|
on Market Microstructure |
| By: | Mehdi Tomas; Iacopo Mastromatteo; Michael Benzaquen |
| 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: | 2021–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2102.02834&r=all |
| By: | Alfelt, Gustav (Department of Mathematics, Stockholm University); Bodnar, Taras (Department of Mathematics, Stockholm University); Javed, Farrukh (Örebro University School of Business); Tyrcha, Joanna (Department of Mathematics, Stockholm University) |
| Abstract: | Realized covariance matrices are often constructed under the assumption that richness of intra-day return data is greater than the portfolio size, resulting in non-singular matrix measures. However, when for example the portfolio size is large, assets suffer from illiquidity issues, or market microstructure noise deters sampling on very high frequencies, this relation is not guaranteed. Under these common conditions, realized covariance matrices may obtain as singular by construction. Motivated by this situation, we introduce the Singular Conditional Autoregressive Wishart (SCAW) model to capture the temporal dynamics of time series of singular realized covariance matrices, extending the rich literature on econometric Wishart time series models to the singular case. This model is furthermore developed by covariance targeting adapted to matrices and a sectorwise BEKK-specification, allowing excellent scalability to large and extremely large portfolio sizes. Finally, the model is estimated to a 20 year long time series containing 50 stocks, and evaluated using out-ofsample forecast accuracy. It outperforms the benchmark Multivariate GARCH model with high statistical significance, and the sectorwise specification outperforms the baseline model, while using much fewer parameters. |
| Keywords: | Covariance targeting; High-dimensional data; Realized covariance matrix; Stock co-volatility; Time series matrix-variate model |
| JEL: | C32 C55 C58 G17 |
| Date: | 2020–10–02 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:oruesi:2021_001&r=all |