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on Market Microstructure |
| By: | Mohammed Salek; Damien Challet; Ioane Muni Toke |
| Abstract: | Equity auctions display several distinctive characteristics in contrast to continuous trading. As the auction time approaches, the rate of events accelerates causing a substantial liquidity buildup around the indicative price. This, in turn, results in a reduced price impact and decreased volatility of the indicative price. In this study, we adapt the latent/revealed order book framework to the specifics of equity auctions. We provide precise measurements of the model parameters, including order submissions, cancellations, and diffusion rates. Our setup allows us to describe the full dynamics of the average order book during closing auctions in Euronext Paris. These findings support the relevance of the latent liquidity framework in describing limit order book dynamics. Lastly, we analyze the factors contributing to a sub-diffusive indicative price and demonstrate the absence of indicative price predictability. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.06724&r=mst |
| By: | Huang, Alan Guoming; Wermers, Russ; Xue, Jinming |
| Abstract: | Using a comprehensive database of corporate news, we find that bond funds trade against the direction of news sentiment. The trading against news phenomenon is concentrated in funds selling on positive news and in the post-financial crisis period when dealer liquidity provision is constrained. Funds in so doing exhibit higher alphas, and a potential source of such alphas is bond price reversals post news events. Our findings highlight that bond mutual funds represent a significant liquidity provider in the corporate bond market and play a complementary role to dealers in corporate news events. |
| Keywords: | Fixed income mutual funds, Corporate bonds, Institutional trading, Public news, Textual analysis |
| JEL: | G12 G14 G23 G39 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:cfrwps:281205&r=mst |
| By: | Philippe Bergault; Leandro S\'anchez-Betancourt |
| Abstract: | We find closed-form solutions to the stochastic game between a broker and a mean-field of informed traders. In the finite player game, the informed traders observe a common signal and a private signal. The broker, on the other hand, observes the trading speed of each of his clients and provides liquidity to the informed traders. Each player in the game optimises wealth adjusted by inventory penalties. In the mean field version of the game, using a G\^ateaux derivative approach, we characterise the solution to the game with a system of forward-backward stochastic differential equations that we solve explicitly. We find that the optimal trading strategy of the broker is linear on his own inventory, on the average inventory among informed traders, and on the common signal or the average trading speed of the informed traders. The Nash equilibrium we find helps informed traders decide how to use private information, and helps brokers decide how much of the order flow they should externalise or internalise when facing a large number of clients. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.05257&r=mst |
| By: | Kenan Wood; Maurice Herlihy; Hammurabi Mendes; Jonad Pulaj |
| Abstract: | An automated market maker (AMM) is a state machine that manages pools of assets, allowing parties to buy and sell those assets according to a fixed mathematical formula. AMMs are typically implemented as smart contracts on blockchains, and its prices are kept in line with the overall market price by arbitrage: if the AMM undervalues an asset with respect to the market, an "arbitrageur" can make a risk-free profit by buying just enough of that asset to bring the AMM's price back in line with the market. AMMs, however, are not designed for assets that expire: that is, assets that cannot be produced or resold after a specified date. As assets approach expiration, arbitrage may not be able to reconcile supply and demand, and the liquidity providers that funded the AMM may have excessive exposure to risk due to rapid price variations. This paper formally describes the design of a decentralized exchange (DEX) for assets that expire, combining aspects of AMMs and limit-order books. We ensure liveness and market clearance, providing mechanisms for liquidity providers to control their exposure to risk and adjust prices dynamically in response to situations where arbitrage may fail. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.04289&r=mst |
| By: | Joseph Najnudel; Shen-Ning Tung; Kazutoshi Yamazaki; Ju-Yi Yen |
| Abstract: | We present a model for price dynamics in the Automated Market Makers (AMM) setting. Within this framework, we propose a reference market price following a geometric Brownian motion. The AMM price is constrained by upper and lower bounds, determined by constant multiplications of the reference price. Through the utilization of local times and excursion-theoretic approaches, we derive several analytical results, including its time-changed representation and limiting behavior. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.01526&r=mst |
| By: | Alessio Brini; Giacomo Toscano |
| Abstract: | This paper introduces SpotV2Net, a multivariate intraday spot volatility forecasting model based on a Graph Attention Network architecture. SpotV2Net represents financial assets as nodes within a graph and includes non-parametric high-frequency Fourier estimates of the spot volatility and co-volatility as node features. Further, it incorporates Fourier estimates of the spot volatility of volatility and co-volatility of volatility as features for node edges. We test the forecasting accuracy of SpotV2Net in an extensive empirical exercise, conducted with high-frequency prices of the components of the Dow Jones Industrial Average index. The results we obtain suggest that SpotV2Net shows improved accuracy, compared to alternative econometric and machine-learning-based models. Further, our results show that SpotV2Net maintains accuracy when performing intraday multi-step forecasts. To interpret the forecasts produced by SpotV2Net, we employ GNNExplainer, a model-agnostic interpretability tool and thereby uncover subgraphs that are critical to a node's predictions. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.06249&r=mst |