Commonality in the LME aluminium and copper volatility processes through a Figarch lens

We consider dynamic representation of spot and three month aluminium and copper volatilities. These are the two most important metals traded in the London Metal Exchange (LME). They share common business cycle factors and are traded under identical contract specifications. We apply the bivariate FIGARCH model which allows parsimonious representation of long memory volatility processes. Our results show that spot and three month aluminium and copper volatilities follow long memory processes, that they exhibit a common degree of fractional integration and that the processes are symmetric. However, there is no evidence that the processes are fractionally cointegrated. This high degree of commonality may result from the common LME trading process

Fractional integration and cointegration in US financial time series data

This paper examines several US monthly financial time series data using fractional integration and cointegration techniques. The univariate analysis based on fractional integration aims to determine whether the series are I(1) (in which case markets might be efficient) or alternatively I(d) with d < 1, which implies mean reversion. The multivariate framework exploiting recent developments in fractional cointegration allows to investigate in greater depth the relationships between financial series. We show that there exist many (fractionally) cointegrated bivariate relationships among the variables examined

Comovements in volatility in the euro money market

This paper assesses the sources of volatility persistence in Euro Area money market interest rates and the existence of linkages relating volatility dynamics. The main findings of the study are as follows. Firstly, there is evidence of stationary long memory, of similar degree, in all series. Secondly, there is evidence of fractional cointegration relationships relating all series, except the overnight rate. Two common long memory factors are found to drive the temporal evolution of the volatility processes. The first factor shows how persistent volatility shocks are trasmitted along the term structure, while the second factor points to excess persistent volatility at the longer end of the yield curve, relative to the shortest end. Finally, impulse response analysis and forecast error variance decomposition point to forward transmission of shocks only, involving the closest maturities. JEL Classification: C32, F30, G10fractional integration and cointegration, fractional vector error correction model, liquidity e¤ect, money market interest rates, realized volatility

Estimating Persistence in the Volatility of Asset Returns with Signal Plus Noise Models

This paper examines the degree of persistence in the volatility of financial time series using a Long Memory Stochastic Volatility (LMSV) model. Specifically, it employs a Gaussian semiparametric (or local Whittle) estimator of the memory parameter, based on the frequency domain, proposed by Robinson (1995a), and shown by Arteche (2004) to be consistent and asymptotically normal in the context of signal plus noise models. Daily data on the NASDAQ index are analysed. The results suggest that volatility has a component of long- memory behaviour, the order of integration ranging between 0.3 and 0.5, the series being therefore stationary and mean-reverting.Fractional integration, long memory, stochastic volatility, asset returns