Results 81 to 90 of about 23,794,745 (244)
Computational aspects of Bayesian spectral density estimation
, 2011 Gaussian time-series models are often specified through their spectral density. Such models present several computational challenges, in particular because of the non-sparse nature of the covariance matrix.
Chopin, Nicolas +2 more
core +5 more sources
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
A Generalized ARFIMA Process with Markov-Switching Fractional Differencing Parameter [PDF]
We propose a general class of Markov-switching-ARFIMA processes in order to combine strands of long memory and Markov-switching literature. Although the coverage of this class of models is broad, we show that these models can be easily estimated with the
Wen-Jen Tsay, Wolfgang Härdle
core
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
پژوهشهای ریاضی, 2020 Introduction The data obtained from observing a phenomenon over time is very common. One of the most popular models in time series and signal processing is the Autoregressive moving average model (ARMA).
Mahmod Afshari +2 more
doaj
Malaysian Journal of Computing, 2020 The main purpose of this study is to compare the performances of univariate and bivariate models on four-time series variables of the crude palm oil industry in Peninsular Malaysia.
Pauline Jin Wee Mah, Nur Nadhirah Nanyan
doaj +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
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
"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
Investigating the Efficacy of ARIMA and ARFIMA Models in Nigeria All Share Index Markets
ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, 2023 Dum Deebom Zorle +7 more
semanticscholar +1 more source