|
|
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
|
|
|
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
|
|
|
Connectedness of high-frequency data
Petras, Petr ; Křehlík, Tomáš (advisor) ; Maršál, Aleš (referee)
This work combines discrete and continuous methods while modeling connect- edness of financial tick data. As discrete method we are using vector autore- gression. For continuous domain Hawkes process is used, which is special case of point process. We found out that financial assets are connected in non- symmetrical fashion. By using two methodologies we were able to model bet- ter how are the series connected. We confirmed existence of price leader in our three stock portfolio and modeled connectedness of jumps between stocks. As conclusion we state that both methods yields important results about price nature on the market and should be used together or at least with awareness of second approach. JEL Classification C32, G11, G14 Keywords Vector Autoregression, Hawkes process, High- frequency analysis, Connectedness Author's e-mail petr.petras@email.cz Supervisor's e-mail krehlik@utia.cas.cz
|
guest ::
