Long Memory in Stock-Market Trading Volume

By Ignacio N. Lobato, Carlos Velasco. 2000

198 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/24e8fd1d57090d3969e5828d778046405163c5b7

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Citations in the corpus

Exact local Whittle estimation of fractionally cointegrated systems (2012, methodology): “As shown by Velasco (1999) and Lobato and Velasco (2000), tapering allows one to accommodate both stationary and nonstationary processes in local Whittle estima- tion.”
The Long Memory of the Efficient Market (2003, unknown): “Another market property that seems to have long-memory properties is stock market trading volume [8].”
Forecasting volatility and volume in the Tokyo stock market: Long memory, fractality and regime switching (2007, background): “Given the evidence on similar long term dependence in both series (Bollerslev and Jubinski, 1999; Lobato and Velasco, 2000; Ray and Tsay, 2000) it seems interesting to explore whether models with this feature are similarly capable of predicting both future volume and volatility.”
Asymptotic normality of narrow-band least squares in the stationary fractional cointegration model and volatility forecasting (2006, background): “This interval is relevant for many applications in finance, see e.g. Lobato & Velasco (2000), Andersen, Bollerslev, Diebold & Ebens (2001), and Andersen, Bollerslev, Diebold & Labys (2001).”
Financial power laws: Empirical evidence, models, and mechanism (2016, background): “Focusing on absolute returns, | ret |, as one of its most frequently analyzed manifestations, the pertinent power law applies to their autocovariance function:”
Local Whittle Analysis of Stationary Fractional Cointegration and the Implied–Realized Volatility Relation (2007, methodology): “Equation (5) is the natural multivariate extension of (3), including some multivariate fractional ARIMA models as special cases, and was also considered in previous work, for example, Robinson (1995b), Lobato (1999), and Robinson and Yajima (2002).”
Estimating Fractional Cointegration in the Presence of Polynomial Trends (2003, unknown): “Even in standard cointegration, recent literature has allowed for linear trends without the above restriction; see LM utkepohl and Saikkonen (2000). The philosophy we adopt here is that the concept of cointegration should entail a relationship solely between the stochastic components of the series, ”
How Markets Slowly Digest Changes in Supply and Demand (2008, unknown): “This property has been verified by many authors on many different financial time series (for example, Mandelbrot [1963], Lux [1996], Gopikrishnan et al.”
Gaussian Semi‐parametric Estimation of Fractional Cointegration (2003, methodology): “First, ~ GuðdoÞ 1⁄4 Gþ opð1Þ from Robinson (1995b) and Velasco (1999b), and”
Gaussian semiparametric estimation of multivariate fractionally integrated processes (2007, background): “Lobato and Velasco (2000) extend it to analyze the two-step Gaussian semiparametric estimation of multivariate nonstationary long-range dependent processes. of da.”
Residual Log-Periodogram Inference for Long-Run-Relationships (2002, unknown): “because from Velasco (1999b) (see also Lemmas 1 and 3 of Lobato and Velasco, 2000 ) and for tapered”
Agent-based Computational Finance (2006, background): “has recently been shown to be a long-memory process with persistence extending out many periods [see, e. g., Logato & Velasco (2000) ].”
Fully modified narrow‐band least squares estimation of weak fractional cointegration (2011, unknown): “That is, when normalized as in Robinson (1994) and Robinson & Marinucci (2003), the NBLS estimator converges to a degenerate distribution (a constant) in the case of non-zero coherence between the regressors and the errors at the origin.”
Determining the cointegrating rank in nonstationary fractional systems by the exact local Whittle approach (2007, background): “Thus, as pointed out by Robinson and Yajima (2002, page 229), the multivariate local Whittle estimation developed by Lobato (1999) and Lobato and Velasco (2000) is not appropriate under cointegration since it assumes full rank of G.”
Long range dependence in financial markets (2005, unknown): “On the empirical side, however, there is a lot of evidence pointing to positive dependence over large time horizons in absolute returns [5, 15, 16, 21, 43, 57] but not in the returns themselves, showing that it is more interesting to use fractional processes as models of volatility rather than for m”
Stochastic behavioral asset pricing models and the stylized facts (2008, background): “Convincing evidence for long-term dependence in volume has been presented in Lobato and Velasco (1998) although the authors also point out that volatility and volume do not share exactly the same degree of long-term dependence (i.e. have di erent decay parameters λ).”