Vector Multiplicative Error Models: Representation and Inference

The Multiplicative Error Model introduced by Engle (2002) for positive valued processes is specified as the product of a (conditionally autoregressive) scale factor and an innovation process with positive support. In this paper we propose a multi-variate extension of such a model, by taking into consideration the possibility that the vector innovation process be contemporaneously correlated. The estimation procedure is hindered by the lack of probability density functions for multivariate positive valued random variables. We suggest the use of copulafunctions and of estimating equations to jointly estimate the parameters of the scale factors and of the correlations of the innovation processes. Empirical applications on volatility indicators are used to illustrate the gains over the equation by equation procedure.

The impact of bank concentration on financial distress: them case of the European banking system

This paper examines the impact of bank concentration on bank financial distress using a balanced panel of commercial banks belonging to EU 25 over the sample period running from 2003 to 2007. Financial distress is proxied by the observations falling below a given threshold of the empirical distribution of a risk adjusted indicator of bank performance: the Shareholder Value ratio. We employ a panel probit regression estimated by GMM in order to obtain consistent and efficient estimates following the suggestion of Bertschek and Lechner (1998). Our findings suggest, after controlling for a number of enviroment variables, a positive effect of bank concentration on financial distress

Climate risk and investment in equities in Europe: a Panel SVAR approach

In this study, we use data on European stocks to construct a green-minus-brown portfolio hedging climate risk and to evaluate its performance in terms of cumulative expected and unexpected returns. More specifically, we estimate a Structural Panel VAR fitted to one month return and realized volatility computed for 40 constituents of a green portfolio (i.e., the low carbon emission portfolio monitored by Refinitiv) and for 41 constituents of a brown portfolio (underlying the Oil&Gas and Utilities industry sectors of the STOXX Europe 600). The common shocks underlying the cross-sectional averages, interpreted as portfolio shocks, are retrieved in a first stage of the analysis and they are used to control for cross-sectional dependence. We compute the historical decomposition (for cumulative returns) in a second stage of the analysis and we find, in line with P´astor, L., Stambaugh, R. F., & Taylor, L. A. (2022). Dissecting green returns. Journal of Financial Economics, 146 (2), 403–424, an out-performance of the expected component of the brown portfolio relative to the one for the green portfolio, and an out-performance of the green portfolio when we turn our focus on the unexpected component. We also extend the analysis of P´astor et al. (2022), assessing, for the top 5 constituents of the green portfolio (e.g., those which are found to have the worst performance in terms of expected return), the role played by idiosyncratic shocks in shaping their out-performance in terms of unexpected component. Finally, after exploiting the non-gaussian time series properties of the financial time series considered for the purpose of statistical identification, we are able to interpret ex post the idiosyncratic shocks in terms of financial leverage and risk aversion

Housing Market Shocks in Italy: a GVAR Approach

In this paper, we use a Global Vector Autoregression (GVAR) model to assess the spatio-temporal mechanism of house price spillovers, also known as “ripple effect”, among 93 Italian provincial housing markets, over the period 2004 − 2016. In order to better capture the local housing market dynamics, we use data not only on house prices but also on transaction volumes. In particular, we focus on estimating, to what extent, exogenous shocks, interpreted as negative housing demand shocks, arising from 10 Italian regional capitals, impact on their house prices and sales and how these shocks spill over to neighbours housing markets. The negative housing market demand shock hitting the GVAR model is identified by using theory-driven sign restrictions. The spatio-temporal analysis carried through impulse response functions shows that there is evidence of a “ripple effect” mainly occurring through transaction volumes

Temperature and Growth: a Panel Mixed Frequency VAR Analysis using NUTS2 data

In this study, we contribute to the existing literature on the impact of temperature on growth by examining the orthogonalized seasonal effect jointly with the feedback from economic activity (hence treating the increase in global temperature as anthropogenic) on a sample of 225 EU NUTS2 regions. For this purpose, we use a Panel Mixed- Frequency VAR. The empirical findings show, first, a worsening impact of temperature on growth over the last sub-sample (2000-2019) relative to the full sample analysis (covering the 1981-2019 time span). Moreover, our findings show that seasonal temperature effects are not restricted only to the agriculture sector, and we also find evidence of a heterogeneous impact of seasonal temperature on growth when we turn our focus on hot and cold regions (using the average EU median annual temperature as a threshold), rich and poor regions (using the average EU median income per capita as a threshold) and between competitiveness (using the median Regional Competitiveness index as a threshold)

On the financial connectedness of the commodity market: a replication of the Diebold and Yilmaz (2012) study

In this paper we replicate the Diebold and Yilmaz (2012) study on the connectedness of the Commodity market and three other financial markets: the stock market, the bond market, and the FX market. We show that both the row and the column normalization schemes of the Generalized Forecast Error Variance Decomposition, suggested by the authors, lead to inaccurate measures of net contribution to risk transmission, in terms of ranking and sign. We show that, considering data generating processes characterized by different degrees of comovement and persistence, a scalar based normalization of the Generalized Forecast Error Variance Decomposition yields consistent (free of sign and ranking errors) net spillovers

Vector Multiplicative Error Models:Representation and Inference

The Multiplicative Error Model introduced by Engle (2002) for positive valued processes is specified as the product of a (conditionally autoregressive) scale factor and an innovation process with positive support. In this paper we propose a multivariate extension of such a model, by taking into consideration the possibility that the vector innovation process be on temporaneously correlated. The estimation procedure is hindered by the lack of probability density functions for multivariate positive valued random variables. We suggest the use of copula functions and of estimating equations to jointly estimate the parameters of the scale factors and of the correlations of the innovation processes. Empirical applications on volatility indicators are used to illustrate the gains over the equation by equation procedure

Vector Multiplicative Error Models: Representation and Inference

The Multiplicative Error Model introduced by Engle (2002) for positive valued processes is specified as the product of a (conditionally autoregressive) scale factor and an innovation process with positive support. In this paper we propose a multivariate extension of such a model, by taking into consideration the possibility that the vector innovation process be contemporaneously correlated. The estimation procedure is hindered by the lack of probability density functions for multivariate positive valued random variables. We suggest the use of copula functions and of estimating equations to jointly estimate the parameters of the scale factors and of the correlations of the innovation processes. Empirical applications on volatility indicators are used to illustrate the gains over the equation by equation procedure

ALTICORE: an initiative for coastal altimetry

ALTICORE (value-added ALTImetry for COastal REgions) is an international initiative whose main objective is to encourage the operational use of altimetry over coastal areas, by improving the quality and availability of coastal altimetry data. The ALTICORE proposal has recently been submitted for funding to the INTAS scheme (www.intas.be) by a consortium of partners from Italy, France, UK, Russia and Azerbaijan. ALTICORE is also meant as a contribution to the ongoing International Altimeter Service effort. In this work we will describe the anticipated project stages, namely: 1) improvement of the most widely distributed, 1 Hz, data by analyzing the corrective terms and providing the best solutions, including those derived from appropriate local modelling; 2) development of a set of algorithms to automate quality control and gap-filling functions for the coastal regions; 3) development of testing strategies to ensure a thorough validation of the data. The improved products will be delivered to ALTICORE users via Grid-compliant technology; this makes it easier to integrate the local data holdings, allows access from a range of services, e.g. directly into model assimilation or GIS systems and should therefore facilitate a widespread and complete assessment of the 1Hz data performance and limitations. We will also outline the design and implementation of the Grid-compliant system for efficient access to distributed archives of data; this consists of regional data centres, each having primary responsibility for regional archives, local corrections and quality control, and operating a set of web-services allowing access to the full functionality of data extraction. We will conclude by discussing a follow-on phase of the project; this will investigate further improvements on the processing strategy, including the use of higher frequency (10 or 20 Hz) data. Phenomena happen at smaller spatial scales near the coast, so this approach is necessary to match the required resolution. The whole project will hopefully promote the 15-year sea surface height from altimetry to the rank of operational record for the coastal areas