A residual-based cointegration test for near unit root variables
Methods of inference based on a unit root assumption in the data are typically not robust to even small deviations from this assumption. In this paper, we propose robust procedures for a residual-based test of cointegration when the data are generated by a near unit root process. A Bonferroni method is used to address the uncertainty regarding the exact degree of persistence in the process. We thus provide a method for valid inference in multivariate near unit root processes where standard cointegration tests may be subject to substantial size distortions and standard OLS inference may lead ...
Frequency of observation and the estimation of integrated volatility in deep and liquid financial markets
Using two newly available ultrahigh-frequency datasets, we investigate empirically how frequently one can sample certain foreign exchange and U.S. Treasury security returns without contaminating estimates of their integrated volatility with market microstructure noise. Using volatility signature plots and a recently-proposed formal decision rule to select the sampling frequency, we find that one can sample FX returns as frequently as once every 15 to 20 seconds without contaminating volatility estimates; bond returns may be sampled as frequently as once every 2 to 3 minutes on days without ...
The Evolution of Price Discovery in an Electronic Market
We study the evolution of the price discovery process in the euro-dollar and dollar-yen currency pairs over a ten-year period on the EBS platform, a global trading venue used by both manual and automated traders. We find that the importance of market orders decreases sharply over that period, owing mainly to a decline in the information share from manual trading, while the information share of market orders from algorithmic and high-frequency traders remains fairly constant. At the same time, there is a substantial, but gradual, increase in the information share of limit orders. Price ...
Should we expect significant out-of-sample results when predicting stock returns?
Using Monte Carlo simulations, I show that typical out-of-sample forecast exercises for stock returns are unlikely to produce any evidence of predictability, even when there is in fact predictability and the correct model is estimated.
What drives volatility persistence in the foreign exchange market?
We analyze the factors driving the widely-noted persistence in asset return volatility using a unique dataset on global euro-dollar exchange rate trading. We propose a new simple empirical specification of volatility, based on the Kyle-model, which links volatility to the information flow, measured as the order flow in the market, and the price sensitivity to that information. Through the use of high-frequency data, we are able to estimate the time-varying market sensitivity to information, and movements in volatility can therefore be directly related to movements in two observable variables, ...
Rise of the machines: algorithmic trading in the foreign exchange market
We study the impact that algorithmic trading, computers directly interfacing at high frequency with trading platforms, has had on price discovery and volatility in the foreign exchange market. Our dataset represents a majority of global interdealer trading in three major currency pairs in 2006 and 2007. Importantly, it contains precise observations of the size and the direction of the computer-generated and human-generated trades each minute. The empirical analysis provides several important insights. First, we find evidence that algorithmic trades tend to be correlated, suggesting that the ...
Predictive regressions with panel data
This paper analyzes panel data inference in predictive regressions with endogenous and nearly persistent regressors. The standard fixed effects estimator is shown to suffer from a second order bias; analytical results, as well as Monte Carlo evidence, show that the bias and resulting size distortions can be severe. New estimators, based on recursive demeaning as well as direct bias correction, are proposed and methods for dealing with cross sectional dependence in the form of common factors are also developed. Overall, the results show that the econometric issues associated with predictive ...
Predicting global stock returns
I test for stock return predictability in the largest and most comprehensive data set analyzed so far, using four common forecasting variables: the dividend- and earnings-price ratios, the short interest rate, and the term spread. The data contain over 20,000 monthly observations from 40 international markets, including 24 developed and 16 emerging economies. In addition, I develop new methods for predictive regressions with panel data. Inference based on the standard fixed effects estimator is shown to suffer from severe size distortions in the typical stock return regression, and an ...
Diversification across characteristics
I study long-short portfolio strategies formed on seven different stock characteristics representing various measures of past returns, value, and size. Each individual characteristic results in a profitable portfolio strategy, but these single-characteristic strategies are all dominated by a diversified strategy that places equal weight on each of the single-characteristic strategies. The benefits of diversifying across characteristic-based long-short strategies are substantial and can be attributed to the mostly low, and sometimes substantially negative, correlation between the returns on ...
Estimation of average local-to-unity roots in heterogenous panels
This paper considers the estimation of average autoregressive roots-near-unity in panels where the time-series have heterogenous local-to-unity parameters. The pooled estimator is shown to have a potentially severe bias and a robust median based procedure is proposed instead. This median estimator has a small asymptotic bias that can be eliminated almost completely by a bias correction procedure. The asymptotic normality of the estimator is proved. The methods proposed in the paper provide a useful way of summarizing the persistence in a panel data set, as well as a complement to more ...