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Price Impact of Order Book Events in Bitcoin Market
Erben, Marek ; Šíla, Jan (advisor) ; Baruník, Jozef (referee)
1 Abstract This thesis examines the price impact of order book events in the Bitcoin mar- ket. Using the data obtained from Binance exchange, the thesis shows that short-term price changes can be explained by high-frequency demand-supply interaction depicted in the Limit Order Book (LOB). The thesis demonstrates that the instantaneous price impact function has a non-linear shape, indicating that small and large orders have di↵erent e↵ects on price, potentially leading to opportunities for price manipulation and quasi-arbitrage. Additionally, the analysis confirms the inverse relation between the price impact coe cient and market depth. Furthermore, the thesis observes that there are no clear intraday patterns for the price impact coe cient. These findings provide valuable insights into the understanding of Bitcoin's price dynamics, benefiting traders, investors, and policymakers seeking to understand the complexities of the cryptocurrency market. 1
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Quoting behaviour of a market-maker under different exchange fee structures
Kiseľ, Rastislav ; Baruník, Jozef (advisor) ; Kočenda, Evžen (referee)
During the last few years, market micro-structure research has been active in analysing the dependence of market efficiency on different market character istics. Make-take fees are one of those topics as they might modify the incen tives for participating agents, e.g. broker-dealers or market-makers. In this thesis, we propose a Hawkes process-based model that captures statistical differences arising from different fee regimes and we estimate the differences on limit order book data. We then use these estimates in an attempt to measure the execution quality from the perspective of a market-maker. We appropriate existing theoretical market frameworks, however, for the pur pose of hireling optimal market-making policies we apply a novel method of deep reinforcement learning. Our results suggest, firstly, that maker-taker exchanges provide better liquidity to the markets, and secondly, that deep reinforcement learning methods may be successfully applied to the domain of optimal market-making. JEL Classification Keywords Author's e-mail Supervisor's e-mail C32, C45, C61, C63 make-take fees, Hawkes process, limit order book, market-making, deep reinforcement learn ing kiselrastislavSgmail.com barunik@f sv.cuni.cz
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The Stigler-Luckock model for a limit order book
Fornůsková, Monika ; Swart, Jan (advisor) ; Večeř, Jan (referee)
THE STIGLER-LUCKOCK MODEL FOR A LIMIT ORDER BOOK Abstract One of the types of modern-day markets are so-called order-driven markets whose core component is a database of all incoming buy and sell orders (order book). The main goal of this thesis is to extend the Stigler-Luckock model for order books to give a better insight into the price forming process and behaviour of the market participants themselves. The model introduced in this thesis focuses on a comparison of behaviour and various strategies of market makers who are sophisticated market participants profiting from extensive trading. The market is described using Markov chains, and the strategies are compared using Monte Carlo simulations and game theory. The results showed that market makers' orders should have small spread and large volumes. The final model compares two strategies in which market makers monitor their portfolio. In case of having more cash than asset (or vice versa), they shift prices of their orders to equalise the portfolio. The model recommends checking the market quite often, but acting conservatively, which means not changing prices that frequently and not jumping to conclusions just from a small imbalance in the portfolio.
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Quoting behaviour of a market-maker under different exchange fee structures
Kiseľ, Rastislav ; Baruník, Jozef (advisor) ; Kočenda, Evžen (referee)
During the last few years, market micro-structure research has been active in analysing the dependence of market efficiency on different market character istics. Make-take fees are one of those topics as they might modify the incen tives for participating agents, e.g. broker-dealers or market-makers. In this thesis, we propose a Hawkes process-based model that captures statistical differences arising from different fee regimes and we estimate the differences on limit order book data. We then use these estimates in an attempt to measure the execution quality from the perspective of a market-maker. We appropriate existing theoretical market frameworks, however, for the pur pose of hireling optimal market-making policies we apply a novel method of deep reinforcement learning. Our results suggest, firstly, that maker-taker exchanges provide better liquidity to the markets, and secondly, that deep reinforcement learning methods may be successfully applied to the domain of optimal market-making. JEL Classification Keywords Author's e-mail Supervisor's e-mail C32, C45, C61, C63 make-take fees, Hawkes process, limit order book, market-making, deep reinforcement learn ing kiselrastislavSgmail.com barunik@f sv.cuni.cz
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