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This manuscript, for the first time, analyses the volatility spillover of oil price uncertainty in the world using data from oil price uncertainty recently developed by Abdul and Qureshi (2023), spanning the time 1996-2019 on a monthly frequency. ARCH/GARCH (Autoregressive Conditional Heteroskedasticity and Generalized Autoregressive Conditional Heteroskedasticity) models are employed as an econometric tool. The findings suggest that ARCH model is more consistent than GARCH model in assessing the volatility of oil price uncertainty in the world. The results show that the volatility of oil price uncertainty is high in the world. The transition to renewable energy sources is proposed as a way to resist unexpected oil shocks since the production of renewables does not depend on the fluctuations of oil prices. Consequently, uncertainties in the oil price do not hinder economic activities.

In an era dominated by increasing global challenges and market volatilities, this study, firstly, embarks on an in-depth exploration of volatility transmission across clean energy stocks, crude oil and financial markets, emphasizing the underlying currents of geopolitical tensions. By using the advanced Multivariate Dynamic Conditional Correlation (MV-DCC) GARCH model, we unravel a landscape where volatility spillovers exhibit a distinct bidirectional nature, and geopolitical risk exerts a substantial impact, cascading from the oil market to financial markets and ultimately to clean energy stocks. Our findings underline the strategic importance of overweighting clean energy assets in a dual-asset portfolio that includes oil and financial equities to enhance investment strategies in turbulent market conditions. Secondly, we investigate the predictive power of oil and market-implied volatilities in forecasting clean energy market volatility by introducing a novel approach that melds the robustness of GARCH models with the flexibility of Long Short-Term Memory (LSTM) networks, creating an innovative hybrid GARCH-LSTM framework. The empirical results demonstrate that this hybrid model significantly outstrips the predictive capabilities of traditional standalone models. Notably, while oil and market-implied volatilities substantially enhance prediction accuracy, the inclusion of historical data does not yield additional predictive value. The implications of our research extend beyond the analytical domain, resonating with financial practitioners and environmentally conscious investors who seek precision in valuation and foresight in market trends. For policymakers, the insights provided offer strategic guidance for developing robust clean energy policies. Overall, our research contributes a fresh perspective to the discourse on renewable energy investment, volatility forecasting, and the interplay between market dynamics and geopolitical risks.

The return of its stock exchange and the companies traded within are one of the important indicators for a national economy. Due to the global structure of stock markets, returns are closely related to both national and international market variables. This study makes a comparative analysis of the volatility structures of the energy companies traded in the Kazakhstan Stock Exchange (KASE) and the combined stock market index and gold and oil prices in international markets for the period between January 01, 2021, and June 31, 2023. The research focused on two issues. The first is the analysis of the volatility structure of the six series examined. For this purpose, four different models were examined. The second focus is to determine whether the returns in international indices have a causal effect on the Kazakhstan stock market (composite stock market index) and the returns of oil and energy companies traded in the stock market. The results revealed that other indices and returns have a similar variable variance structure, except for the KASE. The relevant coefficient estimation was found to be significant in both conditional standard deviation models for the KASE index. The coefficient estimate of the GARCH-M(1,1) model in the OIL index was significant, whereas conditional standard deviation models and the relevant coefficients of both conditional standard deviation models were found to be statistically insignificant in the other returns. This is an indication of the structural compatibility of Kazakhstan's stock market composite index and energy and oil companies with international markets. Furthermore, the causality analysis results showing that international indices have a causal effect on KASE and KZAP is another indicator that the Kazakhstan market works in harmony with the international markets.

This study examines the market for green bonds, which have been in the spotlight as an eco-friendly investment product. We analyze the volatility dynamics and spillovers between the equity and green bond markets. As the return dynamics of financial products typically exhibit asymmetric volatility, we check whether green bonds also share this property. Our analyses confirm that although green bonds do exhibit the asymmetric volatility phenomenon, their volatility, unlike that of equity, is also sensitive to positive return shocks. An analysis of the association between the green bond and equity markets confirms that although the two markets have some volatility spillover effects, neither responds significantly to negative shocks in the other market.

The main objective of this paper is to investigate the spillover effect of global crude oil prices on Indian metal market using dynamic conditional correlation generalized autoregressive conditional heteroskedasticity models. The study considers Indian metal market, Multi Commodity Exchange of India Limited METAL index and two precious metals gold and silver, and three industrial metals aluminium, copper and zinc over the period from 1 June 2006 to 31 March 2017. The results of the study show moderate co-movement between West Texas Intermediate (WTI) crude oil and Indian metal market. Precious metals gold and silver do not show either upward nor downward trend even in global financial crisis 2008–2009 while industrial metals aluminium, copper and zinc are weakly correlated to crude oil prices. In addition, it is found that global crude oil prices have short-term as well as long-term memory effect on Indian metal market and metal prices. The study presents the case for diverse stakeholders to improve strategic oil reserves for stabilizing oil prices during global turmoil. Also, policy makers and practitioners may draw meaningful conclusions from findings of the present study to improve future market for stabilizing spot prices of metals while operating in Indian metal markets.

The behavior of the Bitcoin market is dynamic and erratic, impacted by a range of elements including news developments and investor mood. One well-known aspect of bitcoin is its extreme volatility. This study uses both conventional econometric techniques and deep learning algorithms to anticipate the volatility of Bitcoin returns. The research is based on historical Bitcoin price data spanning October 2014 to February 2022, which was obtained using the Yahoo Finance API. In this work, we contrast the efficacy of generalized autoregressive conditional heteroskedasticity (GARCH) and threshold ARCH (TARCH) models with long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and multivariate Bi-LSTM models. Model effectiveness is evaluated by means of root mean squared error (RMSE) and root mean squared percentage error (RMSPE) scores. The multivariate Bi-LSTM model emerges as mostly effective, achieving an RMSE score of 0.0425 and an RMSPE score of 0.1106. This comparative scrutiny contributes to understanding the dynamics of Bitcoin volatility prediction, offering insights that can inform investment strategies and risk management practices in this quickly changing environment of finance.

This study investigates the volatility dynamics and time-varying correlations between Bitcoin (BTC) and major financial and commodity markets, including gold, oil, NASDAQ, NIKKEI, FTSE, DAX, and the U.S. Dollar Index (USDINX). Using daily data and GARCH-family models, we quantify persistence, asymmetry, and mediumterm memory in BTC volatility. Model selection using  loglikelihood, SIC, and AIC criteria identifies EGARCH as the best model for capturing conditional variance  behavior. We then employ a DCC-MGARCH framework to estimate evolving cross-market correlations. Results indicate that BTC volatility is highly persistent, exhibits stronger reactions to negative shocks, and shows moderate mean reversion. Gold displays the lowest persistence,  confirming its role as a stable diversifier. DCC-MGARCH estimates reveal weak positive BTC-Gold correlations, negative BTC-USDINX correlations, and no significant BTC-Oil or BTC-DAX linkages, implying substantial  diversification potential. Notably, BTC-NIKKEI correlations strengthened during the COVID-19 period, while BTC-Gold correlations modestly increased. These findings  underscore the importance of dynamic portfolio strategies, as optimal weights shift in response to evolving conditional covariances, rendering static allocations suboptimal. For policymakers, volatility persistence and correlation thresholds can inform leverage and exposure limits, particularly when the linkages between BTC and traditional assets intensify.

his study aims to develop a predictive model for stock prices using time-series analysis. The primary objective is to identify volatility patterns through the implementation of the GARCH model and forecast future stock prices for Microsoft company utilizing the ARIMA model based on historical data. The findings of this study contribute to the literature on stock price forecasting and provide insights for investors in making informed investment decisions. Moreover, the effectiveness of the proposed methodology is assessed through a comprehensive set of tests, indicating highly positive results when compared to other similar approaches.

Seasonality and volatility of vegetation in the ecosystem are associated with climatic sensitivity, which can have severe consequences for the environment as well as on the social and economic well-being of the nation. Monitoring and forecasting vegetation growth patterns in ecosystems significantly rely on remotely sensed vegetation indices, such as Normalized Difference Vegetation Index (NDVI). A novel integration of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and the Holt-Winters (H-W) models was used to simulate the seasonality and volatility of the three different agro-climatic zones in Jharkhand, India: the central north-eastern, eastern, and south-eastern agro-climatic zones. MODIS Terra Vegetation Indices NDVI data MOD13Q1, from 2001 to 2021, was used to create NDVI time series volatility and seasonality modeled by the GARCH and the H-W models, respectively. GARCH-based Exponential GARCH (EGARCH) [1,1] and Standard GARCH (SGARCH) [1,1] models were used to check the volatility of vegetation growth in three different agro-climatic zones of Jharkhand. The SGARCH [1,1] and EGARCH [1,1] models for the western agro-climatic zone experienced the best indicator as it has maximum likelihood and minimal Schwarz-Bayesian criterion and Akaike information criterion. The seasonality results showed that the additive H-W model showed better results in the eastern agro-climatic zone with the optimized values of MAE (16.49), MAPE (0.49), NSE (0.86), RMSE (0.49), and R 2 (0.82) followed by the south-eastern and central north-eastern agro-climatic zones. By utilizing the H-W and GARCH models, the finding demonstrates that vegetation orientation and monitoring seasonality can be predicted using NDVI.

This study aims to examine the impact of COVID-19 on stock return volatility in 15 countries worldwide. Using daily data from January 2019 to June 2020, we find that changes in exchange rates have negatively affected stock returns in most countries. We also identify structural changes over the observation period; these structural changes occur not just after the first case of COVID-19 but also earlier in the period. Based on threshold generalized autoregressive conditional heteroskedasticity regressions, we find evidence that the emergence of COVID-19 affected stock return volatility in all observed countries except the United Kingdom. Furthermore, we find that the presence of COVID-19 in a country positively affects return volatility. However, the magnitude of this effect is small in every observed country. This finding suggests the need for in-depth studies of other factors that affect stock return volatility besides the occurrence of COVID-19.