Design and Implementation of Schedule-Based TradingStrategies Based on Uncertainty Bands

Марков, В. А.; Mazur, Slava; Saltz, David

doi:10.3905/jot.2011.6.4.045

Cited by 7 publications

(4 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First, we build the pooled regression model described in the previous section where execution observations from algorithms are aggregated, algorithm labels are discarded and the generic TCA model is fitted on this dataset. This allows for calculation of the posterior distribution of regression coefficients α, β and γ in Equations ( 9), (10), and (12). The posterior distribution has two competing contributions: a single contribution from the prior and likelihood contribution from each observation which increases linearly with the number of observations.…”

Section: Algorithm-specific Tca Regression Modelmentioning

confidence: 99%

Bayesian Trading Cost Analysis and Ranking of Broker Algorithms

Марков¹

2019

Preprint

Self Cite

View full text Add to dashboard Cite

We present a formulation of transaction cost analysis (TCA) in the Bayesian framework for the primary purpose of comparing broker algorithms using standardized benchmarks. Our formulation allows effective calculation of the expected value of trading benchmarks with only a finite sample of data relevant to practical applications. We discuss the nature of distribution of implementation shortfall, volume-weighted average price, participation-weighted price and short-term reversion benchmarks. Our model takes into account fat tails, skewness of the distributions and heteroscedasticity of benchmarks. The proposed framework allows the use of hierarchical models to transfer approximate knowledge from a large aggregated sample of observations to a smaller sample of a particular algorithm.

show abstract

Section: Algorithm-specific Tca Regression Modelmentioning

confidence: 99%

Bayesian Trading Cost Analysis and Ranking of Broker Algorithms

Марков¹

2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…where Q 0 is the total order size, τ is a trading horizon, β, ν -are the shape parameters of strategy q, −1 ≤ β ≤ 0, − 1 ≤ ν ≤ 0. Given the market impact model (19) and volatility scaling rule (3) the expected cost of implementation of any strategy q is Gatheral et al (2011) and Markov et al (2011):…”

Section: Optimal Trading Horizon and Efficient Trading Frontiermentioning

confidence: 99%

“…Further development and application of such an approach can be found in Glukhov (2007) and Markov et al (2011).…”

Section: Optimal Trading Horizon and Efficient Trading Frontiermentioning

confidence: 99%

Modeling market impact and timing risk in volume time

Mazur¹

2013

Self Cite

View full text Add to dashboard Cite

Intraday volatility and market impact models in volume time are proposed. We build an intraday volatility profile to capture non-stationarity of intraday price returns and utilize a fractional Brownian motion process to measure deviations from square root scaling rule of volatility. We propose a generalized, scalable market impact model that encompasses two mainstream approaches: an aggregated impact of a series of trades on a sufficiently long trading horizon and a transient impact of individual trades. We give an intuitive interpretation of the model parameters and provide a generalized formulation of the optimal trading horizon and efficient trading frontier. The self-similarity feature of an aggregated model allows for its application to smaller trading horizons and modeling of transient impact of sliced orders. We formulate conditions when the impact of sliced orders can be consistently aggregated to the total impact of the original order and deduce relationships between parameters of macro and micro level models to enforce such consistency. We demonstrate that the parameters of aggregated and transient impact models are intimately related to the auto-covariance function of trade signs. We give an explicit formulation of such a relationship when the stated auto-covariance function has a power law form.

show abstract

“…Accurate volume prediction is important for controlling and optimizing trading execution costs. All types of trading algorithms use volume prediction in order to choose the optimal trading rate and volume execution trajectory (Markov, Mazur & Saltz (2011)). Traders must estimate volume to choose a proper execution algorithm and its front-end parameters, such as order duration or aggressiveness.…”

Section: Introductionmentioning

confidence: 99%

Quintet Volume Projection

Марков¹,

Vilenskaia²,

Rashkovich³

2017

JOT

Self Cite

View full text Add to dashboard Cite

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

show abstract

Design and Implementation of Schedule-Based TradingStrategies Based on Uncertainty Bands

Cited by 7 publications

References 6 publications

Bayesian Trading Cost Analysis and Ranking of Broker Algorithms

Bayesian Trading Cost Analysis and Ranking of Broker Algorithms

Modeling market impact and timing risk in volume time

Quintet Volume Projection

Contact Info

Product

Resources

About