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on Market Microstructure |
| By: | Wenqian Huang (Bank for International Settlements); Peter O'Neill (University of New South Wales); Angelo Ranaldo (University of St. Gallen; Swiss Finance Institute); Shihao Yu (Columbia University) |
| Abstract: | In this paper, we characterise the liquidity provision and price discovery roles of dealers and HFTs in the FX spot market during the sample period between 2012 and 2015. We find that they have different responses to adverse market conditions: HFT liquidity provision is less sensitive to spikes in market-wide volatility, while dealer bank liquidity is more robust ahead of scheduled macroeconomic news announcements when adverse selection risk is high. In periods of extreme levels of volatility, such as the `Swiss De-peg' event in our sample, HFTs appear to withdraw almost all liquidity while dealers remain. In normal times, we also find that HFTs contribute to market liquidity by passively trading against the pricing errors created by dealers' aggressive trade flows. On price discovery, HFTs contribute the dominant share, mostly through their high-frequency quote updates which incorporate public information. In contrast, dealers contribute to price discovery more through trades that impound private information. |
| Keywords: | HFT, Dealer Banks, Liquidity, Price Discovery, FX |
| JEL: | G14 G21 |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp2348&r=mst |
| By: | David Evangelista; Yuri Thamsten |
| Abstract: | In a fixed time horizon, appropriately executing a large amount of a particular asset -- meaning a considerable portion of the volume traded within this frame -- is challenging. Especially for illiquid or even highly liquid but also highly volatile ones, the role of "market quality" is quite relevant in properly designing execution strategies. Here, we model it by considering uncertain volatility and liquidity; hence, moments of high or low price impact and risk vary randomly throughout the trading period. We work under the central assumption: although there are these uncertain variations, we assume they occur in a fast mean-reverting fashion. We thus employ singular perturbation arguments to study approximations to the optimal strategies in this framework. By using high-frequency data, we provide estimation methods for our model in face of microstructure noise, as well as numerically assess all of our results. |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2307.07024&r=mst |
| By: | Andrea Canidio; Robin Fritsch |
| Abstract: | We consider an automated market maker (AMM) in which all trades are batched and executed at a price equal to the marginal price (i.e., the price of an arbitrary small trade) after the batch trades. We show that such an AMM is a function maximizing AMM (or FM-AMM): for given prices, it trades to reach the highest possible value of a given function. Also, competition between arbitrageurs guarantees that an FM-AMM always trades at a fair, equilibrium price, and arbitrage profits (also known as LVR) are eliminated. Sandwich attacks are also eliminated because all trades occur at the exogenously-determined equilibrium price. We use Binance price data to simulate a lower bound to the return of providing liquidity to an FM-AMM and show that, at least for the token pairs and the period we consider, such lower bound is very close to the empirical returns of providing liquidity on Uniswap v3. |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2307.02074&r=mst |
| By: | Zhou Fang; Haiqing Xu |
| Abstract: | The over-the-counter (OTC) market is characterized by a unique feature that allows market makers to adjust bid-ask spreads based on order size. However, this flexibility introduces complexity, transforming the market-making problem into a high-dimensional stochastic control problem that presents significant challenges. To address this, this paper proposes an innovative solution utilizing reinforcement learning techniques to tackle the OTC market-making problem. By assuming a linear inverse relationship between market order arrival intensity and bid-ask spreads, we demonstrate the optimal policy for bid-ask spreads follows a Gaussian distribution. We apply two reinforcement learning algorithms to conduct a numerical analysis, revealing the resulting return distribution and bid-ask spreads under different time and inventory levels. |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2307.01816&r=mst |
| By: | Zhou Fang; Haiqing Xu |
| Abstract: | Market making of options with different maturities and strikes is a challenging problem due to its high dimensional nature. In this paper, we propose a novel approach that combines a stochastic policy and reinforcement learning-inspired techniques to determine the optimal policy for posting bid-ask spreads for an options market maker who trades options with different maturities and strikes. When the arrival of market orders is linearly inverse to the spreads, the optimal policy is normally distributed. |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2307.01814&r=mst |
| By: | Iota Kaousar Nassr; Ana Sasi-Brodesky |
| Abstract: | This work delves into the liquidations mechanism inherent in Decentralised Finance (DeFi) lending protocols and the connection between liquidations and price volatility in decentralised exchanges (DEXs). The analysis employs transactional data of three of the largest DeFi lending protocols and provides evidence of a positive relation between liquidations and post-liquidations price volatility across the main DEX pools. Without directly observing the behaviour of liquidators, these findings indirectly indicate that liquidators require market liquidity to carry out large liquidations and affect market conditions while doing so. |
| Keywords: | decentralisation, decentralised exchanges, decentralised finance, DeFi, lending protocols, liquidity pools, liquidity providers, tokens |
| JEL: | G12 G14 G23 O39 |
| Date: | 2023–07–31 |
| URL: | http://d.repec.org/n?u=RePEc:oec:dafaad:48-en&r=mst |
| By: | Altmeyer, Patrick (TU Delft); Boneva, Leva (Swiss National Bank); Kinston, Rafael (Bank of England); Saha, Shreyosi (Bank of England); Stoja, Evarist (University of Bristol) |
| Abstract: | Speculative trading activity may either support efficient market functioning or introduce price distortions. Using granular, daily EMIR Trade Repository data on short sterling futures, we investigate the interaction of speculative trading and macroeconomic shocks on UK yield curve pricing over a 16-month sample period from 2018 to 2020. Our results are largely consistent with efficient market functioning throughout the period, although we find some evidence that short speculative positions amplified yield curve moves in response to Brexit shocks, while long speculative positions had a dampening effect. |
| Keywords: | Yield curve; economic data surprises; monetary policy surprises; positioning; market efficiency. |
| JEL: | E43 G14 |
| Date: | 2023–07–21 |
| URL: | http://d.repec.org/n?u=RePEc:boe:boeewp:1029&r=mst |