Mauro BernardiUniversity of Padova | UNIPD · Department of Statistical Sciences
Mauro Bernardi
PhD
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66
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754
Citations
Citations since 2017
Introduction
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October 2013 - March 2015
Publications
Publications (66)
Predicting the future performance of young runners is an important research issue in experimental sports science and performance analysis. We analyse a dataset with annual seasonal best performances of male middle distance runners for a period of 14 years and provide a modelling framework that accounts for both the fact that each runner has typical...
In this paper, we study the price determinants of Airbnb rentals, for the case of New York City, by developing a new dataset, which combines attributes of the property and of the related service, with other information available as open data. This dataset is employed within a spatial quantile semiparametric regression model, able to handle the intr...
We propose an alternative approach towards cost mitigation in volatility-managed portfolios based on smoothing the predictive density of an otherwise standard stochastic volatility model. Specifically, we develop a novel variational Bayes estimation method that flexibly encompasses different smoothness assumptions irrespective of the persistence of...
In functional data analysis, functional linear regression has attracted significant attention recently. Herein, we consider the case where both the response and covariates are functions. There are two available approaches for addressing such a situation: concurrent and nonconcurrent functional models. In the former, the value of the functional resp...
Predicting the future performance of young runners is an important research issue in experimental sports science and performance analysis. We analyse a data set with annual seasonal best performances of male middle distance runners for a period of 14 years and provide a modelling framework that accounts for both the fact that each runner has typica...
We provide an online framework for analyzing data recorded by smart watches during running activities. In particular, we focus on identifying variations in the behavior of one or more measurements caused by changes in physical condition, such as physical discomfort, periods of prolonged de-training, or even the malfunction of measuring devices. Our...
We present an efficient semiparametric variational method to approximate the posterior distribution of Bayesian regression models combining subjective prior beliefs with an empirical risk function. Our results apply to all the mixed models predicting the data through a linear combination of the available covariates, including, as special cases, gen...
Most financial signals show time dependency that, combined with noisy and extreme events, poses serious problems in the parameter estimations of statistical models. Moreover, when addressing asset pricing, portfolio selection, and investment strategies, accurate estimates of the relationship among assets are as necessary as are delicate in a time-d...
We deal with the problem of numerically computing the dual norm, which is important to study sparsity-inducing regularizations (Jenatton et al. 2011,Bach et al. 2012). The dual norms find application in optimization and statistical learning, for example, in the design of working-set strategies, for characterizing dual gradient methods, for dual dec...
We develop a new variational Bayes estimation method for large-dimensional sparse multivariate predictive regression models. Our approach allows to elicit ordering-invariant shrinkage priors directly on the regression coefficient matrix rather than a Cholesky-based linear transformation, as typically implemented in existing MCMC and variational Bay...
The method of simulated quantiles is extended to a general multivariate framework and to provide sparse estimation of the scaling matrix. The method is based on the minimisation of a distance between appropriate statistics evaluated on the true and synthetic data simulated from the postulated model. Those statistics are functions of the quantiles p...
In functional data analysis, functional linear regression has attracted significant attention recently. Herein, we consider the case where both the response and covariates are functions. There are two available approaches for addressing such a situation: concurrent and nonconcurrent functional models. In the former, the value of the functional resp...
We study a message passing approach to power expectation propagation for Bayesian model fitting and inference. Power expectation propagation is a class of variational approximations based on the notion of α-divergence that extends two notable approximations, namely mean field variational Bayes and expectation propagation. An illustration on a simpl...
The light we receive from distant astrophysical objects carries information about their origins and the physical mechanisms that power them. The study of these signals, however, is complicated by the fact that observations are often a mixture of the light emitted by multiple localized sources situated in a spatially-varying background. A general al...
Since the electricity market liberalisation of the mid-1990s, forecasting energy demand
and prices in competitive markets has become of primary importance for energy suppliers, market regulators and policy makers. In this paper, we propose a non-parametric model to obtain point and interval predictions of price and demand. It does not require any p...
Conditional Autoregressive Value-at-Risk and Conditional Autoregressive Expectile have become two popular approaches for direct measurement of market risk. Since their introduction several improvements both in the Bayesian and in the classical framework have been proposed to better account for asymmetry and local non-linearity. Here we propose a un...
The computational revolution in simulation techniques has shown to become a key ingredient in the field of Bayesian econometrics and opened new possibilities to study complex economic and financial phenomena. Applications include risk measurement, forecasting, assessment of policy effectiveness in macro, finance, marketing and monetary economics.
In this paper, we consider a random vector X=X1,X2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$X=\left( X_1,X_2\right) $$\end{document} following a multivariate Skew...
This Chapter reviews the main classes of models that incorporate volatility, with a focus on the most recent advancements in the financial econometrics literature and on the challenges posed by the increased availability of data. There are limits to the feasibility of all models when the cross-sectional dimension diverges, unless strong restriction...
This paper introduces a dominance test that allows to determine whether or not a financial institution can be classified as being more systemically important than another in a multivariate framework. The dominance test relies on a new risk measure, the ΔNetCoVaR that is specifically tailored to capture the joint extreme co-movements between institu...
Conditional Autoregressive Value-at-Risk and Conditional Autoregressive Expectile have become two popular approaches for direct measurement of market risk. Since their introduction several improvements both in the Bayesian and in the classical framework have been proposed to better account for asymmetry and local non-linearity. Here we propose a un...
We propose a shrinkage and selection methodology specifically designed for network inference using high dimensional data through a regularised linear regression model with Spike-and-Slab prior on the parameters. The approach extends the case where the error terms are heteroscedastic, by adding an ARCH-type equation through an approximate Expectatio...
Undirected graphs are useful tools for the analysis of sparse and high-dimensional data sets. In this setting the sparsity helps in reducing the complexity of the model. However, sparse graphs are usually estimated under the Gaussian paradigm thereby leading to estimates that are very sensitive to the presence of outlying observations. In this pape...
In this paper we introduce a family of 2-Sided Skew and Shape distributions that accounts for asymmetry in the tails decay. The proposed distributions account for many of the stylised fact frequently observed in financial time series, except for the time-varying nature of moments of any order. To this aim we extend the model to a dynamic framework...
We propose a Bayesian approach to the problem of variable selection and shrinkage in high dimensional sparse regression models where the regularisation method is an extension of a previous LASSO. The model allows us to include a large number of institutions which improves the identification of the relationship and maintains at the same time the fle...
Parameter estimation of distributions with intractable density, such as the Elliptical Stable, often involves high-dimensional integrals requiring numerical integration or approximation. This paper introduces a novel Expectation–Maximisation algorithm for fitting such models that exploits the fast Fourier integration for computing the expectation s...
One of the ultimate goals of hydrological studies is to assess whether or not the dynamics of the variables of interest are changing. For this purpose, specific statistics are usually adopted: e.g., overall indices, averages, variances, correlations, root-mean-square differences, monthly/annual averages, seasonal patterns, maximum and minimum value...
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. The ability of
econometric models to predict extreme events strongly relies on their flexibility to account for the
highly nonlinear and asymmetric dependence patterns...
Traditional Bayesian quantile regression relies on the Asymmetric Laplace (AL) distribution due primarily to its satisfactory empirical and theoretical performances. However, the AL displays medium tails and it is not suitable for data characterized by strong deviations from the Gaussian hypothesis. An extension of the AL Bayesian quantile regressi...
The sparse multivariate method of simulated quantiles (S‐MMSQ) is applied to solve a portfolio optimization problem under value‐at‐risk constraints where the joint returns follow a multivariate skew‐elliptical stable distribution. The S‐MMSQ is a simulation‐based method that is particularly useful for making parametric inference in some pathologica...
Signals coming from multivariate higher order conditional moments as well as
the information contained in exogenous covariates, can be effectively exploited
by rational investors to allocate their wealth among different risky investment
opportunities. This paper proposes a new flexible dynamic copula model being
able to explain and forecast the tim...
![(Dotted line) VaR0.95(Y)\documentclass[12pt]{minimal}...](publication/317746793/figure/fig1/AS:960243601248256@1605951323384/Dotted-line-VaR095Ydocumentclass12ptminimal-usepackageamsmath_Q320.jpg)
We present a novel methodology to compute conditional risk measures when the conditioning event depends on a number of random variables. Specifically, given a random vector (Formula presented.), we consider risk measures that express the risk of Y given that (Formula presented.) assumes values in an extreme multidimensional region. In particular, t...
This paper presents the R package MCS which implements the model confidence set (MCS) procedure for model comparison. The MCS procedure consists on a sequence of tests which permits to build a set of 'superior' models, where the null hypothesis of equal predictive ability (EPA) is not rejected at a certain confidence level. The EPA statistic test i...
In this paper the method of simulated quantiles (MSQ) of Dominicy and Veredas (2013) and Dominick et al. (2013) is extended to a general multivariate framework (MMSQ) and to provide a sparse estimator of the scale matrix (sparse-MMSQ). The MSQ, like alternative likelihood-free procedures, is based on the minimisation of the distance between appropr...
The dynamic evolution of tail–risk interdependence among institutions is of primary importance when extreme events such as financial crisis occur. In this paper we introduce two new risk measures that generalise the Conditional Value–at–Risk and the Conditional Expected Shortfall in a multiple setting. The proposed risk measures aim to capture extr...
We revisit the notion of Conditional Value-at-Risk (shortly, CoVaR) by weakening the usual assumptions on the joint distribution function of the involved random variables. The new approach exploits the copula methodology and uses the concept of Dini derivatives. A directory of CoVaR values for different families of copulas is provided.
The standard theory of coherent risk measures fails to consider individual institutions as part of a system which might itself experience instability and spread new sources of risk to the market participants. In compliance with an approach adopted by Shapley and Shubik (1969), this paper proposes a cooperative market game where agents and instituti...
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 al. (Econometrica 79(2):453–497, 2011). The direct VaR estimate provided by the Conditional Autoregressive Value-at-Risk (CAViaR) models of Engle and Manganelli (J Bus Eco...
L_p-quantiles represent an important class of generalised quantiles and are defined as the minimisers of an expected asymmetric power function, see Chen (1996). For p=1 and p=2 they correspond respectively to the quantiles and the expectiles. In his paper Koenker (1993) showed that the tau quantile and the tau expectile coincide for every tau in (0...
Traditional Bayesian quantile regression relies on the Asymmetric Laplace distribution (ALD) mainly because of its satisfactory empirical and theoretical performances. However, the ALD displays medium tails and it is not suitable for data characterized by strong deviations from the Gaussian hypothesis. In this paper, we propose an extension of the...
This paper is of methodological nature, and deals with the foundations of Risk Assessment. Several international guidelines have recently recommended to select appropriate/relevant Hazard Scenarios in order to tame the consequences of (extreme) natural phenomena. In particular, the scenarios should be multivariate, i.e., they should take into accou...
We propose a general dynamic model averaging (DMA) approach based on Markov-Chain Monte Carlo for the sequential combination and estimation of quantile regression models with time-varying parameters. The efficiency and the effectiveness of the proposed DMA approach and the MCMC algorithm are shown through simulation studies and applications to macr...
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 property of the underlying stochastic process as well as for the innovation process. Since news are essentially qualitative measures, they are firstly transformed into qu...
Recent financial disasters emphasised the need to investigate the consequences associated with the tail co-movements among institutions; episodes of contagion are frequently observed and increase the probability of large losses affecting market participants' risk capital. Commonly used risk management tools fail to account for potential spillover e...
Background:
Biological agents provide an important therapeutic alternative for rheumatoid arthritis patients refractory to conventional disease-modifying antirheumatic drugs. Few head-to-head comparative trials are available.
Purpose:
The aim of this meta-analysis was to compare the relative efficacy of different biologic agents indicated for us...
Forecasting energy load demand data based on high frequency time series has become of primary importance for energy suppliers in nowadays competitive electricity markets. In this work, we model the time series of Italian electricity consumption from 2004 to 2014 using an exponential smoothing approach. Data are observed hourly showing strong season...
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 al. (2011). The direct VaR estimate provided by
the Conditional Autoregressive Value--at--Risk (CAViaR) models of Eengle and
Manganelli (2004) are compared to those obta...
During the last decades particular effort has been directed towards understanding and predicting the relevant state of the business cycle with the objective of decomposing permanent shocks from those having only a transitory impact on real output. This trend–cycle decomposition has a relevant impact on several economic and fiscal variables and cons...
Background and objective:
There are four efficacious subcutaneous anti-tumor necrosis factor alpha (TNF-alpha) agents used for the therapy of ankylosing spondilitis (AS), but apparently little or no differences in their effectiveness was proven. By this study, we aimed to compare Assessment in Ankylosing Spondylitis Response Criteria 20 response p...
This paper presents the R package MCS which implements the Model Confidence Set (MCS) procedure recently developed by Hansen, Lunde, and Nason (2011). The Hansen's procedure consists on a sequence of tests which permits to construct a set of "superior" models, where the null hypothesis of Equal Predictive Ability (EPA) is not rejected at a certain...
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 property of the underlying stochastic process as well
as for the innovation process. Since news are essentially qualitative measures,
they are firstly transformed into qua...
In this paper we consider a multivariate model-based approach to measure the
dynamic evolution of tail risk interdependence among US banks, financial
services and insurance sectors. To deeply investigate the risk contribution of
insurers we consider separately life and non-life companies. To achieve this
goal we apply the multivariate student-t Mar...
Markov switching models are often used to analyze financial returns because
of their ability to capture frequently observed stylized facts. In this paper
we consider a multivariate Student-t version of the model as a viable
alternative to the usual multivariate Gaussian distribution, providing a
natural robust extension that accounts for heavy-tail...
Finite mixtures of Skew distributions have become increasingly popular in the last few years as a flexible tool for handling data displaying several different characteristics such as multimodality, asymmetry and fat-tails. Examples of such data can be found in financial and actuarial applications as well as biological and epidemiological analysis....
Recent financial disasters emphasised the need to investigate the consequence
associated with the tail co-movements among institutions; episodes of contagion
are frequently observed and increase the probability of large losses affecting
market participants' risk capital. Commonly used risk management tools fail to
account for potential spillover ef...
This article proposes an approximate conditional dynamic finite mixture hurdle model for panel count data with excess of zeros and endogenous initial conditions. We provide parameter estimates by using the Expectation-Maximization (EM) algorithm in a Nonparametric Maximum Likelihood (NPML) framework. An application to a unique data set on traffic v...
The derivation of loss distribution from insurance data is a very interesting research topic but at the same time not an easy task. To find an analytic solution to the loss distribution may be mislading although this approach is frequently adopted in the actuarial literature. Moreover, it is well recognized that the loss distribution is strongly sk...
The series on average hours worked in the manufacturing sector is a key leading indicator of the U.S. business cycle. The paper deals with robust estimation of the cyclical component for the seasonally adjusted time series. This is achieved by an unobserved components model featuring an irregular component that is represented by a Gaussian mixture...
The series on average hours worked in the manufacturing sector is a key leading indicator of the U.S. business cycle. The paper deals with robust estimation of the cyclical component for the seasonally adjusted time series. This is achieved by an unobserved components model featuring an irregular component that is represented by a Gaussian mixture...
The Gaussian model results unsatisfactory and reveals difficulties in fitting data with skewness, heavy tails and multimodality. The use of �-stable distributions allows for modelling skewness and heavy tails but gives rise to inferential problems related to the estimation of the parameters of the distributions. The aim of this work is to generalis...
Forecasting energy load demand based on high frequency time series has became of pri-mary importance for energy suppliers in nowadays competitive electricity markets. The main charac-teristic of this time series is the strong seasonal pattern it displays at different frequencies going from the daily up to the semi-annual or annual cycle, along with...
Projects
Projects (2)
Since the advent of Markov chain Monte Carlo (MCMC) methods in the early 1990s, Bayesian methods have been proposed to a large and growing number of applications. One of the main advantages of Bayesian inference is to deal with different and many sources of uncertainty, including data, model, parameter, parameter restriction uncertainties, in a unified and coherent framework. This Special Issue focuses on exercises where one or more of these features are crucial. Applications include risk measurement in international and financial markets, forecasting, assessment of policy effectiveness in macro and monetary economics. Papers that contain original research on this theme are actively solicited.
Dr. Mauro Bernardi
Dr. Stefano Grassi
Prof. Dr. Francesco Ravazzolo
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