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This study investigates calendar anomalies and their impact on returns and volatility patterns in the cryptocurrency market, focusing on day-of-the-week effects before and during the COVID-19 pandemic. Using advanced statistical models from the GARCH family, we analyze the returns of Binance USD, Bitcoin, Binance Coin, Cardano, Dogecoin, Ethereum, Solana, Tether, USD Coin, and Ripple. Our findings reveal significant shifts in volatility dynamics and day-of-the-week effects on returns, challenging the notion of market efficiency. Notably, Bitcoin and Solana began exhibiting day-of-the-week effects during the pandemic, whereas Cardano and Dogecoin did not. During the pandemic, Binance USD, Ethereum, Tether, USD Coin, and Ripple showed multiple days with significant day-of-the-week effects. Notably, positive returns were generally observed on Sundays, whereas a shift to negative returns on Mondays was evident during the COVID-19 period. These patterns suggest that exploitable anomalies persist despite the market's continuous operation and increasing maturity. The presence of a long-term memory in volatility highlights the need for robust trading strategies. Our research provides valuable insights for investors, traders, regulators, and policymakers, aiding in the development of effective trading strategies, risk management practices, and regulatory policies in the evolving cryptocurrency market. [ABSTRACT FROM AUTHOR]

Focusing on the Russia–Ukraine war, this paper investigates natural gas futures volatilities. Applying several hybrid GARCH and EGARCH models, which innovatively incorporate both fat-tailed distribution errors and structural breaks, we derive the following new evidence. First, our hybrid modeling approach is effective in timely capturing the natural gas futures volatility spike when tensions simmered on the Russia–Ukraine border. Second, the hybrid modeling approach is effective for not only GARCH modeling but also EGARCH modeling. Third, the volatility estimates from our hybrid models have predictive power for the volatilities of nonhybrid models. Fourth, the volatility estimates from the nonhybrid models lag behind the volatilities of our hybrid models. [ABSTRACT FROM AUTHOR]

ABSTRACT This study aimed to test the volatility model of BBCA and BMRI stocks on the IDX. The research problem is whether there is an influence of BBCA and LQ45 volatility on BMRI and vice versa. The study also tested whether BRIS's volatility was influenced by its majority shareholder, BMRI. The EGARCH model analyzed daily return data for 2015-2022 in bearish/bullish markets. The results showed that the data experienced heteroscedasticity problems, and the EGARCH Student's model was selected. The volatility of BBCA and BMRI returns does not affect each other but is influenced by LQ45 when bearish/bullish. The volatility of BRIS returns is influenced by BMRI only when it is bearish and the LQ45 index when bullish. The implications of the research prove the independence of stock investors (BMRI and BBCA) in making decisions. However, it was indicated that both investors were influenced by the decisions of most investors, which was reflected in the significance of the LQ45 index. ABSTRAK Tujuan penelitian ini adalah untuk menguji model volatilitas saham BBCA dan BMRI di BEI. Permasalahan penelitiannya adalah apakah terdapat pengaruh volatilitas return saham BBCA dan LQ45 terhadap BMRI dan sebaliknya. Penelitian tersebut juga menguji apakah volatilitas BRIS dipengaruhi oleh return saham pemegang saham mayoritasnya, BMRI. Model EGARCH digunakan untuk menganalisis data return harian tahun 2015-2022 saat pasar bearish/bullish. Hasil penelitian menunjukkan bahwa data mengalami masalah heteroskedastisitas dan model EGARCH Student's-t yang dipilih. Volatilitas return BBCA dan BMRI tidak saling mempengaruhi, namun dipengaruhi oleh LQ45 saat bearish/bullish. Volatilitas imbal hasil BRIS hanya dipengaruhi oleh BMRI saat bearish dan indeks LQ45 saat bullish. Implikasi penelitian membuktikan independensi investor saham (BMRI dan BBCA) dalam mengambil keputusan. Namun kedua investor tersebut terindikasi dipengaruhi keputusan mayoritas investor yang tercermin signifikansinya indeks LQ45.

The media, speculators, investors, and governments throughout the world have all become increasingly interested in cryptocurrencies in recent years. The price swings of cryptocurrencies are notoriously unstable and have a high level of volatility. This study focused on modeling that volatility of cryptocurrencies, the purpose of this study is to identify the most suitable or appropriate innovation distribution and different GARCH Models to model the returns of the most popular cryptocurrencies. The majority of our work was focused on the top ten cryptocurrencies, but we also extended our analysis to 377 cryptocurrencies. To describe the time dependent volatility of the cryptos, we utilize eleven different GARCH models, including the sGARCH, iGARCH, GJRGARCH, eGARCH, tGARCH, AVGARCH, CSGARCH, ALLGARCH, NGARCH, APARCH, and NAGARCH. For the research period of September 14, 2014 to November 10, 2022, the daily closing prices of cryptocurrencies are collected. The underlying innovation(error) distribution are assumed to be from one of the following eight distributions of Normal, Student's t, Generalized Error, Skew Normal, Skew Student's t, Skew Generalized error, Normal Inverse Gaussian and Generalized Hyperbolic Distribution. Each GARCH-type model was fitted with this eight innovations. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Academic Finance is the property of Academic Finance Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

SCDS (Sovereign Credit Default Swaps) are becoming more widely used as a country risk indicator after 2008 and stand out for providing real-time information rather than periodic reporting. The COVID-19 pandemic has led to economic disruptions and a decline in international trade. Understanding how the Pandemic affects SCDS return volatility in emerging economies like BRICS forms the motivation for our research. With this study, we aim to determine the impact of the COVID-19 Pandemic on SCDS return volatility in Brazil, Russia, India, China and South Africa, known as the BRICS countries. We used the Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model to analyze the data, which consisted of the daily closing price data for SCDS. The date of the first COVID-19 case in each country has been taken as the beginning of the COVID-19 Pandemic in each country. The results of the estimated GARCH models show that the volatility processes of the SCDS return series differ between periods. EGARCH model results indicate that shocks created by news in these countries during the Pandemic have a small and persistent effect on Brazil and Russia's SCDS return volatility, while they have a large and enduring effect on China and South Africa's SCDS return volatility. The findings will guide policymakers and portfolio managers in determining risk management models. [ABSTRACT FROM AUTHOR]

The growing interest and direct impact of carbon trading in the economy have drawn an increasing attention to the evolution of the price of CO2 allowances (European Union Allowances, EUAs) under the European Union Emissions Trading Scheme (EU ETS). As a novel financial market, the dynamic analysis of its volatility is essential for policymakers to assess market efficiency and for investors to carry out an adequate risk management on carbon emission rights. In this research, the main autoregressive conditional heteroskedasticity (ARCH) models were applied to evaluate and analyze the volatility of daily data of the European carbon future prices, focusing on the last finished phase of market operations (phase III, 2013–2020), which is structurally and significantly different from previous phases. Some empirical findings derive from the results obtained. First, the EGARCH (1,1) model exhibits a superior ability to describe the price volatility even using fewer parameters, partly because it allows to collect the sign of the changes produced over time. In this model, the Akaike information criterion (AIC) is lower than ARCH (4) and GARCH (1,1) models, and all its coefficients are significative (p < 0.02). Second, a sustained increase in prices is detected at the end of phase III, which makes it possible to foresee a stabilization path with higher prices for the first years of phase IV. These changes will motivate both companies and individual energy investors to be proactive in making decisions about the risk management on carbon allowances. [ABSTRACT FROM AUTHOR]

The Russia-Ukraine military conflict, commencing on February 24, 2022, notably impacted the international community. This study aims to quantify the volatility engendered by the conflict, drawing from the analysis of stock market indices across 40 countries. Time-series returns data from January 1 to December 31, 2022, were examined utilizing EGARCH econometric models. The relationship between volatility and news regarding the conflict was analyzed through a vector autoregression model, and associations between variables were examined using the Granger causality test. Findings suggest that some markets proximate to Ukraine, notably in Hungary, Polassnd Poland, Serbia, Bosnia and Herzegovina, and the Czech Republic, reacted in anticipation of the conflict, days prior to February 24. Remote markets experienced comparatively lower volatility, along with the primary stock markets. Additionally, a decline in volatility was observed as war-related information became available. Notably, the period between March 2 and March 16, 2022, recorded the highest volatility in 21 countries. Conversely, the value markets of the US, China, Japan, the UK, and Germany navigated the analyzed period with lower volatilities. These results demonstrate that conflict shocks influence stock markets globally. The implications of these findings are significant for investors, decision-makers, portfolio managers, investment funds, and central banks. [ABSTRACT FROM AUTHOR]

Copyright of Cameroon Academy of Sciences Journal is the property of Cameroon Academy of Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent years, digital innovations especially emerged depend on Blockchain technology have caused a substantial transformation in the finance sector as in other sectors. Different financial assets have been revealed and began to be used as an investment tool along with this transformation in the markets. Cryptocurrencies that have a digital structure hold an important place among these assets. Dramatically increases in the daily transaction volume of currencies in the market have brought along different types of risks. These risks raised uncertainty on these currencies. Moreover, because cryptocurrencies are mostly used for the purpose of investment and speculation, it is important to understand the volatility movements and co-movements of cryptocurrencies and is substantially important, particularly because volatility can influence investment decisions. This study aims to determine the volatility transmission between cryptocurrencies to find useful answers about the volatility and the efficiency of markets. Daily logarithmic return series between 18 January 2018 - 14 February 2021 were used to analyze the volatility of five of the most common cryptocurrencies, namely Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), Ripple (XRP), IOTA by applying the RALS-ADF test, EGARCH, and DCC-GARCH models. We determined whether the market is efficient or not, and tested the existence of the asymmetric effect and volatility transmission in the market. According to our results, volatility shocks are not obtained persistent for only BTC. Furthermore, the presence of asymmetric effects and leverage effect valid for four cryptocurrencies. While asymmetric effects observed for BTC, no leverage effect has been observed during the period. We also analyzed nine pair-wise cryptocurrencies applying the DCC-GARCH model and we found that dynamic conditional correlation coefficients are statistically significant and positive for each pair.

AbstractThe Russia-Ukraine military conflict, commencing on February 24, 2022, notably impacted the international community. This study aims to quantify the volatility engendered by the conflict, drawing from the analysis of stock market indices across 40 countries. Time-series returns data from January 1 to December 31, 2022, were examined utilizing EGARCH econometric models. The relationship between volatility and news regarding the conflict was analyzed through a vector autoregression model, and associations between variables were examined using the Granger causality test. Findings suggest that some markets proximate to Ukraine, notably in Hungary, Polassnd Poland, Serbia, Bosnia and Herzegovina, and the Czech Republic, reacted in anticipation of the conflict, days prior to February 24. Remote markets experienced comparatively lower volatility, along with the primary stock markets. Additionally, a decline in volatility was observed as war-related information became available. Notably, the period between March 2 and March 16, 2022, recorded the highest volatility in 21 countries. Conversely, the value markets of the US, China, Japan, the UK, and Germany navigated the analyzed period with lower volatilities. These results demonstrate that conflict shocks influence stock markets globally. The implications of these findings are significant for investors, decision-makers, portfolio managers, investment funds, and central banks.

Across the globe, COVID-19 has disrupted the financial markets, making them more volatile. Thus, this paper examines the market volatility and asymmetric behavior of Bitcoin, EUR, S&P 500 index, Gold, Crude Oil, and Sugar during the COVID-19 pandemic. We applied the GARCH (1, 1), GJR-GARCH (1, 1), and EGARCH (1, 1) econometric models on the daily time series returns data ranging from 27 November 2018 to 15 June 2021. The empirical findings show a high level of volatility persistence in all the financial markets during the COVID-19 pandemic. Moreover, the Crude Oil and S&P 500 index shows significant positive asymmetric behavior during the pandemic. Apart from this, the results also reveal that EGARCH is the most appropriate model to capture the volatilities of the financial markets before the COVID-19 pandemic, whereas during the COVID-19 period and for the whole period, each GARCH family evenly models the volatile behavior of the six financial markets. This study provides financial investors and policymakers with useful insight into adopting effective strategies for constructing portfolios during crises in the future. [ABSTRACT FROM AUTHOR]

This research examines the correlations between the return volatility of cryptocurrencies, global stock market indices, and the spillover effects of the COVID-19 pandemic. For this purpose, we employed a two-stage multivariate volatility exponential GARCH (EGARCH) model with an integrated dynamic conditional correlation (DCC) approach to measure the impact on the financial portfolio returns from 2019 to 2020. Moreover, we used value-at-risk (VaR) and value-at-risk measurements based on the Cornish–Fisher expansion (CFVaR). The empirical results show significant long- and short-term spillover effects. The two-stage multivariate EGARCH model's results show that the conditional volatilities of both asset portfolios surge more after positive news and respond well to previous shocks. As a result, financial assets have low unconditional volatility and the lowest risk when there are no external interruptions. Despite the financial assets' sensitivity to shocks, they exhibit some resistance to fluctuations in market confidence. The VaR performance comparison results with the assets portfolios differ. During the COVID-19 outbreak, the Dow (DJI) index reports VaR's highest loss, followed by the S&P500. Conversely, the CFVaR reports negative risk results for the entire cryptocurrency portfolio during the pandemic, except for the Ethereum (ETH). [ABSTRACT FROM AUTHOR]

Copyright of Revista Contabilidade, Gestao E Governanca is the property of Revista Contabilidade, Gestao E Governanca and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study revisits the empirical relationship between output volatility and output for twelve OECD countries. An extended AR-EGARCH-M model was used to identify the structural break, asymmetric effect, jump effect and spillover effect. In addition to the classical logarithmic definition of growth, the study uses the Hodrick-Prescott filter to compute the deviations from the long-term trend as the output gap. The empirical results show that (i) the effect of output volatility on output differs across countries under the same model specifications; and (ii) while the in-mean effect and spillover effect are stronger for the output gap-based models, the jump effect has a major effect on output volatility under the classical logarithmic definition. [ABSTRACT FROM AUTHOR]

This study examined the effect of sectoral output volatility on economic growth and the determinants of economic growth in the Ethiopian economy. The study used annual time series data spanning from 1981 to 2018 and included capital stock, working-age population, trade balance, and sectoral output volatility as an explanatory variable. Using the Exponential General Autoregressive Conditional Heteroscedasticity (EGARCH) and Autoregressive Distributed Lag (ARDL) cointegration test, the study found a long-run relationship between economic growth and economic growth explanatory variables. From the ARDL model, capital stock and trade balance (which has been negative throughout the study period) was found to have a positive and negative significant effect on the economic growth of Ethiopia, respectively. In the long-run, volatility of industrial and service sector output growth had a negative and statistically significant effect on the economic growth of Ethiopia. In recent years the role of agriculture in the Ethiopian economy, particularly in terms of contribution to the national GDP, has been declining—indicating the growing importance of service and industrial sectors. Therefore, smoothening and maintaining the positive sectoral output growth is advisable for the betterment of the economy. Besides, balancing the foreign trade and curbing unrestricted importation is recommended as long as economic growth is concerned.

We examined volatility spillover effects from five prominent global stock markets to India's stock market during the pre-and-post COVID-19 outbreak using daily adjusted closing prices between January 2019 and September 2021 from six capital markets. The structural breakpoint was identified as 23 March 2020, as per the breakpoint unit root test, to examine and compare the results pre-and-post COVID-19. Results show that previous period news and volatility feeds the next period's volatility significantly and the volatility is found to be persistent. The analysis also shows that during the pre-COVID period there is a negative significant volatility spillover from four of the five selected stock markets (Australia, China, Japan, and Germany) to the Indian stock market, and that spillover continues in the post-COVID period. There is a positive significant return and volatility spillover from the US market to the Indian stock market in the post-COVID-19 period. The results of our study will be useful for retail investors and portfolio managers in understanding the portfolio allocation methods in case of volatility spillover arising due to the crisis caused by the COVID-19 outbreak. [ABSTRACT FROM AUTHOR]

This study proposes an estimation technique for developing the best fit ARIMA-GARCH model to predict the closing values of Nifty 50. The study put forward di¤erent methods to resolve the issue of non-stationarity in mean as well as variance of the series before starting the estimation process. This study has applied autoregressive integrated moving- average (ARIMA), generalized autoregressive conditional heteroscedasticity (GARCH), ex- ponential GARCH (EGARCH) and threshold GARCH (TGARCH) model along with other estimation procedures on the daily closing prices of Nifty 50 from Jan 1, 2009 to Dec 30, 2019. Finally, the study identifies ARIMA(2,1,2)-EGARCH(1,1,1) as best model to predict the closing prices of Nifty 50. The findings indicate that the static forecast provides better results as compared to the dynamic forecast. These research findings will add to the tool kit of domestic as well as international portfolio managers and investors to frame suitable NIFTY trade strategies with least possible risks. [ABSTRACT FROM AUTHOR]

The purpose of this study to investigate the dynamics of return linkages and volatility spillovers between Asian emerging stock markets (China, Hong Kong, Japan, Malaysia, Pakistan, and South Korea). To achieve this task, we used bivariate EGARCH (1) model. We used daily closing stock prices from January 01, 2010, to December 31, 2018. The findings revealed that the own lagged spillovers are statistically significant in all cases at one percent level. Our findings also show that the asymmetric volatility spillovers are significant in all sampled stock markets except China. We find unidirectional volatility spillovers from the markets of China toward Hong Kong, Malaysia toward South Korea, Hong Kong toward South Korea, Pakistan toward Hong Kong, and Japan toward South Korea. Moreover, the volatility spillovers in the majority stock markets are significant and bidirectional. Therefore, these markets are interrelated, and the spillover effect should be taken into consideration by policymakers who are responsible for economic decision making as they can save the financial sector from unexpected financial shockwaves. [ABSTRACT FROM AUTHOR]

We examine the impact of US monetary policy uncertainty (MPU) on the dollar exchange rates of ten Asian economies between February 2006 and January 2019. Our results, which are based on an EGARCH model, indicate that MPU tends to increase the variance of exchange rates rather than affecting the level of exchange rates. The results may have significant implications for the ten economies' conduct of monetary policy. [ABSTRACT FROM AUTHOR]

The debate about the role of speculators in commodity markets has been intensified after the financialization. The current study augments the empirical evidence related to speculators and their impact on volatility of commodity markets. In doing so, the study uses daily data between 12th November 2009 to 30th April 2020 for oil and gold future contracts from Pakistan Mercantile Exchange. The findings suggest that speculative activity positively and significantly affects the returns' volatility. These findings support the destabilizing hypothesis, which argues that speculators create distortions in commodity markets. The findings have important implication for market participants (e.g., absence of welfare gain for hedgers) and regulators.

In this study, we utilize various GARCH-type models to estimate and forecast volatility on S&P 500 returns and compare the results between the two financial crises, the GFC of 2008 (Global Financial Crisis of 2008) and the COVID-19 financial crisis. These two financial crises are different from the forming reasons by whether mainly caused by the financial factors. This study also makes the evaluations on the performance of these GARCH-type models in estimating and forecasting volatility, which may provide the efficient models for reference for the research of the volatility of the future potential financial crisis. We find that as for the AIC/BIC assessments on the estimation of volatility, the GJR-GARCH model performs better during the GFC of 2008, while the EGARCH model has the better performance during the COVID-19 financial crisis. With respect to the QLIKE loss function evaluation on the forecasting ability of volatility, the GJR-GARCH model performs better during the GFC of 2008, while symmetric GARCH model has better volatility forecasting ability during the COVID-19 financial crisis.

Using GARCH(p,q) models, in this study our aim is to examine and search the characteristics of volatility of Turkish sugar price. Due to the ARCH effects on price, ARCH(q), GARCH(p,q) and EGARCH(p,q) including these effects on mean and variance equations were estimated. Normal, t-Student, and generalized error distributions with Maximum Likelihood Estimation Method were estimated for these models. Determining the optimal parameters, Marquardt’s algorithm (1963) was used for maximizing the log-likelihood function. Mean absolute percentage error (MAPE), root mean square error (RMSE) and mean absolute deviation (MAD) were used to determine the fit model for making predicting. In this study, we found the best model as a GARCH (1,1) model. [ABSTRACT FROM AUTHOR]

This paper studies the effect of COVID-19 on the volatility of Australian stock returns and the effect of negative and positive news (shocks) by investigating the asymmetric nature of the shocks and leverage impact on volatility. We employ a generalised autoregressive conditional heteroskedasticity (GARCH) model and extend the analysis using the exponential GARCH (EGARCH) model to capture asymmetry and allegedly leverage. We proxy the news related to the negative effect of COVID-19 on the Australian health system and its economy as bad news, and on the other hand, measures taken by government economic stimulus packages through their monetary and fiscal policies as good news. The S&P ASX200 (ASX-200) index is used as a proxy to the Australian stock market, and we use value-weighted returns of the stocks listed on ASX-200 for the period 27 January 2020 to 29 December 2020. The empirical results suggest the EGARCH model fits better in capturing asymmetry and leverage than the GARCH model in estimating the volatility of the Australian stock returns. However, another interesting finding is that the EGARCH model with volatility equation without news demonstrates a larger (smaller) leverage effect of the negative (positive) shocks on the conditional volatility compared to its variant with the news. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19), the disease caused by the novel coronavirus SARS-CoV-2, has greatly affected financial markets, economies and societies worldwide. This study focusses on the Chinese stock markets. Based on Google Trends data during the period from 1 January 2020 to 12 April 2020, and using the exponential generalised autoregressive conditional heteroskedastic (EGARCH) model, this study finds that the higher uncertainty resulting from the COVID-19 pandemic is significantly associated with the drop in China's composite index, but this impact varies by sectors. Simultaneously, the higher uncertainty due to COVID-19 is significantly associated with greater volatility in stock returns for both the composite index and sector indices. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Finance Letters / Maliye Finans Yazıları Dergisi is the property of Maliye Finans Yazilari Yayimcilik Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Boğaziçi Journal: Review of Social, Economic & Administrative Studies is the property of Bogazici University, Faculty of Economics & Administrative Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Semestre Económico is the property of Sello Editorial de la Universidad de Medellin and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study aims to investigate the potential effects of structural breaks on volatility permanence and asymmetry in stock exchanges of fragile five countries. The study covers the stock market index of 12 countries that were evaluated as fragile fives by various investment institutions on different dates between January 2013 and November 2019. The GARCH and EGARCH models are used for volatility estimates, and ICSS iteration algorithm is used to detect structural breaks. Results of the study reveals that among the 12 stock exchanges Indonesia, India, Brazil, Russia and Turkey are the top five countries with persistent volatility in their stock market indices. According to the empirical results from the study, volatility models in which structural breaks were not included depicts that the permanence effect is high in Indonesia and India, however, it is relatively lower in Argentina and Egypt. In addition, asymmetric volatility and leverage effect was observed in all country indices except Argentina. While where the response to negative shocks are strongest the indices of Pakistan, Qatar, South Africa and India, the response in Russia is lower. No structural breaks were observed in South Africa, Mexico, Argentina and Qatar exchanges during the period considered in study. In addition, it was found that using structural breaks in volatility estimates increased the predictive performance of the models. [ABSTRACT FROM AUTHOR]

The debate about the role of speculators in commodity markets has been intensified after the financialization. The current study augments the empirical evidence related to speculators and their impact on volatility of commodity markets. In doing so, the study uses daily data between 12th November 2009 to 30th April 2020 for oil and gold future contracts from Pakistan Mercantile Exchange. The findings suggest that speculative activity positively and significantly affects the returns' volatility. These findings support the destabilizing hypothesis, which argues that speculators create distortions in commodity markets. The findings have important implication for market participants (e.g., absence of welfare gain for hedgers) and regulators. [ABSTRACT FROM AUTHOR]

Many researchers documented that if stock markets' returns series are significantly skewed, linear-GARCH(1,1) grossly underestimates the forecast values of the returns. However, this study showed that the linear Maximal Overlap Discreet Wavelet Transform MODWT-GARCH(1,1) actually gives an accurate forecast value of the returns. The study used the daily returns of four African countries' stock market indices for the period January 2, 2000, to December 31, 2014. The Maximal Overlap Discreet Wavelet Transform-GARCH(1,1) model and the Maximal Overlap Discreet Wavelet Transform-EGARCH(1,1) model are exhaustively compared. The results show that although both models fit the returns data well, the forecast produced by the Maximal Overlap Discreet Wavelet Transform-EGARCH(1,1) model actually underestimates the observed returns whereas the Maximal Overlap Discreet Wavelet Transform-GARCH(1,1) model generates an accurate forecast value of the observed returns.

Orientation: Modelling of international tourist arrivals’ volatility is vital for marketing, planning, policy formulation and investment purposes among others. Symmetric and asymmetric tourism volatility models for years 2000–2017 were fitted to assess the impact of good and bad news on Zimbabwe’s international tourist arrivals’ volatility. Research purpose: In this article, we model Zimbabwe’s monthly international tourist arrivals’ volatility using both symmetric and asymmetric volatility models and investigate the impact of good and bad news on international tourist arrivals’ volatility. Motivation for the study: Very few, if any, research papers have looked at volatility clustering and the impact of good and bad news on Zimbabwe’s international tourist arrivals’ volatility. These are important aspects for tourism managers, investors and the government in decision-making processes. Research design, approach and method: The ARMA-GARCH and ARMA-EGARCH models were used to model tourism volatility. Main findings: The ARMA(1,1)-GARCH(1,1) model under the t-distribution (STD) innovations shows unexpected tourism shocks having a strong impact, that persists for significant periods of time, on Zimbabwe’s tourist arrivals. The ARMA(1,1)-EGARCH(1,1) model under STD innovations indicated less impact of bad news on future tourism volatility than good news of the same size because the symmetry coefficient gamma (γ) is statistically significant and is above zero. Practical/managerial implications: Tourism volatility models unearth the impact of news on international tourist arrivals’ volatility. Good news attracts tourism investors, while bad news discourages tourism investors from investing. Tourism stakeholders, government and investors can use these informative volatility models to the success of the tourism industry in Zimbabwe. Contribution/value-add: This article highlights the importance of volatility models in the tourism industry.

Orientation: The behaviour of stock market return volatility and implications thereof in Southern African Development Committee (SADC). Research purpose: The main aim of this study was to examine leverage effects and volatility persistence in selected southern African stock markets. Motivation for the study: To examine the volatility of stock markets in SADC which has implications on investment risk. Research approach, design and method: The study adopted exponential generalised autoregressive conditional heteroscedasticity (1.1) model using generalised error distribution and Student’s t-distribution. Main findings: Leverage effects were evidenced in Namibia and South Africa. Other nations reflected mixed results depending on the error distribution assumed. Volatility persistence was noted in all nations save for Malawi. Practical/managerial implications: Investors in Namibia and South Africa are encouraged to include leverage effects in portfolio optimisation and value-at-risk computations. Firms raising funds in these nations should be prepared to incur a risk premium as compensation to creditors for assuming high risk. As such raising capital in such nations is expected to be expensive and difficult coupled by market illiquidity, other things being equal. Except for Malawi, firms operating in other SADC nations are encouraged to hedge their operations as the level of stock market volatility is persistent and notable. Contribution/value-add: The study focused on countries that are excluded from recent studies using current models of volatility. A comparison is therefore possible at country level and using two different error distribution assumptions which concretise the results.

This paper disentangles oil volatility risk to two components. The first component is attributed to crude oil, while the second is related to gasoline. This disentanglement serves the purpose of investigating the extent to which crude oil and gasoline are complementary in impacting return and variance residuals. The Realized-EGARCH model of Hansen et al. (J Appl Econom 29(5):774–799, 2014) is used to test the hypothesis that stock markets show some delay in incorporating oil information. This study shows that both crude oil- and gasoline-based information impact stock markets contemporaneously in a complementary fashion. Unlike the underreaction hypothesis, which is suggested as an explanation to the negative lagged effect of crude oil price change on return, the sequential information hypothesis explains better the ways information about oil is disseminated among U.S. industry portfolios. [ABSTRACT FROM AUTHOR]

This study investigates whether different specifications of univariate GARCH models can usefully forecast volatility on the foreign exchange market. The study uses only forecasts from an asymmetric GARCH model, namely Exponential GARCH (EGARCH) for GBP/USD exchange-rate and compares the estimates with the volatility for the period June 2016 to September 2019. The dataset is obtained from "Investing.com" and covers the period between June 2016 - September 2019. The data encompasses the slump of the GBP to a 31-year low due to a major political crisis in the United Kingdom. Besides averting the decision-making factors about the worrying consequences of Brexit, this paper reaches the conclusion that the EGARCH model could be used to predict volatility of the currencies in the future. [ABSTRACT FROM AUTHOR]

The sub-prime crisis in 2008 illustrated how systemic risk in the financial sector of one country could spread to the financial sectors in other countries, and subsequently result in a global financial crisis. This direct transfer of systemic risk was made possible by phenomena such as contagion and common shocks. The way in which these elements of interconnectedness can magnify seemingly small levels of systemic risk, and subsequently transfer between financial sectors illustrate the necessity for a more in-depth analysis. This measurement is done using three approaches. A dynamic conditional correlation (DCC) model is used to investigate contagion. To analyse the volatility spillover effect from the US to SA, an exponential generalized autoregressive conditional heteroskedastic (EGARCH) model is employed. Finally, a new contribution is made where a marginal expected shortfall (MES) model is used to set the FTSE/JSE All-Share Index (ALSI) as a hypothetical bank in the financial sector of the S&P 500. All approaches show weak evidence for a direct systemic risk transfer and therefore indicate that any systemic risk transfer is more likely to take an indirect form through changes in capital flows or interest rates. [ABSTRACT FROM AUTHOR]

The paper seeks to investigate the effect of both internal and external macroeconomic on stock market return volatility in Ghana and Nigeria over 16 year period of January 2000 to December 2016. The study employs time series approach to investigate the impact of lag of stock market return, consumer price index, 91 days Treasury bill rate exchange rate between domestic currency and US dollar, real domestic product, international crude oil prices, London Stock Exchange all share index and political cycle (independent variables) on stock market volatility of Ghana and Nigeria (dependent variables). It uses Exponential Generalised Autoregressive Conditional Heteroskedascity (EGARCH) as the main model. The results from the study indicate that macroeconomic/political factors affect stock market volatility. This is one of the few studies on Ghana and Nigeria and probably on Africa which tests for stock market volatility as a result of internal and external macroeconomic variable fluctuations. [ABSTRACT FROM AUTHOR]

Volatility Spillover refers to the interaction between volatilities of different financial markets and volatilities can transfer between markets. It is generally understood that the volatility process will show the extent to which new information is assimilated by the market. The paper studies financial innovation management of the Volatility Spillover between the Indian Gold Futures and Spot Gold Commodity Market. This study has used the daily price of futures and spot market of Multi Commodity Exchange, Mumbai for a period of seven years from 1st October 2009 to 30th September 2016. The study employs Bivariate Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) model to investigate innovative management of the volatility spillover dynamics. The Bivariate EGARCH Model result reveals that the GARCH effects are statistically significant, signifying that the degree of volatility persistence exists in the case of both Gold futures and spot market returns. This result indicates that there is a tendency for volatility to continue for long periods in both the markets, once a shock has occurred. The leverage effect parameters are positive and statistically significant for both futures and spot market return, indicating the existence of leverage effect. This indicates that positive shocks have a greater impact on this market than the negative shocks. The spillover coefficients are statistically significant for both futures and spot market return, indicating significant bi-directional spillovers exist across the futures and spot markets of Gold. The result indicates that the investor can use the information of one market to predict the behaviour of the other market. All the available information is reflected in the current price as the market assimilates immediately new information and as a consequence, there are no arbitrage opportunities. In contrast to the widely accepted hypothesis of the futures market, that due to its cost and hedging advantages, the futures market leads the spot market, the Indian Gold Futures market, during the period of study, fails to provide early information to spot market. [ABSTRACT FROM AUTHOR]

There would be rise in LNG shipping business with USA and Australia looking to boosts exports with promising new tonnages from the year 2018. The shipping Charter rates have seen great rise in 2011 but recently have dropping low due increase in supply of ships and limited growth in cargo. The order book of LNG is looking promising there is a need to understand the volatility of LNG charter rates and New Shipbuilding prices as huge capital investment is made in ordering these vessels. The paper analyses asymmetric relationship between New Shipbuilding prices and Short Term Charter rates of LNG shipping using VECM-EGARCH model. The results show existence of long term relationship between new shipbuilding prices and Short term charter rates. [ABSTRACT FROM AUTHOR]