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This paper examines the dynamic linkages of volatility of energy commodities with bullion and the metal market. The proxies of energy commodities are crude oil and natural gas; bullion markets are Gold, silver and platinum and metal markets are copper and zinc. We collect daily data extending from March 18, 2010, to January 15, 2021, a period for about 12 years and employ Granger causality, Dynamic Conditional Correlation (DCC), Diebold Yilmaz (2012), Baruník & Křehlík (2018), and Network analysis for the purpose of examining spillover effect in the data considered. It is observed that there are short-run dynamic spillovers from energy (crude oil) to metal (copper) while long-run linkage is witnessed among all the constituent series. Further, Baruník & Křehlík (2018) test reveals that the total connectedness of the seven data series under study are found to be higher in frequency 2 (6 days to 15 days) than in the short run and long run. Referring to the network analysis, negative correlations are found between each pair of indices considered, i.e., Gold, silver, platinum, zinc, copper with crude oil while positive correlation is witnessed between Gold and silver. In addition, we determine portfolio hedge ratios and portfolio weights for the investors and portfolio managers. It is found that the Crude /Zinc pair had the most expensive optimal hedge ratio, while Crude/Gold had the least expensive hedging.

China is considered the largest emerging economy and thus investors perceived as an attractive investment. We examine the spillover effect from Chinese stock exchange to stock exchanges of Asia and Latin America, namely, India, Indonesia, Mexico, and Brazil. For empirical purpose, the study employs VARMA-Multivariate Generalized Autoregressive Conditional Heteroskedasticity (MGARCH) model with BEKK, diagonal, Constant Conditional Correlation (CCC), and finally, Dynamic Conditional Correlation (DCC) specifications. DCC model outperforms among others and identifies two diversification opportunities with Mexican (MEXICAN) and Indian stock market (BSE). Finally, the hedge ratio and portfolio weights have been calculated. The hedge ratios between China and Mexico (SSE/MEX), and China and India (SSE/BSE) were 0.01 and 0.06, respectively. This implies that a $1 long position in Chinese market (SSE) could be hedged with a 1 cent short position in Mexico (MEXICAN) and a 6 cent short position in the Indian Market (BSE). The findings of this paper provide an insight into investors and policymakers.

Investors become jittery when they do not earn return on their hard earned money. In the same time, they want to make their investment in safe place rather than losing it. For better return, they also want to estimate the volatility in stock market. The basic purpose of the present study is to forecast the volatility in stock return of emerging economies. For the same, the adjusted daily closing price of eleven countries is considered for five years. Generalized Autoregressive Conditional Heteroscedasticity (GARCH) has been applied to predict the stock return of these countries. The different orders of GARCH have been applied in predicting the volatility. It is found that the volatility of every stock return can be forecasted.