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on Market Microstructure |
| By: | Zijian Shi; Yu Chen; John Cartlidge |
| Abstract: | In an order-driven financial market, the price of a financial asset is discovered through the interaction of orders - requests to buy or sell at a particular price - that are posted to the public limit order book (LOB). Therefore, LOB data is extremely valuable for modelling market dynamics. However, LOB data is not freely accessible, which poses a challenge to market participants and researchers wishing to exploit this information. Fortunately, trades and quotes (TAQ) data - orders arriving at the top of the LOB, and trades executing in the market - are more readily available. In this paper, we present the LOB recreation model, a first attempt from a deep learning perspective to recreate the top five price levels of the LOB for small-tick stocks using only TAQ data. Volumes of orders sitting deep in the LOB are predicted by combining outputs from: (1) a history compiler that uses a Gated Recurrent Unit (GRU) module to selectively compile prediction relevant quote history; (2) a market events simulator, which uses an Ordinary Differential Equation Recurrent Neural Network (ODE-RNN) to simulate the accumulation of net order arrivals; and (3) a weighting scheme to adaptively combine the predictions generated by (1) and (2). By the paradigm of transfer learning, the source model trained on one stock can be fine-tuned to enable application to other financial assets of the same class with much lower demand on additional data. Comprehensive experiments conducted on two real world intraday LOB datasets demonstrate that the proposed model can efficiently recreate the LOB with high accuracy using only TAQ data as input. |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2103.01670&r=all |
| By: | Henry Hanifan; Ben Watson; John Cartlidge; Dave Cliff |
| Abstract: | We consider issues of time in automated trading strategies in simulated financial markets containing a single exchange with public limit order book and continuous double auction matching. In particular, we explore two effects: (i) reaction speed - the time taken for trading strategies to calculate a response to market events; and (ii) trading urgency - the sensitivity of trading strategies to approaching deadlines. Much of the literature on trading agents focuses on optimising pricing strategies only and ignores the effects of time, while real-world markets continue to experience a race to zero latency, as automated trading systems compete to quickly access information and act in the market ahead of others. We demonstrate that modelling reaction speed can significantly alter previously published results, with simple strategies such as SHVR outperforming more complex adaptive algorithms such as AA. We also show that adding a pace parameter to ZIP traders (ZIP-Pace, or ZIPP) can create a sense of urgency that significantly improves profitability. |
| Date: | 2021–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2103.00600&r=all |
| By: | Tobias Dieler (University of Bristol - Department of Finance and Accounting); Loriano Mancini (USI Lugano - Institute of Finance; Swiss Finance Institute); Norman Schürhoff (University of Lausanne; Swiss Finance Institute; Centre for Economic Policy Research (CEPR)) |
| Abstract: | Repo markets trade off the efficient allocation of liquidity in the financial sector with resilience to funding shocks. The repo trading and clearing mechanisms are crucial determinants of the allocation-resilience tradeoff. The two common mechanisms, anonymous central-counterparty (CCP) and non-anonymous over-the-counter (OTC) markets, are inefficient and their welfare rankings depend on funding tightness. CCP (OTC) markets inefficiently liquidate high (low) quality assets for large (small) funding shocks. Two innovations to repo market design contribute to maximize welfare: a liquidity-contingent trading mechanism and a two-tiered guarantee fund. |
| Keywords: | repo market, funding run, financial stability, asymmetric information, central clearing, novation, guarantee fund, collateral |
| JEL: | G01 G14 G21 G28 |
| Date: | 2021–02 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp2110&r=all |
| By: | Mustafayeva, Konul; Wang, Weining |
| Abstract: | Estimating spot covariance is an important issue to study, especially with the increasing availability of high-frequency nancial data. We study the estimation of spot covariance using a kernel method for high-frequency data. In particular, we consider rst the kernel weighted version of realized covariance estimator for the price process governed by a continuous multivariate semimartingale. Next, we extend it to the threshold kernel estimator of the spot covariances when the underlying price process is a discontinuous multivariate semimartingale with nite activity jumps. We derive the asymptotic distribution of the estimators for both xed and shrinking bandwidth. The estimator in a setting with jumps has the same rate of convergence as the estimator for di usion processes without jumps. A simulation study examines the nite sample properties of the estimators. In addition, we study an application of the estimator in the context of covariance forecasting. We discover that the forecasting model with our estimator outperforms a benchmark model in the literature. |
| Keywords: | high-frequency data,kernel estimation,jump,forecasting covariance matrix |
| JEL: | C00 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:irtgdp:2020025&r=all |