Asymptotic expansions for some semiparametric program evaluation estimators

We investigate the performance of a class of semiparametric estimators of the treatment effect via asymptotic expansions. We derive approximations to the first two moments of the estimator that are valid to 'second order'. We use these approximations to define a method of bandwidth selection. We also propose a degrees of freedom like bias correction that improves the second order properties of the estimator but without requiring estimation of higher order derivatives of the unknown propensity score. We provide some numerical calibrations of the results

The Effect of Fragmentation in Trading on Market Quality in the UK Equity Market

We investigate the effects of fragmentation in equity markets on the quality of trading outcomes in a panel of FTSE stocks over the period 2008-2011. This period coincided with a great deal of turbulence in the UK equity markets which had multiple causes that need to be controlled for. To achieve this, we use the common correlated effects estimator for large heterogeneous panels. We extend this estimator to quantile regression to analyze the whole conditional distribution of market quality. We find that both fragmentation in visible order books and dark trading that is offered outside the visible order book lower volatility. But dark trading increases the variability of volatility, while visible fragmentation has the opposite effect in particular at the upper quantiles of the conditional distribution. The transition from a monopolistic to a fragmented market is non-monotone

Implications of High-Frequency Trading for Security Markets

High frequency trading (HFT) has grown substantially in recent years, due to fast-paced technological developments and their rapid uptake, particularly in equity markets. This paper investigates how HFT could evolve and, by developing a robust understanding of its effects, to identify potential risks and opportunities that it could present in terms of financial stability and other market outcomes such as volatility, liquidity, price efficiency and price discovery. Despite commonly held negative perceptions, the available evidence indicates that HFT and algorithmic trading (AT) may have several beneficial effects on markets. However, they may cause instabilities in financial markets in specific circumstances. Carefully chosen regulatory measures are needed to address concerns in the shorter term. However, further work is needed to inform policies in the longer term, particularly in view of likely uncertainties and lack of data. This will be vital to support evidence-based regulation in this controversial and rapidly evolving field

Circuit Breakers on the London Stock Exchange: Do they improve subsequent market quality?

This paper uses proprietary data to evaluate the efficacy of single-stock circuit breakers on the London Stock Exchange during July and August 2011. We exploit exogenous variation in the length of the uncrossing periods that follow a trading suspension to estimate the effect of auction length on market quality, measured by volume of trades, frequency of trading and the change in realised variance of returns. We also estimate the effect of a trading suspension in one FTSE-100 stock on the volume of trades, trading frequency and the change in realised variance of returns for other FTSE-100 stocks. We find that auction length has a significant detrimental effect on market quality for the suspended security when returns are negative but no discernible effect when returns are positive. We also find that trading suspensions help to ameliorate the spread of market microstructure noise and price inefficiency across securities during falling markets but the reverse is true during rising markets. Although trading suspensions may not improve the trading process within a particular security, they do play an important role preventing the spread of poor market quality across securities in falling markets and therefore can be effective tools for promoting market-wide stability

Testing for Stochastic Dominance Efficiency

We propose a new test of the stochastic dominance efficiency of a given portfolio over a class of portfolios. We establish its null and alternative asymptotic properties, and define a method for consistently estimating critical values. We present some numerical evidence that our tests work well in moderate sized samples

The Cross-Quantilogram: Measuring Quantile Dependence and Testing Directional Predictability between Time Series

This paper proposes the cross-quantilogram to measure the quantile dependence between two time series. We apply it to test the hypothesis that one time series has no directional predictability to another time series. We establish the asymptotic distribution of the cross quantilogram and the corresponding test statistic. The limiting distributions depend on nuisance parameters. To construct consistent confidence intervals we employ the stationary bootstrap procedure; we show the consistency of this bootstrap. Also, we consider the self-normalized approach, which is shown to be asymptotically pivotal under the null hypothesis of no predictability. We provide simulation studies and two empirical applications. First, we use the cross-quantilogram to detect predictability from stock variance to excess stock return. Compared to existing tools used in the literature of stock return predictability, our method provides a more complete relationship between a predictor and stock return. Second, we investigate the systemic risk of individual financial institutions, such as JP Morgan Chase, Goldman Sachs and AIG. This article has supplementary materials online

Multivariate Variance Ratio Statistics

We propose several multivariate variance ratio statistics. We derive the asymptotic distribution of the statistics and scalar functions thereof under the null hypothesis that returns are unpredictable after a constant mean adjustment (i.e., under the Efficient Market Hypothesis). We do not impose the no leverage assumption of Lo and MacKinlay (1988) but our asymptotic standard errors are relatively simple and in particular do not require the selection of a bandwidth parameter. We extend the framework to allow for a smoothly varying risk premium in calendar time, and show that the limiting distribution is the same as in the constant mean adjustment case. We show the limiting behaviour of the statistic under a multivariate fads model and under a moderately explosive bubble process: these alternative hypotheses give opposite predictions with regards to the long run value of the statistics. We apply the methodology to three weekly size-sorted CRSP portfolio returns from 1962 to 2013 in three subperiods. We find evidence of a reduction of linear predictability in the most recent period, for small and medium cap stocks. We find similar results for the main UK stock indexes. The main findings are not substantially affected by allowing for a slowly varying risk premium

An investigation into Multivariate Variance Ratio Statistics and their application to Stock Market Predictability

We propose several multivariate variance ratio statistics. We derive the asymptotic distribution of the statistics and scalar functions thereof under the null hypothesis that returns are unpredictable after a constant mean adjustment (i.e., under the weak form Efficient Market Hypothesis). We do not impose the no leverage assumption of Lo and MacKinlay (1988) but our asymptotic standard errors are relatively simple and in particular do not require the selection of a bandwidth parameter. We extend the framework to allow for a time varying risk premium through common systematic factors. We show the limiting behaviour of the statistic under a multivariate fads model and under a moderately explosive bubble process: these alternative hypotheses give opposite predictions with regards to the long run value of the statistics. We apply the methodology to five weekly size-sorted CRSP portfolio returns from 1962 to 2013 in three subperiods. period, for small and medium cap stocks. The main findings are not substantially affected by allowing for a common factor time varying risk premium

An investigation into Multivariate Variance Ratio Statistics and their application to Stock Market Predictability

We propose several multivariate variance ratio statistics. We derive the asymptotic distribution of the statistics and scalar functions thereof under the null hypothesis that returns are unpredictable after a constant mean adjustment (i.e., under the weak form Efficient Market Hypothesis). We do not impose the no leverage assumption of Lo and MacKinlay (1988) but our asymptotic standard errors are relatively simple and in particular do not require the selection of a bandwidth parameter. We extend the framework to allow for a time varying risk premium through common systematic factors. We show the limiting behaviour of the statistic under a multivariate fads model and under a moderately explosive bubble process: these alternative hypotheses give opposite predictions with regards to the long run value of the statistics. We apply the methodology to five weekly size-sorted CRSP portfolio returns from 1962 to 2013 in three subperiods. period, for small and medium cap stocks. The main findings are not substantially affected by allowing for a common factor time varying risk premium