Linear models for the impact of order flow on prices I. Propagators: Transient vs. History Dependent Impact

Taranto, Damian Eduardo; Bormetti, Giacomo; Bouchaud, Jean‐Philippe; Lillo, Fabrizio; Tóth, Bence

doi:10.48550/arxiv.1602.02735

Cited by 5 publications

(19 citation statements)

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“…This indicates a market inefficiency which has been observed for self-impact in other markets (see e.g. Figure 1 in Taranto et al [2016]). In the absence of slippage this inefficiency could be exploited by e.g.…”

Section: Decay Kernelmentioning

confidence: 56%

“…We conjecture that such behavior is not observed in because of the rather large time lag of 5 minutes, corresponding to ∼ 80 units of transaction time in Figure 2. In the single asset case this feature is clearly present for the large-tick stock Microsoft in Figure 1 of Taranto et al [2016]. As shown there, the kink could be related to correlations of market order flow with past returns and indicates a forecasting power of current returns on the future order sign imbalance.…”

Section: Response Functionmentioning

confidence: 80%

“…larger in absolute value) than self-response, contrary to the observations in . 10 The figure also shows the prediction from the model of the negative lag impact (see Taranto et al [2016] for details). We observe a clear difference with the empirical data suggesting also for cross-impact a reaction of order flow to past price dynamics of other bonds.…”

Section: Response Functionmentioning

confidence: 99%

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Cross-impact and no-dynamic-arbitrage

Schneider

Lillo

2018

Quantitative Finance

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We extend the "No-dynamic-arbitrage and market impact"-framework of Jim Gatheral [Quantitative Finance, 10 (7): 749-759 (2010)] to the multidimensional case where trading in one asset has a cross-impact on the price of other assets. From the condition of absence of dynamical arbitrage we derive theoretical limits for the size and form of cross-impact that can be directly verified on data. For bounded decay kernels we find that cross-impact must be an odd and linear function of trading intensity and cross-impact from asset i to asset j must be equal to the one from j to i. To test these constraints we estimate cross-impact among sovereign bonds traded on the electronic platform MOT. While we find significant violations of the above symmetry condition of cross-impact, we show that these are not arbitrageable with simple strategies because of the presence of the bid-ask spread.

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Section: Decay Kernelmentioning

confidence: 56%

Section: Response Functionmentioning

confidence: 80%

Section: Response Functionmentioning

confidence: 99%

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Cross-impact and no-dynamic-arbitrage

Schneider

Lillo

2018

Quantitative Finance

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“…The behavior at negative lag indicates that the current return allows some prediction of the sign imbalance, an effect that has been extensively investigated in Refs. [31,33]. § It is worth mentioning that, other than the expected amplitude difference, the off-diagonal response shows the same temporal behaviour than its diagonal counterpart.…”

Section: Price Responsementioning

confidence: 88%

“…§ We will disregard in the following the behavior of returns at negative lags, and only focus on the positive part of the curve, that is equivalent to assuming no price-sign correlation, that is approximately correct for small tick stocks, and breaks down at high frequency and for large tick stocks due to microstructural effects [31,33,34]. This expresses the price variations of contract i as a linear regression on the past sign imbalances of all assets j.…”

Section: The Multivariate Propagator Modelmentioning

confidence: 99%

Dissecting Cross-Impact on Stock Markets: An Empirical Analysis

et al. 2016

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The vast majority of market impact studies assess each product individually, and the interactions between the different order flows are disregarded. This strong approximation may lead to an underestimation of trading costs and possible contagion effects. Transactions in fact mediate a significant part of the correlation between different instruments. In turn, liquidity shares the sectorial structure of market correlations, which can be encoded as a set of eigenvalues and eigenvectors. We introduce a multivariate linear propagator model that successfully describes such a structure, and accounts for a significant fraction of the covariance of stock returns. We dissect the various dynamical mechanisms that contribute to the joint dynamics of assets. We also define two simplified models with substantially less parameters in order to reduce overfitting, and show that they have superior out-of-sample performance.

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Dissecting cross-impact on stock markets: an empirical analysis

et al. 2017

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Abstract. The vast majority of market impact studies assess each product individually, and the interactions between the different order flows are disregarded. This strong approximation may lead to an underestimation of trading costs and possible contagion effects. Transactions in fact mediate a significant part of the correlation between different instruments. In turn, liquidity shares the sectorial structure of market correlations, which can be encoded as a set of eigenvalues and eigenvectors. We introduce a multivariate linear propagator model that successfully describes such a structure, and accounts for a significant fraction of the covariance of stock returns. We dissect the various dynamical mechanisms that contribute to the joint dynamics of assets. We also define two simplified models with substantially less parameters in order to reduce overfitting, and show that they have superior out-of-sample performance.

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Linear models for the impact of order flow on prices I. Propagators: Transient vs. History Dependent Impact

Cited by 5 publications

References 0 publications

Cross-impact and no-dynamic-arbitrage

Cross-impact and no-dynamic-arbitrage

Dissecting Cross-Impact on Stock Markets: An Empirical Analysis

Dissecting cross-impact on stock markets: an empirical analysis

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