|
on Market Microstructure |
| By: | Mike Derksen; Bas Kleijn; Robin de Vilder |
| Abstract: | On January 3, 2018 MiFID II regulations came into effect. This paper compares properties of European stocks for 2017 and 2018. The introduced tick size regime impacted the microstructure in accordance with existing literature on tick size changes. Remarkably, the modification of the microstructure also impacted volatility and transacted volume. Furthermore, it is shown that closing auction volumes increased heavily since MiFID II, leading to higher absolute returns in the auctions. Before MiFID II, high closing auction returns reverted overnight, but after MiFID II this reversion disappeared, showing that closing prices became more efficient. |
| Date: | 2020–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2003.10353&r=all |
| By: | Vladim\'ir Hol\'y; Petra Tomanov\'a |
| Abstract: | We investigate the computational issues related to the memory size in the estimation of quadratic covariation using financial ultra-high-frequency data. In the multivariate price process, we consider both contamination by the market microstructure noise and the non-synchronous observations. We express the multi-scale, flat-top realized kernel, non-flat-top realized kernel, pre-averaging and modulated realized covariance estimators in a quadratic form and fix their bandwidth parameter at a constant value. This allows us to operate with limited memory and formulate such estimation approach as a streaming algorithm. We compare the performance of the estimators with fixed bandwidth parameter in a simulation study. We find that the estimators ensuring positive semidefiniteness require much higher bandwidth than the estimators without such constraint. |
| Date: | 2020–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2003.13062&r=all |
| By: | Andrii Babii |
| Abstract: | This paper introduces a high-dimensional linear IV regression for the data sampled at mixed frequencies. We show that the high-dimensional slope parameter of a high-frequency covariate can be identified and accurately estimated leveraging on a low-frequency instrumental variable. The distinguishing feature of the model is that it allows handing high-dimensional datasets without imposing the approximate sparsity restrictions. We propose a Tikhonov-regularized estimator and derive the convergence rate of its mean-integrated squared error for time series data. The estimator has a closed-form expression that is easy to compute and demonstrates excellent performance in our Monte Carlo experiments. We estimate the real-time price elasticity of supply on the Australian electricity spot market. Our estimates suggest that the supply is relatively inelastic and that its elasticity is heterogeneous throughout the day. |
| Date: | 2020–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2003.13478&r=all |
| By: | Adriano Koshiyama; Sebastian Flennerhag; Stefano B. Blumberg; Nick Firoozye; Philip Treleaven |
| Abstract: | In this work we introduce QuantNet: an architecture that is capable of transferring knowledge over systematic trading strategies in several financial markets. By having a system that is able to leverage and share knowledge across them, our aim is two-fold: to circumvent the so-called Backtest Overfitting problem; and to generate higher risk-adjusted returns and fewer drawdowns. To do that, QuantNet exploits a form of modelling called Transfer Learning, where two layers are market-specific and another one is market-agnostic. This ensures that the transfer occurs across trading strategies, with the market-agnostic layer acting as a vehicle to share knowledge, cross-influence each strategy parameters, and ultimately the trading signal produced. In order to evaluate QuantNet, we compared its performance in relation to the option of not performing transfer learning, that is, using market-specific old-fashioned machine learning. In summary, our findings suggest that QuantNet performs better than non transfer-based trading strategies, improving Sharpe ratio in 15% and Calmar ratio in 41% across 3103 assets in 58 equity markets across the world. Code coming soon. |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2004.03445&r=all |