
on Market Microstructure 
By:  Nikola Gradojevic (Faculty of Business Administration, Lakehead University, Canada; Rouen Business School, France; The Rimini Centre for Economic Analysis, Italy); Camillo Lento (Faculty of Business Administration, Lakehead University, Canada; School of Accounting, Economics and Finance, University of Southern Queensland, Australia) 
Abstract:  This paper investigates the multiscale (frequencydependent) relationship between technical trading profitability and feedback trading effects in the Canada/U.S. dollar foreign exchange market. The results suggest weak evidence that technical trading activities of financial and nonfinancial customers drive frequent violations of the FX market microstructure assumption that exchange rate movements are driven by order flow. After controlling for transaction costs, we find that the contribution of financial customers in feedback trading dominates the contribution of nonfinancial customers at lower frequencies, while the opposite holds at higher frequencies. In addition, the novel finding is that technical indicators constructed from order flows can be profitable. 
Keywords:  Foreign Exchange Markets; Order Flows; Technical Trading; Frequency Domain 
JEL:  F31 G14 C53 
Date:  2012–06 
URL:  http://d.repec.org/n?u=RePEc:rim:rimwps:31_12&r=mst 
By:  Neil Shephard (Nuffield College, Dept of Economics and OxfordMan Institute of Quantitative Finance, University of Oxford.); Dacheng Xiu (University of Chicago Booth School of Business) 
Abstract:  Estimating the covariance and correlation between assets using high frequency data is challenging due to market microstructure effects and Epps effects. In this paper we extend Xiu’s univariate QML approach to the multivariate case, carrying out inference as if the observations arise from an asynchronously observed vector scaled Brownian model observed with error. Under stochastic volatility the resulting QML estimator is positive semidefinite, uses all available data, is consistent and asymptotically mixed normal. The quasilikelihood is computed using a Kalman filter and optimised using a relatively simple EM algorithm which scales well with the number of assets. We derive the theoretical properties of the estimator and prove that it achieves the efficient rate of convergence. We show how to make it achieve the nonparametric efficiency bound for this problem. The estimator is also analysed using Monte Carlo methods and applied on equity data that are distinct in their levels of liquidity. 
Keywords:  EM algorithm; Kalman filter; market microstructure noise; nonsynchronous data; portfolio optimisation; quadratic variation; quasilikelihood; semimartingale; volatility. 
JEL:  C01 C14 C58 D53 D81 
Date:  2012–04–23 
URL:  http://d.repec.org/n?u=RePEc:nuf:econwp:1204&r=mst 
By:  Mauricio Labadie (EXQIM  EXclusive Quantitative Investment Management  EXQIM); CharlesAlbert Lehalle (Head of Quantitative Research  CALYON group) 
Abstract:  We derive explicit recursive formulas for Target Close (TC) and Implementation Shortfall (IS) in the AlmgrenChriss framework. We explain how to compute the optimal starting and stopping times for IS and TC, respectively, given a minimum trading size. We also show how to add a minimum participation rate constraint (Percentage of Volume, PVol) for both TC and IS. We also study an alternative set of risk measures for the optimisation of algorithmic trading curves. We assume a selfsimilar process (e.g. Levy process, fractional Brownian motion or fractal process) and define a new risk measure, the pvariation, which reduces to the variance if the process is a Brownian motion. We deduce the explicit formula for the TC and IS algorithms under a selfsimilar process. We show that there is an equivalence between selfsimilar models and a family of risk measures called pvariations: assuming a selfsimilar process and calibrating empirically the parameter p for the pvariation yields the same result as assuming a Brownian motion and using the pvariation as risk measure instead of the variance. We also show that p can be seen as a measure of the aggressiveness: p increases if and only if the TC algorithm starts later and executes faster. From the explicit expression of the TC algorithm one can compute the sensitivities of the curve with respect to the parameters up to any order. As an example, we compute the first order sensitivity with respect to both a local and a global surge of volatility. Finally, we show how the parameter p of the pvariation can be implied from the optimal starting time of TC, and that under this framework p can be viewed as a measure of the joint impact of market impact (i.e. liquidity) and volatility. 
Keywords:  Quantitative Finance; HighFrequency Trading; Algorithmic Trading; Optimal Execution; Market Impact; Risk Measures; Selfsimilar Processes; Fractal Processes 
Date:  2012–05–18 
URL:  http://d.repec.org/n?u=RePEc:hal:wpaper:hal00705056&r=mst 
By:  Godfrey CharlesCadogan 
Abstract:  We introduce a trade strategy representation theorem for performance measurement and portable alpha in high frequency trading, by embedding a robust trading algorithm that describe portfolio manager market timing behavior, in a canonical multifactor asset pricing model. First, we present a spectral test for market timing based on behavioral transformation of the hedge factors design matrix. Second, we find that the typical trade strategy process is a local martingale with a background driving Brownian bridge that mimics portfolio manager price reversal strategies. Third, we show that equilibrium asset pricing models like the CAPM exists on a set with Pmeasure zero. So that excess returns, i.e. positive alpha, relative to a benchmark index is robust to no arbitrage pricing in turbulent capital markets. Fourth, the path properties of alpha are such that it is positive between suitably chosen stopping times for trading. Fifth, we demonstrate how, and why, econometric tests of portfolio performance tend to under report positive alpha. 
Date:  2012–06 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:1206.2662&r=mst 
By:  Joseph P. Janzen; Colin A. Carter; Aaron D. Smith 
Abstract:  This paper studies the effect of electronic trade on the quality of market price discovery, using the Intercontinental Exchange (ICE) cotton futures market as a laboratory to measure market quality under periods of floor trade, parallel floor and electronic trade, and electroniconly trade. Using randomwalk decomposition methods pioneered by Hasbrouck (2007), we decompose intraday variation in cotton prices into two components: one related to information about market fundamentals and one a “pricing error” related to market frictions such as the cost of liquidity provision and the transient response of prices to trades. We describe the properties of this pricing error to characterize market quality under both floor and electronic trading systems. Unlike previous studies, we analyze more than the average magnitude of the pricing error. Each day, we calculate statistics that describe market quality on that day, and we study their trend, variance and persistence. 
Keywords:  cotton, futures markets, market quality, volume, electronic trading., Marketing, Productivity Analysis, Research Methods/ Statistical Methods, Q0, F0., 
Date:  2012 
URL:  http://d.repec.org/n?u=RePEc:ags:aaea12:124994&r=mst 
By:  Wang, Xiaoyang; Garcia, Philip; Irwin, Scott H. 
Keywords:  Crop Production/Industries, 
Date:  2012–08–12 
URL:  http://d.repec.org/n?u=RePEc:ags:aaea12:124899&r=mst 
By:  Janzen, Joseph P.; Smith, Aaron D.; Carter, Colin A. 
Abstract:  This paper studies the effect of electronic trade on the quality of market price discovery, using the Intercontinental Exchange (ICE) cotton futures market as a laboratory to measure market quality under periods of floor trade, parallel floor and electronic trade, and electroniconly trade. Using randomwalk decomposition methods pioneered by Hasbrouck (2007), we decompose intraday variation in cotton prices into two components: one related to information about market fundamentals and one a “pricing error” related to market frictions such as the cost of liquidity provision and the transient response of prices to trades. We describe the properties of this pricing error to characterize market quality under both floor and electronic trading systems. Unlike previous studies, we analyze more than the average magnitude of the pricing error. Each day, we calculate statistics that describe market quality on that day, and we study their trend, variance and persistence. 
Keywords:  cotton, futures markets, market quality, volume, electronic trading., Crop Production/Industries, Research and Development/Tech Change/Emerging Technologies, Q0, F0, 
Date:  2012 
URL:  http://d.repec.org/n?u=RePEc:ags:aaea12:125024&r=mst 
By:  Phillip Wild (School of Economics, The University of Queensland); John Foster (School of Economics, The University of Queensland) 
Abstract:  In this paper, we present three nonparametric trispectrum tests that can establish whether the spectral decomposition of kurtosis of high frequency financial asset price time series is consistent with the assumptions of Gaussianity, linearity and time reversiblility. The detection of nonlinear and time irreversible probabilistic structure has important implications for the choice and implementation of a range of models of the evolution of asset prices, including BlackSholesMerton (BSM) option pricing model, ARCH/GARCH and stochastic volatility models. We apply the tests to a selection of high frequency Australian (ASX) stocks. 
Date:  2012 
URL:  http://d.repec.org/n?u=RePEc:qld:uq2004:466&r=mst 
By:  Jakub Steiner; Colin Stewart 
Abstract:  We study the effect of frequent trading opportunities and categorization on pricing of a risky asset. Frequent opportunities to trade lead to large distortions in prices if some agents forecast future prices using a simplified model of the world that fails to distinguish between some states. In the limit as the period length vanishes, these distortions take a particular form: the price must be the same in any two states that a positive mass of agents categorize together. Price distortions therefore tend to be large when different agents categorize states in different ways. We characterize the limiting prices in terms of rational expectations prices associated with a coarsened process. Similar results hold if, instead of using a simplified model of the world, some agents overestimate the likelihood of small probability events, as in prospect theory. In this case a set aside may be better than a flat or percentage subsidy. JEL Code: D83, D84, D53 
Keywords:  categorization, price distortion, trading frequency 
Date:  2012–05–25 
URL:  http://d.repec.org/n?u=RePEc:nwu:cmsems:1549&r=mst 