Asset Risk Research Articles

The impact of the COVID-19 pandemic on the financial performance and stability of Islamic banks

Purpose The purpose of this paper is to investigate the impact of the COVID-19 pandemic on the financial performance and stability of Islamic banks (IBs) in the Middle East, North Africa and Southeast Asia (MENASA) region. Design/methodology/approach The sample consists of 50 IBs across 13 MENASA countries. The data covers 11 quarters, starting in Q1 2019 and ending in Q3 2021, and are collected from banks’ quarterly reports. The authors proxy financial performance by three measures, namely, return on assets (ROA), return on equity (ROE) and cost-to-income (Cost/Income). For financial stability, the authors use two indicators: insolvency risk (log Z-score) and asset risk (ROA/SDROA). The methodology is based on the generalized least squares method estimation. Findings The results showed a significant and negative impact of COVID-19 on two performance measures of IBs (ROA and ROE) suggesting that IBs were significantly affected during the earlier pandemic. As well, the authors found strong evidence of the impact of COVID-19 on the insolvency risk and asset risk of the studied banks. Practical implications The study of COVID-19’s impact on the performance and stability of IBs in MENASA countries permits the banks’ regulators and policymakers to ameliorate the banks’ financial performance and reinforce their supervisory actions. Also, it gives them assistance to guarantee the financial stability of these banks in times of crisis. Originality/value This study provides significant financial information and policy implications for stakeholders involved in the banking sector in MENASA countries. Consequently, IBs must guarantee their profits and stability to ensure their competitiveness versus conventional banks during the period of crisis.

Mitigation of the Stranded Asset Risk Due to the Implementation of Coal Plants Early Retirement in Indonesia Using Analytical Hierarchy Process

Based on Presidential Regulation Number 112 of 2022 concerning the Acceleration of Renewable Energy Development for the Supply of Electric Power, Indonesia has a road map in which there is a strategy program for accelerating the end of the operational period of PLTUs. In implementing early retirement, there is a risk of stranded assets that needs to be mitigated. Appropriate decision-making strategies and effective mitigation are needed to reduce the impact of stranded asset risk. Using the Anaytical Hierarchy Process (AHP), this study tries to analyze the implementation of the PLTU early retirement strategy by taking into account the risk of stranded assets and finding appropriate mitigation in reducing the level of impact of the risk of stranded assets that needs to be implemented by stakeholders. The results show that effective mitigation requires comprehensive planning and stakeholder involvement, which can substantially ease the financial transitions from coal to renewable energy sources. The findings suggest that integrating economic and policy considerations into the early retirement planning of coal-fired power plants is crucial for managing the transition effectively and minimizing potential financial losses. This approach ensures a smoother transition to sustainable energy sources, contributing significantly to both national and global decarbonization goals.

The Impact of Trade Openness, Tariff, and Globalization on Food Security in the ASEAN Region

In 2023, many crises hit the world due to heated geopolitical conditions resulting from the war in Russia and Ukraine; the world situation was fragmented because of competition between the USA and PRC blocs, economic recovery due to COVID-19 was shaken again, and food inflation was soaring high. The leading cause of food insecurity in ASEAN, which consists of most developing countries, is the lack of distribution access due to the affordability of logistics and food supplies. Most investors shift their investment preferences from the primary sector to manufacturing due to the volatility and high risk of assets. In 2020, in Asia, there were 78.7 million toddlers who were stunted, with the second majority in Southeast Asia (27.40%). Although most of the ASEAN member countries are connected geographically, the market share of ASEAN member countries in world trade is only 8.8%, so intra-trade relations are considered not optimal enough. This study aims to analyze the effect of three different trade openness indicators (trade openness, tariffs, and globalization) on the food security of Southeast Asian people in 2000–2021. The method used in this research is panel data, which combines pool least squares (PLS) and fixed effect models (FEM) by developing Beck and Katz’s two panels corrected standard errors (PCSE): cross-section weights and SUR. Trade openness significantly affects the two pillars of food security: stability and utilization, with U-shaped results. Ad-valorem tariffs are significant and positive for the two pillars of food security: availability and utilization. Increasing taxes in Southeast Asia can improve the average dietary energy supply’s adequacy; however, this can also increase the prevalence of stunting in children under five. It happens because the availability of abundant food in terms of quantity differs from the quality and safety of its consumption. The increase in globalization from an economic, political, and social perspective in Southeast Asia is significant for the four pillars of food security; if globalization is increased, then this can positively impact reducing cases of stunting and malnutrition; on the other hand, increasing globalization has harmed food availability and stability. Increasing one policy has a different impact. In the first stage, policy improvements can positively impact a pillar of food security. However, after reaching a turning point, there is a possibility that the increased policy will harm the other pillars. Therefore, it is necessary to choose an integrative policy trade-off and be able to solve problems better. This study suggests several main policy implications, namely building a more assertive trade policy based on the WTO and food safety technical rules that comply with WHO rules, increasing intra-trade within the ASEAN group, maintaining food price stability, encouraging investment in agriculture, promoting governance reforms, and strengthening the regional food security system in terms of production, consumption, and distribution. Support policies are also needed regarding solid prevention efforts against stunting and malnutrition and digitalization (Agritech 4.0), which supports food availability. In addition, this research also suggests that ASEAN can continue to increase domestic food production for resilience to the global crisis. Keywords: trade openness, tariff, globalization, food security, ASEAN, dynamic panel

The Time-Consistent Optimal Reinsurance Strategy of Insurance Group under the CEV Model

The article introduces proportional reinsurance contracts under the mean-variance criterion, studying the time-consistence investment portfolio problem considering the interests of both insurance companies and reinsurance companies. The insurance claims process follows a jump-diffusion model, assuming that the risk asset prices of insurance companies and reinsurance companies follow CEV models different from each other. In the framework of game theory, the time-consistent equilibrium reinsurance strategy is obtained by solving the extended HJB equation analytically. Finally, numerical examples are used to illustrate the impact of model parameters on equilibrium strategies and provide economic explanations. The results indicate that the decision weights of insurance companies and reinsurance companies do have a significant impact on both the reinsurance ratio and the equilibrium reinsurance strategy.

Do Investors Underestimate the Risk of Asset Bubbles? ——Evidence from the Goodwill Bubbles of Chinese Listed Companies

ABSTRACT We investigate the extent of investors’ awareness of the bubble risk of assets’ fundamental value by examining Chinese listed companies’ goodwill bubbles. We find that firms with larger goodwill have a higher possibility of goodwill impairment recognition in the future, indicating a higher bubble risk of companies’ assets fundamental. Next, we find that firms with more goodwill experience lower stock returns in subsequent 1–3 years, which means investors do not demand corresponding risk compensation and underestimate the goodwill bubble risk for these firms. Furthermore, we investigate five behavioral biases driving investors to underestimate the bubble risk. The results show that investors’ gambling preference for bubble assets is the key driver, while investors limited attention, extrapolation, opinion divergence, and earnings expectation are not applicable in our setting. Our study suggests that investors’ gambling preference is a channel through which the risk of companies’ assets fundamental bubble transmits to the stock market.

Assessing the attractiveness of chemical and process facilities to terrorism using a situational crime prevention approach

As intentional attacks to industry have been on the rise in the last years, researchers and institutions put effort in addressing risks of industrial assets exposed to acts of interference. One of the key steps of security risk assessment is the evaluation of the attractiveness, i.e. determining which installation or part of it could be of interest to a potential attacker. As security concerns are intrinsically tied to human intention, attractiveness does not only depend on technical characteristics of the facility, but also on the type of threat and its geographic, economic, and socio-political context. Therefore, interdisciplinarity is among the key features of a team tasked with assessing attractiveness. This work is the result of the joint effort of Physical Sciences and Engineering experts and Social Sciences and Humanities experts, and it presents a methodology for the assessment of attractiveness of process facilities to physical terroristic attacks developed using a “terrorist thinking” approach. Technical factors such as plant layout and physical protection systems were combined with modern criminology principles (namely Situational Crime Prevention, i.e. how actors take advantage of situated opportunities to commit a crime) to obtain a comprehensive picture of the possible motives behind a physical terroristic attack to a chemical and process facility (meant as facilities where chemical substances are present and where chemical and/or physical processes occurs). The methodology was applied to a case study featuring facilities located in a critical context; a ranking among facilities was obtained, and the robustness of the results was tested through a Monte Carlo-based uncertainty analysis. The results revealed that it is not possible to obtain a clear ranking of critical facilities accounting only for technical or non-technical factors, but both are necessary. The method developed was demonstrated easy to apply and could be used not only by plant security managers, but also by Institutions and Governments to prioritize critical assets in threatening contexts.

Extreme connectedness between cryptocurrencies and non-fungible tokens: portfolio implications

We analyze the connectedness between major cryptocurrencies and nonfungible tokens (NFTs) for different quantiles employing a time-varying parameter vector autoregression approach. We find that lower and upper quantile spillovers are higher than those at the median, meaning that connectedness augments at extremes. For normal, bearish, and bullish markets, Bitcoin Cash, Bitcoin, Ethereum, and Litecoin consistently remain net transmitters, while NFTs receive innovations. However, spillover topology at both extremes becomes simpler—from cryptocurrencies to NFTs. We find no markets useful for mitigating BTC risks, whereas BTC is capable of reducing the risk of other digital assets, which is a valuable insight for market players and investors.

Realized semibetas in the Australian stock market

ABSTRACT This study examines the explanatory power of the CAPM and downside risk asset pricing models (the downside beta and the realized semibeta models) for the next-month firm-level cross-sectional stock return variation in the Australian stock market. We show that the CAPM beta, downside beta, semibeta BetaNP, and semibeta BetaNN negatively predict future stock returns, which is inconsistent with the findings in the original study by Bollerslev, Patton, and Quaedvlieg (2022). The BetaNN measures the individual stock movement in the same direction as the downward stock market, while BetaNP measures individual stock downward with the upward stock market. These findings are robust, not subsumed by conventional cross-sectional asset pricing factors, and consistent with the existing Australian downside risk study.

Anglo–Dutch financial connections and contrasts in the late eighteenth century: The Amsterdam phase of the 1772–3 credit crisis

AbstractWe examine the Amsterdam phase of the 1772–3 financial crisis using the British experience in the same episode as comparative context. We conclude that, notwithstanding some direct exposures by Amsterdam institutions to the principals of the London crisis, the main linkage between the two outbreaks was the requirement for cash margin on loans backed by British East India Company shares. No significant contagion via the bills of exchange network spread from London to Amsterdam in the period separating the two outbreaks, but some feeding back of distress to London is noticeable after the Dutch outbreak. The crisis was rooted in credit expansion, a deterioration in asset risk profiles, and speculation in West Indian mortgage securities and British equity markets. Speculators were enabled by information inefficiencies in the specialized and layered Amsterdam markets, and by the absence of a gatekeeper who could have regulated the provision of credit to them. To contain the outbreak, the Dutch drew on the cohesion of an informal club of insiders who led business and civic affairs. Though the credit markets resumed normal operations relatively quickly, the incurred losses were transformative for their long term prospects by devastating some of their most important firms.

Nonfungible Tokens—Differentiated Crypto Assets in Need of Accounting and Attestation Guidelines; Analysis and Implications

ABSTRACT Even as recent headlines announce the risks of crypto assets, institutional and governmental adoption continues. Undeterred by short-term price declines and liquidation of speculative instruments, blockchain-based instruments and applications continue to receive billions in investment across the globe. And yet, accounting and attestation standards have failed to keep pace with this growth. This paper focuses specifically on the development of one type of crypto asset—nonfungible tokens (NFTs)—and the relevant accounting and auditing concerns. The first section reviews the nature of NFTs, differentiating them from other existing crypto assets. The second section examines factors that must be considered for the audit and attestation of NFTs. This paper raises questions that both the academician and practitioner must contend with as these crypto assets continue to be adopted.

Temporal Implicit Multimodal Networks for Investment and Risk Management

Many deep learning works on financial time-series forecasting focus on predicting future prices/returns of individual assets with numerical price-related information for trading, and hence propose models designed for univariate, single-task, and/or unimodal settings. Forecasting for investment and risk management involves multiple tasks in multivariate settings: forecasts of expected returns and risks of assets in portfolios, and correlations between these assets. As different sources/types of time-series influence future returns, risks, and correlations of assets in different ways, it is also important to capture time-series from different modalities. Hence, this article addresses financial time-series forecasting for investment and risk management in a multivariate, multitask, and multimodal setting. Financial time-series forecasting, however, is challenging due to the low signal-to-noise ratios typical in financial time-series, and as intra-series and inter-series relationships of assets evolve across time. To address these challenges, our proposed Temporal Implicit Multimodal Network (TIME) model learns implicit inter-series relationship networks between assets from multimodal financial time-series at multiple time-steps adaptively. TIME then uses dynamic network and temporal encoding modules to jointly capture such evolving relationships, multimodal financial time-series, and temporal representations. Our experiments show that TIME outperforms other state-of-the-art models on multiple forecasting tasks and investment and risk management applications.

A Fuzzy Portfolio Model With Cardinality Constraints Based on Differential Evolution Algorithms

Uncertain information in the securities market exhibits fuzziness. In this article, expected returns and liquidity are considered as trapezoidal fuzzy numbers. The possibility mean and mean absolute deviation of expected returns represent the returns and risks of securities assets, while the possibility mean of expected turnover represents the liquidity of securities assets. Taking into account practical constraints such as cardinality and transaction costs, this article establishes a fuzzy portfolio model with cardinality constraints and solves it using the differential evolution algorithm. Finally, using fuzzy c-means clustering algorithm, 12 stocks are selected as empirical samples to provide numerical calculation examples. At the same time, fuzzy c-means clustering algorithm is used to cluster the stock yield data and analyse the stock data comprehensively and accurately, which provides a reference for establishing an effective portfolio.

Can NFTs hedge the risk of traditional assets after the COVID-19 pandemic?

Our study analyzes the dynamic spillover effects between the NFT market, Bitcoin market, oil market, gold futures market, S&P 500 stock index, bond market, and US dollar index over three time periods: the full sample period (2017/6/23–2022/9/2), before the COVID-19 outbreak (2017/6/23–2020/1/12), and after the COVID-19 outbreak (2020/1/13–2022/9/2) by employing the DCC-GARCH-based connectedness model. Furthermore, we apply the DCC-GARCH-t-Copula model, Risk Parity Portfolio (RPP) model, and Minimum Connectedness Portfolio (MCoP) model to evaluate the bilateral dynamic hedge ratios, portfolio weights, and the multivariate portfolio performance of these financial assets, respectively. Our findings suggest that NFTs are a source of volatility spillovers during both periods and that the COVID-19 outbreak accelerates the speed and intensity of volatility spillovers from NFTs to traditional financial markets. Moreover, most of the volatility spillovers from NFTs are caused by own shocks, which implies that NFTs are not susceptible to financial risk contagion. The results of bilateral portfolio analysis show that NFTs can effectively hedge other traditional financial assets when used as long positions. The results of multivariate portfolio analysis demonstrate that NFTs can also reduce investment risk despite their smaller weighting. In the two months after the COVID-19 outbreak, MCoP slightly outperforms RPP, although RPP outperforms MCoP in general.

Predicting tail risks by a Markov switching MGARCH model with varying copula regimes

AbstractTo improve the dynamic assessment of risks of speculative assets, we apply a Markov switching MGARCH approach to portfolio risk forecasting. More specifically, we take advantage of the flexible Markov switching copula multivariate GARCH (MS‐C‐MGARCH) model of Fülle and Herwartz (2022). As an empirical illustration, we take the perspective of a risk‐averse agent and employ the suggested model for assessments of future risks of portfolios composed of a high‐yield equity index (S&P 500) and two safe‐haven investment instruments (i.e., Gold and US Treasury Bond Futures). We follow recent suggestions to employ the expected shortfall as a prime assessment of tail risks. To accurately evaluate the merits of the new model, we back‐test the risk forecasting for daily returns over 10 years for heterogeneous market environments including, for example, the COVID‐19 pandemic. We find that the MS‐C‐MGARCH model outperforms benchmark volatility models (MGARCH, C‐MGARCH) in predicting both value‐at‐risk and expected shortfall. The superiority of the MS‐C‐MGARCH model becomes stronger, when the share of comparably risky assets in the portfolio is relatively large.

The impact of liquidity on common stocks returns: Empirical insights from commercial banks in Nepal

Most developed and emerging economies pay substantial attention to liquidity to understand stock return behavior. However, there is a need for more focus on understanding the impact of such factors on stock returns in developing countries such as Nepal. This study aims to examine the effect of liquidity, size, financial and asset risk, growth potential, and profitability on stock returns in Nepalese commercial banks. A pooled ordinary least squares regression model is utilized, employing data from the Central Bank of Nepal and the Nepal Stock Exchange. There are 249 observations in the data set, which covers the period from 2009/10 to 2019/20. The model considers the impact of trading volume, market capitalization, book-to-market ratio, asset growth, and return on asset on stock returns in Nepalese commercial banks. The results indicate that trading volume, a proxy of liquidity, positively affects stock returns in Nepalese commercial banks. The finding reveals that when other variables are held constant, a 0.288 percent increase in stock returns is expected for a one percent rise in trading volume. However, asset growth and return on assets show a weakly favorable link with stock returns in Nepal. Conversely, the research findings suggest an insignificant inverse correlation between book-to-market and stock returns. A decrease in stock returns of 0.307 percent is expected for a one percent increase in the book-to-market ratio. Similarly, market capitalization has a negligible effect on stock returns in Nepal.

The concept of banking capital risk-sensitivity revisited

The article studies the foundations of the risk-sensitivity concept of regulatory banking capital. The author states that risk of losses, including unexpected ones covered in accordance with the concept by banking capital cannot be measured with satisfactory accuracy. This is the principal obstacle to the concept implementation. The efficacy of Basel III capital regime with parallel use of risk-sensitive and risk-insensitive approaches is under scrutiny. Rationales are adduced that under this regime risk-sensitive approach has no real value. The study of risk-sensitive approach abilities among macro-prudential tools leads to the conclusion that its efficiency is apparently insufficient. The analysis of the academic study on the issue is carried out. This research recommends to impose risk-sensitive capital requirements if the risk of bank assets can be measured quite accurately. It is shown that the study is based on the implicit assumption of the absence of unexpected losses phenomenon in banking activity. This assumption is unrealistic and makes research in effect objectless. The major inference from the study is that the regulatory capital risk-sensitivity concept is not well-founded and its use for capital adequacy regulation is inadvisable. For micro-prudential purposes the simple leverage deems more expedient. It is capable to ensure for capital regulation the reasonable balance of risk-sensitivity to failure, simplicity and comparability.

Sustainable investments in the face of social unrest and risk: A new perspective on corporate social responsibility

In recent years, corporate social responsibility (CSR) activities have become increasingly prevalent as corporate initiatives, owing to their influence on companies and their societal dimensions. To address the gap in the literature on the effect of CSR activities on firms and their value, this study develops a theoretical model of real options considering market uncertainty and analyzes the relationship between a firm’s investment in CSR activities and the risk to its assets. The analysis demonstrates the existence of an optimal level of investment in CSR activities and shows that at this level, the beta of the firm’s assets undergoes the most significant decrease. Furthermore, by examining the relationship between the monetization potential of investment and the beta of assets, this study demonstrates that CSR activities can effectively reduce asset risk. It evaluates a firm’s value and beta from the perspective of investment in CSR activities and the potential for investment success, demonstrating the utility of this investment. It also highlights that the effectiveness of external CSR activities in increasing a firm’s value is contingent on their recognition and visibility in situations of significant social unrest and provides a practical implication that companies should consider engaging in outward-facing CSR activities.

Bridging the Cyber–Physical Divide: A Novel Approach for Quantifying and Visualising the Cyber Risk of Physical Assets

Critical infrastructures and their physical assets are under increasing threat of cyber-attacks as technological integration creates cyber–physical systems (CPSs). This has led to an urgent need to better understand which physical assets in these systems are most at risk, but this requires crossing the divide between cyber and physical risk assessments. However, existing cyber-security methods generally focus solely on the vulnerabilities and security of the cyber network and efforts to quantify the impacts of these cyber vulnerabilities on physical assets are generally limited to the consideration of individual attacks, rather than system-wide risk assessments. Similarly, risk assessments of physical infrastructure systems generally ignore potential impacts due to cyber-attacks. To overcome this cyber–physical divide in risk assessment, we introduce a novel approach for assessing risk across this divide. The proposed approach assesses the cyber risk of physical assets as a function of the vulnerabilities of their connected cyber components, and the contribution of cyber components to this risk. The approach is demonstrated with a case study of the C-Town water distribution system. The results indicate that the approach shows a modified prioritisation of risk compared to that obtained using conventional cyber or physical assessments, highlighting the importance of considering the connection between cyber and physical components in risk assessments of critical infrastructure and their physical assets.

EFFECTS OF NON-PERFORMING LOANS ON RETURN ON ASSETS OF SELECTED COMMERCIAL BANKS IN NIGERIA

The essence of creating Commercial banks is to act as intermediary between the surplus (supply side) and deficit (demand side) units of funds. This is, in addition to other crucial objectives of profitability, growth in assets, and growth in customer base. To achieve these objectives, commercial banks grant loans to individuals, business organizations and governments among other profit yielding ventures. Loan defaults could be rampant resulting from low quality of assets, high non-performing risk assets (credit risk) that may result in huge loan losses, and reduction in bank profitability leading to adverse effect on return on assets of commercial banks. This study investigates the effect of non-performing loans (using loan loss provision as proxy) on return on assets of selected commercial banks in Nigeria. Annual time series secondary data from 2010 to 2021 were sourced from Central Bank of Nigeria Statistical Bulletin and the Annual Financial Reports of affected bank, and panel least square method of analysis was used as our estimation technique. The results revealed that while loan loss provision has negative impact on return on assets of the selected commercial banks, interest rate has a positive impact on return on assets of the selected commercial banks within the period of study. The study recommends, amongst others that effective credit policy, reflected in flexible tenure; restructuring of credit terms; and conversion should be adopted in the selected commercial banks to minimize the negative impacts of loan defaults. This is because decline in loan default will lead to reduction in non-performing loans, and subsequently boost return on assets of affected banks in Nigeria.
 Keywords: Non-Performing Loan, Return on Asset, Interest Rates, Bank Performance, Bank Profitability, and Loan Loss Provisions.

Comparing the Performance of the Expected Returns of Cryptocurrencies Using CAPM and D-CAPM Approaches

The present study compares the expected returns of cryptocurrencies using the capital asset pricing and the downside capital asset pricing models. For this purpose, fifty cryptocurrencies were studied as representative of risky assets during the five-year from 2018 to 2022 with daily frequency. Using the conditional variance test, eighteen cryptocurrencies were accepted and the rest were homogeneously rejected in the variance heterogeneity test. Among the eighteen cryptocurrencies, nine were randomly selected as the portfolio, including high-volatility, low-volatility, and medium-volatility cryptocurrencies. First, using the t-Student statistic, the total return and downside return were compared. Results showed that the capital asset pricing model and the downside risk asset pricing model can be fitted in cryptocurrencies. Using the Wald-Fisher model, we investigated the justification for selecting a more appropriate model based on downside returns. The results of the research showed that D-CAPM and CAPM show the relationship between risk and return in cryptocurrencies appropriately, and the portfolios obtained from the mentioned models show the efficiency of the two models in the cryptocurrency market.