Zobrazeno 1 - 10
of 262
pro vyhledávání: '"Challet, Damien"'
We propose a minimal model of the secured interbank network able to shed light on recent money markets puzzles. We find that excess liquidity emerges due to the interactions between the reserves and liquidity ratio constraints; the appearance of ever
Externí odkaz:
http://arxiv.org/abs/2410.18145
Using the secured transactions recorded within the Money Markets Statistical Reporting database of the European Central Bank, we test several stylized facts regarding interbank market of the 47 largest banks in the eurozone. We observe that the surge
Externí odkaz:
http://arxiv.org/abs/2410.16021
Autor:
Ragel, Vincent, Challet, Damien
Reinforcement learning works best when the impact of the agent's actions on its environment can be perfectly simulated or fully appraised from available data. Some systems are however both hard to simulate and very sensitive to small perturbations. A
Externí odkaz:
http://arxiv.org/abs/2408.02322
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
Externí odkaz:
http://arxiv.org/abs/2401.06724
Autor:
Lefort, Baptiste, Benhamou, Eric, Ohana, Jean-Jacques, Saltiel, David, Guez, Beatrice, Challet, Damien
We used a dataset of daily Bloomberg Financial Market Summaries from 2010 to 2023, reposted on large financial media, to determine how global news headlines may affect stock market movements using ChatGPT and a two-stage prompt approach. We document
Externí odkaz:
http://arxiv.org/abs/2401.05447
Autor:
Bongiorno, Christian, Challet, Damien
The Average Oracle, a simple and very fast covariance filtering method, is shown to yield superior Sharpe ratios than the current state-of-the-art (and complex) methods, Dynamic Conditional Covariance coupled to Non-Linear Shrinkage (DCC+NLS). We pit
Externí odkaz:
http://arxiv.org/abs/2309.17219
Autor:
Challet, Damien, Ragel, Vincent
We extend recurrent neural networks to include several flexible timescales for each dimension of their output, which mechanically improves their abilities to account for processes with long memory or with highly disparate time scales. We compare the
Externí odkaz:
http://arxiv.org/abs/2308.08550
Trading pressure from one asset can move the price of another, a phenomenon referred to as cross impact. Using tick-by-tick data spanning 5 years for 500 assets listed in the United States, we identify the features that make cross-impact relevant to
Externí odkaz:
http://arxiv.org/abs/2305.16915
Using high-quality data, we report several statistical regularities of equity auctions in the Paris stock exchange. First, the average order book density is linear around the auction price at the time of auction clearing and has a large peak at the a
Externí odkaz:
http://arxiv.org/abs/2301.05677
Autor:
Bongiorno, Christian, Challet, Damien
Symbolic transfer entropy is a powerful non-parametric tool to detect lead-lag between time series. Because a closed expression of the distribution of Transfer Entropy is not known for finite-size samples, statistical testing is often performed with
Externí odkaz:
http://arxiv.org/abs/2206.10173