Common persistence in conditional variance: A reconsideration (original) (raw)
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Studies on volatility forecasting models indicate superior performance of generalised autoregressive conditional heteroscedasticity (GARCH) type models in the modelling conditional variance of asset returns. The utility of GARCH parameters lies in their ability in explaining the persistence of the conditional variance. The estimate of persistence provides a quantitative measure of the impact of a sudden significant change in the asset return on its future volatility. This study attempts to analyse the magnitude and time-evolving pattern in the persistence of conditional volatility using data on S&P CNX NIFTY 50 (henceforth, Nifty) benchmark index. The GARCH (1, 1) model is fitted on daily returns and a simple iterative scheme is used to re-estimate GARCH parameters on samples of different sizes and different time periods. The GARCH estimates obtained through repeated estimations furnish empirical evidence on the nature and consistency of the persistence parameter. Findings of the study confirm high persistence in the volatility process and indicate a positive relationship between the conditional volatility and volatility persistence.
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This article shows that the relationship between kurtosis, persistence of shocks to volatility, and first-order autocorrelation of squares is different in GARCH and ARSV models. This difference can explain why, when these models are fitted to the same series, the persistence estimated is usually higher in GARCH than in ARSV models, and, why gaussian ARSV models seem to be adequate, whereas GARCH models often require leptokurtic conditional distributions. We also show that introducing the asymmetric response of volatility to positive and negative returns does not change the conclusions. These results are illustrated with the analysis of daily financial returns.
Journal of Risk and Financial Management
This study rolls out a robust framework relevant for simulation studies through the Generalised Autoregressive Conditional Heteroscedasticity (GARCH) model using the rugarch package. The package is thoroughly investigated, and novel findings are identified for improved and effective simulations. The focus of the study is to provide necessary simulation steps to determine appropriate distributions of innovations relevant for estimating the persistence of volatility. The simulation steps involve “background (optional), defining the aim, research questions, method of implementation, and summarised conclusion”. The method of implementation is a workflow that includes writing the code, setting the seed, setting the true parameters a priori, data generation process and performance assessment through meta-statistics. These novel, easy-to-understand steps are demonstrated on financial returns using illustrative Monte Carlo simulation with empirical verification. Among the findings, the stud...
Estimating persistence in the volatility of asset returns with signal plus noise models
International Journal of Finance & Economics, 2012
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 longmemory behaviour, the order of integration ranging between 0.3 and 0.5, the series being therefore stationary and mean-reverting.