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In this paper, we analyse historical stock market volatility and co-movement behaviour of three emerging markets and three developed economies from January 2001 to December 2012. We find evidence that the sample of emerging economies exhibits higher stock market volatility during the study period and these volatilities increases during the global financial crisis (GFC). There is also evidence that our sample of the emerging economies exhibit higher level of stock market co-movement behaviour during the study period, for example Indonesia and Malaysia exhibit higher R-square values during 2007-2012. However, we do not find any evidence of a statistically significant correlation coefficient between the volatility measures and the co-movement measures for our sample developed and emerging countries, except for Indonesia. Therefore, it is concluded that both these market models capture different aspects of stock market behaviour.
I estimate a GARCH-based volatility factor model that incorporates market volatility and information from high-frequency data. I find that index and stock volatility co-move more after the stock becomes part of SP500. This effect is characteristic to higher frequencies (i.e. hourly) and it is beyond what is predicted by an increase in return comovement. One proposed hypothesis consistent with the findings argues that volatility comovement is induced by 'trading mechanism noise' such as noise generated during index arbitrage operations. Additional behavioral hypotheses may be supported by my results. Moreover, volatility has more uniform intra-day distribution after the addition.
This paper provides a comprehensive analysis of the degree of co-movement among the nominal price returns of 11 major energy, agricultural and food commodities based on monthly data between 1970 and 2013. A uniform-spacings testing approach, a multivariate dynamic conditional correlation model and a rolling regression procedure are used to study the extent and the time-evolution of unconditional and conditional correlations. The results indicate that (i) the price returns of energy and agricultural commodities are highly correlated; (ii) the overall level of co-movement among commodities increased in recent years, especially between energy and agricultural commodities and in particular in the cases of maize and soybean oil, which are important inputs in the production of biofuels; and (iii) particularly after 2007, stock market volatility is positively associated with the co-movement of price returns across markets.
We use data on realized volatility to establish co-movement in volatility on the Saudi Arabian and Kuwaiti stock exchanges. We show, in addition, that the probability of positive and negative co-movement are related to the volatility of international equity prices and volatility of oil prices.
This paper provides a comprehensive analysis of the degree of co-movement among the nominal price returns of 11 major energy, agricultural, and food commodities using monthly data between 1970 and 2013. The authors study the extent and the time evolution of unconditional and conditional correlations using a uniform-spacings testing approach, a multivariate dynamic conditional correlation model and a rolling regression procedure.
An understanding of volatility in stock markets is important for determining the cost of capital and for assessing investment and leverage decisions as volatility is synonymous with risk. Substantial changes in volatility of financial markets are capable of having significant negative effects on risk averse investors. Using daily returns from 1992 to 2002, we investigate volatility co-movement between the Singapore stock market and the markets of US, UK, Hong Kong and Japan. In order to gauge volatility comovement, we employ econometric models of (i) Univariate GARCH, (ii) Vector Autoregression and (iii) a Multivariate and Asymmetric Multivariate GARCH model with GJR extensions. The empirical results indicate that there is a high degree of volatility co-movement between Singapore stock market and that of Hong Kong, US, Japan and UK (in that order). Results support small but significant volatility spillover from Singapore into Hong Kong, Japan and US markets despite the latter three being dominant markets. Most of the previous research concludes that spillover effects are significant only from the dominant market to the smaller market and that the volatility spillover effects are unidirectional. Our study evinces that it is plausible for volatility to spill over from the smaller market to the dominant market. At a substantive level, studies on volatility co-movement and spillover provide useful information for risk analysis.
Fixed income volatility and equity volatility evolve heterogeneously over time, co-moving disproportionately during periods of global imbalances and each reacting to events of different nature. While the methodology for options-based "model-free" pricing of equity volatility has been known for some time, little is known about analogous methodologies for pricing various fixed income volatilities. This book fills this gap and provides a unified evaluation framework of fixed income volatility while dealing with disparate markets such as interest-rate swaps, government bonds, time-deposits and credit. It develops model-free, forward looking indexes of fixed-income volatility that match different quoting conventions across various markets, and uncovers subtle yet important pitfalls arising from naïve superimpositions of the standard equity volatility methodology when pricing various fixed income volatilities.