the first difference of integrated variables in the dynamic OLS estimation of cointegrating regression models. Specifically, we investigate Stock and Watson’s (1993) claim that the role of leads is related to the concept of Granger causality by a Monte Carlo simulation. From the simulation results, we find that the dynamic OLS estimator without ...

We measure asset price jumps by the hedging error they induce on a delta-hedged position of European options. Based on high frequency data, we propose a nonparametric estimator for this measure and a test for its positivity. We further construct a Kolmogorov-type test for the presence of jump hedging errors for a possibly infinite-dimensional family of options based on the worst-case contract i...

We consider selecting a regression model, using a variant of Gets, when there are more variables than observations, in the special case that the variables are impulse dummies (indicators) for every observation. We show that the setting is unproblematic if tackled appropriately, and obtain the finite-sample distribution of estimators of the mean and variance in a simple location-scale model unde...

We study how total factor productivity (TFP), energy prices, and the Great Moderation are linked. First we estimate a joint stochastic process for the energy price and TFP and establish that until the second quarter of 1982, energy prices negatively affected productivity. This spillover has since disappeared. Second, we show that within the framework of a dynamic stochastic general equilibrium ...

This paper describes a Bayesian nonparametric approach to volatility estimation. Volatility is assumed to follow a superposition of an infinite number of Ornstein–Uhlenbeck processes driven by a compound Poisson process with a parametric or nonparametric jump size distribution. This model allows a wide range of possible dependencies and marginal distributions for volatility. The properties of t...

We generalise the Black-Litterman (BL) portfolio management framework to incorporate time-variation in the conditional distribution of returns in the asset allocation process. We evaluate the performance of the dynamic BL model using both standard performance ratios as well as other measures that are designed to capture tail risk in the presence of non-normally distributed asset returns. We fin...

This paper focuses on the performance of various GARCH models in terms of their ability of delivering volatility forecasts for stock return data. Volatility forecasts obtained from a variety of mean and variance specifications in GARCH models are compared to a proxy of actual volatility calculated using daily data. In-sample tests suggest that a regression of volatility estimates on actual vola...