Persistence of volatility in futures markets

Abstract

This article examines the characteristics of key measures of volatility for different types of futures contracts to provide a better foundation for modeling volatility behavior and derivative values. Particular attention is focused on analyzing how different measures of volatility affect volatility persistence relationships. Intraday realized measures of volatility are found to be more persistent than daily measures, the type of GARCH procedure used for conditional volatility analysis is critical, and realized volatility persistence is not coherent with conditional volatility persistence. Specifically, although there is a good fit between the realized and conditional volatilities, no coherence exists between their degrees of persistence, a counterintuitive finding that shows realized and conditional volatility measures are not a substitute for one another.

Modelling day ahead Nord Pool forward price volatility: Realized volatility versus GARCH models

“…However, the KPSS-test (which test the opposite hypothesis -1(0», provide a somewhat different conclusion. These results are consistent with findings in traditional financial futures markets and provide evidence of long-memory and thus hyperbolic decay of the autocorrelation function [15].…”

Covariance estimation using high-frequency data: Analysis of Nord Pool electricity forward data

“…The hyperbolic-decay process is a fractionally integrated process with a fractional order ranging from 0 to 1. When the fractional order is between 0 and 0.5, the process is mean-reverting stationary [12]. To test the decay rate both the unit-root ADF test [22] …”

“…These studies aim to document the statistical characteristics of the financial returns or the "stylized facts", as it is often named in the econometric and finance literature, where the understanding of the market is of interest in itself and can give useful information for the specification and estimation of predictive models. Notable examples of studies within the financial field of stylized facts are those of individual stocks and stock indices [9][10][11][12], bonds [11], currencies [2,4,11,12] and agricultural commodities [12]. Some of these studies, in addition to variance and volatility analyses, also focus on the stylized facts of realized covariances and correlations between assets investigated [4,9,11].…”

Dual Long-Memory, Structural Breaks and the Link Between Turnover and the Range-Based Volatility

“…We can choose from among several alternative measures, each of which uses di¤erent information from the available daily price data. To avoid the microstructure biases introduced by high frequency data, and based on the conclusion of Chen et al (2006) that the range-based and highfrequency integrated volatility provide essentially equivalent results, we employ the classic range-based estimator of Garman and Klass (1980) to construct the daily volatility (y gt ) as follows…”