Optimal VWAP trading under noisy conditions

Humphery‐Jenner, Mark

doi:10.1016/j.jbankfin.2011.01.028

Cited by 32 publications

(17 citation statements)

References 31 publications

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“…McCulloch and Kazakov () view it as a quadratic hedging problem under partial information, whereas Kakade et al. () and Białkowski et al () use online learning and dynamic volume approaches and Humphery‐Jenner () gives a VWAP trading rule, which takes intraday noise into consideration. Finally, Bouchard and Dang () formulate it as a stochastic target problem and derive a viscosity solution characterization of the value function.…”

Section: Introductionmentioning

confidence: 99%

Optimal Execution of a Vwap Order: A Stochastic Control Approach

Frei

Westray

2013

Mathematical Finance

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We consider the optimal liquidation of a position of stock (long or short) where trading has a temporary market impact on the price. The aim is to minimize both the mean and variance of the order slippage with respect to a benchmark given by the market volume-weighted average price (VWAP). In this setting, we introduce a new model for the relative volume curve which allows simultaneously for accurate data fit, economic justification, and mathematical tractability. Tackling the resulting optimization problem using a stochastic control approach, we derive and solve the corresponding Hamilton-Jacobi-Bellman equation to give an explicit characterization of the optimal trading rate and liquidation trajectory.

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Section: Introductionmentioning

confidence: 99%

Optimal Execution of a Vwap Order: A Stochastic Control Approach

Frei

Westray

2013

Mathematical Finance

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“…Fuh, Teng, and Wang (2010) investigate the performance of heuristic trading strategies to track VWAP both in a simulation study and in an empirical study for 20 stocks in the Taiwan Stock Exchange. Bia lkowski et al (2008) and Humphery-Jenner (2011) suggest using an adaptive, fixed-schedule, trading strategy, with trades adjusted to match the expected market volume over the remaining time intervals in two models of volume that allow for auto-regression and jumps, but do not allow for correlation between price return variance and volume and do not optimize the trading strategy. Kakade, Kearns, Mansour,…”

Section: Literature Reviewmentioning

confidence: 99%

Quintet Volume Projection

Марков¹,

Vilenskaia²,

Rashkovich³

2017

JOT

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We consider the problem of finding a strategy that tracks the volume weighted average price (VWAP) of a stock, a key measure of execution quality for large orders used by institutional investors. We obtain the optimal, dynamic, VWAP tracking strategy in closed form in a model without trading costs with general price and volume dynamics. We build a model of intraday volume using the Trade and Quote dataset to empirically test the strategy, both without trading costs and when trading has temporary and permanent effects, and find that the implementation cost is lower than the cost charged by brokerage houses.JEL classification: G12, G29, C61

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“…The VWAP often becomes an optimal benchmark and an implementation strategy. An order with enough large volume is decomposed into smaller suborders, then trade the small suborders throughout a specified period gradually by sequence so that their trading cost is less than or equal to the VWAP value in the corresponding time period [32]. By doing so, it can reduce the market impact, increase the profitability of investors' transactions, and making the selling or the buying in large amounts of shares more secret.…”

Section: B Prediction Of Stock Volume Weighted Average Pricementioning

confidence: 99%

“…α satisfies the following inequality in probability η [1] According to (31) and (32), the following inequality is established in probability 1 2mη − :…”

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confidence: 99%

Global Nonlinear Kernel Prediction for Large Data Set With a Particle Swarm-Optimized Interval Support Vector Regression

Ding

Cheng

Pedrycz

et al. 2015

IEEE Trans. Neural Netw. Learning Syst.

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Abstract-A new global nonlinear predictor with a particle swarm optimized interval support vector regression (PSO-ISVR) is proposed to address three issues (viz. kernel selection, model optimization, kernel method speed) encountered when applying support vector regression (SVR) in the presence of large datasets. The novel prediction model can reduce the SVR computing overhead by dividing input space and adaptively selecting the optimized kernel functions to obtain optimal SVR parameter by PSO. To quantify the quality of the predictor, its generalization performance and execution speed are investigated based on statistical learning theory. In addition, experiments using synthetic data as well as the stock volume weighted average price (VWAP) are reported to demonstrate the effectiveness of the developed models. The experimental results show that the proposed PSO-ISVR predictor can improve the computational efficiency and the overall prediction accuracy compared with the results produced by the SVR and other regression methods. The proposed PSO-ISVR provides an important tool for nonlinear regression analysis of big data.Index Terms-global nonlinear predictor, interval support vector regression, particle swarm optimization, kernel function, sliding adaptive model, large data I. INTRODUCTIONUPPORT vector regression (SVR) model is constructed based on statistical learning theory [1], which uses a kernel function to map the data from some input space to a highdimensional feature space where the problem becomes amenable for handling by linear regression [2]. Owing to its robustness to noise and its generalization abilities, it has been widely employed in various areas such as adaptive flight identification [3], ore grade estimation [4] , and stock market price forecasting [5][6][7].Many researchers have pointed out that three crucial problems existing in SVR urgently need to be addressed: (1) How to choose or construct an appropriate kernel to complete forecasting problems [8,9]; (2) How to optimize parameters of SVR to improve the quality of prediction [10,11]; (3) How to construct a fast algorithm to operate in presence of large datasets [12,13]. With unsuitable kernel functions or hyperparameter settings, SVR may lead to poor prediction results. In fact, a kernel function forms a certain nonlinear transformation function. Due to data uncertainty in practical regression problems, it is difficult to determine which kernel function is the best one for a specific problem without any prior knowledge [14]. If the adjustable kernel parameters in SVR are not properly selected, it will result in the undesirable phenomena of over-fitting or under-fitting [1]. Furthermore, SVR is typically confronted with a heavy computing overhead due to processing large Gram matrices being associated with the kernels [13]. This computing burden becomes an essential barrier when dealing with massive data, such as those encountered in protein structure prediction and time series prediction [15].During the past several years, various methods h...

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Optimal VWAP trading under noisy conditions

Cited by 32 publications

References 31 publications

Optimal Execution of a Vwap Order: A Stochastic Control Approach

Optimal Execution of a Vwap Order: A Stochastic Control Approach

Quintet Volume Projection

Global Nonlinear Kernel Prediction for Large Data Set With a Particle Swarm-Optimized Interval Support Vector Regression

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