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on Market Microstructure |
| By: | Ibrahim Ekren; Brad Mostowski; Gordan \v{Z}itkovi\'c |
| Abstract: | We construct an equilibrium for the continuous time Kyle's model with stochastic liquidity, a general distribution of the fundamental price, and correlated stock and volatility dynamics. For distributions with positive support, our equilibrium allows us to study the impact of the stochastic volatility of noise trading on the volatility of the asset. In particular, when the fundamental price is log-normally distributed, informed trading forces the log-return up to maturity to be Gaussian for any choice of noise-trading volatility even though the price process itself comes with stochastic volatility. Surprisingly, we find that in equilibrium both Kyle's Lambda and its inverse (the market depth) are submartingales. |
| Date: | 2022–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2204.11069&r= |
| By: | Ali Raheman; Anton Kolonin; Alexey Glushchenko; Arseniy Fokin; Ikram Ansari |
| Abstract: | Crypto-currency market uncertainty drives the need to find adaptive solutions to maximise gain or at least to avoid loss throughout the periods of trading activity. Given the high dimensionality and complexity of the state-action space in this domain, it can be treated as a "Narrow AGI" problem with the scope of goals and environments bound to financial markets. Adaptive Multi-Strategy Agent approach for market-making introduces a new solution to maximise positive "alpha" in long-term handling limit order book (LOB) positions by using multiple sub-agents implementing different strategies with a dynamic selection of these agents based on changing market conditions. AMSA provides no specific strategy of its own while being responsible for segmenting the periods of market-making activity into smaller execution sub-periods, performing internal backtesting on historical data on each of the sub-periods, doing sub- agent performance evaluation and re-selection of them at the end of each sub- period, and collecting returns and losses incrementally. With this approach, the return becomes a function of hyper-parameters such as market data granularity (refresh rate), the execution sub-period duration, number of active sub-agents, and their individual strategies. Sub-agent selection for the next trading sub-period is made based on return/loss and alpha values obtained during internal backtesting as well as real trading. Experiments with the AMSA have been performed under different market conditions relying on historical data and proved a high probability of positive alpha throughout the periods of trading activity in the case of properly selected hyper-parameters. |
| Date: | 2022–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2204.13265&r= |
| By: | Huiling Yuan; Guodong Li; Junhui Wang |
| Abstract: | This paper introduces one new multivariate volatility model that can accommodate an appropriately defined network structure based on low-frequency and high-frequency data. The model reduces the number of unknown parameters and the computational complexity substantially. The model parameterization and iterative multistep-ahead forecasts are discussed and the targeting reparameterization is also presented. Quasi-likelihood functions for parameter estimation are proposed and their asymptotic properties are established. A series of simulation experiments are carried out to assess the performance of the estimation in finite samples. An empirical example is demonstrated that the proposed model outperforms the network GARCH model, with the gains being particularly significant at short forecast horizons. |
| Date: | 2022–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2204.12933&r= |
| By: | Masanori Hirano; Hiroki Sakaji; Kiyoshi Izumi |
| Abstract: | This study proposes a new generative adversarial network (GAN) for generating realistic orders in financial markets. In some previous works, GANs for financial markets generated fake orders in continuous spaces because of GAN architectures' learning limitations. However, in reality, the orders are discrete, such as order prices, which has minimum order price unit, or order types. Thus, we change the generation method to place the generated fake orders into discrete spaces in this study. Because this change disabled the ordinary GAN learning algorithm, this study employed a policy gradient, frequently used in reinforcement learning, for the learning algorithm. Through our experiments, we show that our proposed model outperforms previous models in generated order distribution. As an additional benefit of introducing the policy gradient, the entropy of the generated policy can be used to check GAN's learning status. In the future, higher performance GANs, better evaluation methods, or the applications of our GANs can be addressed. |
| Date: | 2022–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2204.13338&r= |
| By: | Czech, Robert (Bank of England); Della Corte, Pasquale (Imperial College London and Centre for Economic Policy Research (CEPR)); Huang, Shiyang (University of Hong Kong); Wang, Tianyu (Bank of England) |
| Abstract: | We study the information content of foreign exchange (FX) option volume using a unique dataset on over-the-counter FX options with disclosed counterparty identities and contract characteristics. Our study shows that FX option volume can predict future exchange rate returns, especially when the demand for the US dollar is high. In support of information-based arguments, we also document that the exchange rate predictability is stronger around macro-announcement days or when using options with higher embedded leverage. Finally, we show that hedge funds and real money investors have superior skills in predicting future exchange rates compared to other investor types. |
| Keywords: | Currency return; foreign exchange option; Informed trading; dollar demand |
| JEL: | F31 G12 G14 G15 |
| Date: | 2022–03–04 |
| URL: | http://d.repec.org/n?u=RePEc:boe:boeewp:0964&r= |