68 citations · 98 across the 10 of their papers we have counts for
12 papers
Gaia method paper: an algorithm to detect flare events in Gaia time-series
E. Distefano, A. C. Lanzafame, A. F. Lanza +14
The third Gaia data release (GDR3) includes the gdr3_rotmod catalogue of 474026 stars with variability that is attributed to magnetic activity analogous to that observed in the Sun…
Simultaneous Bandwidths Determination for DK-HAC Estimators and Long-Run Variance Estimation in Nonparametric Settings
Federico Belotti, Alessandro Casini, Leopoldo Catania +2
We consider the derivation of data-dependent simultaneous bandwidths for double kernel heteroskedasticity and autocorrelation consistent (DK-HAC) estimators. In addition to the usu…
Value-at-Risk Prediction in R with the GAS Package
David Ardia, Kris Boudt, Leopoldo Catania
GAS models have been recently proposed in time-series econometrics as valuable tools for signal extraction and prediction. This paper details how financial risk managers can use GA…
Generalized Autoregressive Score Models in R: The GAS Package
David Ardia, Kris Boudt, Leopoldo Catania
This paper presents the R package GAS for the analysis of time series under the Generalized Autoregressive Score (GAS) framework of Creal et al. (2013) and Harvey (2013). The disti…
Dynamic Model Averaging for Practitioners in Economics and Finance: The eDMA Package
Leopoldo Catania, Nima Nonejad
Raftery, Karny, and Ettler (2010) introduce an estimation technique, which they refer to as Dynamic Model Averaging (DMA). In their application, DMA is used to predict the output s…
Density Forecasts and the Leverage Effect: Some Evidence from Observation and Parameter-Driven Volatility Models
Leopoldo Catania, Nima Nonejad
The leverage effect refers to the well-established relationship between returns and volatility. When returns fall, volatility increases. We examine the role of the leverage effect…
Dynamic Adaptive Mixture Models
Leopoldo Catania
In this paper we propose a new class of Dynamic Mixture Models (DAMMs) being able to sequentially adapt the mixture components as well as the mixture composition using information…
Dynamic Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances
Leopoldo Catania, Anna Gloria Billé
We propose a new class of models specifically tailored for spatio-temporal data analysis. To this end, we generalize the spatial autoregressive model with autoregressive and hetero…
Portfolio Optimisation Under Flexible Dynamic Dependence Modelling
Mauro Bernardi, Leopoldo Catania
Signals coming from multivariate higher order conditional moments as well as the information contained in exogenous covariates, can be effectively exploited by rational investors t…
Switching-GAS Copula Models With Application to Systemic Risk
Mauro Bernardi, Leopoldo Catania
Recent financial disasters have emphasised the need to accurately predict extreme financial losses and their consequences for the institutions belonging to a given financial market…
Comparison of Value-at-Risk models: the MCS package
Mauro Bernardi, Leopoldo Catania
This paper compares the Value--at--Risk (VaR) forecasts delivered by alternative model specifications using the Model Confidence Set (MCS) procedure recently developed by Hansen et…
Are news important to predict large losses?
Mauro Bernardi, Leopoldo Catania, Lea Petrella
In this paper we investigate the impact of news to predict extreme financial returns using high frequency data. We consider several model specifications differing for the dynamic p…