Effect Of Volatility Research Articles

Oil price uncertainty and capital structure dynamics

Purpose The literature is remarkably silent on questions concerning the nexus between firms’ capital structure adjustment speed and the uncertainty of oil prices. This study aims to examine how oil price fluctuations affect a firm’s capital structure and adjustment speed. Design/methodology/approach This research focuses on a set of corporations from the Gulf Cooperation Council spanning from 2011 to 2022. The methods applied are panel fixed effects and dynamic two-step system Generalized Method of Moments models. Findings The findings indicate that changes in oil prices significantly impact corporate capital structure. Specifically, high oil price volatility leads to a reduction in both market leverage and book leverage. In addition, increased volatility in oil prices results in higher costs for leverage adjustment speed, which subsequently influences how quickly companies move toward their optimal leverage ratio. It is observed that when there is an increase in oil price volatility, firms adjust their leverage more slowly. At the same time, they do so more rapidly during phases of lower oil price volatility. Practical implications The findings of this study hold significant implications for corporate managers, investors and lenders. The observed negative relationship between oil price uncertainty and leverage suggests that corporate managers may benefit from prioritizing equity financing over debt during periods of heightened oil price volatility. In addition, managers should integrate oil price uncertainty into their liquidity management strategies by maintaining sufficient cash reserves. This proactive approach can help mitigate the challenges posed by reduced access to external debt financing during volatile periods. Furthermore, understanding the influence of oil price fluctuations on firms’ cost of debt is crucial, as it directly impacts firms’ adjustment costs in achieving their optimal capital structures. Creditors, too, should consider the adverse effects of oil price volatility on corporate financing when designing credit policies, ensuring they remain responsive to the financial constraints faced by firms under such conditions. Originality/value At best, this study presents new evidence that sheds light on the nexus between oil price volatility and the speed at which firms adjust their leverage toward the trade-off theory’s optimal target.

Europe's extra-territorial mineral trade and clean energy metamorphosis in a landscape of multifaceted risks.

Despite modest reserves of energy transition minerals (ETMs) like copper, nickel, and aluminium, European nations largely depend on imports, a dynamic often marred by financial uncertainties, price volatility, and geopolitical tensions. The purpose of this study is to investigate the response of extra-territorial ETM imports to the clean energy objectives of 24 European nations, taking into account financial stress, mineral price volatility, and geopolitical instability. We analyse monthly data from January 2005 to October 2022, using the quantiles via moments (QvM) and the linear models multiway fixed effects (LMFE) approaches to account for heterogeneity, spatial dependence, heteroscedasticity, and endogeneity in the data. Our results indicate that during flourishing market conditions, Europe's extra-territorial mineral imports are positively associated with the clean energy transition. Financial stress, however, negatively affects these imports across various market conditions. Moreover, the EU's mineral imports are adversely influenced by both individual and combined effects of financial stress and mineral price volatility, whereas the impacts of geopolitical risk velocity and financial stress are minimal. Using the LMFE method, we find the influence of geopolitical risk intensification to be insignificant on its own, but when combined with financial stress, it has a negative effect, particularly when addressing endogeneity. Hence, our conclusion highlights the adverse effects of multifarious risk factors on Europe's mineral trade, necessitating the efficient allocation of financial resources and strategic risk management to expedite the region's energy transition.

Analyzing Shock Transmission and Spillover Effect in the Day-Ahead and Intraday Markets: Key Implications for Price Forecasting

Abstract The increasing capacity in renewables (RES) and batteries in the Romanian power system stirred discussions on increasing interdependency between day-ahead market (DAM) and intraday continuous (IDC) market. In this paper, we analyze hourly market-cleared prices and selling and buying quantities in the DAM and IDC over 3 months (4th of June to 11th of September 2024) to verify the connectedness between prices of the two markets, possible shock transmission and spillover effects. We employ generalized autoregressive conditional heteroskedasticity (GARCH) models to examine volatilities. The Diebold–Yilmaz spillover index is 95.69%, demonstrating an extremely high degree of volatility interdependence. This suggests that nearly all volatility in both markets is driven by cross-market shocks. Price shocks in the DAM initially raise IDC prices by 0.3 units, with this effect fading after 8 to 10 periods. Conversely, IDC shocks similarly raise DAM prices by 0.3 units, but both effects are temporary, indicating a short-term dynamic link between the two markets. Moreover, numerical results from the BEKK-GARCH model highlight significant volatility spillovers between Price_DAM and Price_IDC, with stronger cross-market volatility effects and evidence of interdependence, particularly from Price_IDC to Price_DAM. The findings suggest implications for market players to create bidding strategies and decision-makers to mitigate price volatility connectedness in DAM and IDC. Furthermore, our findings underscore the critical role of knowledge creation and diffusion in understanding the interconnected dynamics of electricity markets. By leveraging advanced econometric tools and spillover analysis, our research contributes to the body of knowledge essential for innovation in market strategies and decision-making processes.

Analysis of The Effect of Volatility, Uncertainty, Complexity, and Ambiguity on The Performance of Culinary Msmes in Enrekang Regency

The purpose of this study was to determine and analyze the effect of Volatility, Uncertainty, Complexity, and Ambiguity on the Performance of Culinary MSMEs in Enrekang Regency. The population in this study were culinary MSME players, with data collection techniques through observation, questionnaires and documentation. The data analysis technique uses multiple linear regression analysis. From the results of the research conducted, empirical findings were obtained that volatility has a positive and significant effect on the performance of culinary MSMEs. Uncertainty has a positive and insignificant effect on the performance of culinary MSMEs. Complexity has a positive and significant effect on the performance of culinary MSMEs, and ambiguity has a negative and significant effect on the performance of culinary MSMEs in Enrekang Regency.

Impact of Exchange Rate Volatility on Sectoral Output in Liberia

This paper investigates the effects of exchange rate volatility on sectoral output in Liberia, focusing on agriculture, mining, and manufacturing. Using monthly time series data ranging from 2010 to 2022 and estimating the volatility in exchange rate using the ARCH model, the study found a strong sector-specific impact along with an overall economic impact on output with a significant coefficient of (-2.868) at 10%, 5% and 1% level of significance .We found that the agriculture with a coefficient of (-0.2127) and manufacturing sectors (-0.5171) proxy by value-added as % of GDP are negatively affected by exchange rate fluctuations due to their heavy reliance on imported raw material, which increases production costs and reduces output. Conversely, the mining sector, particularly gold production as measured by key output volume, shows a positive relationship with exchange rate volatility with a coefficient of (3.9677) significant at 10% level of significance, as global demand for gold rises in response to currency instability. The findings underline that policy interventions are needed to stabilize the exchange rate, particularly for agriculture and manufacturing. In fact, given the dual currency system of the country and the high level of dollarization, the country needs targeted policies to enhance sectoral resilience in its way to attain economic growth.

Cryptocurrencies fall from grace: Snatched safe flight to which haven during Russia-Ukraine conflict?

The unprecedented upheaval in an economic and financial system facilitates herding behaviour and flight-to-safety (FTS) episodes from a riskier asset into a safer one. The Russia-Ukraine conflict shows that the financial market assets are still prone to external shock. In line with this, the study explores the inconclusive insights on an FTS from cryptocurrencies to US treasury securities. The paper employs dynamic conditional correlation – generalized autoregressive conditional heteroskedasticity (DCC-GARCH) to test the FTS episode for a period from February 24, 2022 to February 23, 2023. The findings hold a sizeable negative and significant volatility coefficient, particularly DCCα, which directs to support the notion of short-lived FTS from cryptocurrencies to treasury securities during the invasion period. Nevertheless, some evidence of the positive volatility effect points out the risk diversification benefits. The results also show flight-to-quality from BTC, ETH, USDT, BNB, ADA and MATIC to the US dollar index (USDX), however, for other cryptocurrencies, it acts as a diversifier. We unfolded several implications that could be interesting for a market participant looking for evidence on the behaviour of cryptocurrencies and govt. backed securities during times of market uncertainty in the future.

The asymmetric effects of cross-border equity flow volatility on equity market returns in SANEK countries

The asymmetric effects of cross-border equity flow volatility on equity market returns in SANEK countries

Volatility dynamics of stock returns, liquidity and exchange rates in ASEAN Countries

In this study, we examined the volatility trend of stock return in eight ASEAN stock markets. These includes the Singapore Exchange (SGX), Bursa Malaysia Stock Exchange (YSX), the Stock Exchange of Thailand (SET), Indonesia stock exchange, the Vietnam Stock Exchange (VNX), the Cambodia Securities Exchange (CSX), the Lao Securities Exchange (LSX), and the Philippine Stock Exchange. Secondly, we evaluated the factors that influence the level of return in those stock markets with exchange rate volatility as a control variable. By employing FIGARCH-DCC and ARDL models, the study aimed to provide a more robust understanding of stock market dynamics. The findings reveal significant negative returns effect of market volatilities and liquidity crisis in all the stock exchanges of all sample countries in the study. In Singapore, money supply variation, market volatility, liquidity risks, and exchange rate volatility significantly influenced stock returns positively. The short-run model explains 52.26% of the variation in stock returns. Only in Malaysia, we had significant positive returns from exchange rate volatility. Nevertheless, the Russian model explains just 22.22% of the variation in stock returns. In Thailand and Indonesia alike, returns significantly and positively responded to variation in money supply, while the volatility in the market and currency rate exchange adversely impacted returns. The short-run models explain 53.66% and 65.21% of the variation in stock returns for Vietnam and Indonesia, respectively. The variation in money supply does not significantly affect stock returns and has no significant contribution to returns in Cambodia. The Cambodia model explains around 48.34% of the variation in returns. For Lao Stock Exchange, return effects of liquidity risk, and exchange rate instability were significant and negative. Market volatility had insignificantly impacted stock returns in Nigeria. The Lao model explains 50.38% of the variation in stock returns. In the Philippine Stock Exchange, the returns effect of exchange rate volatility and liquidity crisis are adverse and significant. Money supply variation and market volatility had insignificant influence on returns. The model explains 68.11% of the variation in returns. In the Philippines, market volatility, liquidity risks, and exchange rate volatility adversely impacted returns. Money supply variation had no such significant influence on returns. The panel model of the Philippines explains 62.9% of the variation in stock returns. The research accentuates the need for governments to stabilize exchange rates, boost liquidity, through targeted policies aimed at managing stock market dynamics especially as it relates to stock volatility in order to foster meaningful growth and development of the financial market.

Conditional threshold effects of stock market volatility on crude oil market volatility

Conditional threshold effects of stock market volatility on crude oil market volatility

Volatility, correlation and risk spillover effect between freight rates in BCI and BPI markets: Evidence from static and dynamic GARCH-Copula and dynamic CoVaR models.

The dry bulk shipping market plays a crucial role in global trade. To examine the volatility, correlation, and risk spillover between freight rates in the BCI and BPI markets, this paper employs the GARCH-Copula-CoVaR model. We analyze the dynamic behavior of the secondary market freight index for dry bulk cargo, highlighting its performance in a complex financial environment and offering empirical support for the shipping industry and financial markets. The findings reveal that: (1) There are significant differences in correlation across various routes, with the correlation between BCI and BPI routes fluctuating over time. Among all route combinations, C5 and P3A_03 exhibit the highest positive correlation. (2) A one-way risk spillover exists between P1A_03 an C5, while two-way positive risk spillover is observed between other routes. This suggests that when a risk materializes on a specific route, other routes are also exposed to potential risks, with varying intensities of spillover. (3) The distance and geographical location of routes may be key factors influencing the differing intensities of risk spillover. This highlights the need to consider the geographical characteristics of routes in understanding risk transmission. This paper aims to provide risk management strategies based on these empirical findings, assisting shipping companies and investors in developing more effective responses to market volatility.

FOOD PRICE VOLATILITY AND ITS ECONOMIC EFFECTS ON RURAL LABOR MARKETS BY PRODUCT TYPES

Food price volatility is a key concern in global agricultural markets, influencing economic outcomes in both urban and rural areas. In rural labor markets, where a significant portion of households depends on agriculture, the impacts of food price fluctuations are multifaceted. This paper investigates the effects of food price volatility on rural labor markets, focusing on different agricultural product types. We analyze the direct and indirect consequences on employment, wages, income distribution, and migration patterns. The study highlights that the impacts vary across regions and product categories, with staple crops, cash crops, and livestock products exhibiting distinct economic dynamics. Using both theoretical frameworks and empirical data, the paper offers insights into policy strategies that can mitigate the adverse effects of price volatility, fostering more resilient rural labor markets.

The Effect of Earnings Volatility on Capital Structure: A Case of Oligopolistic Banking Sector

The article investigates the impact of earnings volatility on the South African bank’s capital structure using a sample of registered banks from 2012 to 2021. None of the research on this relationship has examined the oligopolistic market of the banking sector, which stands as a structurally distinctive economic segment. Using the generalized method of moments (GMM) model, the results show a negative and significant effect of earnings volatility on leverage. These findings help finance professionals understand the oligopolistic banking sector’s leverage dynamics and earnings volatility within the context of unexpected economic shocks. Thus, the central banks should require banks to diversify in a bid to minimize earnings variance, thereby providing them with financing flexibility. Bank executives and regulators will find the findings pertinent, providing them with perspectives for making well-informed decisions regarding the capital structure based on the degree of earnings variability.

Analyzing the Effects of Oil Price Volatility on Agricultural Pricing and Outsourcing Dynamics: A Vector Autoregression Approach

The investigation delved into the dynamic interplay between oil price fluctuations and their ramifications on the pricing of agricultural products, employing the Vector Autoregression methodology. The dataset spanned 3 months, commencing from January 2010 and concluding in December 2022. Upon subjecting the data to an in-depth analysis, it was ascertained that it possesses a unit root, indicating an integrated order of one (I [1]), and achieves stationarity subsequent to the first-order differencing. The findings of the inquiry revealed that the paramount driver influencing agricultural product prices is the inherent volatility within the agricultural sector itself. Contrary to initial expectations, the impact of oil price fluctuations on agricultural prices was discerned to be comparatively modest. Intriguingly, the outcomes underscored that the preeminent factor contributing to fluctuations in agricultural product prices is the influence wielded by oil prices. This implies that alterations in oil prices exert a more pronounced effect on the variability in agricultural product prices as opposed to the overall revenue generated from agricultural endeavors.

DeepONet-Inspired Architecture for Efficient Financial Time Series Prediction

Financial time series prediction is a fundamental problem in investment and risk management. Deep learning models, such as multilayer perceptrons, Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM), have been widely used in modeling time series data by incorporating historical information. Among them, LSTM has shown excellent performance in capturing long-term temporal dependencies in time-series data, owing to its enhanced internal memory mechanism. In spite of the success of these models, it is observed that in the presence of sharp changing points, these models fail to perform. To address this problem, we propose, in this article, an innovative financial time series prediction method inspired by the Deep Operator Network (DeepONet) architecture, which uses a combination of transformer architecture and a one-dimensional CNN network for processing feature-based information, followed by an LSTM based network for processing temporal information. It is therefore named the CNN–LSTM–Transformer (CLT) model. It not only incorporates external information to identify latent patterns within the financial data but also excels in capturing their temporal dynamics. The CLT model adapts to evolving market conditions by leveraging diverse deep-learning techniques. This dynamic adaptation of the CLT model plays a pivotal role in navigating abrupt changes in the financial markets. Furthermore, the CLT model improves the long-term prediction accuracy and stability compared with state-of-the-art existing deep learning models and also mitigates adverse effects of market volatility. The experimental results show the feasibility and superiority of the proposed CLT model in terms of prediction accuracy and robustness as compared to existing prediction models. Moreover, we posit that the innovation encapsulated in the proposed DeepONet-inspired CLT model also holds promise for applications beyond the confines of finance, such as remote sensing, data mining, natural language processing, and so on.

The Effects of Exchange Rates Volatility and Inflation on Tanzania’s Trade Balance: Evidence from J-Curve Effect

All economies today are experiencing the volatility of exchange rates and inflation in global markets. Consequently, this affects the trade performance among trading partners globally. This study examines the effects of exchange rate volatility and inflation on Tanzania’s trade balance. Time series data spanning 1990 to 2020 were used from the World Bank and Bank of Tanzania (BOT). Moreover, J-Curve and Marshall Lerner's theories provided the theoretical arguments. Systematically, a Vector Error Correction Model (VECM) was employed for short- and long-run analysis. The findings reveal that, in the long run, inflation, Foreign Direct Investment, and Gross Domestic Product have a positive impact on the trade balance, while the Exchange Rate exerts a negative influence. In the short run, the exchange rate negatively affects Foreign Direct Investment, while Gross Domestic Product has a positive influence. Further, the gross domestic product also positively influences inflation. Furthermore, the exchange rate and gross domestic product significantly affect the trade balance negatively and positively respectively. Analysis of impulse response functions confirms the presence of the J-Curve effect, showing an initial deterioration in the trade balance following a depreciation of the domestic currency, which reverses after three periods. These findings suggest the need for effective exchange rate management, promotion of export competitiveness, and a good environment for foreign direct investment to enhance trade performance in Tanzania

Extended Multivariate EGARCH Model: A Model for Zero‐Return and Negative Spillovers

ABSTRACTThis paper introduces an extended multivariate EGARCH model that overcomes the zero‐return problem and allows for negative news and volatility spillover effects, making it an attractive tool for multivariate volatility modeling. Despite limitations, such as noninvertibility and unclear asymptotic properties of the QML estimator, our Monte Carlo simulations indicate that the standard QML estimator is consistent and asymptotically normal for larger sample sizes (i.e., ). Two empirical examples demonstrate the model's superior performance compared to multivariate GJR‐GARCH and Log‐GARCH models in volatility modeling. The first example analyzes the daily returns of three stocks from the DJ30 index, while the second example investigates volatility spillover effects among the bond, stock, crude oil, and gold markets. Overall, this extended multivariate EGARCH model offers a flexible and comprehensive framework for analyzing multivariate volatility and spillover effects in empirical finance research.

Do geopolitical risks affect stock market returns and volatilities: an analysis based on the TVP-VAR model

This study examines the effects of geopolitical risk (GPR) shocks on stock market returns and volatility across G7, BRICS, and Gulf countries, using a time-varying parameter vector autoregression (TVP-VAR) model. By analyzing responses over short, medium, and long-term horizons, our findings reveal significant variations in how geopolitical risks impact stock markets across different countries and timelines. We observe that GPR-related impacts on stock returns weaken over time, while volatility effects tend to strengthen, suggesting persistent risks for investors in these markets. These insights provide new perspectives for portfolio management and investment strategies during times of geopolitical uncertainty.

Natural gas volatility prediction via a novel combination of GARCH-MIDAS and one-class SVM

Research has focused on whether information spillovers from external influences play a role in clean energy–natural gas volatility forecasts. However, the climate and energy crises caused by the intensification of extreme events, such as recent extreme weather and geopolitical risks, have led the public to turn their attention to research in the field of clean energy. Therefore, this paper uses one-class SVM (support vector machine) techniques to identify extreme volatility in natural gas prices induced by significant occurrences (e.g., wars, financial crises, and COVID-19) and then investigates whether considering extreme volatility in natural gas over different volatile periods (short- and long-term periods) improves volatility forecasting accuracy within the context of a GARCH-MIDAS framework. The in-sample analyses demonstrate that extreme shocks increase natural gas price volatility and that the asymmetric effects are more influential than the short- and long-term extreme volatility effects. The out-of-sample results indicate that the GJR-GARCH-MIDAS-one-class-SVM-SLES model outperforms the other models and achieves the best forecasting performance of the remaining extended models. In addition, robustness tests confirm these findings.

Volatility Spillover Between the Carbon Market and Traditional Energy Market Using the DGC-t-MSV Model

This study employed the dynamic conditional correlation algorithm and incorporated the temporal dynamics of spillover effect to enhance the Multivariate Stochastic Volatility (MSV) model. Consequently, a DGC-t-MSV model (multiple stochastic volatility model of dynamic correlation coefficient with Granger causality test) was constructed to simulate and examine the volatility spillover effects between China’s carbon market and the traditional energy market. The findings reveal the following: (1) A significant spillover effect in price volatility exists between China’s carbon and traditional energy markets, with a notably fluctuating spillover index. The traditional energy market in China exerts a stronger unidirectional volatility spillover effect on the carbon market. Price fluctuations in the traditional energy market impact carbon market prices through mechanisms such as cost transmission and market expectations. (2) In the initial stages, the dynamic correlation between China’s carbon and traditional energy markets showed an overall downward trend, underscoring the positive influence of policy incentives and technological advancements on the growth of alternative energy. A mutual weakening effect exists between the carbon and traditional energy markets. (3) Price fluctuations in China’s carbon and traditional energy markets display a high degree of interdependence and short-term persistence, with evidence of a long memory and significant inertia in these price movements. Integration of the DGC-t-MSV model with the Bayesian approach and the Markov Chain Monte Carlo (MCMC) method and the introduction of a time-varying factor enabled the efficient measurement of the volatility spillover effect between China’s carbon and traditional energy markets.

Pengaruh Tangibility Asset, Size, dan Volatilitas Terhadap Financial Distress Pada Bumn di Bursa Efek Indonesia

This research aims to determine the effect of asset tangibility, size and volatility on financial distress. In estimating financial distress in this research, the Altman Z-score model was used. With a sample size of 27 state-owned companies for 4 years or during COVID-19. The results of the analysis obtained are that the asset tangibility variable states that it has no effect on financial distress. Because, a high asset tangibility value will not necessarily prevent the company from financial distress. The size variable shows that it has no effect on financial distress. Companies that have a large number of assets will not necessarily avoid financial distress or financial difficulties. The volatility variable shows an influence on financial distress. Companies that have low volatility values ​​do not necessarily always experience financial distress or financial difficulties, and vice versa. This is because the volatility value will affect investors who will invest. This research concludes that state-owned companies listed on the Indonesia Stock Exchange for the 2019-2022 period or during the COVID-19 pandemic did not have a significant influence on financial distress. However, during COVID-19, banking and health companies made very rapid profits.