Zobrazeno 1 - 10
of 646
pro vyhledávání: '"Lillo, Fabrizio"'
We propose a theory of unimodal maps perturbed by an heteroscedastic Markov chain noise and experiencing another heteroscedastic noise due to uncertain observation. We address and treat the filtering problem showing that by collecting more and more o
Externí odkaz:
http://arxiv.org/abs/2411.13939
We investigate the well-posedness in the Hadamard sense and the absence of price manipulation in the optimal execution problem within the Almgren-Chriss framework, where the temporary and permanent impact parameters vary deterministically over time.
Externí odkaz:
http://arxiv.org/abs/2410.04867
Autor:
Lillo, Fabrizio, Macrì, Andrea
The use of reinforcement learning algorithms in financial trading is becoming increasingly prevalent. However, the autonomous nature of these algorithms can lead to unexpected outcomes that deviate from traditional game-theoretical predictions and ma
Externí odkaz:
http://arxiv.org/abs/2408.11773
Change points in real-world systems mark significant regime shifts in system dynamics, possibly triggered by exogenous or endogenous factors. These points define regimes for the time evolution of the system and are crucial for understanding transitio
Externí odkaz:
http://arxiv.org/abs/2407.16376
Autor:
Lillo, Fabrizio, Rizzini, Giorgio
Modelling how a shock propagates in a temporal network and how the system relaxes back to equilibrium is challenging but important in many applications, such as financial systemic risk. Most studies so far have focused on shocks hitting a link of the
Externí odkaz:
http://arxiv.org/abs/2407.09340
In financial risk management, Value at Risk (VaR) is widely used to estimate potential portfolio losses. VaR's limitation is its inability to account for the magnitude of losses beyond a certain threshold. Expected Shortfall (ES) addresses this by pr
Externí odkaz:
http://arxiv.org/abs/2407.06619
Autor:
Ravagnani, Adele, Lillo, Fabrizio, Deriu, Paola, Mazzarisi, Piero, Medda, Francesca, Russo, Antonio
Identification of market abuse is an extremely complicated activity that requires the analysis of large and complex datasets. We propose an unsupervised machine learning method for contextual anomaly detection, which allows to support market surveill
Externí odkaz:
http://arxiv.org/abs/2403.00707
Autor:
Macrì, Andrea, Lillo, Fabrizio
Optimal execution is an important problem faced by any trader. Most solutions are based on the assumption of constant market impact, while liquidity is known to be dynamic. Moreover, models with time-varying liquidity typically assume that it is obse
Externí odkaz:
http://arxiv.org/abs/2402.12049
Financial order flow exhibits a remarkable level of persistence, wherein buy (sell) trades are often followed by subsequent buy (sell) trades over extended periods. This persistence can be attributed to the division and gradual execution of large ord
Externí odkaz:
http://arxiv.org/abs/2307.02375
Autor:
Mazzarisi, Piero, Ravagnani, Adele, Deriu, Paola, Lillo, Fabrizio, Medda, Francesca, Russo, Antonio
Identifying market abuse activity from data on investors' trading activity is very challenging both for the data volume and for the low signal to noise ratio. Here we propose two complementary unsupervised machine learning methods to support market s
Externí odkaz:
http://arxiv.org/abs/2212.05912