› › Volatility Co Movement

Volatility Co-Movement

Author : Radu Gabudean

Publisher :

Page : 25 pages

File Size : 46,93 MB

Release : 2005

Category :

ISBN :

Get eBook

Book Description

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.



Volatility and Co-Movement

Author : Sarod Khandaker

Publisher :

Page : 19 pages

File Size : 45,48 MB

Release : 2015

Category :

ISBN :

Get eBook

Book Description

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.



Volatility Co-Movement in Saudi Arabian and Kuwaiti Stock Markets

Author : Hyginus Leon

Publisher :

Page : 10 pages

File Size : 47,62 MB

Release : 2008

Category :

ISBN :

Get eBook

Book Description

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.





High Frequency Volatility Co-Movements in Cryptocurrency Markets

Author : Paraskevi Katsiampa

Publisher :

Page : 31 pages

File Size : 27,4 MB

Release : 2019

Category :

ISBN :

Get eBook

Book Description

Through the application of Diagonal BEKK and Asymmetric Diagonal BEKK methodologies to intra-day data for eight cryptocurrencies, this paper investigates not only conditional volatility dynamics of major cryptocurrencies, but also their volatility co-movements. We first provide evidence that all conditional variances are significantly affected by both previous squared errors and past conditional volatility. It is also shown that both methodologies indicate that cryptocurrency investors pay the most attention to news relating to Neo and the least attention to news relating to Dash, while shocks in OmiseGo persist the least and shocks in Bitcoin persist the most, although all of the considered cryptocurrencies possess high levels of persistence of volatility over time. We also demonstrate that the conditional covariances are significantly affected by both cross-products of past error terms and past conditional covariances, suggesting strong interdependencies between cryptocurrencies. It is also demonstrated that the Asymmetric Diagonal BEKK model is a superior choice of methodology, with our results suggesting significant asymmetric effects of positive and negative shocks in the conditional volatility of the price returns of all of our investigated cryptocurrencies, while the conditional covariances capture asymmetric effects of good and bad news accordingly. Finally, it is shown that time-varying conditional correlations exist, with our selected cryptocurrencies being strongly positively correlated, further highlighting interdependencies within cryptocurrency markets.



Volatility Comovement

Author : Laurent E. Calvet

Publisher :

Page : 60 pages

File Size : 46,58 MB

Release : 2004

Category : Economics

ISBN :

Get eBook

Book Description

We implement a multifrequency volatility decomposition of three exchange rates and show that components with similar durations are strongly correlated across series. This motivates a bivariate extension of the Markov-Switching Multifractal (MSM) introduced in Calvet and Fisher (2001, 2004). Bivariate MSM is a stochastic volatility model with a closed-form likelihood. Estimation can proceed by ML for state spaces of moderate size, and by simulated likelihood via a particle filter in high-dimensional cases. We estimate the model and confirm its main assumptions in likelihood ratio tests. Bivariate MSM compares favorably to a standard multivariate GARCH both in- and out-of-sample. We extend the model to multivariate settings with a potentially large number of assets by proposing a parsimonious multifrequency factor structure.



Co-Movement of Major Commodity Price Returns

Author : Francesca de Nicola

Publisher :

Page : 38 pages

File Size : 23,79 MB

Release : 2017

Category :

ISBN :

Get eBook

Book Description

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.



Stock Market Volatility

Author : Gamini Premaratne

Publisher :

Page : 0 pages

File Size : 32,95 MB

Release : 2003

Category :

ISBN :

Get eBook

Book Description

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.



Does Co-Movement of Conditional Volatility Matter in Asset Pricing? Further Evidence in the Downside and Conventional Pricing Frameworks

Author : Song Li

Publisher :

Page : 29 pages

File Size : 43,51 MB

Release : 2008

Category :

ISBN :

Get eBook

Book Description

In this paper we model country-specific equity market return and association between country-specific equity market volatility and that of the world market in the downside and conventional asset pricing frameworks. For this a Factor- ARCH type process is adopted where world market risk (beta) is estimated in the mean equation and exposure of country-specific market volatility to world market volatility (volatility beta) is estimated in the variance equation. Generally, the beta is estimated higher for developed markets than for emerging markets and the reverse is observed in volatility beta. Even though the two types of betas are positive and significant, a cross-sectional analysis reveals that volatility beta is not priced. We observe these results when the analysis is carried out from an international investor perspective. When we repeat the analysis in sub-periods delineated via breakpoints in the world market return series and with alternative specifications of the variance equation our findings remain largely unchanged.