Heteroscedastic Model Research Articles

Disentangled oil shocks and stock market volatility in Nigeria and South Africa: A GARCH-MIDAS approach

In this study, we investigate the effects of disentangled oil shocks on the volatility of the stock markets of Nigeria and South Africa using the Mixed Data Sampling variant of the Generalized Autoregressive Conditional Heteroscedasticity (GARCH-MIDAS) model. The disentangled oil shocks involve oil supply shock, economic activity shock, oil consumption demand shock, and oil inventory demand shock covering January 2010 and July 2022. Overall, we find that the stock market volatilities of Nigeria and South Africa respond similarly to oil supply shock and oil consumption demand shock but differently to economic activity shock and oil inventory demand shock. The unusual increase in the volatility of Nigeria’s stock market has been attributed to the loss of investors’ confidence which takes a too long time to be restored. Our results are robust to alternative forecast sampling techniques, particularly fixed and rolling windows.

Russia–Ukrainian war: measuring the intraday risk dynamics of energy futures contracts using VaR and CVaR

PurposeThis paper investigates the influence of the ongoing crisis of Russia's incursion on Ukraine on the risk dynamics of energy futures contracts with high-frequency data on four different futures contracts using risk metrics of value at risk (VaR) and conditional value at risk (CVaR) for the USA market.Design/methodology/approachThe author used different generalised autoregressive conditional heteroscedasticity - Extreme Value Theory (GARCH)-EVT models and compared the performance of each of the competing models. Backtesting evidence shows that VaR and CVaR combined with GARCH-EVT better estimate risk.FindingsThe study results show that combined risk metrics are efficient and adaptive to estimating the risk dynamics and backtesting of the models, revealing that the autoregressive moving average (ARMA) (1,1)-asymmetric power autoregressive conditional heteroscedasticity (APARCH) model performs relatively better than other models.Practical implicationsThe paper has practical implications for different market participants. From the risk manager's and day traders' angles, the market participants can estimate the risk exposure in the energy futures contract and take positions accordingly. The results are important for oil-importing countries due to the developing supply crisis and price escalation, which can brew inflation in the economy.Originality/valueTo the best of the author's knowledge, the paper is the first to throw light on the risk angle of energy futures contracts during the ongoing crisis of the Russia–Ukraine war.

The volatility spillover effect of macroeconomic indicators on inbound tourism in India

Diseño/metodología/enfoqueLos modelos GARCH bivariantes se aplican para estimar el efecto indirecto de la volatilidad (VSE) de un mercado a otro. En comparación con otros métodos, el GARCH bivariante goza de gran aceptación para estimar el VSE. Para el análisis empírico se recopilan los MI mensuales y los datos de demanda turística (2012–2021).ObjetivoEl estudio se centra en medir la relación mutua entre la volatilidad de los indicadores macroeconómicos (MI) y la demanda turística de la India.ConclusionesSe observan indicios de GLT (demanda turística impulsada por el crecimiento). A corto plazo, se evidencia el TLG (crecimiento impulsado por el turismo). Sin embargo, este TLG no se mantiene a largo plazo. Existen pruebas significativas a favor del VSE de los MI a la demanda turística que garantizan el GLT en India.Implicaciones prácticasLa principal implicación del presente estudio es desestimar la influencia a corto plazo de la demanda turística en la economía porque no se sostiene a largo plazo. Sin embargo, la influencia a largo plazo de los indicadores macroeconómicos en la demanda turística debe considerarse con cautela. Por ello, la cobertura de riesgos puede plantearse para proteger los intereses del sector turístico de las repercusiones económicas adversas.Originalidad/valorExiste una carencia de estudios sobre la volatilidad (especialmente en el VSE) entre los MI y la demanda turística. En consecuencia, este estudio realiza una aportación investigadora mediante una contribución novedosa y única en la ampliación del conocimiento sobre el tema de análisis.

Detection and Estimation of Change Point in Volatility Function of Foreign Exchange Rate Returns

This work aims at detection and estimation of a change point in conditional variance function of a Nonparametric Auto-Regressive Conditional Heteroscedastic model. The conditional mean and conditional variance functions are not specified a priori but estimated using Nadaraya Watson kernel. This is because inferences based on nonparametric approaches are robust against misspecification of the conditional mean function and the conditional variance function of returns. The squared residuals obtained after estimating the regression function of the returns are used in estimating the conditional variance function. Further, the squared residuals are used in developing a test statistic for unknown abrupt change point in volatility of the exchange rate returns. The test statistic takes into consideration the conditional heteroskedasticity of the disturbances, dependence of the returns, heterogeneity and fourth moment of returns. This does not require prior knowledge of the marginal or the conditional densities of the returns as opposed to maximum likelihood estimation methods. The estimator for change point is considered as the augmented maximum of the test statistic. The consistency of the estimator is stated as a theorem. The asymptotic distribution associated with the test for unknown break points is the Bessel process distribution. The Bessel process distributions have no known simple closed-form expression for the distribution function which makes it difficult to compute exact p-values. Also, the Bessel process distributions depend on two parameters which makes it hard to tabulate the critical values hence one needs to simulate them. After simulating the critical values, hypothesis testing is done in the presence and absence of a change point in volatility of a simulated time series and the test is shown to reject the null hypothesis in the presence of a change point at alpha level of significance. Further, the test fails to reject the null hypothesis in the absence of a change point at alpha level of significance. An application to United States Dollar/Kenya Shilling historical exchange rates returns is made from 1^st January 2010 to 27^th November 2020 where the sample size n = 2839 is done. Through binary segmentation method, three change points are detected, estimated and accounted for. A significant improvement in describing a time series is expected if a point in time for volatility change has been detected and estimated.

MODELLING THE LEVERAGE EFFECT AND VOLATILITY PERSISTENCE IN THE EAST AFRICAN SECURITIES EXCHANGES DURING GLOBAL SHOCKS

<p>The study examines volatility dynamics during the 2007/09 global financial crisis in three main East African Exchanges; Nairobi Securities Exchange, Kenya, Uganda Securities Exchange, Uganda and Dar-es-Salaam Stock Exchange, Tanzania. A modified Asymmetric Generalized Autoregressive Conditional Heteroscedastic (E-GARCH 4,1) model was used to test leverage effects and volatility persistence in the markets and the USA as the ground zero market. The data consisted of daily closing indices covering 2006-2010. The period was divided into three phases before the crisis, during and after the crisis. The study found that the leverage effect was eminent in the Ugandan and Tanzania markets. The effect is more prevalent in the Ugandan market in all three phases of the crisis, and the pre-crisis phase for the Tanzania market but absent in the Kenyan market in all the phases. Explosive volatility was observed in the Kenyan and Ugandan markets meaning that volatility takes a longer period to decay off in these markets.</p><p><strong>JEL:</strong> E44; G10; G18</p>

A methodology to [formula omitted]-augment experimental designs

One of the main criticisms of optimal experimental designs is that they tend not to adequately meet the practical needs of the experimenter. For example, optimal designs for estimation of the parameters in a model frequently have too few designs points to check the model adequacy, to discriminate between rival models or to estimate a particular function of the parameters. Further, some experimenters like toxicologists have been schooled to using many doses in an animal experiment and there is great resistance to using a design with just a few doses. This paper uses the General Equivalence Theorem to define regions where the practitioner may select points flexibly to augment a design that meets the practical needs more adequately and has a user-specified minimum efficiency requirement. As examples, we demonstrate the usefulness of our theory-based method in various setups using statistical models, such as Antoine’s Equation, the Michaelis–Menten model and a quadratic heteroscedastic model. We also explore the power of the lack-of-fit test for several D-augmented designs in a simulation study. We provide user-friendly codes, either through the R package optedr or through a graphical user interface in Shiny, to facilitate practitioners implement our methodology to find an improved D-augmented designs for their problems.

Multivariate Garch Models Comparison in Terms of the Symmetric and Asymmetric Models

Modelling the Multivariate Generalised Autoregressive Conditional Heteroscedasticity (GARCH) model included both symmetric and asymmetric processes. The information includes monthly data for the Consumer Price Index, Crude Oil Price, Exchange Rate, and Inflation Rate from January 2005 to December 2021. For the analysis, E-view Statistical software was employed. The aforementioned four macroeconomic variables show a tendency for volatility to cluster across time. Both symmetrical and asymmetrical processes had the volatility condition. The residual recursive plot was used to analyse the structural fluctuation in the series. The plot showed continual movement in the inflation rate as well as a downward and upward movement in the consumer price index, exchange rate, and crude oil price. The Akaike Information Criterion (AIC), Hannan-Quinn Information Criterion (HQIC), and Schwarz Information Criterion were chosen as the best models based on information criteria (SIC). Symmetric and asymmetric modelling techniques were used to create the Economic Variables Multivariate GARCH (M-GARCH) models. To calculate the covariance and correlation between the four variables, M-GARCH models were utilised. The main finding of the estimation of all M-GARCH models is that the symmetric models (Diagonal BEKK and Constant Conditional Correlation, or "CCC") have the lowest values of the model information criteria compared to the asymmetric models (Diagonal BEKK and CCC), while the asymmetric models (Diagonal VECH) have the lowest values of the model information criteria compared to the symmetric models (Diagonal VECH). Based on the findings, it was discovered that while analysing the interaction between the four economic variables returns series, the Symmetric Diagonal BEKK and CCC model outperformed the Asymmetric Diagonal VECH model.

Garch Models Comparison with Symmetric and Asymmetric Process for Univariate Econometric Series

The Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model was modeled using both symmetric and asymmetric processes. Secondary data from January 2005 to December 2021 on the Consumer Price Index, Exchange Rate, Crude Oil Price, and Inflation Rate were used for this study. The research was conducted using the statistical software packages Minitab and E-view. The aforementioned four macroeconomic variables show a tendency for volatility to cluster across time. In both symmetric and asymmetric processes, the volatility condition and leverage impact coefficients were present. By contrasting the symmetric models (ARCH, GARCH, and GARCH-M) and the asymmetric models, the best model was chosen using Akaike Information Criteria (E-GARCH, T-GARCH and APARCH). For the investigated univariate economic variables, the results indicated that the found asymmetric model GARCH models outperformed the symmetric model GARCH models. Therefore, these models can be applied to the forecasting of these series of economic indicators. Models include the Asymmetric E-GARCH (1, 1) Model for Consumer Price Index, Crude Oil Price, and Inflation Rate Series and the Asymmetric T-GARCH (1, 1) Model for Exchange Rate Series.

A new method for customized fetal growth reference percentiles.

Customized fetal growth charts assume birthweight at term to be normally distributed across the population with a constant coefficient of variation at earlier gestational ages. Thus, standard deviation used for computing percentiles (e.g., 10th, 90th) is assumed to be proportional to the customized mean, although this assumption has never been formally tested. In a secondary analysis of NICHD Fetal Growth Studies-Singletons (12 U.S. sites, 2009-2013) using longitudinal sonographic biometric data (n = 2288 pregnancies), we investigated the assumptions of normality and constant coefficient of variation by examining behavior of the mean and standard deviation, computed following the Gardosi method. We then created a more flexible model that customizes both mean and standard deviation using heteroscedastic regression and calculated customized percentiles directly using quantile regression, with an application in a separate study of 102, 012 deliveries, 37-41 weeks. Analysis of term optimal birthweight challenged assumptions of proportionality and that values were normally distributed: at different mean birthweight values, standard deviation did not change linearly with mean birthweight and the percentile computed with the normality assumption deviated from empirical percentiles. Composite neonatal morbidity and mortality rates in relation to birthweight < 10th were higher for heteroscedastic and quantile models (10.3% and 10.0%, respectively) than the Gardosi model (7.2%), although prediction performance was similar among all three (c-statistic 0.52-0.53). Our findings question normality and constant coefficient of variation assumptions of the Gardosi customization method. A heteroscedastic model captures unstable variance in customization characteristics which may improve detection of abnormal growth percentiles. ClinicalTrials.gov identifier: NCT00912132.

War build-up and stock returns: evidence from Russian and Ukrainian stock markets

PurposeThis study examined the impact of war/conflict-related news on the Russian and Ukrainian stock markets in the build-up and beginning of the war that sparked in the year 2022.Design/methodology/approachIn order to examine the impact of war-related news on stock returns, data were gathered from the United States (US) and Russian stock indices, oil price and volatile index (VIX) from Yahoo.finance; Ukrainian stock values from pfts.ua website and daily related news retrieved from nexis.com were analysed. The data were gathered from January 1, 2022 to February 24, 2022. Seeming unrealated regressions (SUR) and exponential generalised autoregressive conditional heteroscedastic (EGARCH) models were carried out to determine the formulated correlations. This study controlled the oil price, US stock returns, Chicago Board Options Exchange (CBOE) VIX and difference in stock returns of Russia and Ukraine.FindingsThe results are presented two-fold: first, war-related news between the two countries enhanced volatility and caused a significant decline in the stock market indices for both countries. Second, the Russian stock market faced a steeper decline in the build-up and the actual beginning of the war than the Ukrainian stock market. Notably, the Russian markets feared the adverse economic consequences that stemmed from the sanctions the US and the Western world imposed.Research limitations/implicationsAs this study was based on early evidence, future studies with a longer window may provide better insights. This present study is restricted to the stock returns of the countries directly involved in the build-up towards war. Studies focusing on the impact of other asset classes, currencies, commodities and global stock markets might offer holistic insights.Practical implicationsThe study outcomes suggest that global portfolio investors should stay away from stock markets of the war-raged countries and equity markets in general, but instead look for safe-haven assets.Originality/valueThe paper evaluates stock markets' performance during the pre-war period, considering the context of this historical war between the neighbours. It is important to understand this issue as this war is subject to sanctions by the US and leads to a global supply chain crisis.

Spillover Effect of Chinese Export on New ASEAN-5 Stock Markets using Markov Regime Switching Model

China as the second largest economy supposes to produce spillover effect on the emerging market economies especially the ASEAN nations. The main objective of this paper is to study the spillover effect of the Chinese export on the new ASEAN-5 stock markets (Indonesia, Malaysia, Philippines, Thailand, and Vietnam). In this paper, multivariate Markov-Switching Intercept Autoregressive Heteroscedasticity (MSIAH) model is employed to analyze the linkage between Chinese export and the new ASEAN-5 stock markets over the sample period of August 2000 to December 2018. The monthly data have been analysed using EViews. Their relationship is also strong and positive. The findings report that the spillover effects of China export on new ASEAN-5 stock markets is significant. There is positive relationship between China export and the stock markets for both regimes. The conclusion can be made is China exports should be one of the important factors in determining the stock prices in new ASEAN-5 stock markets. Investors should alert China export information when investing in new ASEAN-5 stock market. New ASEAN-5 are important emerging economies in Asia Pacific region and China is a rising economic power, but there is very least literature to study the Spillover effect of China export on stock market in new ASEAN-5.

Time series model for GLWB with surrender benefit and stochastic interest rate: Dynamic withdrawal approach

AbstractThis study investigates the pricing problem of a variable annuity (VA) contract embedded with a guaranteed lifetime withdrawal benefit (GLWB) rider. VAs are annuities in which the value is linked to a bond and equity sub‐account fund. The guaranteed lifetime withdrawal benefit rider regularly provides a series of payments to the policyholder for the term of the policy while he/she is alive, regardless of portfolio performance. At the time of the policyholder's death, the remaining fund value is given to his nominee. Therefore, proper fund modeling is critical in the pricing of VA products. Several writers in the literature used a GBM model in which variance is considered to be constant to represent the fund value in a variable annuity contract. However, on the other hand, the returns on financial assets are non‐normally distributed in real life. A bit much Kurtosis, leverage effect, and Non‐zero Skewness characterize the returns. The generalized autoregressive conditional heteroscedastic (GARCH) models are also used for presenting a discrete framework for the pricing of GLWB. Still, the interest rate was kept constant without including the surrender benefit and the static withdrawal approach, which keeps the model far from the real scenario. Thus, in this research, the generalized GARCH models are used with surrender benefit and dynamic withdrawal strategy to develop a time series model for the pricing of annuity that overcomes the constraints of previous models. A numerical illustration and sensitivity analysis are used to examine the suggested model.

Valuing the EQ Health and Wellbeing Short Using Time Trade-Off and a Discrete Choice Experiment: A Feasibility Study

ObjectivesThe EQ Health and Wellbeing Short (EQ-HWB-S) is a new generic measure that covers health and wellbeing developed for use in economic evaluation in health and social care. The aim was to test the feasibility of using composite time trade-off (cTTO) and a discrete choice experiment (DCE) based on an international protocol to derive utilities for the EQ-HWB-S and to generate a pilot value set. MethodsA representative UK general population was recruited. Online videoconference interviews were undertaken where cTTO and DCE tasks were administered using EuroQol Portable Valuation Technology. Quality control (QC) was used to assess interviewers’ performance. Data were modeled using Tobit, probit, and hybrid models. Feasibility was assessed based on the distribution of data, participants, and reports of understanding from the interviewer, QC and modeling results. ResultscTTO and DCE data were available for 520 participants. Demographic characteristics were broadly representative of the UK general population. Interviewers met QC requirements. cTTO values ranged between −1 to 1 with increasing disutility associated with more severe states. Participants understood the tasks and the EQ-HWB-S states; and the interviewers reported high levels of understanding and engagement. The hybrid Tobit heteroscedastic model was selected for the pilot value set with values ranging from −0.384 to 1. Pain, mobility, daily activities, and sad/depressed had the largest disutilities, followed by loneliness, anxiety, exhaustion, control, and cognition in the selected model. ConclusionsEQ-HWB-S can be valued using cTTO and DCE. Further methodological work is recommended to develop a valuation protocol specific to the EQ-HWB-S.

Interest rate, foreign exchange and stock performance in a dual banking industry: evidence from Saudi Arabia

PurposeThis study aims to extend the literature by extensively investigating the impact of foreign exchange and interest rate changes on the returns and volatility of bank stocks in Saudi Arabia, which is the largest dual banking industry.Design/methodology/approachThis study employs the generalized autoregressive conditional heteroscedasticity (GARCH) model on stock returns of four fully Islamic Saudi banks and eight conventional Saudi banks.FindingsThe results showed that the foreign exchange rate return has a positive impact on Saudi conventional bank returns, while it has an adverse impact on Saudi Islamic bank returns. Moreover, a higher interest rate return has a positive impact on Saudi bank stock returns implying that the assets side is more sensitive to changes in interest rates than the liability side. Finally, higher foreign exchange and interest rates volatility increases the volatility of Saudi bank returns, where the former has the largest significant impact. Therefore, Saudi regulators should pay more attention to the risk management of their banks because this could threaten the stability of their financial system.Originality/valueTo the best knowledge of the author, this is the first study that tries to extensively analyze the joint impact of foreign exchange and interest rates on bank stock returns and volatility in Saudi Arabia by applying the GARCH model. The study uses a long data set from 2010 to 2019 that includes all Saudi banks and employs four measures of interest rates to increase the robustness of the results.

PRICE VOLATILITY ANALYZE IN EARLY PANDEMIC COVID 19 OUTBREAKS: CASE STUDY IN GORONTALO PROVINCE SHALLOT MARKET

The Covid-19 pandemic in early 2020 led to unpredictable price fluctuation in agricultural commodities such as Shallots. The purpose of this research was to analyze the price fluctuation behavior of shallots in Gorontalo Province during the Covid-19 Pandemic and the price volatility surge as well as the effect of the pandemic during 2020. This study used data from The National Food Strategy Information Center in the form of weekly price in time-series from January 2018 to December 2020. The data analysis on price volatility was conducted using the Auto-Regressive Conditional Heteroscedastic and Generalized Auto-Regressive Conditional Heteroscedastic (ARCH / GARCH) Models. The results show that shallot price behavior randomly fluctuates every year, is more unpredictable, and has significant fluctuation over 2020. The ARCH / GARCH models prove that Pandemic Covid-19 in 2020 triggered the unpredictable price fluctuation and led to price volatility of shallots commodity in Gorontalo Province.

Problems of modeling the stability of the financial market as a dynamic system

Subject. The article addresses the stable functioning of the financial market and its protection against financial crises as the main indicator of financial system’s stability. It considers the history of the issue, enabling to conclude that financial markets are built mainly on the principle of unstable equilibrium in contrast to the more stable equilibrium underlying the commodity markets and industrial production. Objectives. The article attempts to compare well-known stochastic models with dynamic and chaotic systems. Methods. The study employs stochastic modeling (autoregressive conditional heteroscedasticity (ARCH) and generalized autoregressive conditional heteroscedasticity (GARCH) models), investigates methods and approaches to solving some types of differential stochastic equations, in particular, the Ito and Fokker-Planck-Kolmogorov equations. Results. Financial markets are considered within the theory of dynamic systems as an example of a non-linear system. It is extremely difficult to predict the behavior of such a system, precisely because of the non-linearity, which is reduced to random and chaotic processes. Through mathematical transformations, the paper shows that solutions are reduced to multidimensional stochastic volatility models. Conclusions. Stochastic volatility models, despite their relative theoretical elaboration and practical applicability, can lead to dynamic chaos, when there is a vector of asset return, the conditional covariance matrix of which changes over time.

Banking sector reforms in Nigeria: an empirical appraisal

PurposeThis paper provides evidence that the banking sector reforms of 2004 and 2009 enhanced prudential performance of the banking industry and financial system stability in Nigeria.Design/methodology/approachThis study uses regression analysis with regime shift to confirm results from tests of two means and variances model to examine the effectiveness of banking sector reforms in Nigeria.FindingsEvidence from the regression model agrees with findings from the test of means model (not controlling for trend effects) that capital to assets ratio rose while non-performing loan ratio declined after the reforms, and that capital to earning assets ratio rose when trend effects were accounted for. Both the regression model and the tests of means model controlling for trend effects show that return on asset, return on equity and return on earning assets ratios declined after the reforms.Research limitations/implicationsThis paper evaluated the effectiveness of banking sector reforms in Nigeria using models that avoid weaknesses that besieged many previous studies. It however used data covering 1983–2020 period, due to data availability. A larger scope of data may improve the results, and future research may re-examine this theme as more data become available. Furthermore, banking stability issues could be examined using specialised techniques such as the generalised autoregressive conditional heteroscedasticity model and related family.Practical implicationsThese results suggest that the reforms led to improvement in the sector’s resilience (risks-absorbing capacity) and asset quality, and that profitability had not been the primary focus of the reforms.Social implicationsThe authors recommend that regulatory and supervisory authorities in Nigeria continue to implement and improve on banking sector reforms for a more resilient and functional banking system. As a contribution to social research, this study shows that studies on policy evaluation should be located within appropriate theoretical framework: the theory of change. It shows that an appropriate use of attribution analysis and contribution analysis within this theoretical framework engenders robust analysis and results. Otherwise, the analytical findings would be erroneous and policy advice misguided.Originality/valueThe statistical significance of our findings establishes that the banking sector reforms in Nigeria have been effective in promoting financial system stability in Nigeria. By deploying both the test of means with and without trend effects (an attribution analysis) and the multivariate regression analysis with regulatory shift (a contribution analysis), and relying more on the later for its superiority, this study contributes to the body of knowledge in that, it not only determined the true effects of banking sector reforms in Nigeria for appropriate policy guidance but also demonstrated that, in research, an inappropriate methodology produces results that may diverge from the more accurate ones that were derived from the correct methodology.

Risks in Major Cryptocurrency Markets: Modeling the Dual Long Memory Property and Structural Breaks

This study estimates the effects of the dual long memory property and structural breaks on the persistence level of six major cryptocurrency markets. We apply the Bai and Perron structural break test, Inclán and Tiao’s iterated cumulative sum of squares (ICSS) algorithm, and the fractionally integrated generalized autoregressive conditional heteroscedasticity (FIGARCH) model, with different distributions. The results show that long memory and structural breaks characterize the conditional volatility of cryptocurrency markets, confirming our hypothesis that ignoring structural breaks leads to an underestimation of the persistence of volatility modeling. The ARFIMA-FIGARCH model, with structural breaks and a skewed Student-t distribution, fits the cryptocurrency market’s price dynamics well.

Guidelines for Increasing the Effectiveness of Thailand’s Sustainable Development Policy based on Energy Consumption: Enriching the Path-GARCH Model

The objective of this study is to develop a model for forecasting energy consumption and to increase the effectiveness of Thailand's sustainable development policy based on energy consumption by using the best model, the Path Analysis-Generalized Autoregressive Conditional Heteroscedasticity Model (Path-GARCH model). To improve the effectiveness of sustainability policies, the researcher has envisioned the final energy consumption over a 20-year period (AD 2023–2022) by defining a new scenario policy. Comparing the performance of the Path-GARCH model to other previous models, the Path-GARCH model was found to have the lowest mean absolute percentage error (MAPE) and root mean square error (RMSE) values. In addition, the study found that energy consumption continued to rise to 125,055 ktoe by 2042, with a growth rate of 115.05% between 2042 and 2023, which exceeded the carrying capacity limit of 90,000 ktoe. When a new scenario policy is implemented, however, the final energy consumption continues to rise to 74,091 ktoe (2042). Consequently, defining a new scenario policy is a crucial development guideline for enhancing the effectiveness of Thailand's sustainable development policy.

Volatility Spillover Effects between Indian Stock Market and Global Stock Markets: A DCC-GARCH Model

The present article empirically estimates the volatility spillover transmission in Indian equity market represented by Sensex from world economies composite index (Euro Stoxx 50) using the dynamic conditional correlation generalized autoregressive conditional heteroscedasticity (DCC-GARCH) model. The study uses secondary data spanning between 1 April 2012 and March 2022 on weekly basis. The DCC-GARCH model is applied to examine the spillover from developed stock markets to Indian stock market (Sensex). The findings of the study revealed that in short run there is a spillover effect from global markets to Indian stock markets. Investors can invest in the Indian stock market for the long period of time as there is no volatility spillover or volatility transmission from Euro and Nasdaq however in short run the investment in the Indian stock market is not safe due to the presence of volatility effect from all developed stock markets.