Spillover effect and the dynamic contagion channels across NFTs news attention and the banking sector: Experience from the United Kingdom.
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Authors: with A. Danso, Gregory Jame, and A. Boateng
Abstract: Sustainable responsible investing is gaining traction globally because of its contribution to climate solutions and 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 and hence the need to ascertain to what extent ESG Scores could relate to a company's riskiness as confirmed by the European Banking Authority (EBA) that ESG scores could contribute to risk. To assess these risk typologies inherent in the financial markets in relation to a mix of (non) ESG-linked assets, we employ an extension of the extreme downside hedge (EDH) and the extreme downside correlation (EDC) to investigate the sensitivity of assets to downside risk of other financial assets under severe firm/company-level and sector market conditions, and therefore account for three different economic scenarios: namely before COVID 19 outbreak, during COVID-19 outbreak and the recovering phase. We examine various hidden interconnections on the topology of the insurance market sector through the lenses of methods from innovative network analysis. Our empirical findings show various interconnections typical of givers of tail contagion and receivers of tail contagion as well as identifying the safest companies that favor diversification of investments. Next, we account for the role of ESG Scores via (non)-ESG related assets and therefore provide new perspectives for institutional investors and other financial market participants for the formulation of better data-driven actionable investment decisions and regulatory policies. In addition, the impacts of COVID-19 have been examined.
Keywords: Equity indices, extreme downside hedge, extreme downside correlation, network models, systematic risk, systemic risk, volatility, (non) ESG-linked assets, the insurance sector
Nachhaltiges, verantwortungsbewusstes Investieren gewinnt weltweit an Zugkraft, da es einen Beitrag zur Lösung des Klimaproblems leistet, und hat daher zu einer Zunahme professionell verwalteter Vermögenswerte geführt, die mit Nachhaltigkeitscharakteristika verbunden sind. In diesem Sinne sind Investitionen in die Bereiche Umwelt, Soziales und Unternehmensführung (ESG) zu einem einzigartigen Merkmal der Kapitalmärkte geworden. Daher ist es notwendig, festzustellen, inwieweit ESG-Scores mit dem Risiko eines Unternehmens in Verbindung stehen könnten, wie die Europäische Bankenaufsichtsbehörde (EBA) bestätigt hat, dass ESG-Scores zum Risiko beitragen könnten. Um diese den Finanzmärkten inhärenten Risikotypologien in Bezug auf einen Mix aus (nicht) ESG-gebundenen Vermögenswerten zu bewerten, verwenden wir eine Erweiterung des Extreme Downside Hedge (EDH) und der Extreme Downside Correlation (EDC), um die Sensitivität von Vermögenswerten gegenüber dem Abwärtsrisiko anderer Finanzanlagen unter schwierigen Marktbedingungen auf Unternehmens- und Sektorebene zu untersuchen und dabei drei verschiedene wirtschaftliche Szenarien zu berücksichtigen: nämlich vor dem Ausbruch von COVID 19, während des Ausbruchs von COVID 19 und in der Erholungsphase. Wir untersuchen verschiedene versteckte Zusammenhänge in der Topologie des Versicherungsmarktsektors mit Hilfe von Methoden der innovativen Netzwerkanalyse. Unsere empirischen Ergebnisse zeigen verschiedene Verflechtungen, die typisch für die Verursacher von Tail Contagion und die Empfänger von Tail Contagion sind, sowie die Identifizierung der sichersten Unternehmen, die eine Diversifizierung der Investitionen begünstigen. Darüber hinaus berücksichtigen wir die Rolle von ESG-Scores über (nicht) ESG-bezogene Vermögenswerte und bieten institutionellen Anlegern und anderen Finanzmarktteilnehmern neue Perspektiven für die Formulierung besserer datengestützter handlungsfähiger Investitionsentscheidungen und regulatorischer Maßnahmen. Darüber hinaus wurden die Auswirkungen von COVID-19 untersucht.
Stichwörter: Aktienindizes, extreme Abwärtsabsicherung, extreme Abwärtskorrelation, Netzwerkmodelle, systematisches Risiko, systemisches Risiko, Volatilität, (nicht) ESG-gebundene Vermögenswerte, der Versicherungssektor
Abstract: Socially responsible investing (SRI) continues to gain momentum in the financial market space for various reasons starting from the looming effect of climate change and the drive towards a net-zero economy. Existing SRI approaches have included environmental, social, and governance (ESG) criteria as a further dimension to portfolio selection problems, 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 like Solvency II, we measure risk by the solvency ratio, i.e., by the ratio of the insurer's market-based equity capital over the Value-at-Risk of all modeled risk categories. In 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.
Keywords: Socially responsible investments, life insurance companies, Portfolio optimization, Solvency regulation.
Abstract: 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 method, 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, depict some interesting characterization of price volatility. Our findings show that in terms of the model-specific forecast accuracy measures, CEEMDAN-ELM, EEMD-ELM, and EMD-ELM provide the best forecast for US gas, Japan gas, crude oil, respectively in the pre-COVID-19 period and however, we obtain a mixed result during the COVID-19 outbreak. Furthermore, EEMD-ELM shows resilience in providing an accurate forecast for Japan gas in the two scenarios are examined and discussed.
Keywords: Energy commodity price analysis; Ensemble empirical decomposition; forecasting; Intrinsic mode function
Abstract: The price volatility of energy assets such as natural gas, crude oil, and coal among others do influence electricity prices, which altogether directly have significant economic impacts on different sectors of the economy. From this viewpoint, accurate energy price volatility predictions are very valuable for the reliable and stable operational security of energy systems. However, forecasting energy prices volatility can be less satisfactory, especially in the era of big data because of the complexity and non-linearity inherent in the energy system. This paper, therefore, utilizes a hybridization of techniques that seamlessly cut across artificial intelligence and computational intelligence for forecasting energy price volatility. In particular, we propose three parallel forecast combinations of Markov Switching GARCH-type and extreme learning machine model (MS-GARCH-type-ELM) alongside the selection of an appropriate model for price volatility forecasting in the energy market. Using energy time series data on coal, gas, and crude oil, we examine the forecasting performance of the different models. Our findings show that the Mean Absolute Error (MAE), Mean Square Error (MSE), Theil's U and Mean Absolute Percentage Error (MAPE) indicate that the MS-GARCH-type-ELM model with a simple structure could obtain more accurate forecasting results and as such the best forecasting results at all steps among all related models and hence can help to predict short-run price volatility fluctuations in the energy market. In addition, investigating both the Pre-COVID and during COVID outbreak periods reveal that OLS-MSGARCH-t-GARCH-GJR-GARCH-ELM with combined student-t and skewed student-t distribution is robust and show resilience as competing combined accurate forecast model for crude oil price volatility forecasting while the remaining show mixed results in the two paradigms namely (i) Pre-COVID period and (ii) during the COVID-19 outbreak.
Keywords: Markov-switching GARCH models, Extreme learning machines, Stochastic volatility, commodity prices, forecasting, accuracy forecast measures
Publications:
Ahelegbey, D. F., Casarin, R., Fianu, E. S., & Grossi, L. (2024). Structural changes in contagion channels: the impact of COVID-19 on the Italian electricity market. Annals of Operations Research, 1-26.
Schlütter, S., Fianu, E. S., & Gründl, H. (2023). Responsible investments in life insurers’ optimal portfolios under solvency constraints. Zeitschrift für die gesamte Versicherungswissenschaft, (1), 53-81.
Fianu, E. S., Ahelegbey, D. F., & Grossi, L. (2022). Modeling risk contagion in the Italian zonal electricity market. European Journal of Operational Research.
Fianu, E. S., Ahelegbey, D. F., & Grossi, L. (2021). “Risk Management via Contemporaneous and Temporal Dependence Structures with Applications” MethodsX
Fianu, E. S. (2018), “Portfolio optimization of power futures market: Evidence from France and Germany”, International Journal of Public Policy, Vol. 14, Nos.1/2, 2018.
Fianu, E. S. (2017), “Exploring the resilience of crude oil prices via nonlinear dynamics and wavelet-based analysis: an international experience”, Int. Journal of Decision Sciences, Risk and Management, Vol. 7, No. 4, 2017, Copyright © 2018 Inderscience Enterprises Ltd.
Fianu, E. S. (2017), “A concise note on risk externalities: a critical review”, Advances in Economics and Business, Vol. 5 (10), pp. 568 - 573.
Fianu, E.S., (2016), “The Delay vector variance (DVV) method and recurrence quantification analysis of energy markets”, The International Journal of Energy and Statistics, Vol. 04, No. 01, 1650001.
Fianu et al (2016), “A network framework of investigating systemic risk in zonal energy markets”; Available at: http://dx.doi.org//10.2139/ssrn.2833175.
Fianu, E. S., and L., Grossi (2015), “Estimation of risk measures on electricity markets with fat tailed distributions”, The Journal of Energy Markets, volume 8 (4), pp 29–54, 2015.
Fianu, E. S., (2015), “Portfolio optimization in zonal energy markets: Evidence from Italy”, The International Journal of Energy and Statistics, Volume 03, Issue 2 2015, Pages 155000.
Herzberg, F. S. Lauwers, L. Van Liedekerke, L., and E. S. Fianu (2010), “Addendum to L. Lauwers and L. Van Liedekerke, “Ultraproducts and aggregation”. Journal of Mathematical Economics Volume 46, Number 2 pp 277–278.