Papers by Matteo Pelagatti
British Accounting Review, Mar 1, 2017
This paper addresses the issue of systemic risk in insurance and investigates how financial marke... more This paper addresses the issue of systemic risk in insurance and investigates how financial markets evaluate the introduction of a new regulation addressed to global systemically important insurers (G-SIIs). We analysed the stock price reactions and the evolution of the distance-to-default of a sample of 44 of the world's largest insurers to the publication of the first list of 9 G-SIIs and the release of information regarding their new capital requirements and other policy measures. The results of our event study suggest that, overall, investors doubt the effectiveness of the new regulatory framework in reducing systemic risk in the insurance sector and curbing the moral hazard implications of a "too systemic to fail" policy.
Journal of Statistical Software, 2011
The use of state space models and their inference is illustrated using the package SsfPack for Ox... more The use of state space models and their inference is illustrated using the package SsfPack for Ox. After a rather long introduction that explains the use of SsfPack and many of its functions, four case-studies illustrate the practical implementation of the software to real world problems through short sample programs. The first case consists in the analysis of the well-known (at least to time series analysis experts) Nile data with a local level model. The other case-studies deal with ARIMA and RegARIMA models applied to the (also well-known) Airline time series, structural time series models applied to the Italian industrial production index and stochastic volatility models applied to the FTSE100 index. In all applications inference on the model (hyper-) parameters is carried out by maximum likelihood, but in one case (stochastic volatility) also an MCMC-based approach is illustrated. Cubic splines are covered in a very short example as well.
Social Science Research Network, 2015
We address the issue of systemic risk in insurance and investigate whether financial markets reac... more We address the issue of systemic risk in insurance and investigate whether financial markets reacted to the regulatory changes implied by the publication of the list of global systemically important insurers (G-SIIs) and the new rules designed to address the systemic risk posed by large and interconnected insurers. By applying event study methodology to a sample of 44 of the world’s largest insurers, we assess whether the stock prices of G-SIIs reacted significantly and differently from those of other large insurers not deemed to be systemically important, following the publication of the first list of 9 G-SIIs and the release of information regarding their new capital requirements and other policy measure. Overall, we determine that financial markets did not react to the new regulation regarding G-SIIs, confirming the results obtained in the banking sector. Financial markets question the capability of the new rules to effectively curb the moral hazard implications of a too-big-to-fail policy.
RePEc: Research Papers in Economics, Dec 1, 2020
On 4 November 2020 the Italian government introduced a new policy to address the second wave of C... more On 4 November 2020 the Italian government introduced a new policy to address the second wave of COVID-19. Based on a battery of indicators, the 21 administrative regions of Italy were assigned a risk level among yellow, orange, red, and, starting on 6 November 2020, different type of restrictions were applied accordingly. This event represents a natural experiment that allows the evaluation of the effects of non-pharmaceutical interventions, free from those nuisance factors affecting cross-national studies. In this work, we extract the daily growth rate of new cases, hospitalizations and patients in ICU from official data using an unobserved components model and assess how the different restrictions had different impacts in reducing the speed of spread of the virus. We find that all the three packages of restrictions have an effect on the speed of spread of the disease, but while the mildest (yellow) policy leads to a constant number of hospitalizations (zero growth rate), the strictest (red) policy is able to halve the number of accesses to regular wards and intensive care units in about one month. The effects of the intermediate (orange) policy are more volatile and seem to be only slightly more effective than the milder (yellow) policy.
International journal of business and social science, 2017
Chapman and Hall/CRC eBooks, Jul 23, 2015
We propose a likelihood ratio (LR) test of stationarity based on a widely-used correlated unobser... more We propose a likelihood ratio (LR) test of stationarity based on a widely-used correlated unobserved components model. We verify the asymptotic distribution and consistency of the LR test, while a bootstrap version of the test is at least first-order accurate. Given empiricallyrelevant processes estimated from macroeconomic data, Monte Carlo analysis reveals that the bootstrap version of the LR test has better small-sample size control and higher power than commonly used bootstrap Lagrange multiplier (LM) tests, even when the correct parametric structure is specified for the LM test. A key feature of our proposed LR test is its allowance for correlation between permanent and transitory movements in the time series under consideration, which increases the power of the test given the apparent presence of non-zero correlations for many macroeconomic variables. Based on the bootstrap LR test, and in some cases contrary to the bootstrap LM tests, we can reject trend stationarity for U.S. real GDP, the unemployment rate, consumer prices, and payroll employment in favor of nonstationary processes with volatile stochastic trends.
RePEc: Research Papers in Economics, Jul 29, 2016
We clarify a point regarding the appropriate measure(s) of the variance of smoothed disturbances ... more We clarify a point regarding the appropriate measure(s) of the variance of smoothed disturbances in the context of linear state-space models. This involves explaining how two different concepts, which are sometimes given the same name in the literature, relate to each other. We also describe the behavior of several common software packages is in this regard.
Time Series Modelling with Unobserved Components By Matteo M. Pelagatti Despite the unobserved co... more Time Series Modelling with Unobserved Components By Matteo M. Pelagatti Despite the unobserved components model (UCM) having many advantages over more popular forecasting techniques based on regression analysis, exponential smoothing, and ARIMA, the UCM is not well known among practitioners outside the academic community. Time Series Modelling with Unobserved Components rectifies this deficiency by giving a practical overview of the UCM approach, covering some theoretical details, several applications, and the software for implementing UCMs. The book's first part discusses introductory time series and prediction theory. Unlike most other books on time series, this text includes a chapter on prediction at the beginning because the problem of predicting is not limited to the field of time series analysis. The second part introduces the UCM, the state space form, and related algorithms. It also provides practical modeling strategies to build and select the UCM that best fits the needs of time series analysts. The third part presents real-world applications, with a chapter focusing on business cycle analysis and the construction of band-pass filters using UCMs. The book also reviews software packages that offer ready-to-use procedures for UCMs as well as systems popular among statisticians and econometricians that allow general estimation of models in state space form. This book demonstrates the numerous benefits of using UCMs to model time series data. UCMs are simple to specify, their results are easy to visualize and communicate to non-specialists, and their forecasting performance is competitive. Moreover, various types of outliers can easily be identified, missing values are effortlessly managed, and working contemporaneously with time series observed at different frequencies poses no problem. Download Time Series Modelling with Unobserved Components ...pdf Read Online Time Series Modelling with Unobserved Components ...pdf
Social Science Research Network, 2008
Abstract: Ageing and technological change play a major role in the governance of healthcare resou... more Abstract: Ageing and technological change play a major role in the governance of healthcare resources, with cohorts living longer and consuming increasing amounts of intensive, previously unavailable treatments. Equity of access to pharmaceutical treatment on the basis of clinical need alone remains the central principle of the public healthcare system, raising the issue of an equitable distribution of resources in proportion to the population needs. The primary objective of this study was to discuss the adoption of a ...
Social Science Research Network, 2006
In this paper we analyze the time series of daily mean prices generated in the Italian electricit... more In this paper we analyze the time series of daily mean prices generated in the Italian electricity market, which started to operate as a Pool in April 2004. The objective is to characterize the high degree of autocorrelation and multiple seasonalities in the electricity prices. We use periodic models with GARCH disturbances and leptokurtic distribution and compare their performance with more classical ARMA-GARCH processes. The within-year seasonal component is built using the low frequencies components of physical quantities, which are very regular throughout the sample. Results reveal that much of the variability of the price series is explained by deterministic multiple seasonalities which interact with each other. Periodic AR-GARCH models seem to perform quite well in mimicking the features of the stochastic part of the price process.
International Advances in Economic Research, Jul 26, 2007
This paper analyses the interdependencies existing in the European electricity prices. The result... more This paper analyses the interdependencies existing in the European electricity prices. The results of a multivariate dynamic analysis of weekly median prices reveal the presence of strong integration (but not perfect integration) among the markets considered in the sample and the existence of a common trend among electricity prices and oil prices. This implies that there are no long-run arbitrage opportunities. The latter result appears to be relevant also in the context of the discussion of efficient hedging instruments to be used by medium-long term investors.
International Journal of Forecasting, 2023
Forecasting, Dec 30, 2022
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
In this paper we consider an oligopolistic market in which one firm can be monopolist on her resi... more In this paper we consider an oligopolistic market in which one firm can be monopolist on her residual demand function and derive implications on the shape of her profit function, which we show may not be concave in price. We propose a simple price-capping rule that induce the pivotal operator to compete for quantity instead of taking advantage of her monopoly. Then, we analyze the bidding behaviour of the dominant electricity producer operating in the Italian wholesale power market (IPEX). This firm is vertically integrated and in many instances she acts as a monopolist on the residual demand. We find that, contrary to expectations, this pivotal firm refrains to exploit totally her unilateral market power and, therefore, bids at levels well below the cap. We discuss such a behaviour and derive implications for the setting of the price cap.
RePEc: Research Papers in Economics, Nov 1, 2007
Energy: Expectations and Uncertainty,39th IAEE International Conference,Jun 19-22, 2016, Jun 19, 2016
Econometrics, 2005
The Dynamic Conditional Correlation (DCC) model of Engle has made the estimation of multivariate ... more The Dynamic Conditional Correlation (DCC) model of Engle has made the estimation of multivariate GARCH models feasible for reasonably big vectors of securities' returns. In the present paper we show how Engle's twosteps estimate of the model can be easily extended to elliptical conditional distributions and apply different leptokurtic DCC models to the evaluation of the Value at Risk (VaR) of a portfolio of realistic dimensions. A free software (Ox class) written by the authors to carry out all the required computations is presented as well.
Energy Policy, Mar 1, 2013
In this paper we consider an oligopolistic market in which one firm can be monopolist on her resi... more In this paper we consider an oligopolistic market in which one firm can be monopolist on her residual demand function and derive implications on the shape of her profit function, which we show may not be concave in price. We propose a simple price-capping rule that induce the pivotal operator to compete for quantity instead of taking advantage of her monopoly. Then, we analyze the bidding behaviour of the dominant electricity producer operating in the Italian wholesale power market (IPEX). This firm is vertically integrated and in many instances she acts as a monopolist on the residual demand. We find that, contrary to expectations, this pivotal firm refrains to exploit totally her unilateral market power and, therefore, bids at levels well below the cap. We discuss such a behaviour and derive implications for the setting of the price cap.
arXiv (Cornell University), Oct 24, 2022
Event Studies (ES) are statistical tools that assess whether a particular event of interest has c... more Event Studies (ES) are statistical tools that assess whether a particular event of interest has caused changes in the level of one or more relevant time series. We are interested in ES applied to multivariate time series characterized by high spatial (cross-sectional) and temporal dependence. We pursue two goals. First, we propose to extend the existing taxonomy on ES, mainly deriving from the financial field, by generalizing the underlying statistical concepts and then adapting them to the time series analysis of airborne pollutant concentrations. Second, we address the spatial cross-sectional dependence by adopting a twofold adjustment. Initially, we use a linear mixed spatio-temporal regression model (HDGM) to estimate the relationship between the response variable and a set of exogenous factors, while accounting for the spatio-temporal dynamics of the observations. Later, we apply a set of sixteen ES test statistics, both parametric and nonparametric, some of which directly adjusted for cross-sectional dependence. We apply ES to evaluate the impact on NO 2 concentrations generated by the lockdown restrictions adopted in the Lombardy region (Italy) during the COVID-19 pandemic in 2020. The HDGM model distinctly reveals the level shift caused by the event of interest, while reducing the volatility and isolating the spatial dependence of the data. Moreover, all the test statistics unanimously suggest that the lockdown restrictions generated significant reductions in the average NO 2 concentrations.
Uploads
Papers by Matteo Pelagatti