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Volatility Dynamics (volatility + dynamics)

Distribution by Scientific Domains

Business, Economics, Finance and Accounting	80%

Selected Abstracts

Return and Volatility Dynamics in the Spot and Futures Markets in Australia: An Intervention Analysis in a Bivariate EGARCH-X Framework

THE JOURNAL OF FUTURES MARKETS, Issue 9 2001
Ramaprasad Bhar
This article provides evidence of linkages between the equity market and the index futures market in Australia, where the futures market has experienced a major structural event due to the futures contract respecification. A bivariate Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model is developed that includes a cointegrating residual as an explanatory variable for both the conditional mean and the conditional variance. The conditional mean returns from both markets are influenced by the long-run equilibrium relationship, and these markets are informationally linked through the second moments. The crossmarket spillovers exhibit asymmetric behavior in that the volatility responses to past standardized innovations are different for market advances and market retreats. An intervention analysis shows that some of the parameters describing the return-generating process have shifted after the contract respecification by the futures exchange. © 2001 John Wiley & Sons, Inc. Jrl Fut Mark 21:833,850, 2001 [source]

Volatility dynamics and heterogeneous markets

INTERNATIONAL JOURNAL OF FINANCE & ECONOMICS, Issue 2 2006
David G. McMillan
Abstract Recent research has suggested that intra-day volatility may possess a component structure, resulting either from the arrival of heterogeneous information or the actions of heterogeneous market agents. This paper reports direct evidence for the existence of such components in S&P500 index and DM/$ exchange rate data. Estimation of a FIGARCH model supports the contention that volatility dynamics result from multiple sources. Using a HARCH conditional variance model which defines volatility components over differing time horizons, confirmatory evidence of heterogeneous components is reported, in which context the impact of high-frequency speculation and noise-trading are particularly apparent. Copyright © 2006 John Wiley & Sons, Ltd. [source]

Return Dynamics when Persistence is Unobservable

MATHEMATICAL FINANCE, Issue 4 2001
Timothy C. Johnson
This paper proposes a new theory of the sources of time-varying second (and higher) moments in financial time series. The key idea is that fully rational agents must infer the stochastic degree of persistence of fundamental shocks. Endogenous changes in their uncertainty determine the evolution of conditional moments of returns. The model accounts for the principal observed features of volatility dynamics and implies some new ones. Most strikingly, it implies a relationship between ex post trends, or momentum, and changes in volatility. [source]

Delivery horizon and grain market volatility

THE JOURNAL OF FUTURES MARKETS, Issue 9 2010
Berna Karali
We study the difference in the volatility dynamics of CBOT corn, soybeans, and oats futures prices across different delivery horizons via a smoothed Bayesian estimator. We find that futures price volatilities in these markets are affected by inventories, time to delivery, and the crop progress period and that there are important differences in the effects across delivery horizons. We also find that price volatility is higher before the harvest starts in most cases compared to the volatility during the planting period. These results have implications for hedging, options pricing, and the setting of margin requirements. © 2010 Wiley Periodicals, Inc. Jrl Fut Mark 30:846,873, 2010 [source]

Empirical modelling of the DEM/USD and DEM/JPY foreign exchange rate: Structural shifts in GARCH-models and their implications

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 1 2002
Helmut Herwartz
Abstract We analyse daily changes of two log foreign exchange (FX) rates involving the Deutsche Mark (DEM) for the period 1975,1998, namely FX-rates measured against the US dollar (USD) and the Japanese yen (JPY). To account for volatility clustering we fit a GARCH(1,1)-model with leptokurtic innovations. Its parameters are not stable over the sample period and two separate variance regimes are selected for both exchange rate series. The identified points of structural change are close to a change of the monetary policies in the US and Japan, the latter of which is followed by a long period of decreasing asset prices. Having identified subperiods of homogeneous volatility dynamics we concentrate on stylized facts to distinguish these volatility regimes. The bottom level of estimated volatility turns out be considerably higher during the second part of the sample period for both exchange rates. A similar result holds for the average level of volatility and for implied volatility of heavily traded at the money options. Copyright © 2002 John Wiley & Sons, Ltd. [source]