Results 71 to 80 of about 4,901 (213)
TESTING THE LONG RANGE-DEPENDENCE FOR THE CENTRAL EASTERN EUROPEAN AND THE BALKANS STOCK MARKETS [PDF]
Annals of the University of Oradea: Economic Science, 2013 In this study we tested the existence of long memory in the the return series for major Central Eastern European and Balkans stock markets, using the following statistical methods: Hurst Exponent, GPH method, Andrews and Guggenberger method, Reisen ...
Pece Andreea Maria +3 more
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Lecturas de Economía, 2011 Sesgos en estimación, tamaño y potencia de una prueba sobre el parámetro de memoria larga en modelos ARFIMA Resumen: Castaño et al. (2008) proponen una prueba para investigar la existencia de memoria larga, basada en el parámetro de diferenciación ...
Elkin Castaño Vélez +2 more
doaj +1 more source
Local Whittle estimation with (quasi‐)analytic wavelets
Journal of Time Series Analysis, Volume 45, Issue 3, Page 421-443, May 2024.In the general setting of long‐memory multivariate time series, the long‐memory characteristics are defined by two components. The long‐memory parameters describe the autocorrelation of each time series. And the long‐run covariance measures the coupling between time series, with general phase parameters.
Sophie Achard, Irène Gannaz
wiley +1 more source
International Journal of Finance &Economics, Volume 29, Issue 2, Page 1581-1608, April 2024.Abstract This article contributes to our understanding of the macro‐financial linkages in the high‐frequency domain during the recent health crisis. Building on the extant literature that mainly uses monthly or quarterly macro proxies, we examine the daily economic impact on intra‐daily financial volatility by applying the macro‐augmented HEAVY model ...
Guglielmo Maria Caporale +2 more
wiley +1 more source
, 2016 In this paper, we show that the central limit theorem (CLT) satisfied by the data-driven Multidimensional Increment Ratio (MIR) estimator of the memory parameter d established in Bardet and Dola (2012) for d $\in$ (--0.5, 0.5) can be extended to a ...
Bardet, Jean-Marc, Dola, Béchir
core +2 more sources
IET Intelligent Transport Systems, Volume 18, Issue 4, Page 691-708, April 2024.Based on multi‐source data, this study couples the travel characteristics identifying by introducing a concept of service dependency degree and a Bayesian optimization–long short time memory–convolutional neural network method to conduct the multi‐task online car‐hailing demand prediction. This method is applied to the main scenic spots in Beijing, and
Zile Liu +3 more
wiley +1 more source
Investigating Inflation Dynamics and Structural Change with an Adaptive ARFIMA Approach [PDF]
Previous models of monthly CPI inflation time series have focused on possible regime shifts, non-linearities and the feature of long memory. This paper proposes a new time series model, named Adaptive ARFIMA; which appears well suited to describe ...
Claudio Morana, Richard T. Baille
core
WIREs Data Mining and Knowledge Discovery, Volume 14, Issue 1, January/February 2024.A review of recent research on the application of deep learning models to price forecast of financial time series, with information on model architectures, applications, advantages and disadvantages, and directions for future research. Abstract Accurately predicting the prices of financial time series is essential and challenging for the financial ...
Cheng Zhang +2 more
wiley +1 more source
"Realized Volatility Risk" [PDF]
In this paper we document that realized variation measures constructed from high-frequency returns reveal a large degree of volatility risk in stock and index returns, where we characterize volatility risk by the extent to which forecasting errors in ...
David E. Allen +2 more
core +3 more sources
Dynamics of Inflation and Inflation Uncertainty Using ARFIMA- GARCH Model [PDF]
Faslnāmah-i Pizhūhish/Nāmah-i Iqtisādī, 2010 In this paper, we study inflation dynamics and then examine the relation of inflation and inflation uncertainty. At first, for filtering of predictable term of inflation series, we used time series model.
Teymour Mohammadi, Reza Teleblou
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