2 citations · 2 across the 6 of their papers we have counts for
6 papers
A high-frequency approach to Realized Risk Measures
Federico Gatta, Fabrizio Lillo, Piero Mazzarisi
We propose a new approach, termed Realized Risk Measures (RRM), to estimate Value-at-Risk (VaR) and Expected Shortfall (ES) using high-frequency financial data. It extends the Real…
Tackling estimation risk in Kelly investing using options
Fabrizio Lillo, Piero Mazzarisi, Ioanna-Yvonni Tsaknaki
The Kelly criterion provides a general framework for optimizing the growth rate of an investment portfolio over time by maximizing the expected logarithmic utility of wealth. Howev…
Modeling metaorder impact with a Non-Markovian Zero Intelligence model
Adele Ravagnani, Fabrizio Lillo
Devising models of the limit order book that realistically reproduce the market response to exogenous trades is extremely challenging and fundamental in order to test trading strat…
Bayesian Autoregressive Online Change-Point Detection with Time-Varying Parameters
Ioanna-Yvonni Tsaknaki, Fabrizio Lillo, Piero Mazzarisi
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 ti…
CAESar: Conditional Autoregressive Expected Shortfall
Federico Gatta, Fabrizio Lillo, Piero Mazzarisi
In financial risk management, Value at Risk (VaR) estimates potential portfolio losses but fails to account for losses beyond a certain threshold. Expected Shortfall (ES) addresses…
Dimensionality reduction techniques to support insider trading detection
Adele Ravagnani, Fabrizio Lillo, Paola Deriu +3
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…