Effect Of Volatility Research Articles

A decision-making framework for the funding of shipping decarbonization initiatives in non-EU countries: insights from Türkiye

The decarbonization of the shipping industry is a critical imperative in the global fight against climate change. Non-EU countries, being significant contributors to shipping emissions, play a crucial role in shaping the industry's sustainable future. However, securing funding for shipping decarbonization initiatives in these countries presents challenges, such as limited access to capital, lack of financial initiatives, political and regulatory uncertainties, technological risks, lack of local expertise, and the effects of global economic volatility. Addressing these challenges demands innovative strategies. The paper explores ways of effectively allocating funds for decarbonization projects in the shipping industry of non-EU countries, Türkiye in particular, using a structured decision-making framework tailored to their specific needs and challenges. We adopt the Moment Integrated Solution Method (THEMIS) to identify the “best” option from the range of alternative strategies identified in the framework. Our findings show that the use of incentive mechanisms is the most prioritized funding alternative, followed by the implementation of a sound Cap-and-Trade system and the promotion of strict local regulations to combat emissions. These imply that the prioritization of funding mechanisms, market-driven approaches, and more stringent regulation are key drivers in maritime decarbonization efforts. The Türkiye case study on strategies to achieve a greener maritime industry in developing countries also shows that securing funding for decarbonization requires a concerted effort by governments, private entities, and international organizations.

Developing Exp-FIGARCH Hybrid Models for Time Series Modelling

In this paper, we introduced a new hybrid model namely Exponential Autoregressive-Fractional Integrated Generalized Autoregressive Conditional Heteroscedasticity (ExpAR-FIGARCH) model and study financial data. The Daily Nigeria All Share Stock Index that exhibit nonlinear, volatility and long memory effect were analyzed in the study. The existing ExpAR-Generalized Autoregressive Conditional Heteroscedasticity (ExpAR-GARCH) model were estimated and compared with the proposed ExpAR-FIGARCHmodel. Results showed that the new hybrid model is better based on efficient parameters, serial correlation analysis and forecast measures of accuracy. Therefore, as a conclusion, the current study indicates that the ExpAR-FIGARCHmodel performed better compared to the ExpAR-GARCHhybrid model. Therefore, the ExpAR-FIGARCHmodel is a better option for modeling nonlinear, volatility and long memory characteristics of time series. Future study should focus on the application of the developed hybrid ExpAR-FIGARCHmodel using health, meteorological and economic data.

Analysis Volatility Spillover of Stock Index in ASEAN (Case Study: Indonesia, Singapore, Malaysia)

Every country has its own income, including ASEAN countries such as Indonesia, Singapore, and Malaysia. One source of national income can come from stocks, which can be measured by the stock index. The income of each country depends on each other and can be influenced by a phenomenon, such as the Covid-19 pandemic. The Covid-19 pandemic can also cause volatility spillover. This research aims to analyze volatility spillover in ASEAN countries (Indonesia, Singapore, and Malaysia) before and during Covid-19 by looking at the effects of asymmetric volatility. Volatility spillover testing in this study uses the Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) model, starting with creating a time series model and then modeling the residuals from that model, then finding the estimated parameter results of asymmetric volatility effects. The results of this study indicate that during the period before Covid-19, there is volatility spillover for Indonesia and Malaysia. Then, during the Covid-19 period, there is volatility spillover for Indonesia and Malaysia, for Indonesia and Singapore, and for Singapore and Malaysia.

Risk sensitive linear approximations

We propose a linear approximation to the solution of DSGE models that is sensitive to the effects of risk. If variables remain close to the approximation point in expectation, a second-order Taylor expansion to the equilibrium conditions reduces to a fixed-point problem characterized by a system of linear equations that depends on the second-order moments of the variables. The latter can be solved recursively using standard linear rational expectation methods. The resulting approximation captures the effects of risk in models with constant volatility, stochastic volatility, and GARCH effects through the intercept and/or the slopes of the decision rules. Relative to alternative approximations, our method yields approximation errors that are up to two orders of magnitude smaller, all while preserving a linear structure in the state variables. Finally, we show how to accommodate asymmetric effects from non-normal shocks within our linear approximation using information from a third-order Taylor expansion.

Monetary policy and distributional effects of agricultural commodity price volatility in Sub-Saharan Africa

Monetary policy and distributional effects of agricultural commodity price volatility in Sub-Saharan Africa

Return and Volatility Spillover in Equity and Commodity Market: Some Indian Evidence

This study empirically investigates the return and volatility spillover among equity markets, Agro and non-Agro commodity markets. The study has used daily return data of BSE SENSEX as equity index, MCX iCOMEDEX, including Gold, Silver, and Crude oil, as non-agro indices, and NCDEX (AGRIDEX) as agro commodity indices to analyze volatility transmission between assets and markets using Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and Exponential Generalized Autoregressive Conditional Heteroscedastic (EGARCH) model. The study has used dummy variables in the variance equation to identify the structural breaks during the COVID 19 pandemic. Further, Vector Auto Regression (VAR) pair-wise Granger causality test has been used to understand returns’ causality. The study has found a bidirectional and unidirectional causal relationship between variables. The study indicates that NCDEX could influence all the variables except MCX Gold. In structural breaks with deterministic dummy variables during COVID-19, we have found mixed evidence of asymmetric impact on conditional volatility. The study also indicates a positive and significant asymmetric volatility effect in the case of MCX Gold and MCX Silver during the COVID-19 period. The study has specified that SENSEX dominates the inflow of information to all commodity markets. Also, the findings signpost that information flows from NCDEX to MCX Gold, MCX Silver, and MCX Crude Oil. JEL Codes: C32, E30, E44, F41, G15, Q43

Interest Rate Volatility and Economic Growth in Nigeria: New Insight from the Quantile Autoregressive Distributed Lag (QARDL) Model

AbstractThis is a study on interest rate volatility, a crucial form of volatility which affects local and foreign investments in the real and financial sectors. Whether to prioritize interest rate stability to prevent distortions in the market mechanism or to prioritize other macroeconomic objectives while allowing interest rates to independently react to market forces is a key question for Nigeria’s apex monetary authority. Answering this question is the primary motivation for this research. This paper is an attempt to establish the effect of interest rate volatility on economic growth and further conclude on the suitability of the financial liberalization policy in Nigeria. To reach an evidence-based conclusion, the paper analyzes the relationship between interest rate volatility and economic growth in Nigeria for the period 1981–2020. The QARDL procedure was employed to establish the short-run and long-run quantile-specific impacts of interest rate volatility. As a final step, Granger causality tests are conducted to investigate the predictive powers of the variables. It is discovered from the econometric analysis that interest rate volatility adversely affects the economic performance of Nigeria in both the short run and long run. Consequently, full liberalization is not suitable for the economy. Moreover, we find that the short-run adverse growth effect of interest rate volatility is greater when the economy is already in a relatively weak state, whereas the long-run adverse growth effect is greater when the economy is already in a relatively strong position. The findings sufficiently prove that full interest rate liberalization is not Pareto efficient for Nigeria. Hence, greater supervision of the interest rate corridor system to reduce volatility in the rates and minimize chances of persistent upward or downward bias is advised. Study limitations and directions for further research are also provided.

Bank ownership, credit supply volatility, and macroeconomic volatility

We examine the real effects of credit supply volatility in emerging economies. In countries with highly effective governments, government-owned banks play a significant role in reducing the effect of credit supply volatility on macroeconomic volatility. Conversely, foreign banks do not significantly change this effect. Furthermore, the presence of government-owned banks as development banks plays a positive role in stabilizing the economy during a sovereign or currency crisis. In countries where foreign banks dominate the banking sector, these banks amplify the adverse effect of a volatile credit supply on the volatilities in output, consumption, and investment growth rates, especially during a banking crisis.

Towards efficient similarity embedded temporal Transformers via extended timeframe analysis

Price prediction remains a crucial aspect of financial market research as it forms the basis for various trading strategies and portfolio management techniques. However, traditional models such as ARIMA are not effective for multi-horizon forecasting, and current deep learning approaches do not take into account the conditional heteroscedasticity of financial market time series. In this work, we introduce the similarity embedded temporal Transformer (SeTT) algorithms, which extend the state-of-the-art temporal Transformer architecture. These algorithms utilise historical trends in financial time series, as well as statistical principles, to enhance forecasting performance. We conducted a thorough analysis of various hyperparameters including learning rate, local window size, and the choice of similarity function in this extension of the study in a bid to get optimal model performance. We also experimented over an extended timeframe, which allowed us to more accurately assess the performance of the models in different market conditions and across different lengths of time. Overall, our results show that SeTT provides improved performance for financial market prediction, as it outperforms both classical financial models and state-of-the-art deep learning methods, across volatile and non-volatile extrapolation periods, with varying effects of historical volatility on the extrapolation. Despite the availability of a substantial amount of data spanning up to 13 years, optimal results were primarily attained through a historical window of 1–3 years for the extrapolation period under examination.

Combining low-volatility and momentum: recent evidence from the Nordic equities

ABSTRACT This paper investigates the profitability of combined low-volatility and momentum investment strategies in the Nordic stock markets from January 1999 to September 2022. Confirming earlier studies, our results first indicate that both the volatility and momentum effects persist as pure-play strategies. Further, we explore combined strategies using 50/50, double screening, and ranking strategies. Among the long-only portfolios, the momentum-first strategy generates the best Sharpe ratio using the double screening method−slightly outperforming the ranking method. Additionally, all long-only combination portfolios outperform the market in terms of risk-adjusted returns. Combination long-short strategies produce significantly higher risk-adjusted returns than pure-play strategies. Surprisingly, novel evidence suggests that none of the combination long-short strategies outperforms the pure momentum strategy after risk-adjusting the returns using the Fama and French five-factor model, implying that while momentum may enhance the returns from the low-volatility strategy, the reverse is not true for the Nordic stock markets.

INFECTIOUS DISEASE AND ASYMMETRIC INDUSTRIAL VOLATILITY

We examine the time-varying effect of stock market volatility due to infectious diseases on industrial sectorsin the US from 2012 to 2021. We extend the current literature by exploring the diverse impact of infectiousdiseases on various industrial sectors and decomposing industrial volatility into good and bad volatility toquantify how good and bad components vary in response to the transmission of shocks due to infectiousdiseases. The results show that the transmission of volatile shocks from the stock market more stronglyenhances the good component of industrial volatility as compared with bad volatility during COVID-19. Weconclude that the relationship between infectious disease equity market volatility and industrial volatilitydepends on the good and bad volatile components and their respective conditions at different quantiles.

Exchange Rate Volatility and International Trade in Turkey

Purpose: The aim of the study was to investigate the exchange rate volatility and international trade in Malaysia. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Exchange rate volatility significantly impacts international trade by affecting exporters, importers, and trade volumes. It introduces uncertainty, raises hedging costs, and alters price competitiveness, leading to shifts in trade flows. While policymakers implement measures to manage volatility, their effectiveness is debated. Regional trade agreements can mitigate its impact, but challenges persist. Effective management of exchange rate volatility is essential for fostering stable international trade relationships. Unique Contribution to Theory, Practice and Policy: The theory of purchasing power parity (PPP), the theory of optimum currency areas (OCA) & the balassa- samuelson effect may be used to anchor future studies on the exchange rate volatility and international trade in Malaysia. Implement risk management strategies to mitigate the adverse effects of exchange rate volatility on international trade, including the use of financial derivatives, hedging instruments, and diversification strategies. Design and implement policies that strike a balance between exchange rate flexibility and stability, recognizing the trade-offs between exchange rate volatility and export competitiveness.

Exchange Rate Volatility and Export Performance: Case of Malaysia

Purpose: The aim of the study was to investigate the exchange rate volatility and export performance: case of Malaysia
 Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries.
 Findings: Exchange rate volatility negatively impacts Malaysia's exports, reducing both volumes and revenues. Stable exchange rates are crucial for export competitiveness and attracting foreign investment. Effective hedging and exchange rate policies are recommended to mitigate volatility's adverse effects. Macroeconomic stability and structural reforms are essential for Malaysia's resilience to external shocks. Managing exchange rate volatility is key for sustaining Malaysia's export-led growth and fostering economic development.
 Unique Contribution to Theory, Practice and Policy: Portfolio balance theory, j-curve theory & competitive devaluation theory may be used to anchor future studies on the exchange rate volatility and export performance: case of Malaysia. Malaysia can provide practical insights into how exporters can effectively integrate risk management strategies into their operations to mitigate the adverse effects of exchange rate volatility. Malaysia can serve as a model for proactive policy responses to exchange rate volatility, offering lessons and strategies that can be adapted by policymakers in other emerging economies.

Assessing the Drivers of Economic Growth in Pakistan: A Time-Series Analysis of Foreign Direct Investment and Oil Price Effects

This research looks at the factors that drive Pakistan's economic growth, with a particular emphasis on the influence of foreign direct investment (FDI) and oil prices. Using data from 1998 to 2022, the study applies the Auto Regression Distributed Lag (ARDL) model to examine the link between FDI, oil prices, and economic growth. The empirical findings show that FDI has a beneficial impact on economic growth, highlighting its importance as a driver for economic development in Pakistan. In contrast, the study finds that rising oil prices harm economic growth, emphasizing the negative effects of oil price volatility. Considering these findings, the study underlines the need for policies that encourage FDI inflows and industrialization to drive economic growth. Furthermore, the study emphasizes the importance of government involvement to mitigate the negative effects of rising oil costs, since unchecked rises might stymie economic progress. Understanding the determinants of economic growth in Pakistan is critical for both policymakers and investors. FDI is critical for increasing productivity and creating jobs, yet oil price volatility can have a substantial influence on the country's macroeconomic stability and growth trajectory. Analyzing their effects gives useful insights for long-term economic growth initiatives. Overall, the study suggests a dual strategy for Pakistani policymakers: create a climate conducive to attracting FDI while taking steps to reduce the negative consequences of unpredictable oil prices. By prioritizing these policies, Pakistan may accelerate its economic growth and protect itself from foreign economic shocks.

Modelling The Volatility of Frankfurt Stock Exchange (DAX) Returns Using hybrid Models

Recently, the interest of researchers in the use of hybrid models in the process of analyzing model time series with fluctuations and forecasting fluctuations in financial time series has increased significantly. Hybrid ARMA-GARCH models were created for medium- and long-term forecasts of time series of financial market index prices: ARMA models are used to analyze their linear component, which is a combination of autoregressive models and moving average models, and GARCH models are used to analyze the nonlinear component. which are generalized autoregressive models that depend on the nonconstancy of variance models. Hybrid ARMA-GARCH models eliminate the weaknesses and gaps that exist in each group of models (ARMA and GARCH) separately, which increases their forecasting accuracy and reliability, so they have already been successfully applied to model and forecast daily stock returns for three standard indices in the USA. The purpose of this article is to investigate which of the hybrid ARMA-GARCH models is optimal for forecasting the return of the DAX index, which is the most important stock index of the German securities market. It is the German equivalent of the American Dow Jones Index, has been calculated since 1988 by Deutsche Börse AG and reflects the total return on capital of the largest stock companies listed on the Frankfurt Stock Exchange (currently 40; by 2021 – 30): calculated as a weighted average of capitalization of the value of Free Float share prices on the Xetra electronic exchange, and also takes into account dividends on shares, assuming that the dividend is reinvested in the share on which it was accrued. The database of this study consisted of the daily closing prices of the DAX index presented on the official website of the Frankfurt Stock Exchange during the period from 01.01.2018 to 09.30.2023 (altogether about 1,500 observations), the stability of the time series was assessed using Expanded Dickey Fuller Liquidity (ADF). The article proposes 7 hybrid models, from which the one that is best suited for modeling the volatility of the DAX index is selected. It is an ARMA (2,3)-EGARCH (1,1) model because it captures volatility and leverage effects on DAX returns and its expected returns more than other models. The selection of the best alternative from the developed array of hybrid models was carried out according to the following criteria: AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion), H-QIC (Hannan-Quinn Information Criterion).

Ultra-short term wind power prediction based on quadratic variational mode decomposition and multi-model fusion of deep learning

To address the challenges associated with the intricate selection of decomposition technical parameters and the adverse impact of high-frequency non-stationary components on prediction accuracy, a differential evolution (DE) and sparrow search algorithm (SSA) parameter optimization model is formulated. Through the optimization of variational mode decomposition (VMD) and bidirectional short and long-term memory (BiLSTM) neural networks, enhancements are made to the overall performance of the prediction method. Subsequently, DESSA–VMD is employed to quadratically decompose the high-frequency unstable component, mitigating the detrimental effects of randomness and volatility on the prediction accuracy of the primary component. Following this, the DESSA–BiLSTM neural network is applied to predict the high-frequency strong non-stationary component, whereas the autoregressive integrated moving average is utilized for predicting the low-frequency periodic component. Finally, a case analysis is conducted using actual data comprising 2000 sets of wind power in a province. When the test set consisted of 600 groups, the root mean square error index value of the proposed prediction model exhibited reductions of 1.95 MW, 2.86 MW, and 1.16 MW, respectively, compared with the BP, SVM, and LSTM methods. Additionally, the mean absolute percentage error index showed decreases of 2.26 %, 2.96 %, and 1.73 %, respectively.

Quantifying the impact of interest rate volatility on Asian energy companies: A comparative study of fossil and renewable sectors

This study examines the impact of government bond yield volatility on companies in the fossil and renewable energy sectors in five Asian countries from 2015 to 2023. The analysis uses quantile-based methods to explore the spillover effects of yield volatility on companies with high and low debt levels in these sectors. The findings show that yield volatility tends to transmit shocks at lower quantiles and over shorter periods. The network connectedness is more critical at extreme upper and lower quantiles in the short term. The results also reveal significant differences in how yield volatility affects companies with high and low leverage levels, and these effects vary across the sample countries. Overall, this research provides valuable insights into the influence of yield volatility on financial decision-making in different contexts and industries. The study's results underscore the imperative for policymakers and stakeholders in the energy industry to give precedence to improving risk management methods.

Volatility spillovers of cloud stocks: Evidence from China using the dynamic connectedness approach

AbstractBased on daily data from 2013 to 2022, this study examines the spillover effects of volatility between cloud stocks and other asset classes (global stocks, treasury bonds, gold and crude oil) using the VAR connectedness approach. The results show that there is a significant spillover effect from global stocks and crude oil markets to the cloud stock market. The spillover effects become stronger whenever there are shocks such as economic crisis, turbulence in the international financial markets, COVID‐19 and global inflation. However, nearly 91% of the variations of cloud stocks come from within, suggesting that the diversification/hedging value of cloud stocks is potentially high.

Structural changes in contagion channels: the impact of COVID-19 on the Italian electricity market

AbstractOperating on electricity markets requires accurately identifying, quantifying, and measuring risk coupled with their corresponding return: this appears as a crucial point, particularly during and after the COVID-19 pandemic. The aim of the present paper is twofold. First, we propose a novel econometric approach to identifying relevant market factors that capture several elements of the risk transmission mechanism inherent in energy systems. The proposed model extends Bayesian graphical models with change points to a multiple-layer set-up. Multilayer graphs encompass the two relevant channels of shock transmission: volatility and price contagion effects. The choice of these two layers seems natural because electricity prices and their spiky nature, coupled with inherent volatility, constitute essential influential elements for market players to maximize their profits. The change-point specification allows for detecting relevant changes in the electricity market. Second, we apply the proposed econometric framework to the Italian zonal markets analyzing the effects of returns and volatility contagion in several periods detected by the model. The last time intervals identified by the change-point methodology overlap the COVID-19 pandemic period. The model captures relevant abrupt changes in prices and volatility in the zonal electricity market and provides new evidence of interconnections in the zones of the Italian market related to the risk alone, price process alone, and risk versus price process relationship and their interactions.

Systemic risk prediction using machine learning: Does network connectedness help prediction?

The global financial crisis not only highlights the important role of financial network connectedness, but also urges regulators pay more attention to systemic risk. We study whether network connectedness helps systemic risk prediction by using machine learning techniques, including k-nearest neighbor (KNN), support vector regression (SVR), extreme gradient boosting (XGB), and feedforward neural network (FNN). Based on prediction results, we use the fingerprint model to deliver interpretability evaluations of financial variables and network connectedness. We find that (1) the linear and nonlinear effects of stock market volatility and network connectedness effectively interpret systemic risk; (2) the interaction effects between stock market volatility and other drivers have a stronger ability to predict system risk in SVR, KNN, and XGB approaches, while network connectedness performs more profoundly in FNN approach; and (3) the interaction effect between stock market volatility and network connectedness is evident in all approaches.