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Deep learning, jumps, and volatility bursts
Bashchenko, Oksana; Marchal, Alexis - 2020
We develop a new method that detects jumps nonparametrically in financial time series and significantly outperforms the … generated by a process that experiences both jumps and volatility bursts. As a result, the network learns how to disentangle the …: we obtain fewer spurious detection and identify a larger number of true jumps. When applied to real data, our approach …
Persistent link: https://www.econbiz.de/10012181300
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High-frequency jump analysis of the bitcoin market
Scaillet, Olivier; Treccani, Adrien; Trevisan, Christopher - 2017
trader identifiers at a tick transaction level. Jumps are frequent events and they cluster in time. The order flow imbalance … and the preponderance of aggressive traders, as well as a widening of the bid-ask spread predict them. Jumps have short …
Persistent link: https://www.econbiz.de/10011762219
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A penalized two-pass regression to predict stock returns with time-varying risk premia
Bakalli, Gaetan; Guerrier, Stéphane; Scaillet, Olivier - 2021
We develop a penalized two-pass regression with time-varying factor loadings. The penalization in the first pass enforces sparsity for the time-variation drivers while also maintaining compatibility with the no arbitrage restrictions by regularizing appropriate groups of coefficients. The second...
Persistent link: https://www.econbiz.de/10012487589
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Deep learning for asset bubbles detection
Bashchenko, Oksana; Marchal, Alexis - 2020
We develop a methodology for detecting asset bubbles using a neural network. We rely on the theory of local martingales in continuous-time and use a deep network to estimate the diffusion coefficient of the price process more accurately than the current estimator, obtaining an improved detection...
Persistent link: https://www.econbiz.de/10012181227
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Apparent criticality and calibration issues in the Hawkes self-excited point process model : application to high-frequency financial data
Filimonov, Vladimir; Sornette, Didier - 2013
We present a careful analysis of possible issues of the application of the self-excited Hawkes process to high-frequency financial data and carefully analyze a set of effects that lead to significant biases in the estimation of the "criticality index'' n that quantifies the degree of endogeneity...
Persistent link: https://www.econbiz.de/10010257507
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Portfolio selection with active risk monitoring
Paolella, Marc S.; Polak, Pawel - 2015
The paper proposes a framework for large-scale portfolio optimization which accounts for all the major stylized facts of multivariate financial returns, including volatility clustering, dynamics in the dependency structure, asymmetry, heavy tails, and nonellipticity. It introduces a so-called...
Persistent link: https://www.econbiz.de/10011410659
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Machine learning for predicting stock return volatility
Filipović, Damir; Khalilzadeh, Amir - 2021
We use machine learning methods to predict stock return volatility. Our out-of-sample prediction of realised volatility for a large cross-section of US stocks over the sample period from 1992 to 2016 is on average 44.1% against the actual realised volatility of 43.8% with an R2 being as high as...
Persistent link: https://www.econbiz.de/10012800743
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A deep learning approach to estimate forward default intensities
Divernois, Marc-Aurèle - 2020 - Preliminary draft
This paper proposes a machine learning approach to estimate physical forward default intensities. Default probabilities are computed using artificial neural networks to estimate the intensities of the inhomogeneous Poisson processes governing default process. The major contribution to previous...
Persistent link: https://www.econbiz.de/10012419329
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A diagnostic criterion for approximate factor structure
Gagliardini, Patrick; Ossola, Elisa; Scaillet, Olivier - 2016 - This version: July 2016
We build a simple diagnostic criterion for approximate factor structure in large panel datasets. Given observable factors, the criterion checks whether the errors are weakly cross-sectionally correlated or share at least one unobservable common factor (interactive effects). A general version...
Persistent link: https://www.econbiz.de/10011518993
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Stable mixture GARCH models
Broda, Simon A.; Haas, Markus; Krause, Jochen; … - 2011
A new model class for univariate asset returns is proposed which involves the use of mixtures of stable Paretian distributions, and readily lends itself to use in a multivariate context for portfolio selection. The model nests numerous ones currently in use, and is shown to outperform all its...
Persistent link: https://www.econbiz.de/10009313940