LOBATO, I.N. and C. VELASCO, 2000. Long memory in stock-market trading volume. Journal of Business & Economic Statistics, Vol. 18, No. 4. (Oct., 2000), pp. 410-427. [Cited by 30] (4.75/year)
Abstract: "This article examines consistent estimation of the long-memory parameters of stock-market trading volume and volatility. The analysis is carried out in the frequency domain by tapering the data instead of detrending them. The main theoretical contribution of the article is to prove a central limit theorem for a multivariate two-step estimator of the memory parameters of a nonstationary vector process. Using robust semiparametric procedures, the long-memory properties of trading volume for the 30 stocks in the Dow Jones Industrial Average index are analyzed. Two empirical results are found. First, there is strong evidence that stock-market trading volume exhibits long memory. Second, although it is found that volatility and volume exhibit the same degree of long memory for most of the stocks, there is no evidence that both processes share the same long-memory component."
LUX, T. and T. KAIZOJI, 2004. Forecasting Volatility and Volume in the Tokyo Stock Market: The Advantage of Long Memory Models. [Cited by 1] (0.43/year)
Abstarct: "We investigate the predictability of both volatility and volume for a large sample of Japanese stocks. The particular emphasis of this paper is on assessing the performance of long memory time series models in comparison to their short-memory counterparts. Since long memory models should have a particular advantage over long forecasting horizons, we consider predictions of up to 100 days ahead. In most respects, the long memory models (ARFIMA, FIGARCH and the recently introduced multifractal models) dominate over GARCH and ARMA models. However, while FIGARCH and ARFIMA also have a number of cases with dramatic failures of their forecasts, the multifractal model does not suffer from this shortcoming and its performance practically always improves upon the naïve forecast provided by historical volatility. As a somewhat surprising result, we also find that, for FIGARCH and ARFIMA models, pooled estimates (i.e. averages of parameter estimates from a sample of time series) give much better results than individually estimated models."
LIESENFELD, R., Identifying Common Long-Range Dependence in Volume and Volatility Using High-Frequency Data. papers.ssrn.com. [Cited by 1] (?/year)
Abstract: "This paper examines the joint long-run dynamics of trading volume and return volatility in futures contracts on the German stock index DAX using a sample of 5-minute returns and trading volume. Employing robust semiparametric methods of inference on memory parameters, I find that volume and volatility exhibit the same degree of long-memory which is consistent with a mixture-of-distributions (MOD) model in which the latent number of information arrivals follows a long-memory process. However, there is some evidence that volume and volatility are not driven by the same long-memory process suggesting that the MOD model cannot explain the joint long-run dynamics of volatility and volume."
FERA, F., et al., 1999. Hippocampal volume correlates with long-term memory impairment following severe closed head injury: …. NEUROIMAGE. [not cited] (0/year)