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on Market Microstructure |
| By: | Trent Spears; Stefan Zohren; Stephen Roberts |
| Abstract: | In this work we show that prediction uncertainty estimates gleaned from deep learning models can be useful inputs for influencing the relative allocation of risk capital across trades. In this way, consideration of uncertainty is important because it permits the scaling of investment size across trade opportunities in a principled and data-driven way. We showcase this insight with a prediction model and find clear outperformance based on a Sharpe ratio metric, relative to trading strategies that either do not take uncertainty into account, or that utilize an alternative market-based statistic as a proxy for uncertainty. Of added novelty is our modelling of high-frequency data at the top level of the Eurodollar Futures limit order book for each trading day of 2018, whereby we predict interest rate curve changes on small time horizons. We are motivated to study the market for these popularly-traded interest rate derivatives since it is deep and liquid, and contributes to the efficient functioning of global finance -- though there is relatively little by way of its modelling contained in the academic literature. Hence, we verify the utility of prediction models and uncertainty estimates for trading applications in this complex and multi-dimensional asset price space. |
| Date: | 2020–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2007.15982&r=all |
| By: | FUKUMA Noritaka (Bank of Japan); KADOGAWA Yoichi (Bank of Japan) |
| Abstract: | In recent years, the foreign exchange market has seen a growing presence of algorithmic trading, that is, a process of automated transactions based on pre-determined programs. Concurrently, the need to better understand its characteristics has become more important. In this paper, we construct proxy indicators of algorithmic trading in the USD/JPY spot market by focusing on its general features - high-speed and high-frequency transactions. Based on the proxy indicators, algorithmic trading has been on an upward trend since around 2016 and is more active in European and U.S. time zones than in Japan. Our analysis shows that algorithmic trading on average helps improve market liquidity in normal times. Its liquidity-providing function was generally maintained under market stress triggered by the COVID-19 pandemic from late-February to end-March 2020, though it could have been dampened albeit temporarily in times of severe stress when the market experienced sudden and sharp price fluctuation. |
| Keywords: | Foreign exchange; Algorithmic trading; Market liquidity |
| JEL: | F31 G10 G14 |
| Date: | 2020–08–03 |
| URL: | http://d.repec.org/n?u=RePEc:boj:bojrev:rev20e05&r=all |
| By: | Daniel Barth (Board of Governors of the Federal Reserve System); Jay Kahn (Office of Financial Research) |
| Abstract: | The Treasury basis trade exploits the price difference between Treasury bonds and futures. The trade is exposed to financing and liquidity risks that can affect market liquidity. This brief summarizes evidence on the size and extent of basis trading by hedge funds, and on whether these trades contributed to Treasury market illiquidity in March 2020. Timely intervention by the Federal Reserve in the Treasury and repurchase agreement markets may have limited the extent of spillovers that could affect financial stability. |
| Keywords: | Treasury, repurchase agreement, futures, basis trade, hedge fund, securities dealers, liquidity |
| Date: | 2020–07–16 |
| URL: | http://d.repec.org/n?u=RePEc:ofr:briefs:20-01&r=all |
| By: | Anouk Faure; Marc Baudry; Simon Quemin |
| Abstract: | We develop an equilibrium model of emissions permit trading in the presence of fixed and proportional trading costs in which the permit price and firms’ participation in and extent of trading are endogenously determined. We analyze the sensitivity of the equilibrium to changes in the trading costs and firms’ allocations, and characterize situations where the trading costs alternatively depress or raise permit prices relative to frictionless market conditions. We calibrate our model to annual transaction and compliance data in Phase II of the EU ETS (2008-2012) which we consolidate at the firm level. We find that trading costs in the order of 10 k€ per annum plus 1€ per permit traded substantially reduce discrepancies between observations and theoretical predictions for firms’ behavior (e.g. autarkic compliance). Our simulations suggest that ignoring trading costs leads to an underestimation of the price impacts of supply-curbing policies, this difference varying with the incidence on firms. |
| Keywords: | Emissions trading, Transaction costs, Policy design and evaluation, EU ETS |
| JEL: | D22 D23 H32 L22 Q52 Q58 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:drm:wpaper:2020-19&r=all |