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The concept of volatility transmission and co-movement has witnessed a resurgence in the international finance literature in recent years after the black swan events which gave evidence of financial market linkages. The purpose of this paper is to examine the dynamic sources of volatility transmission in the foreign exchange market in recent financial market integration in Africa.

A conceptual framework was adapted from the extant literature and was used as the basis of modeling exchange rate volatility transmission. This paper adopts a quantitative research approach and opts for augmented DCC model to empirically unearth the sources of exchange rate volatility transmission.

The key findings of the study are that, the African market is more prone to shock from outside than in the region. Macroeconomic news surprises influence volatility transmission and co-movements. Robust support is found for trade balance, interest rate and gross domestic product. These findings clearly demonstrate the low level of financial development and challenges that sometimes exist in exchange rate-policy implementation by policy makers.

Interested academics and practitioners working in the area might incorporate bilateral investment into the model of exchange rate correlation in future research.

Unilaterally considering exchange rate volatility transmission and subsequent augmentation of the DCC model, this study makes a modest contribution to the examination of exchange rate correlations in Africa. This study makes an important contribution in not only addressing this imbalance, but more importantly improving the relative literature on exchange rate volatility transmission.

The purpose of this study is to investigate the volatility and forecast accuracy of the Islamic stock market for the period 1999–2017. This period is characterized by the occurrence of several economic and political events such as the September 11, 2001, terrorist attack and the 2007–2008 global financial crisis.

This study constructs a new hybrid generalized autoregressive conditional heteroskedasticity (GARCH)-type model based on an artificial neural network (ANN). This model is applied to the daily Dow Jones Islamic Market World Index during the period June 1999–January 2017.

The in-sample results show that the volatility of the Islamic stock market can be better described by the fractionally integrated asymmetric power ARCH (FIAPARCH) approach that takes into account asymmetry and long memory features. Considering the out-of-sample analysis, this paper has applied a hybrid forecasting model, which combines the FIAPARCH approach and the ANN. Empirical results reveal that the proposed hybrid model (FIAPARCH-ANN) outperforms all other single models such as GARCH, fractional integrated GARCH and FIAPARCH in terms of all performance criteria used in the study.

The results have some implications for Islamic investors, portfolio managers and policymakers. These implications are related to the optimal portfolio diversification decision, the hedging strategy choice and the risk management analysis.

The paper develops a new framework that combines an ANN and FIAPARCH model that introduces two important features of time series, namely, asymmetry and long memory.

Guar Seed crop is ruling the Indian International business mainly due to its application as a drilling fluid in shale energy industry concentrated in the USA. One of the allegations against futures market is its possible role in increasing the volatility of underlying physical market prices. Suspension of guar seed futures contract in 2012 at National Commodity Derivatives Exchange of India (NCDEX)-India, has reignited the controversy and raised an alarm bell to peek into obscure world of Indian commodity derivatives market. Against the backdrop of fiasco in guar futures trading, the purpose of this paper is to investigate whether sudden surge in futures trading volume leads to increase in the volatility of spot market prices.

Guar seed spot returns volatility is modeled as a GARCH (1, 1) process. Futures trading volume and open interest are segregated into expected and unexpected components. The data are analyzed from 2004 to 2011 using Augmented GARCH model to study the contemporaneous relationship between spot volatility and unexpected futures trading activity and Granger Causality test for examining the dynamic relationship between them and ascertaining causality.

Augmented GARCH model reports positive relationship between unexpected futures trading volume (UTV) and spot returns volatility, and, Granger Causality flows from UTV to spot volatility. Therefore, when the level of futures trading volume increases unexpectedly, the volatility of spot prices increases pointing toward the destabilizing impact of futures trading. However, hedger’s activity, represented by open interest is not seen to have any causal/destabilizing impact on spot price volatility of guar seed.

The study provides empirical evidence to support the concern of regulators, genuine hedgers and other traders about the presence of excessive speculation and market manipulations perpetrated through futures market that is disturbing the underlying physical market instead of strengthening it by aiding in price discovery and risk mitigation.

There are very few studies which have empirically investigated the temporal relation between volume and volatility in Indian agricultural commodity markets. With guar seed as a special case the present study investigates statistically the impact of futures trading on spot price volatility. In light of the findings of the study, the curb imposed on guar seed futures trading in 2012 was justified.

The purpose of this paper is to analyze the effect of investor sentiment, measured with Google internet search data, on volatility forecasts of the US REIT market.

The author uses the S&P US REIT index and collects search volume data from Google Trends for all US REIT. Two different Generalized Autoregressive Conditional Heteroskedastic models are then estimated, namely, the baseline model and the Google augmented model. Using these models, one-step-ahead forecasts are conducted and the forecast accuracies of both models are subsequently compared.

The empirical results reveal that search volume data can be used to predict volatility on the REIT market. Especially in periods of high volatility, Google augmented models outperform the baseline model.

The results imply that Google data can be used on the REIT market as a market indicator. Investors could use Google as an early warning system, especially in periods of high volatility.

This is the first paper to use Google search query data for volatility forecasts of the REIT market.

In any stock market, volatility is a significant factor in strengthening their asset pricing. The upsurge in volatility in the stock market can activate and bring changes in the financial risk. According to financial conventional theory, the stakeholders (investors) are selected to be balanced and variations in pertinent risk are also to be anticipated due to the outcome of the drive-in basic factors in Indian stock markets. The hypothesis shows that there are actions in systematic and unsystematic risks that are determined by volatility. It is allied to sentiment-driven in the trader movement.

The paper used the methodology of generalized autoregressive conditional heteroskedasticity-in mean GARCH-M and exponential GARCH-M (E-GARCH-M) methods on the Indian stock market. The data have been covered from 2000 to 2019.

Finally, the study suggests that due to the unfitness of the capital asset pricing model (CAPM), the selection has enhanced with sentiment is an important risk factor.

The investor sentiment and stock return volatility statement are established by using the investor sentiment amalgamated stock market index built.

The outcome of the study shows that there is an important association between stakeholder (investor) sentiment and stock return, in case of volatility behavioural finance can significantly explain the behaviour of stock returns on the Indian Stock Exchange.

The purpose of this paper is to ascertain the possible consequences of ban on futures trading of agriculture commodities in India by examining three critical issues: first, the author explores whether price discovery dominance changes between futures and spot in the pre-ban and post-relaunch phase both in the long run and short run. Second, the author examines the impact of ban and relaunch of futures trading on its underlying spot volatility for five sample cases of agriculture commodities (Wheat, Sugar, Soya Refined Oil, Rubber and Chana) using both parametric and non-parametric tests. Third, the author revisits the destabilization hypothesis in the light of ban on futures trading by examining the impact of unexpected component of liquidity of futures on spot volatility.

The author uses widely adopted methodology of co-integration to examine long-run relationship between spot and futures, while the short-run relationship is investigated using vector error correction model (VECM) and Granger causality to test price discovery in the pre-ban and post-relaunch phases. The second objective is explored using a combination of parametric and non-parametric tests such as Welch one-way ANOVA and Kruskal–Wallis test, respectively, to gauge the impact of ban on futures trading on spot volatility along with post hoc tests to investigate pairwise comparison of spot volatility among three phases (pre-ban, ban and post-relaunch) using Dunn Test. In addition, extensive robustness test is undertaken by adopting augmented E-GARCH model to ascertain the impact of ban and relaunch of futures trading on spot volatility. The third objective is investigated using Granger causality test between spot volatility and unexpected component of liquidity of futures estimated using Hodrick and Prescott (HP) filter to re-visit the destabilization hypothesis.

The author found extensive evidence for the dominance of futures market in the price discovery of agriculture commodities both in the pre-ban and post-relaunch phases in India. The ban on futures trading is found to have a destabilizing impact on spot volatility as evident from the findings of Wheat, Sugar and Rubber. In addition, it is observed that spot volatility was highest during the ban phase as compared to the pre-ban and post-relaunch phases for all four commodities barring Chana. The author found that destabilisation hypothesis holds true during the pre ban phase, while weakening of destabilization hypothesis is observed in the post-relaunch phase as unexpected futures liquidity has no role in driving the spot volatility.

This study is a novel attempt to empirically examine the potential impact of ban and relaunch of futures trading of agriculture commodities on two key market quality dimensions – price discovery and spot volatility. In addition, destabilization hypothesis is revisited to investigate the impact of futures trading on spot volatility during the pre-ban and post-relaunch period.

The study aims to analyze the interaction between macroeconomic uncertainty and stock market return and volatility for China and USA and tries to draw some invaluable inferences for the investors, portfolio managers and policy analysts.

Empirically the study uses GARCH family models to capture the time-varying volatility of stock market and macroeconomic risk factors by using monthly data ranging from 1995:M7 to 2018:M6. Then, these volatility series are further used in the multivariate VAR model to analyze the feedback interaction between stock market and macroeconomic risk factors for China and USA. The study also incorporates the impact of Asian financial crisis of 1997–1998 and the global financial crisis of 2007–2008 by using dummy variables in the GARCH model analysis.

The empirical results of GARCH models indicate volatility persistence in the stock markets and the macroeconomic variables of both countries. The study finds relatively weak and inconsistent unidirectional causality for China mainly running from the stock market to the macroeconomic variables; however, the volatility spillover transmission reciprocates when the impact of Asian financial crisis and Global financial crisis is incorporated. For USA, the contemporaneous relationship between stock market and macroeconomic risk factors is quite strong and bidirectional both at first and second moment level.

This study investigates the interaction between stock market and macroeconomic uncertainty for China and USA. The researchers believe that none of the prior studies has made such rigorous comparison of two of the big and diverse economies (China and USA) which are quite contrasting in terms of political, economic and social background. Therefore, this study also tries to test the presumed conception that macroeconomic uncertainty in China may have different impact on the stock market return and volatility than in USA.

The purpose of this paper is to examine the impact of structural breaks on the conditional variance of carbon emission allowance prices.

The authors employ the symmetric GARCH model, and two asymmetric models, namely the exponential GARCH and the threshold GARCH.

The authors show that the forecast performance of GARCH models improves after accounting for potential structural changes. Importantly, we observe a significant drop in the volatility persistence of emission prices. In addition, the effects of positive and negative shocks on carbon market volatility increase when breaks are taken into account. Overall, the findings reveal that when structural breaks are ignored in the emission price risk, the volatility persistence is overestimated and the news impact is underestimated.

The authors are the first to examine how the conditional variance of carbon emission allowance prices reacts to structural breaks.

The purpose of this paper is to examine the impact of terrorism in the Organization of the Petroleum Exporting Countries (OPEC) on the return and volatility of world price of oil.

GARCH models and daily data from 1987 till 2015 will be used.

The empirical results reveal that terrorism in the OPEC affects positively oil returns and negatively its volatility. Results also show that the different characteristics of the attacks are likely to have different impact on the return and volatility of oil prices. In overall terms, terrorism has a much larger positive impact on the return of oil prices and negative impact on its volatility if it targets the oil industry in the OPEC. This marginal effect is even greater if those attacks were successful.

The distinguishing feature of this paper is that the authors use a framework that takes into account different attributes for terrorism the success of the attacks, the intensity of the attacks and the associated targets.

The purpose of this paper is to understand the volatility in commodity futures and spot markets. The study starts with a few questions: first, the effect of seasonality on the volatility is studied. Thereafter, the presence of structural breaks in the variance is identified. At last the seasonality, structural shifts and spillover effects are examined together to find out their effects on volatility.

The methodology heavily employs econometric tools and techniques. The monthly seasonal dummies are incorporated to identify the effects of seasonality on volatility. Then, the presence of break in volatility is tested by cumulative sum of squares (CUSUM test), followed by generalized autoregressive conditional heteroscedastictity and EGARCH models are measured by including seasonal dummies, break dummies and the residuals of other market in the variance equation to determine spillover effects.

It is found that the effects of seasonality on volatility cannot be ignored as the effects are significant. The presence of asymmetry is detected in all the commodities. The presence of seasonality and structural breaks in the variance equation are statistically able to reduce the volatility but the magnitude is very negligible with an exception in cumin futures markets. Bi-directional volatility spillover between futures and spot markets is observed in all the commodities and the effect of spillover is more from spot markets to the futures markets.

This study is limited to a few agro commodities which are well traded. This study could have been extended to the other thinly traded commodities. This study has also taken only near month futures contracts as it contains more information but the same could have been studied by taking far month contracts also.

The present study attempted to understand the conjugated effects of seasonality, structural breaks and spillover on volatility of commodity markets which is not apparent in the previous studies. This study has also employed methodological rigor to identify the breaks in the variance equation. In addition to this it has also investigated whether Indian commodity futures markets are informationally more efficient than the spot markets.