|
on Market Microstructure |
| By: | Richard Y. Chen; Per A. Mykland |
| Abstract: | In this paper, we investigate the implication of non-stationary market microstructure noise to integrated volatility estimation, provide statistical tools to test stationarity and non-stationarity in market microstructure noise, and discuss how to measure liquidity risk using high frequency financial data. In particular, we discuss the impact of non-stationary microstructure noise on TSRV (Two-Scale Realized Variance) estimator, and design three test statistics by exploiting the edge effects and asymptotic approximation. The asymptotic distributions of these test statistics are provided under both stationary and non-stationary noise assumptions respectively, and we empirically measure aggregate liquidity risks by these test statistics from 2006 to 2013. As byproducts, functional dependence and endogenous market microstructure noise are briefly discussed. Simulation studies corroborate our theoretical results. Our empirical study indicates the prevalence of non-stationary market microstructure noise in the New York Stock Exchange. |
| Date: | 2015–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1512.06159&r=mst |
| By: | Nicholas Barberis; Robin Greenwood; Lawrence Jin; Andrei Shleifer |
| Abstract: | We present an extrapolative model of bubbles. In the model, many investors form their demand for a risky asset by weighing two signals?an average of the asset?s past price changes and the asset?s degree of overvaluation. The two signals are in conflict, and investors ?waver? over time in the relative weight they put on them. The model predicts that good news about fundamentals can trigger large price bubbles. We analyze the patterns of cash-flow news that generate the largest bubbles, the reasons why bubbles collapse, and the frequency with which they occur. The model also predicts that bubbles will be accompanied by high trading volume, and that volume increases with past asset returns. We present empirical evidence that bears on some of the model?s distinctive predictions. |
| Date: | 2015–12 |
| URL: | http://d.repec.org/n?u=RePEc:qsh:wpaper:357401&r=mst |
| By: | Ilze KALNINA |
| Abstract: | We consider the problem of conducting inference on nonparametric high-frequency estimators without knowing their asymptotic variances. We prove that a multivariate subsampling method achieves this goal under general conditions that were not previously available in the literature. We suggest a procedure for a data-driven choice of the bandwidth parameters. Our simulation study indicates that the subsampling method is much more robust than the plug-in method based on the asymptotic expression for the variance. Importantly, the subsampling method reliably estimates the variability of the Two Scale estimator even when its parameters are chosen to minimize the finite sample Mean Squared Error; in contrast, the plugin estimator substantially underestimates the sampling uncertainty. By construction, the subsampling method delivers estimates of the variance-covariance matrices that are always positive semi-definite. We use the subsampling method to study the dynamics of financial betas of six stocks on the NYSE. We document significant variation in betas within year 2006, and find that tick data captures more variation in betas than the data sampled at moderate frequencies such as every five or twenty minutes. To capture this variation we estimate a simple dynamic model for betas. The variance estimation is also important for the correction of the errors-in-variables bias in such models. We find that the bias corrections are substantial, and that betas are more persistent than the naive estimators would lead one to believe. |
| Date: | 2015 |
| URL: | http://d.repec.org/n?u=RePEc:mtl:montec:13-2015&r=mst |
| By: | Abhijit Sharang; Chetan Rao |
| Abstract: | We use machine learning for designing a medium frequency trading strategy for a portfolio of 5 year and 10 year US Treasury note futures. We formulate this as a classification problem where we predict the weekly direction of movement of the portfolio using features extracted from a deep belief network trained on technical indicators of the portfolio constituents. The experimentation shows that the resulting pipeline is effective in making a profitable trade. |
| Date: | 2015–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1512.06228&r=mst |
| By: | Darrell Duffie; Haoxiang Zhu |
| Abstract: | Size discovery refers to the use of trade mechanisms by which large quantities of an asset can be exchanged at a price that does not respond to price pressure. Primary examples of size discovery include "workup," a trade protocol used in the markets for U.S. Treasuries and swaps, and block-trading "dark pools," used in equity markets. By freezing the execution price, a size-discovery mechanism does not clear the market, but overcomes large investors' concerns over their price impacts. Price-discovery mechanisms, which determine a market-clearing price by matching supply and demand, cause investors to internalize their price impacts, inducing costly delays in the reduction of position imbalances. We show that augmenting a price-discovery mechanism with a size-discovery mechanism such as workup or dark pools improves allocative efficiency. Because of adverse selection regarding the order imbalances of other investors, size discovery is used only by investors with large position imbalances. |
| JEL: | D82 G14 |
| Date: | 2015–11 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:21696&r=mst |