Search Results (646)

The cryptocurrency market offers attractive but risky investment opportunities, characterized by rapid growth, extreme volatility, and uncertainty. Traditional risk management models, which rely on probabilistic assumptions and historical data, often fail to capture the market’s unique dynamics and unpredictability. In response to these challenges, this paper introduces a novel portfolio optimization model tailored for the cryptocurrency market, leveraging a credibilistic CVaR framework. CVaR was chosen as the primary risk measure because it is a downside risk measure that focuses on extreme losses, making it particularly effective in managing the heightened risk of significant downturns in volatile markets like cryptocurrencies. The model employs credibility theory and trapezoidal fuzzy variables to more accurately capture the high levels of uncertainty and volatility that characterize digital assets. Unlike traditional probabilistic approaches, this model provides a more adaptive and precise risk management strategy. The proposed approach also incorporates practical constraints, including cardinality and floor and ceiling constraints, ensuring that the portfolio remains diversified, balanced, and aligned with real-world considerations such as transaction costs and regulatory requirements. Empirical analysis demonstrates the model’s effectiveness in constructing well-diversified portfolios that balance risk and return, offering significant advantages for investors in the rapidly evolving cryptocurrency market. This research contributes to the field of investment management by advancing the application of sophisticated portfolio optimization techniques to digital assets, providing a robust framework for managing risk in an increasingly complex financial landscape. Full article

We examine the impact of physical climate risks on energy markets in China, distinguishing between traditional energy and new energy stock markets, and the energy commodity market, utilizing a time-varying parameter vector autoregressive model with stochastic volatility (TVP-SV-VAR). Specifically, we investigate the dynamic effects of five specific subtypes of physical climate risks, namely waterlogging by rain, drought, typhoon, cryogenic freezing, and high temperature, on WTI oil prices and coal prices. The findings reveal that these physical climate risks exhibit time-varying similar effects on the returns of traditional energy and new energy stocks, but heterogeneous effects on the returns of WTI oil prices and coal prices. Finally, we categorize and examine the impact of both acute and chronic physical risks on the energy commodity market. Full article

This study examined the connectedness and propagation of risk in the South African equity sectors during the Global Financial Crisis (GFC), the European Debt Crisis (EDC), the US–China trade war, and the COVID-19 pandemic. Daily returns of nine Johannesburg Stock Exchange (JSE) super sectors were examined from 3 January 2006 to 31 December 2021. Applying the connectedness matrix and time-varying parameter vector autoregressive (TVP-VAR) model, in full sample and sub-periods, the study showed that dynamic total connectedness of the super sectors is high in absolute form (62%). Furthermore, it was found that the highest volatility connectedness was during the EDC (68.83%) and during the COVID-19 pandemic (68.57%), followed by the GFC (63.16%) and lastly the US–China trade war (42.09%), respectively. This suggests that the tendency for a systemic risk is highest during the EDC, COVID-19, and GFC periods, and lowest during the US–China trade war. The financial sector was the primary net-transmitter of shocks during the COVID-19 period, while the automobile and parts sector was the strongest net-transmitter of shocks during the GFC, EDC, and US–China trade war. Similarly, the strongest net recipient of shocks during GFC, EDC, and COVID-19 is the chemical super sector. The study concludes that there is a significant volatility connectedness among JSE super sectors. In addition, the JSE super sectors exhibit time-varying connectedness during extreme events. Moreover, the net-transmitter and net-receiver of shock do not change significantly during different crisis periods. The policy implications of the findings are highlighted in the concluding section. Full article

This paper develops and implements an Autoregressive Integrated Moving Average model with an Adaptive Neuro-Fuzzy Inference System (ARIMA-ANFIS) for BTCUSD price prediction and risk assessment. The goal of these forecasts is to identify patterns from past data and achieve an understanding of the future behavior of the price and its volatility. The proposed ARIMA-ANFIS model is compared with a benchmark ARIMA-GARCH model. To evaluated the adequacy of the models in terms of risk assessment, we compare the confidence intervals of the price and accuracy measures for the testing sample. Additionally, we implement the diebold and Mariano test to compare the accuracy of the two volatility forecasts. The results revealed that each volatility model focuses on different aspects of the data dynamics. The ANFIS model, while effective in certain scenarios, may expose one to unexpected risks due to its underestimation of volatility during turbulent periods. On the other hand, the GARCH(1,1) model, by producing higher volatility estimates, may lead to excessive caution, potentially reducing returns. Full article

This paper explores the influence of the risk–return relationship and volatility spillover on stock market returns of emerging economies, with a particular focus on the BRICS countries. This research is undertaken in a context where discussions on de-dollarization and the expansion of BRICS membership are gaining momentum, making it a novel and distinct exercise compared to prior studies. Utilizing econometric techniques to investigate daily market returns from 1 April 2008 to 31 March 2023, a period that witnessed major events like the global financial crisis, the COVID-19 pandemic, and the Russia–Ukraine conflict, linear and non-linear models like ARCH, GARCH, GARCH-M, EGARCH, and TGARCH, are employed to assess stock return volatility behaviour, assuming a Gaussian distribution of error terms. The diagnostic test confirms that the distribution is non-normal, stationary, and heteroscedastic. The key findings indicate a lack of the risk–return relationship across all BRICS stock markets, except for South Africa; a more pronounced effect of unpleasant news over pleasant news; a slow mean-reverting process in volatility; the EGARCH model is the best fit model as evidenced by a higher log likelihood and lower Akaike information criterion and Schwardz information criterion parameters; and finally, the presence of significant bidirectional and unidirectional spillover effects in the majority of instances. These findings are valuable for investors, regulators, and policymakers in enhancing returns and mitigating risk through portfolio diversification and informed decision making. Full article

This paper aims to leverage Bayesian nonlinear expectations to construct Bayesian lower and upper estimates for prices of Ether options, that is, options written on Ethereum, with conditional heteroscedasticity and model uncertainty. Specifically, a discrete-time generalized conditional autoregressive heteroscedastic (GARCH) model is used to incorporate conditional heteroscedasticity in the logarithmic returns of Ethereum, and Bayesian nonlinear expectations are adopted to introduce model uncertainty, or ambiguity, about the conditional mean and volatility of the logarithmic returns of Ethereum. Extended Girsanov’s principle is employed to change probability measures for introducing a family of alternative GARCH models and their risk-neutral counterparts. The Bayesian credible intervals for “uncertain” drift and volatility parameters obtained from conjugate priors and residuals obtained from the estimated GARCH model are used to construct Bayesian superlinear and sublinear expectations giving the Bayesian lower and upper estimates for the price of an Ether option, respectively. Empirical and simulation studies are provided using real data on Ethereum in AUD. Comparisons with a model incorporating conditional heteroscedasticity only and a model capturing ambiguity only are presented. Full article

In this study, we explore the financial and economic integration of BRICS nations (Brazil, Russia, India, China, and South Africa) and key emerging economies (Egypt, Saudi Arabia, and the UAE) using graph theory, aiming to map intersectoral connections and their impact on financial stability and market risk. The research addresses a critical gap in the literature; while political and economic linkages between nations have been widely studied, the specific connectivity between sectors within these economies remains underexplored. Our methodology utilizes eigenvector centrality and Euclidean distance to construct a comprehensive network of 106 publicly listed firms from 2013 to 2022, across sectors such as energy, telecommunications, retail, and technology. The primary hypothesis is that sectors with higher centrality scores—indicative of their interconnectedness within the broader financial network—demonstrate greater resilience to market volatility and contribute disproportionately to sectoral profitability. The analysis yielded several key insights. For instance, BHARTI AIRTEL LIMITED in telecommunications exhibited an eigenvector centrality score of 0.9615, positioning it as a critical node in maintaining sectoral stability, while AMBEV SA in the retail sector, with a centrality score of 0.9938, emerged as a pivotal player influencing both profitability and risk. Sectors led by companies with high centrality showed a 20% increase in risk-adjusted returns compared to less connected entities, supporting the hypothesis that central firms act as stabilizers in fluctuating market conditions. The findings underscore the practical implications for policymakers and investors alike. Understanding the structure of these networks allows for more informed decision making in terms of investment strategies and macroeconomic policy. By identifying the central entities within these economic systems, both policymakers and investors can target their efforts more effectively, either to support growth initiatives or to mitigate systemic risks. This study advances the discourse on emerging market integration by providing a quantitative framework to analyze intersectoral connections, offering critical insights into how sectoral dynamics in emerging economies influence global financial trends. Full article

The excess levels of investor participation coupled with irrational behaviour in the South African bond market causes excess volatility, which in turn exposes investors to losses. Consequently, the study aims to examine the effect of market-wide investor sentiment on government bond index returns of varying maturities under changing market conditions. This study constructs a new market-wide investor sentiment index for South Africa and uses the two-state Markov regime-switching model for the sample period 2007/03 to 2024/01. The findings illustrate that the effect investor sentiment has on government bond indices returns of varying maturities is regime-specific and time-varying. For instance, the 1–3-year government index return and the over-12-year government bond index were negatively affected by investor sentiment in a bull market condition and not in a bear market condition. Moreover, the bullish market condition prevailed among the returns of selected government bond indices of varying maturities. The findings suggest that the government bond market is adaptive, as proposed by AMH, and contains alternating efficiencies. The study contributes to the emerging market literature, which is limited. That being said, it uses market-wide investor sentiment as a tool to make pronunciations on asset selection, portfolio formulation, and portfolio diversification, which assists in limiting investor losses. Moreover, the findings of the study contribute to settling the debate surrounding the efficiency of bond markets and the effect between market-wide sentiment and bond index returns in South Africa. That being said, it is nonlinear, which is a better modelled using nonlinear models and alternates with market conditions, making the government bond market adaptive. Full article

Different refund policies offered by e-commerce platforms provide diverse options for consumers and are crucial for enhancing after-sales service. This study constructs a refund and ordering decision model based on three typical refund policies: both basic refund and refund guarantee option (‘Policy I’), basic refund only (‘Policy II’), and refund guarantee option only (‘Policy III’). We examine scenarios where demand is influenced by price, refund policies, and stochastic factors, and returns are affected by refund policies, aiming to determine the optimal refund and ordering decisions for fresh produce e-commerce platforms. Our results indicate that, under the same parameters, the platform achieves the maximum order quantity and highest expected profit with Policy I. The return rate under Policy I is always higher than under Policy III, but not consistently higher than under Policy II. Additionally, as the sensitivity of demand to the refund policy increases, both the order quantity and basic refund price rise, while the refund guarantee option price decreases. Conversely, as the sensitivity of returns to the refund policy increases, the opposite occurs. Although market demand uncertainty does not impact the basic refund or refund guarantee option prices, the platform must increase order quantities to manage market volatility. Full article

Efficient sizing and economic analysis of community battery systems is crucial for enhancing energy efficiency and sustainability in rooftop PV panel-rich communities. This paper proposes a comprehensive model that integrates key technical and economic factors to optimize the size and operation of the prosumer-owned battery, maximizing the financial returns over the life span of the battery. Sensitivity and uncertainty analyses were also conducted on a number of factors that are constantly changing over the years such as per-unit cost of the battery and interest rate. Monte Carlo simulations were utilized to replicate the unpredictable PV generations and the volatility of house load demands. The developed model is evaluated under three scenarios: a shared community battery for all houses, individual batteries for each house, and a combined system with an additional large load. Particle Swarm Optimization (PSO) is utilized to maximize the formulated objective function subject to the considered constraints. The findings indicate that integrating community batteries offered a substantial economic advantage compared to individual home batteries. The additional revenue stream of incorporating larger consumers looking to reduce their carbon footprint (e.g., commercial) returned a further augmented net present value (NPV). The influence of different tariff structures was also assessed and it was found that critical peak pricing (CPP) was the most prolific. The outcomes offer valuable insights for policymakers and stakeholders in the energy sector to facilitate a more sustainable future. Full article

We employ wavelet analysis using the maximum overlap discrete wavelet transform (MODWT) to examine the return and volatility interconnectedness between the German equity market (a prominent representative of the Eurozone market) and the BRICS countries over the period 2005–2017. Specifically, we investigate the presence of the pure form of financial contagion in the stock markets of Brazil, Russia, India, China, and South Africa subsequent to the Eurozone Sovereign Debt Crisis (EZDC). Our results indicate the presence of financial contagion between the Eurozone equity market and its counterparts in South Africa and Russia, characterised by co-movement and volatility spillover effects. This contagion is particularly evident at higher frequencies, suggesting that the transmission of shocks occurs rapidly across these markets in the short term. No financial contagion is observed in the Brazilian, Chinese, and Indian stock markets during the European Sovereign Debt Crisis. The absence of financial contagion observed in these three BRICS countries during the European Sovereign Debt Crisis suggests that policymakers in these countries should prioritise addressing idiosyncratic shock channels. Full article

In this article, I develop a critique of Chantal Mouffe’s leftist populism and its construction of ‘the people’ against an opposed ‘them’, from a perspective informed by the thought of Didier Eribon. I draw on both his public interventions and his theoretical work, employing his concepts of return, society as verdict, and his two principles of critical thinking to question the desirability of crafting ‘the people’ in the first place. I contend that Eribon’s critique renders Mouffe’s proposal problematic on three accounts. First, her approach is too politically volatile; its instability leaves it devoid of a critical analysis of the differences between concrete social positions, struggles, and subjectivities within ‘the people’. Consequently, the political becomes merely a function of the social. Yet, the social and its determining power remain mostly unaddressed by her framework. Second, its simplistic opposition of an overly generalised ‘the people’ against ‘the oligarchy’ is susceptible to right-wing populist appropriations. Third, for a shot at hegemony and a general appeal, it eclipses plurality and dissensus within ‘the people’. In contrast, Eribon encourages a connection between the social and the political by suggesting that a self-critical analysis be mutually intertwined with social analysis. Instead of merely mobilising affects, they must be critically interrogated. Instead of summoning ‘the people’, a return to their respective genesis must be attempted. Unless both principles of critical thinking, the insights of return, and societal verdicts are deployed to come to terms with the social determinisms at hand, the ‘people’s’ mobilisation against an opposed ‘them’ risks sacrificing pluralism and equality alike and neglecting the criteria of the desirability of specific changes in favour of a “whatever it costs” attempt at hegemony. Full article

This study rigorously investigates the impact of COVID-19 on Tunisian stock market volatility. The investigation spans from January 2020 to December 2022, employing a GJR-GARCH model, bias-corrected wavelet analysis, and an ARDL approach. Specific variables related to health measures and government interventions are incorporated. The findings highlight that confirmed and death cases contribute significantly to the escalation in TUNINDEX volatility when using both the conditional variance and the realized volatility. Interestingly, aggregate indices related to government interventions exhibit substantial impacts on the realized volatility, indicating a relative resilience of the Tunisian stock market amidst the challenges posed by COVID-19. However, the application of the bias-corrected wavelet analysis yields more subtle outcomes in terms of the correlations of both measures of volatility to the same metrics. Our econometric implications bear on the application of such a technique, as well as on the use of the realized volatility as an accurate measure of the “true” value of volatility. Nevertheless, the measures and actions undertaken by the authorities do not exclude fear and insecurity from investors due to another virus or any other crisis. The positive and long-term impact on the volatility of US equity market uncertainty, VIX, economic policy uncertainty (EPU), and the infectious disease EMV tracker (IDEMV) is obvious through the autoregressive distributed lag model (ARDL). A potential vulnerability of the Tunisian stock market to future shocks is not excluded. Government and stock market authorities should grapple with economic and financial fallout and always instill investor confidence. Importantly, our results put mechanisms such as overreaction to public news and (in)efficient use of information under test. Questioning the accuracy of announcements is then recommended. Full article

Stock market indices are important tools to measure and compare stock market performance. The Selective Stock Price (SSP) index reflects fluctuations in a set value of financial instruments of Santiago de Chile’s stock exchange. Stock indices also reflect volatility linked to high uncertainty or potential investment risk. However, economic shocks are altering volatility. Evidence of long memory in SSP time series also exists, which implies long-term persistence. In this paper, we studied the volatility of SSP time series from January 2010 to September 2023 using fractionally heteroskedastic models. We considered the Autoregressive Fractionally Integrated Moving Average (ARFIMA) process with Generalized Autoregressive Conditional Heteroskedasticity (GARCH) innovations—the ARFIMA-GARCH model—for SSP log returns, and the fractionally integrated GARCH, or FIGARCH model, was compared with a classical GARCH one. The results show that the ARFIMA-GARCH model performs best in terms of volatility fit and predictive quality. This model allows us to obtain a better understanding of the observed volatility and its behavior, which contributes to more effective investment risk management in the stock market. Moreover, the proposed model detects the influence volatility increments of the SSP index linked to external factors that impact the economic outlook, such as China’s economic slowdown in 2012 and the subprime crisis in 2008. Full article

This research examines the impact of the COVID-19 pandemic on the volatility behavior of Amman Stock Exchange (ASE) returns using ARMA–GARCH-type models for three sub-periods: pre-COVID-19, during COVID-19, and post-COVID-19. The research finds that volatility persistence is significant across all periods, with the pandemic period showing the highest impact of shocks. Bad news has no statistically significant impact on volatility in the pre-COVID-19 period or during the pandemic, while in the post-pandemic period, good news significantly influences volatility. Additionally, there exist notable changes in the autocorrelation and the shock structure of the AR and MA components. Considering these alterations in the asymmetric effects, the AR and MA components suggest significant shifts in market dynamics, investor sentiments, and economic policies in response to pandemic experiences. Full article