Modeling risk contagion in the Italian zonal electricity market

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Ensuring the security of stable, efficient, and reliable energy supplies has intensified the interconnections between energy markets. Imbalances between supply and demand due to operational failures, congestion, and other sources of risk faced by market connections can lead to a system that is vulnerable to the spread of risk and its spillover. The main contribution of this paper is the development and estimation of a Bayesian Graphical Vector-AutoRegression and a Bayesian Graphical Structural Equation Modelling with external regressors - BG-VARX and BG-SEMX, respectively - enhancing the proper analysis of market connections. The Italian electricity market has been chosen because it is a clear example of a zonal market where risk can spread over connected zones. We estimate, for the first time, within-day and across-day zonal market interconnections with a multivariate time series of hourly prices, actual and forecast power demand, and forecast wind generation over the period 2014-2019 and evaluate the dynamics and persistence of zonal market connections, examining the spread of risk in the zones of the Italian electricity market. Our findings provide an improved, accurate explanation of risk contagion, identifying the zones that are most influential in terms of hub centrality (major transmitters) and authority centrality (major recipients), respectively, for intra-day and inter-day risk propagation in the Italian electricity market. In addition, the policy implications in terms of market monitoring are discussed

Understanding Environmental, Social and Governance (ESG) contributions in the downside systematic and systemic risk measurement of the insurance sector in (non)-stressful times

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Sustainable responsible investing is gaining traction globally because of its contribution to climate solutions and this has therefore led to the growth in professionally managed assets linked to sustainability characterizations. In this sense, environmental, social, and governance (ESG) investing has become a unique feature of capital markets, necessitating the need to determine the extent to which ESG Scores can be related to a company’s riskiness, as confirmed by the European Banking Authority (EBA) that ESG scores can contribute to risk. To assess these risk typologies inherent in the financial markets concerning a mix of (non) ESG-linked assets, we employ an extension of the extreme downside hedge (EDH) and the extreme downside correlation (EDC) coupled with innovative network analysis and unsupervised machine learning technique to investigate the sensitivity of assets to the downside risk of other financial assets under severe firm/company-level and sector market conditions. We account for three different economic scenarios: namely before the COVID-19 outbreak, during the COVID-19 outbreak and the recovering phase, and the results reveal monotonically increasing levels of market integration during the three periods, respectively. Furthermore, we account for the role of ESG Scores implicitly via (non) ESG-related assets, which shows that firms with no or low ESG ratings are vulnerable to the spread of risk in the insurance industry and vice-versa. In consequence, firms that are safest for the purpose of diversification are identified

Analyzing and Forecasting Multi-Commodity Prices Using Variants of Mode Decomposition-Based Extreme Learning Machine Hybridization Approach

Because of the non-linearity inherent in energy commodity prices, traditional mono-scale smoothing methodologies cannot accommodate their unique properties. From this viewpoint, we propose an extended mode decomposition method useful for the time-frequency analysis, which can adapt to various non-stationarity signals relevant for enhancing forecasting performance in the era of big data. To this extent, we employ variants of mode decomposition-based extreme learning machines namely: (i) Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-based ELM Model (CEEMDAN-ELM), (ii) Ensemble Empirical Mode Decomposition-based ELM Model (EEMD-ELM) and (iii) Empirical Mode Decomposition Based ELM Model (EMD-ELM), which cut-across soft computing and artificial intelligence to analyze multi-commodity time series data by decomposing them into seven independent intrinsic modes and one residual with varying frequencies that depict some interesting characterization of price volatility. Our findings show that in terms of the model-specific forecast accuracy measures different dynamics in the two scenarios namely the (non) COVID periods. However, the introduction of a benchmark, namely the autoregressive integrated moving average model (ARIMA) reveals a slight change in the earlier dynamics, where ARIMA outperform our proposed models in the Japan gas and the US gas markets. To check the superiority of our models, we apply the model-confidence set (MCS) and the Kolmogorov-Smirnov Predictive Ability test (KSPA) with more preference for the former in a multi-commodity framework, which reveals that in the pre-COVID era, CEEMDAN-ELM shows persistence and superiority in accurately forecasting Crude oil, Japan gas, and US gas. Nonetheless, this paradigm changed during the COVID-era, where CEEMDAN-ELM favored Japan gas, US gas, and coal market with different rankings via the Model confidence set evaluation methods. Overall, our numerical experiment indicates that all decomposition-based extreme learning machines are superior to the benchmark model

Risk Management for Energy Markets.

ABSTRACT: Questa tesi si occupa di gestione del rischio. Il tema unificante è la gestione del rischio di mercato dell'energia. I diversi capitoli trattano la gestione del rischio in modo diverso e considerano diversi mercati energetici. Nel primo capitolo viene affrontato il tema della stima del rischio applicato a due principali mercati elettrici europei: Powernext (Francia) ed EEX (Germania e Austria). Nel secondo capitolo la misurazione del rischio viene effettuata mediante l’applicazione dell’allocazione ottima di portafoglio nei mercati dell'energia elettrica utilizzando i rendimenti calcolati sui prezzi futures. Nel terzo capitolo, la teoria di allocazione ottima di portafoglio viene applicata al mercato zonale italiano (utilizzando le zone fisiche). La modellazione, la misurazione e la contabilità del rischio sono importanti dal punto di vista teorico e, in particolare, dal punto di vista pratico. In pratica, esso svolge un ruolo chiave nella strategia di allocazione del portafoglio sui mercati energetici. Nel contesto attuale gli operatori attivi sui mercati energetici devono affrontare livelli di rischio senza precedenti. L'importanza di una corretta gestione del rischio e di una corretta comprensione dei rischi è fondamentale per l’effettuazione di un buon investimento e per guidare le decisioni contrattuali. La gestione del rischio deve condurre al raggiungimento di un equilibrio mix di rischio e rendimento attraverso una particolare strategia di trading. Con “strategia di trading” si intende un insieme definito di regole da seguire per effettuare buone decisioni di negoziazione. Questa tesi si compone di tre capitoli autonomi. Nel primo capitolo, effettuiamo un'analisi econometrica del rischio nel mercato elettrico utilizzando prezzi spot. Il mercato dell'energia, ed in particolare il mercato dell'energia elettrica, sta attraversando una fase di transizione in tutto il mondo. Le fluttuazioni dei prezzi e a la loro stretta correlazione con la domanda sono una caratteristica comune a tutti i mercati elettrici liberalizzati. Il test più importante per i nuovi mercato liberalizzati è la capacità di gestire l'eccessiva volatilità connessa ad un sistema con sostanziali variazioni temporali di capacità di generazione. In questo primo lavoro abbiamo proposto di utilizzare AR-GARCH - tipo – EVT (Estreme Value Theory) con diverse distribuzioni delle innovazioni e con varianti che tengano conto della risposta asimmetrica della volatilità per la stima del Value at Risk nei mercati elettrici. Quindi, il rischio di investimento sui mercati dell'energia elettrica viene calcolato in base alla stima del VaR e del VaR condizionato mediante filtri di tipo GARCH con distribuzioni a code pesanti. L'attenzione si è fissata sia dal punto di vista dei regolatori (code superiori) e degli investitori (code inferiori). Le autorità di vigilanza e i regolatori sono infatti più preoccupati del rischio che si verifichino prezzi elevati poichè il loro obiettivo è quello di garantire l'efficienza del mercato. Il secondo capitolo, presenta l’applicazione della teoria dell’allocazione ottimale di portafoglio ai mercati energetici attraverso una versione modificata del classico approccio mean-variance suggerito originariamente da Markowitz. Il risultato principale del capitolo mostra che i portafogli con scadenze diverse potrebbero fornire agli operatori di mercato delle linee guida per una buona strategia di gestione del rischio nei mercati energetici. Le tecniche di ottimizzazione vengono utilizzate per ottenere pesi ottimali per l'allocazione degli investimenti finanziari, al fine di analizzare il rischio di investimento connesso al mercato dell'energia elettrica utilizzando prezzi futures. Si tratta di un'applicazione originale di una tecnica di ottimizzazione già nota in letteratura, ma che non è ancora stata esplorata nello studio del mercato energetici. In particolare, la tecnica di ottimizzazione basata sul VaR condizionale come misura del rischio non è ancora stata utilizzata per l'analisi del rischio insito nei mercati dell'energia elettrica. Nel terzo capitolo, effettuiamo un’analisi spaziale del rischio di investimento nei mercati zonali. Da quando il mercato elettrico italiano è stato liberalizzato, non esistono documenti a conoscenza dell’autore che abbiano considerato l'ottimizzazione portafoglio nel mercato zonale italiano. Nei mercati liberalizzati dell'energia elettrica con prezzi zonali, il mercato è suddiviso in alcune zone, a ciascuna delle quali è assegnato un prezzo di mercato al quale i partecipanti reagiscono in un qualsiasi istante temporale. Il nostro contributo consiste nell’applicazione dell’allocazione di portafoglio basata sul VaR come misura di rischio al mercato zonale italiano per prendere decisioni ponderate di investimento nelle diverse zone in cui è suddiviso il mercato. Lo scopo principale consiste nel mitigare il rischio connesso ad investimenti sul mercato. L' analisi si sposta quindi dalla prospettiva di una diversificazione temporale a quella di una diversificazione spaziale.ABSTRACT: This thesis is concerned with risk management. The unifying theme is the risk management of the energy market. The different chapters deal with risk management in a different way and consider different energy markets. The first chapter addressed the issue of risk assessment applied to two major European electricity markets: Powernext (France) and EEX (Germany and Austria). In the second chapter the measurement of risk is done through the application of the optimal portfolio in the electricity markets calculated using the returns on the futures prices. In the third chapter, the theory of optimal allocation of the portfolio is applied to the zonal Italian market (using physical zones). The modeling, measurement and accounting of risk are important from a theoretical point of view and, in particular, from the practical point of view. In practice, it plays a key role in the strategy of portfolio allocation in the energy markets. In the present context, the operators active on energy markets are facing unprecedented levels of risk. The importance of a proper risk management and a proper understanding of the risk are crucial to the making of a good investment and to guide decisions contract. Risk management must lead to the achievement of a balance mix of risk and return through a particular trading strategy. While “trading strategy" means a defined set of rules to follow to make good trading decisions. This thesis consists of three self-contained chapters. In the first chapter, we carry out an econometric analysis of the risk in the electricity market using spot prices. The energy market, and in particular the electricity market is going through a transition phase in the world. Price fluctuations and their correlation with demand are common features of all liberalized electricity markets. The most important test for the new liberalized market is the ability to manage excessive volatility connected to a system with substantial temporal variations of generation capacity. We have proposed the use of AR-GARCH-type-EVT (Extreme Value Theory) with different distributions of the innovations and variations that take into account the asymmetric response of volatility to estimate the value at risk in the electricity markets. Thus, the risk of investment in electricity markets is calculated based on the estimated VaR and conditional VaR using GARCH filters distributions with heavy tails. The focus is fixed from the point of view of the regulators (upper tails) and investors (lower tails). Supervisors and regulators are in fact more concerned with the risk of experiencing high prices because their aim is to ensure the efficiency of the market. The second chapter suggests the application of the theory of the optimal portfolio to energy markets through a modified version of the classical mean-variance approach originally suggested by Markowitz. The main result of the chapter shows that portfolios with different maturities could provide market operators with guidelines for a good strategy of risk management in energy markets. Optimization techniques are used to obtain optimal weights for the allocation of financial investments, in order to analyze the investment risk connected to the electricity market using futures prices. It is an original application of an optimization technique already known in the literature, but which has not yet been explored in the study of the energy market. In particular, the optimization technique based on conditional VaR as a risk measure has not yet been used for the analysis of the risk inherent in the electricity markets. In the third chapter; we carry out a spatial analysis of the risks of investing in local markets. Since the Italian electricity market has been liberalized, there are no documents as the author knows that they have considered the zonal portfolio optimization in the Italian market. In liberalized electricity markets with zonal prices, the market is divided into several zones, each of which is assigned a market price at which participants react at any moment in time. Our contribution consists in the application of the allocation of the portfolio based on VaR as a risk measure to the market to make informed decisions about zonal Italian investment in the different areas that comprise the market. The main purpose is to mitigate the risk associated with investments in the market. The analysis therefore moves from the perspective of a temporal diversification to that of a spatial diversification

Estimation of risk measures on electricity markets with fat tailed distributions

This paper proposes AR\u2013GARCH\u2013type\u2013EVT model with various innovations based on Value\u2013at\u2013Risk (VaR) and Conditional Value\u2013at\u2013Risk (CVaR) for energy price risk quantification for different emerging energy markets. We assess the models of best fit, AR\u2013EGARCH\u2013EVT and AR\u2013TGARCH\u2013EVT models forPowernext and European Energy Exchange, respectively. Extreme Value theory (EVT) is adopted explicitly to model the tails of the return distribution in order to capture extremal events. One of the main contributions of this paper is the estimation of Value\u2013at\u2013Risk and Conditional Value\u2013at\u2013Risk via EVT on both the lower and the upper tails of the return distribution in order to capture the extreme events of the distribution.This study also contributes to the literature in by analyzing both the upper and the lower tails because of the different perspective of regulators and investors present in the energy market

Responsible investments in life insurers' optimal portfolios under solvency constraints

Socially responsible investing (SRI) continues to gain momentum in the financial market space for various reasons, starting with the looming effect of climate change and the drive toward a net-zero economy. Existing SRI approaches have included environmental, social, and governance (ESG) criteria as a further dimension to portfolio selection, but these approaches focus on classical investors and do not account for specific aspects of insurance companies. In this paper, we consider the stock selection problem of life insurance companies. In addition to stock risk, our model set-up includes other important market risk categories of insurers, namely interest rate risk and credit risk. In line with common standards in insurance solvency regulation, such as Solvency II, we measure risk using the solvency ratio, i.e. the ratio of the insurer’s market-based equity capital to the Value-at-Risk of all modeled risk categories. As a consequence, we employ a modification of Markowitz’s Portfolio Selection Theory by choosing the “solvency ratio” as a downside risk measure to obtain a feasible set of optimal portfolios in a three-dimensional (risk, return, and ESG) capital allocation plane. We find that for a given solvency ratio, stock portfolios with a moderate ESG level can lead to a higher expected return than those with a low ESG level. A highly ambitious ESG level, however, reduces the expected return. Because of the specific nature of a life insurer’s business model, the impact of the ESG level on the expected return of life insurers can substantially differ from the corresponding impact for classical investors

Responsible investments in life insurers’ optimal portfolios under solvency constraints

Socially responsible investing (SRI) continues to gain momentum in the financial market space for various reasons, starting with the looming effect of climate change and the drive toward a net-zero economy. Existing SRI approaches have included environmental, social, and governance (ESG) criteria as a further dimension to portfolio selection. But, these approaches focus on classical investors and do not account for specific aspects of insurance companies. In this paper, we consider the stock selection problem of life insurance companies. In addition to stock risk, our model set-up includes other important market risk categories of insurers, namely interest rate risk and credit risk. In line with common standards in insurance solvency regulation, such as Solvency II, we measure risk using the solvency ratio, i. e. the ratio of the insurer’s market-based equity capital to the Value-at-Risk of all modeled risk categories. As a consequence, we employ a modification of Markowitz’s Portfolio Selection Theory by choosing the “solvency ratio” as a downside risk measure to obtain a feasible set of optimal portfolios in a three-dimensional (risk, return, and ESG) capital allocation plane. We find that for a given solvency ratio, stock portfolios with a moderate ESG level can lead to a higher expected return than those with a low ESG level. A highly ambitious ESG level, however, reduces the expected return. Because of the specific nature of a life insurer’s business model, the impact of the ESG level on the expected return of life insurers can substantially differ from the corresponding impact for classical investors

Risk management via contemporaneous and temporal dependence structures with applications

This paper presents the estimation methods of the Bayesian Graphical Vector Auto-regression with and without innovations such as external regressors (BG-VAR(X)) and Bayesian Graphical Systems Equation Modelling with and without exogenous variables (BG-SEM(X)), which are developed to examine risk network structures embedded in multivariate time series. This methodical approach allows for the analysis of various dynamics and persistence in the multivariate time series in terms of risk propagation. For instance, both the BG-SEMX and BG-VARX can reveal the within-day and across-day major risk transmitters as well as risk recipients from other univariate time series, which better explain risk contagion using complex network models. In addition, the procedures for models with and without exogenous variables have been explored, which shows that the former produce more network structures compared to the latter and therefore depict their influential role. This approach, therefore, provides a platform for future research in terms of extension of the method to encompass different types of multivariate data with additional innovations that might aid feasible analysis and the design of policy instruments and the implementation of relevant policy implications

Modeling risk contagion in the Italian zonal electricity market

none3siEnsuring the security of stable, efficient and reliable energy supplies has intensified the interconnections between energy markets. Imbalances between supply and demand due to operational failures, congestion and other sources of risk faced by market connections can lead to a system that is vulnerable to the spread of risk and its spill-over. The main contribution of this paper is the development and estimation of a Bayesian Graphical Vector-AutoRegression and a Bayesian Graphical Structural Equation Modelling with external regressors - BG-VARX and BG-SEMX, respectively - enhancing the proper analysis of market connections. The Italian electricity market has been chosen because it is a clear example of a zonal market where risk can spread over connected zones. We estimate, for the first time, within-day and across-day zonal market interconnections with a multivariate time series of hourly prices, actual and forecast power demand and forecast wind generation over the period 2014-2019 and evaluate the dynamics and persistence of zonal market connections, examining the spread of risk in the zones of the Italian electricity market. Our findings provide an improved, accurate explanation of risk contagion, identifying the zones that are most influential in terms of hub centrality (major transmitters) and authority centrality (major recipients), respectively, for intra-day and inter-day risk propagation in the Italian electricity market. In addition, the policy implications in terms of market-monitoring are discussed.noneFianu, Emmanuel Senyo; Ahelegbey, Daniel Felix; Grossi, LuigiFianu, Emmanuel Senyo; Ahelegbey, Daniel Felix; Grossi, Luig