Medium-term Horizon Research Articles

Assessing the Fiscal Implications of Flood Disasters in India: Insights for General and Special Category States

Abstract This study provides a comprehensive analysis of the fiscal consequences arising from flood disasters in India, with a specific focus on the challenges encountered by General Category States (GCS) and Special Category States (SCS). Recognizing India's vulnerability to floods due to its diverse geographical and climatic landscape, the research emphasizes the need for a coordinated disaster response framework involving both central and state governments. Utilizing a panel vector autoregression (VAR) methodology alongside impulse response functions (IRFs), we reveal that flood disasters significantly impact fiscal variables over a medium-term horizon of 3–5 years. Our findings indicate that fiscal deficits widen for up to three years post-disaster, while expenditures on flood control surge in the following years. Notably, SCS face a disproportionate fiscal strain, exacerbated by their geographical disadvantages and heightened susceptibility to disasters, leading to a decline in non-tax revenue after flood shocks. The study advocates for tailored fiscal policies that enhance the resilience and recovery capacity of both GCS and SCS in addressing flood-induced fiscal challenges.

Time-frequency comovements between news sentiments, Non-fungible tokens, and DeFi assets: evidence from the wavelet analysis

ABSTRACT Growth in digitalization has created a potential boost for Non-fungible tokens (NFTs) and decentralized finance (DeFis) assets in the modern world. Therefore, this study aims to examine the comovement between the recently developed comprehensive measure of news sentiment index (NSI) and selected digital assets. For this purpose, we have utilized the wavelet transform, wavelet correlation, and wavelet coherence econometric model to assess interdependency in both time and frequency between news sentiments and digital assets. Our wavelet correlation and covariance results suggest that almost all the digital assets exhibit a negative relationship with NSI. Moreover, the wavelet coherence results confirm that there is no significant comovement in the short to medium-term horizon, suggesting that both NFTs and DeFi can be used as hedges against the NSI. Furthermore, we observe small patches of significant negative comovement between NSI and digital assets in the long term, which correspond to the initial days of COVID-19. Our results confirm selected digital assets’ hedging role against news-driven uncertainty. This study finding provides essential information to policymakers, international investors, and investment managers to make effective decisions.

A new approach to forecasting Islamic and conventional oil and gas stock prices

In order to make more informed investment decisions, it becomes increasingly critical to forecast stock prices. Given the abnormality of financial markets, predicting the stock market with high accuracy is challenging, necessitating the selection of a reliable method. This paper aims to predict oil and gas stocks in both Islamic and conventional markets before and during COVID-19 using a model based on recurrent long short-term memory (LSTM) networks. The study employs an LSTM network combined with maximum overlap discrete wavelet transformation (MODWT) to predict the Islamic oil and gas stocks (IOG) index as well as the conventional oil and gas stocks (COG) index. Data spanning from 2018.06.27 to 2021.11.23 is divided into two periods: pre-COVID-19 and COVID-19. Prediction accuracy is assessed using root mean square error (RMSE). The study reveals that the network forecasts both indices better during the crisis period than in normal conditions. Additionally, the model generates more accurate forecasts of COG than IOG in both periods across most scales. LSTM predicts COG more accurately at the long-term horizon of the pre-crisis period, whereas it only forecasts IOC at a medium-term horizon in the same market state. In the COVID-19 era, LSTM performs best at predicting both stock markets in the medium-term, but the longest-term forecast is the least accurate. These findings have important implications for investors trading in oil and gas stocks across different market conditions, as well as policymakers regulating oil and gas-related markets.

Analysis of aggregated load consumption forecasting in short, medium and long term horizons using Dynamic Mode Decomposition

In response to the growing need for precise energy demand forecasting in distribution grids, this study examines the effectiveness of the Dynamic Mode Decomposition (DMD) algorithm for short, medium, and long-term demand forecasting in distribution grids, leveraging diverse time series energy consumption data. The DMD algorithm, a data-driven approach renowned for its capacity to discern frequencies and trends, will be compared against established forecasting techniques, including XGBoosting Regressor, Long Short-Term Memory, and PROPHET. The research is conducted through a comprehensive case study comprising three distinct phases: study on pattern capturing of DMD, comparison in Long term horizon with all models studied and robustness study of DMD. It recalls upon real aggregated hourly energy consumption data sourced from a Spanish distribution grid. The practical applications and benefits of DMD in time series forecasting are examined carefully in order to show its comparative advantages over alternative forecasting models. To ensure a rigorous assessment of the findings, the analysis will employ four key statistical metrics: Root Mean Square Error (RMSE), Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The results obtained with the case studies showed a 75% reduction on computational waste compared to other models and the results are closer to classical models, obtaining an improvement in the best case study of 44% comparing RMSE with Prophet. The insights gained can help future applications and research in the field.

Interplay of crises: Unpacking intraday spillovers in oil and European equities in the shadow of the COVID-19 and the Ukraine-Russia war

This study investigates the time-varying frequency of spillovers between European stock markets and oil during the COVID-19 pandemic and the Russia-Ukraine war. Using the spillover index by Diebold & Yilmaz, 2012 and Baruník & Křehlík, 2018, we analyze high-frequency data at a 5-min interval to analyze the interplay between crude oil market returns and the Stoxx 600 index returns, including sectors such as auto, basic material, banks, chemicals, food and beverage, health, industrials, insurance, oil and gas, retail, real estate, technology (tech), telecommunication (telecom), and utilities. The sample period is January 3, 2022, to March 25, 2022. Our findings reveal a substantial degree of connectedness within this financial network, with specific sectors—auto, chemicals, food, industrials, insurance, real estate, retail, tech, and telecom—acting as net transmitters of shocks, while other sectors assume the role of net receivers. The Russia-Ukraine war is a significant driver of these interconnected dynamics. Spillovers are most prevalent in the medium-term horizon, with the time dimension affecting the role of sectors and oil. Furthermore, our research highlights the potential benefits of adding oil assets to portfolios, enhancing risk-adjusted performance. However, ongoing risk management remains crucial due to the dynamic hedge ratios. This study offers practical insights for investors and policy makers, aiding risk management and investment strategies in turbulent markets.

ОСОБЛИВОСТІ ІНФЛЯЦІЙНИХ ПРОЦЕСІВ В УКРАЇНІ: ПРОБЛЕМИ І ПРИЧИНИ

Research objective. The main purpose of this article is to examine the evolution and dynamics of inflationary processes in Ukraine during 1991–2023 on the basis of historical and systemic analysis, methods of dynamic analysis, structural analysis, and comparative analysis. Separate goals are to reveal and theoretically substantiates the main regularities of inflationary processes influence on the economic development and to analyze the features of anti-inflation regulation in the economy of Ukraine. Research methodology. In the paper, the authors examined inflationary processes with the aim of identifying their consequences for the economy of Ukraine. A review of publications has shown varying studies of the inflationary process. The paper describes the chronology of inflationary processes, the dynamics of the consumer price index, the socio-economic consequences and causes of inflation in Ukraine. Attention is focused on the need for state regulation of inflationary processes. The effectiveness of the anti-inflationary policy was determined. In line with the findings of the study, the prospects for the implementation of an effective monetary anti-inflationary policy aimed at effective regulation of inflationary processes are outlined. Conclusions. The authors conclude that the study of inflationary processes is important because inflation affects the exchange rate, economic growth and welfare. Economic agents always adjust financial risks, taking into account that inflation has historically been one of the most acute problems of Ukraine, which has uneven constant development. This study is aimed at determining the essence and causes of inflation in Ukraine during the period of restoration of its independence, as well as the possibility of its regulation by monetary methods. Inflation in Ukraine has specific features related to pricing, purchasing power, money circulation, anti-inflationary and anti-cyclical policies. Practical implications. The results of this study are of interest to the Ministry of Economy of Ukraine, the National Bank of Ukraine, as well as the Committee on Ukraine's Integration into the European Union, whereas the processes of European integration contribute to reducing inflation in the future. Originality/value. This study highlights the discussion on inflationary process in the transition economy of Ukraine. The authors emphasize that inflation in Ukraine has a permanent character and requires constant regulation to achieve significant progress in its reducing. The authors discoveredthat authorities should focus on the medium-term horizon of inflation control to ensure a reduction in inflation, as well as a level of inflation appropriate for price stability.

A blending ensemble learning model for crude oil price forecasting

AbstractTo efficiently capture diverse fluctuation profiles in forecasting crude oil prices, we here propose to combine heterogenous predictors for forecasting the prices of crude oil. Specifically, a forecasting model is developed using blended ensemble learning that combines various machine learning methods, including k-nearest neighbor regression, regression trees, linear regression, ridge regression, and support vector regression. Data for Brent and WTI crude oil prices at various time series frequencies are used to validate the proposed blending ensemble learning approach. To show the validity of the proposed model, its performance is further benchmarked against existing individual and ensemble learning methods used for predicting crude oil price, such as lasso regression, bagging lasso regression, boosting, random forest, and support vector regression. We demonstrate that our proposed blending-based model dominates the existing forecasting models in terms of forecasting errors for both short- and medium-term horizons.

The inflation targeting regime in Ukraine and Czech Republic: a comparative analysis

The main purpose of this article is to determine the impact of the inflation targeting re-gime on the inflation rates in Ukraine and the Czech Republic, to identify the advantages and disadvantages of its use, to study the evolution and dynamics of inflationary processes in Ukraine and the Czech Republic, as well as the nature and causes of inflation in Ukraine and the Czech Republic. A separate goal is to analyze the conditions and features of the Czech Re-public's experience with the introduction of inflation targeting and the formation of recommen-dations of successful measures to achieve inflation goals for Ukraine. In the paper, the authors examined the practice of applying the inflation targeting regime in Ukraine and the Czech Re-public in order to identify the positive and negative consequences of the implementation of this monetary policy regime. A review of publications has shown varying attitudes towards the in-flation targeting regime. The paper describes the chronology of inflationary processes in the re-searched countries, the dynamics of the consumer price index, the socio-economic consequences and causes of inflation. Attention is focused on the need for state regulation of inflationary pro-cesses. The effectiveness of the anti-inflationary policy was determined. In line with the findings of the study, the prospects for the implementation of an effective monetary anti-inflationary policy aimed at effective regulation of inflationary processes are outlined. The authors conclude that the study of the National Bank of Ukraine's (NBU) monetary policy is important because it affects inflation, the exchange rate, and economic growth. Economic agents always adjust fi-nancial risks, keeping in mind that historically inflation has been one of the most acute prob-lems for Ukraine , which has an uneven constant development. This study is aimed at determin-ing the nature and causes of modern inflation in Ukraine and in Czech Republic and the possi-bility of its regulation by monetary methods. Differences in the dynamics of inflation processes in the presented countries indicate that inflation in each country has specific features related to pricing, purchasing power, money circulation, anti-inflationary and countercyclical policies. However, unevenness and cyclical development of inflation is typical for the studied countries. The results of this study are of interest to the Ministry of Economy of Ukraine, the National Bank of Ukraine, as well as the Committee on Ukraine's Integration into the European Union, whereas the processes of European integration contribute to reducing inflation in the future. The authors conclude that in the process of creating effective economic policy, it is necessary to use the experience of other countries that have achieved stable, multi-year success in this pro-cess. This study highlights the discussion about inflation targeting experience in the transition economies of Ukraine and Czech Republic. The authors emphasize that circumstances that led to the transition from exchange rate pegging to inflation targeting have made significant pro-gress in reducing inflation. The authors discovered that the focus of inflation-targeting by cen-tral banks should be on the medium-term horizon in order to ensure inflation reduction as well as inflation level appropriate for price stability.

Financial-cycle ratios and medium-term predictions of GDP: Evidence from the United States

Using a large quarterly macroeconomic dataset for the period 1960–2017, we document the ability of specific financial ratios from the housing market and firms’ aggregate balance sheets to predict GDP over medium-term horizons in the United States. A cyclically adjusted house price-to-rent ratio and the liabilities-to-income ratio of the non-financial non-corporate business sector provide the best in-sample and out-of-sample predictions of GDP growth over horizons of one to five years, based on a wide variety of rankings. Small forecasting models that include these indicators outperform popular high-dimensional models and forecast combinations. The predictive power of the two ratios appears strong during both recessions and expansions, stable over time, and consistent with well-established macro-finance theory.

Neuroforecasting of daily streamflows in the UK for short- and medium-term horizons: A novel insight

Predicting streamflows, which is crucial for flood defence and optimal management of water resources for drinking, irrigation, hydropower generation and ecosystem conservation, is a challenging task in most practical cases. The limitations of physically based models and the increasing availability of time series data on flow rates and other weather and climate variables of interest are increasingly driving the use of models based on Machine Learning algorithms. Of these, neural networks have proven to be among the best-performing prediction tools.In this study, four types of neural networks, Multilayer Perceptron (MLP), Radial Basis Function Neural Network (RBF-NN), Long Short-Term Memory Network (LSTM), and Bi-directional Long Short-Term Memory Network (Bi-LSTM), were compared in the prediction of short-term (1–3 days ahead) and medium-term (7–15 days ahead) daily flow rates of six different rivers in the United Kingdom. The predictors consisted only of the lagged values of flow rates and daily cumulative precipitation. The optimal number of these and the hyperparameters of the different algorithms were selected according to a Bayesian optimization procedure.The various algorithms demonstrated comparable and strong short-term forecasting abilities, with a slight inclination to underestimate the maximum flood flows. In particular, the coefficient of determination (R2) for 1-day ahead forecasts ranged from 0.909 to 0.986, and the Mean Absolute Percentage Error (MAPE) ranged from 3.36% to 13.94%. However, as the forecast horizon extended, a reduction in forecasting accuracy was identified, despite all models being able to predict the overall flow pattern, even up to 7–15 days ahead. Compared to LSTM- and Bi-LSTM-based models, RBF-NN-based models showed less of a tendency to underestimate flood peaks and overestimate low flows and could predict both with good accuracy. Additionally, the relative error distribution exhibited a general skew in all models. The findings of this study suggest that RBF-NNs are a powerful tool for obtaining accurate forecasts in both the short- and medium-term while requiring a limited number of parameters to be optimized, thus reducing the calculation time required.

Deep decarbonisation pathways of the energy system in times of unprecedented uncertainty in the energy sector

Unprecedented investments in clean energy technology are required for a net-zero carbon energy system before temperatures breach the Paris Agreement goals. By performing a Monte-Carlo Analysis with the detailed ETSAP-TIAM Integrated Assessment Model and by generating 4000 scenarios of the world's energy system, climate and economy, we find that the uncertainty surrounding technology costs, resource potentials, climate sensitivity and the level of decoupling between energy demands and economic growth influence the efficiency of climate policies and accentuate investment risks in clean energy technologies. Contrary to other studies relying on exploring the uncertainty space via model intercomparison, we find that the CO2 emissions and CO2 prices vary convexly and nonlinearly with the discount rate and climate sensitivity over time. Accounting for this uncertainty is important for designing climate policies and carbon prices to accelerate the transition. In 70% of the scenarios, a 1.5 °C temperature overshoot was within this decade, calling for immediate policy action. Delaying this action by ten years may result in 2 °C mitigation costs being similar to those required to reach the 1.5 °C target if started today, with an immediate peak in emissions, a larger uncertainty in the medium-term horizon and a higher effort for net-zero emissions.

Central bank capital management

This paper offers general guidelines for central bank capital management. Capital adequacy is important to be a credible, independent monetary authority over a medium-term horizon. Central banks, however, face several challenges in determining their capital adequacy. Firstly, the amount of capital only plays an auxiliary role in central banks' effectiveness given that they cannot default as long as they have the right to issue legal tender. Secondly, central banks face two types of financial risks: calculable risks from current exposures and latent risks from future exposures. These latent risks, in particular, are difficult to quantify because they stem from contingent policy measures such as quantitative easing and lending of last resort. It is argued that a central bank's target level of capital (1) can be calibrated with a confidence level that is lower than that used for commercial banks and (2) takes latent risks into account that are related to GDP or the size of the financial sector in the economy.

Design Exploration for Sustainable Regional Hybrid-Electric Aircraft: A Study Based on Technology Forecasts

The environmental impact of aviation in terms of noise and pollutant emissions has gained public attention in the last few years. In addition, the foreseen financial benefits of an increased energy efficiency have motivated the transport industry to invest in propulsion alternatives. This work is collocated within the Clean Sky 2 project GENESIS, focused on the environmental sustainability of 50-passenger hybrid-electric aircraft from a life-cycle-based perspective to support the development of a technology roadmap for transitioning towards sustainable and competitive electric aircraft systems. While several studies have already focused on the definition of possible aircraft designs combining several propulsion systems, the novelty of the present work is to consider technology forecasts and more comprehensive indicators in the design phase. These include the performance and emissions on a 200 nmi typical mission, which reflects the most economically attractive range for aircraft in the regional class. The work proposes a complete exploration of three major technology streams for energy storage: batteries, fuel cells, and turbine internal combustion engine generators, also including possible combinations of those technologies. The exploration was carried out through the execution of several designs of experiments aiming at the identification of the most promising solutions in terms of aircraft configuration for three different time horizons: short-term, 2025–2035; medium-term, 2035–2045; and long-term, 2045–2050+. As a result, in the short-term scenario, fuel energy consumption is estimated to be reduced by around 24% with respect to conventional aircraft with the same entry-into-service year thanks to the use of hybrid propulsive systems with lithium batteries. Fuel saving increases to 45% in the medium-term horizon due to the improvement in the energy density of storage systems. By the year 2050, when hydrogen fuel cells are estimated to be mature enough to completely replace kerosene-based engines, the forthcoming hybrid-electric aircraft promise no NOx and CO2 direct emissions, while being approximately 50% heavier than conventional ones.

Minimizing patients total clinical condition deterioration in operating theatre departments

The operating theatre is the most crucial and costly department in a hospital due to its expensive resources and high patient admission rate. Efficiently allocating operating theatre resources to patients provides hospital management with better utilization and patient flow. In this paper, we tackle both tactical and operational planning over short-term to medium-term horizons. The main goal is to determine an allocation of blocks of time on each day to surgical specialties while also assigning each patient a day and an operating room for surgery. To create a balance between improving patients welfare and satisfying the expectations of hospital administrators, we propose six novel deterioration rates to evaluate patients total clinical condition deterioration. Each deterioration rate is defined as a function of the clinical priorities of patients, their waiting times, and their due dates. To optimize the objective functions, we present mixed integer programming (MIP) models and two dynamic programming based heuristics. Computational experiments have been conducted on a novel well-designed and carefully chosen benchmark dataset, which simulates realistic-sized instances. The results demonstrate the capability of the MIP models in finding excellent solutions (maximum average gap of 4.71% across all instances and objective functions), though, requiring large run-times. The heuristic algorithms provide a time-efficient alternative, where high quality solutions can be found in under a minute. We also analyse each objective function’s ability in generating high quality solutions from different perspectives such as patients waiting times, the number of scheduled patients, and operating rooms utilization rates. We provide managerial insights to the decision makers in cases where their intention is to meet KPIs and/or maintaining trade-offs between patients and administrators expectations, more fair assignments, or ensuring that the most urgent patients are taken care of first.

Effect of Integrated Reporting on Firm’s Value: The Nigeria Manufacturing Sector Experience

The main objective of this study was to investigate the effect of integrated reporting on firms’ value drawing samples from listed manufacturing firms in Nigeria between the periods of 2011-2020. In this study, human capital disclosure index, manufacturing capital disclosure index, and social and relationship capital disclosure index were the integrated reporting proxies adopted to evaluate the effect on value of Nigeria manufacturing firms. Firms’ value was measured in terms of Tobin Q, and year price index was adopted as the control variable. Ex post factor research design was used and the study made use of secondary data sourced from the sampled companies’ annual reports and Nigeria Exchange Group Fact book. Data for integrated reporting variables were derived using disclosure checklist developed in accordance with the integrated reporting framework disclosure guidelines. Purposive sampling technique was used to select 51 out of the 59 manufacturing companies listed on the Nigeria Exchange Group. In order to examine the cause-effect relationship between the dependent variable and independent variables as well as to test the formulated hypotheses, the researcher used a robust regression analysis. The results of the analysis showed that the disclosure of human capital information in the annual report of listed manufacturing firms in Nigeria significantly improves the firm’s value. Based on this findings, it was concluded that only the variable of human capital disclosure has significant effect on firms’ value. Finally, the study recommended that the management of manufacturing firms in Nigeria should capture all disclosure items concerning human capital in their financial statements as this tends to improve the value of the firm and increase shareholders’ wealth. It was also recommended that management should develop an inclusive organizational culture for disclosing non-financial information with long term value creating capacity as they can maximise the market value of the firm over the short, medium and long term horizon.

Predicting the Dynamics of Covid-19 Propagation in Azerbaijan based on Time Series Models

The study is dedicated to developing an econometric model that can be used to make medium-term forecasts about the dynamics of the spread of the coronavirus in different countries, including Azerbaijan. We examine the number of COVID-19 cases and deaths worldwide to understand the data's intricacies better and make reliable predictions. Though it’s essential to quickly obtain an acceptable (although not perfect) prediction that shows the critical trends based on incomplete and inaccurate data, it is practically impossible to use standard SIR models of the epidemic spread. At the same time the similarity of the dynamics in different countries, including those which were several weeks ahead of Azerbaijan in the epidemic situation, and the possibility of including the heterogeneity factors into the model allowed as early as March 2020 to develop the extrapolation working relatively well on the medium-term horizon. The SARS-CoV-2 virus, which causes COVID-19, has affected societies worldwide, but the experiences have been vastly different. Countries' health-care and economic systems differ significantly, making policy responses such as testing, intermittent lockdowns, quarantine, contact tracing, mask-wearing, and social distancing. The study presented in this paper is based on the Exponential Growth Model method, which is used in statistical analysis, forecasting, and decision-making in public health and epidemiology. This model was created to forecast coronavirus spread dynamics under uncertainty over the medium term. The model predicts future values of the percentage increase in new cases for 1–2 months. Data from previous periods in the United States, Italy, Spain, France, Germany, and Azerbaijan were used. The simulation results confirmed that the proposed approach could be used to create medium-term forecasts of coronavirus spread dynamics. The main finding of this study is that using the proposed approach for Azerbaijan, the deviation of the predicted total number of confirmed cases from the actual number was within 3-10 percent. Based on March statistics on the spread of the coronavirus in the US, 4 European countries: Italy, Spain, France, Germany (most susceptible to the epidemic), and Azerbaijan, it was shown how the trajectory would deviate exponentially from a shape; a trial was carried out to identify and assess the key factors that characterize countries. One of the unexpected results was the impact of quarantine restrictions on the number of people infected. We also used the medium-term forecast set by the local government to assess the adequacy of health systems.

Is the ECB already following albeit implicitly an average inflation targeting strategy?

A fundamental aspect of the ECB's monetary policy strategy is that it aims to pursue price stability over the medium term. However, the ECB has not defined the medium term with reference to a predetermined horizon. To shed some light on how the horizon of price stability is being achieved in practice, we have estimated a regression where the ECB policy interest rate is expressed as function of a j-period centered moving average of inflation forecasts, which may capture the length of the medium-term horizon orientation of monetary policy. We have tested for different combinations of policy horizons, and forecast horizons, to achieve the best fit. Allowing j to vary from 1 year (corresponding the standard inflation targeting regime) to a normal length of an economic cycle, we keep the model sufficiently general, and we let the data speak for itself. We have found that the ECB is targeting price stability over a period in between four years and a half and five years, implying that the ECB is area1dy following, albeit implicitly, a hybrid approach to price stability in line with average inflation target. This result is robust when we control for the Central Bank desire to avoid excessive policy interest rate variability due to the presence of policy interest rate adjustment costs.

Analysis of Technologies for Hydrogen Consumption, Transition and Storage at Operating Thermal Power Plants

The paper analyses operating and developing technologies for hydrogen implementation, transition, and storage at operating thermal power plants (TPPs) to make recommendations for realization of perspective projects for evaluation of the use of hydrogen as a fuel. Over the medium-term horizon of the next decade, it is suggested that using the technology of burning a mixture of hydrogen and natural gas in gas turbines and gas-and-oil-fired boilers in volume fractions of 20% and 80%, respectively, be implemented at operating gas fired TPPs. We consider the construction of the liquefied hydrogen and natural gas storage warehouses for the required calculated quantities of the gas mixture as a reserve energy fuel for operating the TPPs. We consider the possibility of the reserve liquid fuel system being replaced by the technology involving storage of liquefied hydrogen in combination with natural gas. An economic assessment of the storing cost of reserve fuel on the TPP site is given. The paper suggests that the methane-hydrogen mixture be supplied to the TPP site by two independent gas pipelines for the possibility of using the mixture as the main fuel and to exclude fuel storage at the plant.

The Risk of the COVID-19 Pandemic and Its Influence on the Business Insurance Market in the Medium- and Long-Term Horizon

The aim of this article is to identify the risk and the likelihood of potential consequences of the COVID-19 pandemic on the business insurance market in the medium- and long-term horizon. The first section of this elaboration presents the theoretical approach connected with the nature of the pandemic. The second section outlines the presentation of the COVID-19 measurement rules and the third describes the potential effects of COVID-19 on the insurance market. Contrary to other elaborations on this topic which have appeared so far (these are mostly reports describing the financial market in short- and long-term horizons), here, the authors present the medium-term horizon as well. The possible consequences of COVID-19 are outlined both in relation to the insurance company client, e.g., change in the amount of the insurance premium under the insurance agreement, as well as in relation to the insurer, e.g., appearance of innovative and competitive offers (Trott’s concept Special attention has been paid to the way in which the insurer’s strategy (scenario analysis) may be used to build resilience to other crises as well as to the planning of emergency solutions. Actual events confirm the hypothesis that changes in the business insurance market dominated the losses in the aftermath of the pandemic.

A note on regulatory responses to COVID-19 pandemic: Balancing banks’ solvency and contribution to recovery

We discuss the implications on banks and the economy of prudential regulatory intervention to soften the treatment of non-performing exposures (NPEs) and ease bank capital buffers. We apply these easing measures on a sample of Globally Systemically Important Banks (G-SIBs) and show that these banks can play a constructive role in sustaining economic growth during the COVID-19 pandemic. In addition, an empirical analysis shows that prudential regulatory responses to COVID-19 along with high regulatory capital and low non-performing loans ratios are positively associated with economic growth. Thus, banks should maintain high capital ratios in the medium-term horizon to absorb future losses, as the effect of COVID-19 on the economy might take time to fully materialize.