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This work investigates financial volatility cascades generated by SARS-CoV-2 related news using concepts developed in the field of seismology. We analyze the impact of socio-economic and political announcements, as well as of financial stimulus disclosures, on the reference stock markets of the United States, United Kingdom, Spain, France, Germany and Italy. We quantify market efficiency in processing SARS-CoV-2 related news by means of the observed Omori power-law exponents and we relate these empirical regularities to investors' behavior through the lens of a stylized Agent-Based financial market model. The analysis reveals that financial markets may underreact to the announcements by taking a finite time to re-adjust prices, thus moving against the efficient market hypothesis. We observe that this empirical regularity can be related to the speculative behavior of market participants, whose willingness to switch toward better performing investment strategies, as well as their degree of reactivity to price trend or mispricing, can induce long-lasting volatility cascades. [ABSTRACT FROM AUTHOR]

We propose a dynamic method to hedge foreign exchange risk of international equity portfolios. The method is based on the currency return forecasts derived from a set of alternative machine learning models, built on the main factor components of the currency return time series. To illustrate our method, we take the perspective of a US portfolio manager investing in a global equity portfolio of developing and developed economies. The analysis of several model performance indicators, back-tested on data from 27 October 2008 to 30 December 2022, allows to conclude that accurate predictions of global currency factor returns, such as those obtained with non linear machine learning models, can improve currency risk hedging of global equity portfolios. • We propose a dynamic method to hedge foreign exchange risk of international equity portfolios. • The method is based on machine learning models built on the main factor components of currency returns. • Hedging strategy outperforms leading alternative approaches on a large set of performance metrics. • Accurate ML predictions of global factor returns improve linear currency risk hedging. [ABSTRACT FROM AUTHOR]

Mobility restrictions have been identified as key non-pharmaceutical interventions to limit the spread of the SARS-COV-2 epidemics. However, these interventions present significant drawbacks to the social fabric and negative outcomes for the real economy. In this paper we propose a real-time monitoring framework for tracking the economic consequences of various forms of mobility reductions involving European countries. We adopt a granular representation of mobility patterns during both the first and second waves of SARS-COV-2 in Italy, Germany, France and Spain to provide an analytical characterization of the rate of losses of industrial production by means of a nowcasting methodology. Our approach exploits the information encoded in massive datasets of human mobility provided by Facebook and Google, which are published at higher frequencies than the target economic variables, in order to obtain an early estimate before the official data becomes available. Our results show, in first place, the ability of mobility-related policies to induce a contraction of mobility patterns across jurisdictions. Besides this contraction, we observe a substitution effect which increases mobility within jurisdictions. Secondly, we show how industrial production strictly follows the dynamics of population commuting patterns and of human mobility trends, which thus provide information on the day-by-day variations in countries' economic activities. Our work, besides shedding light on how policy interventions targeted to induce a mobility contraction impact the real economy, constitutes a practical toolbox for helping governments to design appropriate and balanced policy actions aimed at containing the SARS-COV-2 spread, while mitigating the detrimental effect on the economy. Our study reveals how complex mobility patterns can have unequal consequences to economic losses across countries and call for a more tailored implementation of restrictions to balance the containment of contagion with the need to sustain economic activities. [ABSTRACT FROM AUTHOR]

To investigate the relationships between the market performance of the renewable energy sector and that of the fossil fuel energy sector in Europe, this paper develops a multi-resolution analysis of the series by leveraging the tools derived from the wavelet analysis. Wavelet, by decomposing time series into their time scale components, each associated to a specific frequency range, are instrumental for studying co-movements of the fossil fuel and renewable index prices over several time horizons. The European Renewable Index, is used to describe the market price of the renewable energy sector, while the MSCI Europe Energy is designed to capture the large and mid cap companies' performance across Integrated Oil & Gas segments in Europe. The findings demonstrate the existence of areas with significant co-movements between the series at scales ranging from two-months to two-years, up to 2011. After that, areas of strong coherence are kept for a shorter amount of time, from two to six months. Moreover, despite the fact that the variables are in phase, the renewable index price is outpacing the fossil fuel price dynamic at low frequency. This relation is reversed at higher frequencies. Finally, time scale regression and nonparametric analysis suggest the cross-effects of the price series are frequency-dependent, with the indices being negatively related only in the long-run. • The relationship between fossil fuel and renewable energy prices in Europe is examined. • A multi-resolution analysis of the series is derived from the wavelet framework. • Existence of regions co-movements between the ERI and MSCI-EE prices at scales corresponding to durations. • The cross-effects of the price series are frequency-dependent. [ABSTRACT FROM AUTHOR]

In this article we propose to exploit topological information embedded in forecast error variance decomposition derived from large Bayesian vector autoregressive models (VAR) to study network connectedness and risk transmission of multivariate time series observations. Firstly, we design a robust link classification procedure based on shortest paths, so to identify salient directional spillovers in a high-dimensional framework. Secondly, we study recurrent and statistically significant sub-graphs, i.e. network motifs, on the induced network backbone by means of null models which account for local node heterogeneity. The methodology is applied to analyze spillover networks of a set of global commodity prices. We demonstrate that spillovers become key drivers of the system variance during commodity price bubbles and bursts, giving raise to complex triadic structures which do not manifest during normal business periods. By accounting for local node connectivity, we observe a departure from the null models due to the high participation of Crude Oil, Food and Beverages and Raw Materials in complex recurrent sub-graphs. • Forecast error variance decomposition from large Bayesian vector autoregressive models to study network connectedness. • Robust link classification to identify salient directional spillovers. • Recurrent and statistically significant sub-graphs, network motifs, on the induced network backbone. • Analysis of spillover networks of a set of global commodity prices. [ABSTRACT FROM AUTHOR]

This paper aims to provide a statistical analysis of the impacts of worldwide climate change and consequent natural disasters on international stock markets. By means of a suited event study methodology, we investigate the effects of biological, climatological, geophysical, hydrological and metereological disasters occurred in 104 countries across the world on 27 global stock market indexes over the period 8 February 2001 to 31 December 2019. We find heterogeneous stock markets responses to natural hazard shocks depending on the type of event under consideration, as well as on the location in which the event has occurred. Climatological and biological calamities seem the disaster types which, overall, induce the most extreme reactions of international financial markets. Furthermore, the analysed stock indexes are more responsive to shocks occurring in European countries. Finally, to predictively validate our model, we build a natural disaster risk hedging strategy, which sheds light on the investment opportunities derived from the mitigation of natural risks. • We investigate the impact of natural hazards on global financial markets • Climatological and biological hazards induce most extreme market reactions • Market response is heterogeneous depending on hazard type and location • A natural disaster-risk hedging strategy is proposed and assessed [ABSTRACT FROM AUTHOR]

In this paper we analyze the topological properties of the network of the Euro Area banking market network, with the primary aim of assessing the importance of a bank in the financial system with respect to ownership and control of other credit institutions. The network displays power law distributions in both binary and weighted degree metrics indicating a robust yet fragile structure and a direct link between an increase of control diversification and a rise in the market power. Therefore while in good time the network is seemingly robust, in bad times many banks can simultaneously go into distress. This behavior paves the way for Central bank’s actions. In particular we investigate whether the Single Supervisory Mechanism introduced by the European Central Banks and based on banks’ total asset is a good proxy to quantify their systemic importance. Results indicate that not all the financial institutions with high valued total assets are systemically important but only few of them. Moreover the network structure reveals that control is highly concentrated, with few important shareholders approximately controlling a separate subset of banks. [ABSTRACT FROM AUTHOR]

This paper studies the global production network using a general equilibrium model calibrated on world input–output data. The analysis of propagation of idiosyncratic productivity shocks in the calibrated model allows to define a model-based network centrality measure. Such measure is used to investigate the topology of the global input–output network in 2014 and its evolution from 2000 to 2014. We find that new influential sectors have emerged over time. Moreover, we show that the global production system has evolved to become more sensitive to idiosyncratic productivity shocks and that this result is related to the increase of the intermediate input intensity of production. [ABSTRACT FROM AUTHOR]

Abstract: We study the international interbank market through a geometric analysis of empirical data. The geometric analysis of the time series of cross-country liabilities shows that the systematic information of the interbank international market is contained in a space of small dimension. Geometric spaces of financial relations across countries are developed, for which the space volume, multivariate skewness and multivariate kurtosis are computed. The behavior of these coefficients reveals an important modification acting in the financial linkages since 1997 and allows us to relate the shape of the geometric space that emerges in recent years to the globally turbulent period that has characterized financial systems since the late 1990s. Here we show that, besides a persistent decrease in the volume of the geometric space since 1997, the observation of a generalized increase in the values of the multivariate skewness and kurtosis sheds some light on the behavior of cross-border interdependencies during periods of financial crises. This was found to occur in such a systematic fashion, that these coefficients may be used as a proxy for systemic risk. [Copyright &y& Elsevier]

Time series forecasting is of fundamental importance for financial market prediction and, consequently, for portfolio allocation strategies. However, non-stationarity and non-linearity of most financial time series often make these tasks difficult to perform. In this paper, we propose a methodology based on chaos and dynamical systems theory for non-linear time series forecasting and investment strategy development, which is able to correctly make predictions at long time horizons. We construct Constant Chaoticity Portfolios (CCP) and evaluate their performances on the survival components of the STOXX Europe 50 index and the Hang-Seng index. Results show that the CCP overwhelms several competing alternatives, both in terms of net profits and risk-return profiles. Our findings are confirmed by a sensitivity analysis on the parameters of the underlying model and over different choices of forecast horizons. • We propose a portfolio selection method based on chaos and dynamical systems theory. • We build Constant Chaoticity Portfolios (CCPs) via nonlinear time series forecasting. • The CCP overwhelms several competing methods in terms of risk-return profiles. • A sensitivity analysis on model configurations confirms the CCP's overperformance. [ABSTRACT FROM AUTHOR]

Abstract: The recent financial crisis has stressed the need to understand financial systems as networks of interdependent countries, where cross-border financial linkages play the fundamental role. It has also been emphasized that the relevance of these networks relies on the representation of changes follow on the occurrence of stress events. Here, from series of interbank liabilities and claims over different time periods, we have developed networks of positions (net claims) between countries. Besides the Minimal Spanning Tree analysis of the time-constrained networks, a coefficient of residuality is defined to capture the structural evolution of the network of cross-border financial linkages. Because some structural changes seem to be related to the role that countries play in the financial context, networks of debtor and creditor countries are also developed. Empirical results allows to relate the network structure that emerges in the last years to the globally turbulent period that has characterized financial systems since the latest nineties. The residuality coefficient highlights an important modification acting in the financial linkages across countries in the period 1997–2011, and situates the recent financial crises as replica of a larger structural change going on since 1997. [Copyright &y& Elsevier]

The SARS-CoV-2 epidemics outbreak has shocked global financial markets, inducing policymakers to put in place unprecedented interventions to inject liquidity and to counterbalance the negative impact on worldwide financial systems. Through the lens of statistical physics, we examine the financial volatility of the reference stock and bond markets of the United States, United Kingdom, Spain, France, Germany and Italy to quantify the effects of country-specific socio-economic and political announcements related to the epidemics. Main results show that financial markets exhibit heterogeneous behaviours towards news on the epidemics, with the Italian and German bond markets responding with major delays to shocks. Additionally, credit markets tend to be slower than equity markets in adjusting prices after shocks, hence being slower at incorporating the effects of such news. • We study financial effects of socio-economic and political news related to SARS-CoV-2 pandemics. • We investigate the impact of SARS-CoV-2 related news on major equity and bond markets through the lens of seismology. • We show that financial markets exhibit heterogeneous behaviours towards news on the epidemic. • Bond markets tend to be slower than equity markets in adjusting prices after shocks. • Italian and German bond markets respond with major delays to shock. [ABSTRACT FROM AUTHOR]

The spread of SARS-COV-2 has affected many economic and social systems. This paper aims at estimating the impact on regional productive systems in Italy of the interplay between the epidemic and the mobility restriction measures put in place to contain the contagion. We focus then on the economic consequences of alternative lockdown lifting schemes. We leverage a massive dataset of human mobility which describes daily movements of over four million individuals in Italy and we model the epidemic spreading through a metapopulation SIR model, which provides the fraction of infected individuals in each Italian district. To quantify economic backslashes this information is combined with socio-economic data. We then carry out a scenario analysis to model the transition to a post-lockdown phase and analyze the economic outcomes derived from the interplay between (a) the timing and intensity of the release of mobility restrictions and (b) the corresponding scenarios on the severity of virus transmission rates. Using a simple model for the spreading disease and parsimonious assumptions on the relationship between the infection and the associated economic backlashes, we show how different policy schemes tend to induce heterogeneous distributions of losses at the regional level depending on mobility restrictions. Our work shed lights on how recovery policies need to balance the interplay between mobility flows of disposable workers and the diffusion of contagion. [ABSTRACT FROM AUTHOR]

Robot advisory services are rapidly expanding, responding to a growing interest people have in directly managing their savings. Robot-advisors may reduce costs and improve the quality of asset allocation services, making user's involvement more transparent. Against this background, there exists the possibility that robot advisors underestimate market risks, especially during crisis times, when high order interconnections arise. This may lead to a mismatch between investors' expected and actual risk. The aim of this paper is to overcome this issue, taking into account not only investors' risk preference but also their attitude towards interconnectdness. To achieve this aim, we combine random matrix theory with correlation networks and extend the Markowitz' optimisation problem to a third dimension. To demonstrate the practical advantage of our proposed approach we employ daily returns of a large set of Exchange Traded Funds, which are representative of the financial products employed by robot-advisors. [ABSTRACT FROM AUTHOR]

We develop a minimalist compartmental model to study the impact of mobility restrictions in Italy during the Covid-19 outbreak. We show that, while an early lockdown shifts the contagion in time, beyond a critical value of lockdown strength the epidemic tends to restart after lifting the restrictions. We characterize the relative importance of different lockdown lifting schemes by accounting for two fundamental sources of heterogeneity, i.e. geography and demography. First, we consider Italian Regions as separate administrative entities, in which social interactions between age classes occur. We show that, due to the sparsity of the inter-Regional mobility matrix, once started, the epidemic spreading tends to develop independently across areas, justifying the adoption of mobility restrictions targeted to individual Regions or clusters of Regions. Second, we show that social contacts between members of different age classes play a fundamental role and that interventions which target local behaviours and take into account the age structure of the population can provide a significant contribution to mitigate the epidemic spreading. Our model aims to provide a general framework, and it highlights the relevance of some key parameters on non-pharmaceutical interventions to contain the contagion. [ABSTRACT FROM AUTHOR]

We leverage a granular representation of mobility patterns before and during the first wave of SARS-COV2 in Italy to investigate the economic consequences of various forms of lockdown policies when accounting for mobility restrictions between and within local jurisdictions, i.e. municipalities, provinces and regions. We provide an analytical characterization of the rate of economic losses using a network-based spectral method. The latter treats the spread of contagion of economic losses due to commuting restrictions as a dynamical system stability problem. Our results indicate that the interplay between lower level of smartworking and the polarization of commuting flows to fewer local labor hubs in the South of Italy makes Southern territories extremely important in spreading economic losses. We estimate an economic contraction of total income derived from commuting restrictions in the range of 10–30% depending on the economic assumptions. However, alternative policies proposed during the second wave of SARS-COV2 can pose a greater risk to Northern areas due to their higher degree of mobility between jurisdictions than Southern ones. The direction of economic losses tend to propagate from large to medium-small jurisdictions across all alternative lockdown policies we tested. Our study shows how complex mobility patterns can have unequal consequences to economic losses across the country and call for more tailored implementation of restrictions to balance the containment of contagion with the need to sustain economic output. [ABSTRACT FROM AUTHOR]

To design appropriate pension or insurance plans it is crucial to understand mortality heterogeneity across demographic features, such as age, gender, and country. To this aim, we propose a coherent mortality forecasting methodology, which leverages the four-way CANDECOMP/PARAFAC and Vector-Error Correction models. We examine how age groups, years, countries, and gender impact target variables, namely log-centered mortality rates and compositional transformation of mortality data using the Human Mortality Database. The CANDECOMP/PARAFAC model synthesizes the behavior of the target variables through a few latent components and highlights the evolution of the temporal patterns. These patterns are employed to forecast future trajectories of mortality with Vector-Error Correction models, which account for the non-stationarity of the series. We carry out Monte Carlo simulations to obtain the distributions of the time component over the forecasted period 2001–2015, and we evaluate the goodness of the prediction by computing the Root Mean Square Error and the Mean Absolute Error. Our analysis underlines that understanding mortality dynamics in a high-dimensional framework is crucial for demographic assessments and could help design appropriate pension plans that mitigate the burden of increased longevity. The paper provides two steps further on methodological developments in the field of mortality analysis and forecasting in a high-dimensional space by (i) offering a comprehensive picture of mortality data through the four-way decomposition and (ii) designing appropriate forecasting of mortality data which relies on the projection of the temporal component through Vector-Error Correction models. [ABSTRACT FROM AUTHOR]

This paper proposes a new methodology based on non-negative matrix factorization to detect communities and to identify central nodes in a network as well as within communities. The method is specifically designed for directed weighted networks and, consequently, it has been applied to the interbank network derived from the e-MID interbank market. In an interbank network indeed links are directed, representing flows of funds between lenders and borrowers. Besides distinguishing between Systemically Important Borrowers and Lenders, the technique complements the detection of systemically important banks, revealing the community structure of the network, that proxies the most plausible areas of contagion of institutions’ distress. [ABSTRACT FROM AUTHOR]

In this paper, we propose a multivariate procedure based on multidimensional visibility graphs to detect changes in the market volatility of UK financial indices, considered both before and after Brexit main events. We produce a graph-theoretical representation of volatility time series derived from equity indexes, government rates and currencies to investigate the behavior of the aggregate market volatility through the use of global centrality measures. By employing a stylized agent-based model, we show that the proposed approach is able to discriminate between periods of high and low volatility, both in the temporal dimension and cross-sectionally among multiple time series. We aim at recognizing whether external news related to the Brexit process could induce significant 'after-shocks' (and also 'pre-shocks') in the system, by producing dynamic relaxation in the values of centrality measures, in line with the cascade effects described by the Omori earthquake law. In particular, high volatility cascades dissipate into the market via power-law relaxation. When compared with other categories of events, such as Bank of England monetary policy announcements, we observe significant market inefficiency in processing Brexit related news. We also find that strong market surprise related to specific Brexit news or a correct discount of some Brexit announcements can produce an inverse Omori law exhibiting convex relaxation. [ABSTRACT FROM AUTHOR]

By exploiting a bipartite network representation of the relationships between mutual funds and portfolio holdings, we propose an indicator that we derive from the analysis of the network, labelled the Average Commonality Coefficient (ACC), which measures how frequently the assets in the fund portfolio are present in the portfolios of the other funds of the market. This indicator reflects the investment behavior of funds' managers as a function of the popularity of the assets they held. We show that ACC provides useful information to discriminate between funds investing in niche markets and those investing in more popular assets. More importantly, we find that ACC is able to provide indication on the performance of the funds. In particular, we find that funds investing in less popular assets generally outperform those investing in more popular financial instruments, even when correcting for standard factors. Moreover, funds with a low ACC have been less affected by the 2007–2008 global financial crisis, likely because less exposed to fire sales spillovers. [ABSTRACT FROM AUTHOR]