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on Market Microstructure |
| By: | Mehdi Tomas; Iacopo Mastromatteo; Michael Benzaquen (LadHyX - Laboratoire d'hydrodynamique - X - École polytechnique - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | We introduce a linear cross-impact framework in a setting in which the price of some given financial instruments (derivatives) is a deterministic function of one or more, possibly tradeable, stochastic factors (underlying). We show that a particular cross-impact model, the multivariate Kyle model, prevents arbitrage and aggregates (potentially non-stationary) traded order flows on derivatives into (roughly stationary) liquidity pools aggregating order flows traded on both derivatives and underlying. Using E-Mini futures and options along with VIX futures, we provide empirical evidence that the price formation process from order flows on derivatives is driven by cross-impact and confirm that the simple Kyle cross-impact model is successful at capturing parsimoniously such empirical phenomenology. Our framework may be used in practice for estimating execution costs, in particular hedging costs. |
| Date: | 2021–10–14 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-03378903&r= |
| By: | Riccardo Marcaccioli; Jean-Philippe Bouchaud; Michael Benzaquen (LadHyX - Laboratoire d'hydrodynamique - X - École polytechnique - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | Synchronising a database of stock specific news with 5 years worth of order book data on 300 stocks, we show that abnormal price movements following news releases (exogenous) exhibit markedly different dynamical features from those arising spontaneously (endogenous). On average, large volatility fluctuations induced by exogenous events occur abruptly and are followed by a decaying power-law relaxation, while endogenous price jumps are characterized by progressively accelerating growth of volatility, also followed by a power-law relaxation, but slower than for exogenous jumps. Remarkably, our results are reminiscent of what is observed in different contexts, namely Amazon book sales and YouTube views. Finally, we show that fitting power-laws to {\it individual} volatility profiles allows one to classify large events into endogenous and exogenous dynamical classes, without relying on the news feed. |
| Date: | 2021–10–14 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-03378876&r= |
| By: | Winkelmann, Lars; Yao, Wenying |
| Abstract: | This paper develops high-frequency econometric methods to test for jumps in the spread of bond yields. We derive a coherent inference procedure that detects a jump in the yield spread only if at least one of the two underlying bonds displays a jump. We formalize the test as a sequential procedure in the context of an intersection union test in multiple testing and introduce a new bivariate jump test for pre-averaged intra-day returns. In an empirical application involving high-frequency data of U.S. government bonds, we contrast response patterns of term spreads and break-even in ation across monetary policy announcements, in ation, and employment news releases. |
| Keywords: | High-frequency data,sequential testing,news announcements,term spread,break-even inflation |
| JEL: | C58 C12 E43 E44 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:fubsbe:202115&r= |
| By: | Xiufeng Yan |
| Abstract: | This paper explores the duration dynamics modelling under the Autoregressive Conditional Durations (ACD) framework (Engle and Russell 1998). I test different distributions assumptions for the durations. The empirical results suggest unconditional durations approach the Gamma distributions. Moreover, compared with exponential distributions and Weibull distributions, the ACD model with Gamma distributed innovations provide the best fit of SPY durations. |
| Date: | 2021–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2111.02300&r= |
| By: | Reiß, Markus; Winkelmann, Lars |
| Abstract: | We study the rank of the instantaneous or spot covariance matrix ΣX(t) of a multidimensional continuous semi-martingale X(t). Given highfrequency observations X(i/n), i = 0,...,n, we test the null hypothesis rank (ΣX(t)) |
| Keywords: | empirical covariance matrix,rank detection,signal detection rate,matrix concentration,eigenvalue perturbation,principal component analysis,factor model,term structure |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:fubsbe:202114&r= |
| By: | Xiufeng Yan |
| Abstract: | This paper proposes a multiplicative component intraday volatility model. The intraday conditional volatility is expressed as the product of intraday periodic component, intraday stochastic volatility component and daily conditional volatility component. I extend the multiplicative component intraday volatility model of Engle (2012) and Andersen and Bollerslev (1998) by incorporating the durations between consecutive transactions. The model can be applied to both regularly and irregularly spaced returns. I also provide a nonparametric estimation technique of the intraday volatility periodicity. The empirical results suggest the model can successfully capture the interdependency of intraday returns. |
| Date: | 2021–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2111.02376&r= |