A Simple, Positive Semi-Definite, Heteroskedasticity and AutocorrelationConsistent Covariance Matrix

This paper describes a simple method of calculating a heteroskedasticity and autocorrelation consistent covariance matrix that is positive semi-definite by construction. It also establishes consistency of the estimated covariance matrix under fairly general conditions.

Automatic Lag Selection in Covariance Matrix Estimation

We propose a nonparametric method for automatically selecting the number of autocovariances to use in computing a heteroskedasticity and autocorrelation consistent covariance matrix. For a given kernel for weighting the autocovariances, we prove that our procedure is asymptotically equivalent to one that is optimal under a mean squared error loss function. Monte Carlo simulations suggest that our procedure performs tolerably well, although it does result in size distortions.

Combination Forecasts of Bond and Stock Returns: An Asset Allocation Perspective

We investigate the out-of-sample forecasting ability of the HML, SMB, momentum, short-term and long-term reversal factors along with their size and value decompositions on U.S. bond and stock returns for a variety of horizons ranging from the short run (1 month) to the long run (2 years). Our findings suggest that these factors contain significantly more information for future bond and stock market returns than the typically employed financial variables. Combination of forecasts of the empirical factors turns out to be particularly successful, especially from an an asset allocation perspective. Similar findings pertain to the European and Japanese markets

Long memory conditional volatility and asset allocation

Pre-print version dated May 2011 issued as Discussion paper by University of Exeter. A definitive version was subsequently published in International Journal of Forecasting Volume 29, Issue 2, April–June 2013, Pages 258–273. Available online at http://www.sciencedirect.com/In this paper, we evaluate the economic benefits that arise from allowing for long memory when forecasting the covariance matrix of returns over both short and long horizons, using the asset allocation framework of Engle and Colacito (2006) In particular, we compare the statistical and economic performances of four multivariate long memory volatility models (the long memory EWMA, long memory EWMA–DCC, FIGARCH-DCC and component GARCH-DCC models) with those of two short memory models (the short memory EWMA and GARCH-DCC models). We report two main findings. First, for longer horizon forecasts, long memory models generally produce forecasts of the covariance matrix that are statistically more accurate and informative, and economically more useful than those produced by short memory models. Second, the two parsimonious long memory EWMA models outperform the other models–both short and long memory–across most forecast horizons. These results apply to both low and high dimensional covariance matrices and both low and high correlation assets, and are robust to the choice of the estimation window

How useful are no-arbitrage restrictions for forecasting the term structure of interest rates?

International audienceWe develop a general framework for analyzing the usefulness of imposing parameter restrictions on a forecasting model. We propose a measure of the usefulness of the restrictions that depends on the forecaster's loss function and that could be time varying. We show how to conduct inference about this measure. The application of our methodology to analyzing the usefulness of no-arbitrage restrictions for forecasting the term structure of interest rates reveals that: (1) the restrictions have become less useful over time; (2) when using a statistical measure of accuracy, the restrictions are a useful way to reduce parameter estimation uncertainty, but are dominated by restrictions that do the same without using any theory; (3) when using an economic measure of accuracy, the no-arbitrage restrictions are no longer dominated by atheoretical restrictions, but for this to be true it is important that the restrictions incorporate a time-varying risk premium

Can currency-based risk factors help forecast exchange rates?

This paper examines time-series predictability of bilateral exchange rates from linear factor models that utilize unconditional and conditional expectations of three currency-based risk factors. Exploiting a comprehensive set of statistical criteria, we find that all versions of the linear factor models largely fail to outperform the benchmark of random walk with drift model in the out-of-sample forecasting of monthly exchange rate returns. This holds true for individual currencies and currency portfolios formed on forward discounts. We also show that the information embedded in the currency-based risk factors does not generate systematic economic value to investors

Exchange rate predictability in a changing world

An expanding literature articulates the view that Taylor rules are helpful in predicting exchange rates. In a changing world, however, Taylor rule parameters may be subject to structural instabilities, for example in the aftermath of the Global Financial Crisis. This paper forecasts exchange rates using Taylor rules with Time-Varying Parameters (TVP) estimated by Bayesian methods. Focusing on the data from the crisis, we improve upon the random walk for at least half, and for as many as seven out of 10, of the currencies considered. Results are stronger when we allow the TVP of the Taylor rules to differ between countries

On the sources of uncertainty in exchange rate predictability

In a unified framework, we examine four sources of uncertainty in exchange rate forecasting models: (i) random variations in the data, (ii) estimation uncertainty, (iii) uncertainty about the degree of time-variation in coefficients, and (iv) uncertainty regarding the choice of the predictor. We find that models which embed a high-degree of coefficient variability yield forecast improvements at horizons beyond 1-month. At the 1-month horizon, and apart from the standard variance implied by unpredictable fluctuations in the data, the second and third sources of uncertainty listed above are key obstructions to predictive ability. The uncertainty regarding the choice of the predictors is negligible

The predictive performance of commodity futures risk factors

This paper investigates the time-series predictability of commodity futures excess returns from factor models that exploit two risk factors – the equally weighted average excess return on long positions in a universe of futures contracts and the return difference between the high- and low-basis portfolios. Adopting a standard set of statistical evaluation metrics, we find weak evidence that the factor models provide out-of-sample forecasts of monthly excess returns significantly better than the benchmark of random walk with drift model. We also show, in a dynamic asset allocation environment, that the information contained in the commodity-based risk factors does not generate systematic economic value to risk-averse investors pursuing a commodity stand-alone strategy or a diversification strategy