The study described in this paper develops a new technique which permits the execution of an open straddle strategy based on the superior volatility forecast for analyzing historical data. We extend the current litearure by measuring the volatility of an underlying asset in the last predefined period and comparing the actual volatility in currency with historical volatility in currency to make predictions of implied volatility. We calculated stock price volatility through an optimal holding period (OHP) and set up bars of volatility in currency. To obtain this, we solved optimization equations to find maximum and minimum movements in the volatility in currency within the defined range. We placed volatility in currency into percentile rankings and designed a straddle trading strategy based on the last OHP’s volatility in currency. The technique allows for an investor (or trader) to open either short or long positions based on calculations for a selected OHP’s volatility in currency. We applied this strategy to 130 stocks which are traded on CBOE. We developed a trading algorithm which can be used by institutional as well as individual investors. The algorithm is set to determine historical volatility in currency and forecast upcoming volatilities in currency through the understanding of the market sentiment. The empirical findings show that the stocks analyzed with the algorithm generate positive returns along a spectrum of changing volatilities of the underlying assets. © 2024 by the authors.
On the one hand, economies, particularly developing ones, need to grow. On the other hand, climate change is the most pressing issue globally, and nations should take the necessary measures. Such a complex task requires new theoretical and empirical models to capture this complexity and provide new insights. Our study uses a newly developed theoretical framework that involves renewable energy consumption (REC) and total factor productivity (TFP) alongside traditional factors of CO2 emissions. It provides policymakers with border information compared to traditional models, such as the Environmental Kuznets Curve (EKC), being limited to income and population. Advanced panel time series methods are also employed, addressing panel data issues while producing not only pooled but also country-specific results. 20 Renewable Energy Country Attractiveness Index (RECAI) nations are considered in this study. The results show that REC, TFP, and exports reduce CO2 emissions with elasticities of 0.3, 0.4, and 0.3, respectively. Oppositely, income and imports increase emissions with elasticities of 0.8 and 0.3. Additionally, we show that RECAI countries are commonly affected by global and regional factors. Moreover, we find that shocks can create permanent changes in the levels of the factors but only temporary changes in their growth rates. The main policy implication of the findings is that authorities should implement measures boosting TFP and REC. These factors are driven mainly by technological progress, innovation, and efficiency gains. Thus, they can simultaneously reduce emissions while promoting long-run green economic growth, which addresses the complexity mentioned above to some extent. © 2024 The Authors
Most countries have tried to decline fossil fuels dependency by supporting clean energy transition. In light of this, this study investigates the impact of energy security risk (ESR) and geopolitical risk (GPR) on the load capacity factor (LCF) in four fragile countries (Brazil-BRA; India-IND; South Africa-ZAF, Türkiye-TUR). The study applies quantile approaches for the period between 1985/m2 and 2018/m12, which represents the largest amount of accessible data. The results show that (i) at higher quantiles, ESR declines the LCF in IND and ZAF, while it has an increasing impact in BRA and a mixed impact in TUR; (ii) GPR increases the LCF in BRA, ZAF, and TUR at lower and middle quantiles, while GPR decreases ecological quality at higher quantiles in all countries; (iii) ESR and GPR have a causal effect on the LCF across various quantiles; (iv) ESR and GPR are strong predictors of the LCF, but their predictive power varies by quantile and becomes significantly weaker with increasing lags. With these fresh outcomes, the study underlines the significant influence of ESR and GPR in ensuring ecological sustainability across all quantiles and countries. The overall findings of the study emphasize that risks and uncertainties degrade the ecological quality of four fragile countries and that policymakers should turn to clean energy sources in case of an increase in geopolitical and energy risks. © 2024 The Authors
The present study aimed to investigate the presence of asymmetric stochastic volatility and leverage effects within the Nasdaq-100 index. This index is widely regarded as an important indicator for investors. We focused on the nine leading stocks within the index, which are highly popular and hold significant weight in the investment world. These stocks are Netflix, PayPal, Google, Intel, Microsoft, Amazon, Tesla, Apple, and Meta. The study covered the period between 3 January 2017 and 30 January 2023, and we employed the EViews and WinBUGS applications to conduct the analysis. We began by calculating the logarithmic difference to obtain the return series. We then performed a sample test with 100,000 iterations, excluding the first 10,000 samples to eliminate the initial bias of the coefficients. This left us with 90,000 samples for analysis. Using the results of the asymmetric stochastic volatility model, we evaluated both the Nasdaq-100 index as a whole and the volatility persistence, predictability, and correlation levels of individual stocks. This allowed us to evaluate the ability of individual stocks to represent the characteristics of the Nasdaq-100 index. Our findings revealed a dense clustering of volatility, both for the Nasdaq-100 index and the nine individual stocks. We observed that this volatility is continuous but has a predictable impact on variability. Moreover, apart from Intel, all the stocks in the model exhibited both leverage effects and the presence of asymmetric relationships, as did the Nasdaq-100 index. Overall, our results show that the characteristics of stocks in the model are like the volatility characteristic of the Nasdaq-100 index and can represent it. © 2024 by the authors.
Exploring the relationship between international oil prices, income, and carbon dioxide (CO2) emissions in Saudi Arabia, this study examines if renewable energy consumption plays a lowering tool in international oil prices' impact on CO2 emissions, employing conventional econometric methods and the functional coefficient approach. The study reveals that the interaction between renewable energy consumption and international oil prices has a negative and statistically significant impact on CO2 emissions. This emphasizes the potential for Saudi Arabia to reduce carbon emissions by prioritizing renewable energy projects. In addition, a positive and statistically significant relationship between income and CO2 emissions is found, emphasizing the need to decouple economic growth from emissions growth. Furthermore, an interesting decoupling effect between oil price elasticity of CO2 emissions and per capita GDP is noted from the early 2000s–2015. This indicates that economic growth driven by rising oil prices can be managed to mitigate environmental impact, showcasing Saudi Arabia's commitment to sustainable development. Policy recommendations involve intensifying efforts to promote renewable energy implementation, lowering fossil fuel dependence in power generation, and incentivizing emissions reduction for a more sustainable energy future. © The Author(s) 2024.