Lea Petrella

Sapienza University of Rome | la sapienza · Department of Methods and Models for Economics, Territory and Finance

In this paper we develop a linear expectile hidden Markov model for the analysis of cryptocurrency time series in a risk management framework. The methodology proposed allows to focus on extreme returns and describe their temporal evolution by introducing in the model time-dependent coefficients evolving according to a latent discrete homogeneous M...

This paper introduces inter-order formulas for partial and complete moments of a Student $t$ distribution with $n$ degrees of freedom. We show how the partial moment of order $n - j$ about any real value $m$ can be expressed in terms of the partial moment of order $j - 1$ for $j$ in $\{1,\dots, n \}$. Closed form expressions for the complete moment...

In this paper, we investigate the interconnections among and within the Energy, Agricultural, and Metal commodities, operating in a risk management framework with a twofold goal. First, we estimate the Value-at-Risk (VaR) employing GARCH and Markov-switching GARCH models with different error term distributions. The use of such models allows us to t...

This paper develops a quantile hidden semi-Markov regression to jointly estimate multiple quantiles for the analysis of multivariate time series. The approach is based upon the Multivariate Asymmetric Laplace (MAL) distribution, which allows to model the quantiles of all univariate conditional distributions of a multivariate response simultaneously...

An M-quantile regression model is developed for the analysis of multiple dependent outcomes by introducing the notion of directional M-quantiles for multivariate responses. In order to incorporate the correlation structure of the data into the estimation framework, a robust marginal M-quantile model is proposed extending the well-known generalized...

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...

The identification of factors associated with mental and behavioural disorders in early childhood is critical both for psychopathology research and the support of primary health care practices. Motivated by the Millennium Cohort Study, in this paper we study the effect of a comprehensive set of covariates on children's emotional and behavioural tra...

In this paper we introduce the use of mixed-frequency variables in a quantile regression framework to compute high-frequency conditional quantiles by means of low-frequency variables. We merge the well-known Quantile Regression Forest algorithm and the recently proposed Mixed-Data-Sampling model to build a comprehensive methodology to jointly model...

The high level of integration of international financial markets highlights the need to accurately assess contagion and systemic risk under different market conditions. To this end, we develop a quantile graphical model to identify the tail conditional dependence structure in multivariate data across different quantiles of the marginal distribution...

Background Nonthyroidal Illness Syndrome (NTIS) can be detected in many critical illnesses. Recently, we demonstrated that this condition is frequently observed in COVID-19 patients too and it is correlated with the severity the disease. However, the exact mechanism through which thyroid hormones influence the course of COVID-19, as well as that of...

The identification of factors associated with mental and behavioral disorders in early childhood is critical both for psychopathology research and the support of primary health care practices. Motivated by the Millennium Cohort Study, in this paper we study the effect of a comprehensive set of covariates on children's emotional and behavioural traj...

This paper provides a quantitative assessment of equity options priced at the Zero Lower Bound, i.e., when interest rates are set essentially to zero. We obtain closed form formulas for American options when the Zero Lower Bound policy holds. We perform numerical implementation of American put options written on the stock Federal National Mortgage...

This paper shows the effects of the COVID-19 pandemic on energy markets. We estimate daily volatilities and correlations among energy commodities relying on a mixed-frequency approach that exploits information from the number of weekly deaths related to COVID-19 in the United States. The mixed-frequency approach takes advantage of the MIxing-Data S...

In this paper, we propose a multivariate quantile regression framework to forecast Value at Risk (VaR) and Expected Shortfall (ES) of multiple financial assets simultaneously, extending (Taylor, 2019). We generalize the Multivariate Asymmetric Laplace (MAL) joint quantile regression of (Petrella and Raponi, 2019) to a time-varying setting, which al...

In this paper we propose a multivariate quantile regression framework to forecast Value at Risk (VaR) and Expected Shortfall (ES) of multiple financial assets simultaneously, extending Taylor (2019). We generalize the Multivariate Asymmetric Laplace (MAL) joint quantile regression of Petrella and Raponi (2019) to a time-varying setting, which allow...

We reviewed surgical cases from 4 Thoracic Surgery departments in the Lombardia region of Italy, the area mostly affected by Coronavirus pandemic in Europe, with the aim to describe the impact of COVID-19 on the treatment of thoracic surgical patients. Clinical, radiological and laboratory data from patients who underwent lung resection from Decemb...

This article develops a two-part finite mixture quantile regression model for semi-continuous longitudinal data. The proposed methodology allows heterogeneity sources that influence the model for the binary response variable to also influence the distribution of the positive outcomes. As is common in the quantile regression literature, estimation a...

A hidden semi-Markov-switching quantile regression model is introduced as an extension of the hidden Markov-switching one. The proposed model allows for arbitrary sojourn-time distributions in the states of the Markov-switching chain. Parameters estimation is carried out via maximum likelihood estimation method using the Asymmetric Laplace distribu...

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...

Quantile regression is an efficient tool when it comes to estimate popular measures of tail risk such as the conditional quantile Value at Risk. In this paper we exploit the availability of data at mixed frequency to build a volatility model for daily returns with low-- (for macro--variables) and high--frequency (which may include an \virg{--X} ter...

This paper develops a two-part finite mixture quantile regression model for semi-continuous longitudinal data. The proposed methodology allows heterogeneity sources that influence the model for the binary response variable, to influence also the distribution of the positive outcomes. As is common in the quantile regression literature, estimation an...

In recent years, three-dimensional reconstruction (3DR) models have become a standard tool in several medical fields such as education, surgical training simulation, patient–doctor communication, and surgical planning. Postoncologic reconstructive surgery in thoracic diseases might benefit from 3DR models; however, limited data on this application...

This work is intended to assess the contribution to systemic risk of major companies in the European stock market on a geographical basis. We use the EuroStoxx 50 Index as a proxy for the financial system and we rely on the CoVaR and Δ-CoVaR risk measures to estimate the contribution of each European country belonging to the index to systemic risk....

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...

This paper proposes a maximum likelihood approach to jointly estimate marginal conditional quantiles of multivariate response variables in a linear regression framework. We consider a slight reparameterization of the multivariate asymmetric Laplace distribution proposed by Kotz et al. (2001) and exploit its location–scale mixture representation to...

This paper proposes a maximum-likelihood approach to jointly estimate marginal conditional quantiles of multivariate response variables in a linear regression framework. We consider a slight reparameterization of the Multivariate Asymmetric Laplace distribution proposed by Kotz et al (2001) and exploit its location-scale mixture representation to i...

In this paper we investigate different VaR forecasts for daily energy commodities returns using GARCH, EGARCH, GJR-GARCH, Generalized Autoregressive Score (GAS) and the Conditional Autoregressive Value at Risk (CAViaR) models. We further develop a Dynamic Quantile Regression (DQR) one where the parameters evolve over time following a first order st...

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...

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...

The multi-cycle organization of modern university systems stimulates the interest in studying the progression to higher level degree courses during the academic career. In particular, after the achievement of the first level qualification (Bachelor degree), students have to decide whether to continue their university studies, by enrolling in a seco...

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...

Due to their heterogeneity, insurance risks can be properly described as a mixture of different fixed models, where the weights assigned to each model may be estimated empirically from a sample of available data. If a risk measure is evaluated on the estimated mixture instead of the (unknown) true one, then it is important to investigate the commit...

Inventory optimization of high-value spare parts may generate a significant reduction of cost to allow a better allocation of resources in maintenance management. Sherbrooke’s METRIC (Multi Echelon Technique for Recoverable Item Control) is the most common method to define an overall optimization process adopting a system-approach. Its main assumpt...

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 consider method-of-quantiles estimators of unknown one-dimensional parameters, namely the analogue of method-of-moments estimators obtained by matching empirical and theoretical quantiles at some probability level lambda in (0,1). The aim is to present large deviation results for these estimators as the sample size tends to infinity. We study in...

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...

Background Use of biologic drugs is approved for treatment in rheumatoid arthritis (RA), both in established disease and at the early stage of RA (ERA). Identification of ERA and an early therapeutic strategy would lead to greater clinical improvement. Only a few indirect comparisons of the efficacy of different biologic agents in established RA ha...

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...

Bronchopleural fistulas are a major therapeutic challenge. We have reviewed our experience to establish the best choice of treatment. From January 2001 to December 2013, the records of 3,832 patients who underwent pulmonary anatomic resections were retrospectively reviewed. The overall incidence of bronchopleural fistulas was 1.4% (52 of 3,832): 1....

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 shows that regular fractional polynomials can approximate regular cost, production and utility functions and their first two derivatives on closed compact subsets of the strictly positive orthant of Euclidean space arbitrarily well. These functions therefore can provide reliable approximations to demand functions and other economically r...

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...

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...

Empirical study of university student performance is often complicated by missing data, due to student drop-out of the university. If drop-out is non-ignorable, i.e. it depends on either unobserved values or an underlying response process, it may be a pervasive problem. In this paper, we tackle the relation between the primary response (student per...

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...

University drop-out is a topic of increasing concern in Italy as well as in other countries. In empirical analysis, university drop-out is generally measured by means of a binary variable indicating the drop-out versus retention. In this paper, we argue that the withdrawal decision is one of the possible outcomes of a set of four alternatives: rete...

University students’ drop-out is a crucial issue for the universities’ efficiency evaluation and funding. In this paper, we analyze the drop-out rate of the Economics and Business faculty of Sapienza University of Rome. We use administrative data on 9,725 undergraduates students enrolled in three-years bachelor programs from 2001 to 2007 and perfor...

In this paper, we present large deviation results for estimators of some unknown parameters concerning stationary Gaussian processes. We deal with both maximum likelihood estimators and posterior distributions; moreover, we illustrate the differences between short-range- and long-range-dependent processes. As a typical feature the rate functions fo...

We sought to evaluate factors influencing long-term survival of patients with locally advanced thymoma/thymic carcinoma (Masaoka stages III and IVa) treated by immediate surgery or induction therapy plus surgery. From January 1991 to April 2007, we surgically treated 61 patients with locally advanced thymoma/thymic carcinoma (Masaoka stages III and...

In this article, we consider several statistical models for censored exponential data. We prove a large deviation result for the maximum likelihood estimators (MLEs) of each model, and a unique result for the posterior distributions which works well for all the cases. Finally, comparing the large deviation rate functions for MLEs and posterior dist...

We seek to evaluate the comparative merits of thoracoscopic versus open decortication in the surgical management of patients with chronic postpneumonic pleural empyema. From January 1996 to December 2006, 308 patients (180 males, 128 females, mean age: 56.3 years, range: 17-82 years) with chronic postpneumonic pleural empyema underwent decorticatio...

Neurogenic tumours of the mediastinum are uncommon neoplasms arising from nerve tissues within the thorax. We sought to evaluate and compare the outcome following surgical resection of such tumours by VATS, open thoracotomy, and by either combined with hemilaminectomy. From February 1992 to March 2007, 93 patients underwent surgical resection of ne...

Some concern still exists regarding long-term lung function following videothoracoscopic talc poudrage for primary spontaneous pneumothorax (PSP). We evaluated lung function at 5 years in a series of 100 patients surgically treated for PSP. Out of 1065 patients treated for PSP by means of videothoracoscopic talc poudrage from September 1995 to Janu...

We sought to evaluate the outcome of 861 patients treated with videothoracoscopic talc poudrage for primary spontaneous pneumothorax. From September 1995 through January 2004, a total of 861 patients (578 male, 283 female, mean age 28.6 years) underwent videothoracoscopy for recurrent and complicated primary spontaneous pneumothorax. Patients were...

In this paper we present asymptotic estimation of level crossing probabilities from a Bayesian point of view, based on large deviations. For the Bayesian analysis we choose a finite mixture of conjugate prior distributions to model the uncertainty on the unknown parameters of the two classes of stochas-tic processes considered: the Brownian motion...

Although the technique for the surgical repair of rectal prolapse has advanced over the years, no ideal procedure has been found. We aim to test a new surgical procedure for abdominal rectopexy that uses the greater omentum to support the rectum below the rectopexy, to reconstruct the anorectal angle and dispense with the need for synthetic mesh, t...

We develop a Bayesian semiparametric procedure for the analysis of stationary long-range dependent time series. We use frequency domain methods to partition the infinite-dimensional parameter space into regions where genuine prior information on the form of the spectral density is available, and others where vague prior beliefs are adopted; the sol...

This paper considers point null hypothesis testing when the sampling distribution belongs to a particular class, defined in L. J. Gleser and J. T. Hwang [Ann. Stat. 15, 1351–1362 (1987; Zbl 0638.62035)]. We discuss the drawbacks of frequentist and likelihood solutions and we show how proper Bayesian analysis encounters relatively similar difficulti...

This paper provides a generic, very fast method for computing exact density ratio class bounds on posterior expectations, given the output of a posterior simulator. It illustrates application of the method in an econometric model of typical complexity. In this model, the exact bounds for expectations of some functions of interest are well approxima...

This article provides a generic, very fast method for computing exact density-ratio class bounds on posterior expectations, given the output of a posterior simulator. It illustrates application of the method in an econometric model of typical complexity. In this model, the exact bounds for expectations of some functions of interest are well approxi...

The development of Bayesian robustness has been growing in the last decade. The theory has extensively dealt with the univariate parameter case. Among the vast amount of proposals in the literature, only a few of them have a straightforward extension to the multivariate case. In this paper we consider the multidimensional version of the class of ε-...

In this work we consider, under a Bayesian perspective, the problem of order selection within a finite class, M, of stationary and invertible ARMA models. Assuming that the final porpose is to predict one or more future realisations of a stochastic process, model selection can be seen as a statistical decision problem, and we want to maximise a sui...

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...