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A New Implementation of Network GARCH Model for Stock Volatility and Co‐Volatility Forecasting
Journal of Forecasting, EarlyView.ABSTRACT Volatility clustering and spillovers are key features of financial time series with many cross‐sectional assets. While network analysis links similar or correlated stocks and helps trace volatility spillovers, contemporary multivariate ARCH‐GARCH formulations struggle to represent structured network dependence and remain parsimonious.
Peiyi Zhou
wiley +1 more source
Journal of Forecasting, EarlyView.ABSTRACT Deep learning‐based long time series forecasting (LTSF) has achieved high accuracy by effectively capturing the underlying trends, seasonality, and temporal dependencies within time series data. However, at the individual entity level, termed the low aggregation level (LAL), intermittency, irregularity, and data sparsity undermine the ...
Hanbyeol Park +5 more
wiley +1 more source
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2021
Spatial autocorrelation is an assessment of the correlation between two random variables which describe the same aspect of the phenomenon under study, referred to two locations of the domain. The suffix “auto” is justified since in some sense the spatial autocorrelation quantifies the correlation of a variable with itself over space.
Posa D., De Iaco S.
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Spatial autocorrelation is an assessment of the correlation between two random variables which describe the same aspect of the phenomenon under study, referred to two locations of the domain. The suffix “auto” is justified since in some sense the spatial autocorrelation quantifies the correlation of a variable with itself over space.
Posa D., De Iaco S.
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Spatial Interaction and Spatial Autocorrelation
2008The objective is to combine insights from two research traditions, spatial interaction modelling and spatial autocorrelation modelling, to deal with the issue of spatial autocorrelation in spatial interaction data analysis. First, the problem is addressed from an exploratory perspective for which a generalisation of the Getis–Ord G statistic is ...
Fischer, Manfred M. +2 more
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A Biparametric Approach to Spatial Autocorrelation [PDF]
Environment and Planning A: Economy and Space, 1979 In spatial econometric models, autocorrelation among error terms is usually incorporated by means of the so-called contiguity matrix W, determining the interdependence between the spatial observations on the dependent variable. In this paper, the analysis is generalized by introducing two contiguity matrices, related to two autocorrelation parameters ...
A S Brandsma, R H Ketellapper
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Spatial pattern and spatial autocorrelation
1995The spatial pattern of a distribution is defined by the arrangement of individual entities in space and the geographic relationships among them. The capability of evaluating spatial patterns is a prerequisite to understanding the complicated spatial processes underlying the distribution of a phenomenon.
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2018
This chapter respects the six procedures of spatial autocorrelation covered by myGeoffice©, including variogram setup and fitness, Moran I correlograms, an innovative version of the conventional Moran scatterplot and the recent Moran variance scatterplot.
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This chapter respects the six procedures of spatial autocorrelation covered by myGeoffice©, including variogram setup and fitness, Moran I correlograms, an innovative version of the conventional Moran scatterplot and the recent Moran variance scatterplot.
openaire +1 more source
Spatial autocorrelation and toponym ambiguity
Proceedings of the 5th Workshop on Geographic Information Retrieval, 2008In this paper, we explore the spatial distribution of the referents of ambiguous toponyms and compare it to the distribution of randomly selected unambiguous toponym pairs. We show that for a number of gazetteers, ambiguous toponyms are spatially autocorrelated and that typical autocorrelations are similar to the size of document scopes for a newspaper
Brunner, Tobias J, Purves, Ross S
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Hidden negative spatial autocorrelation
Journal of Geographical Systems, 2006Mostly lip service treatments of negative spatial autocorrelation (NSA) appear in the literature, although spatial scientists confront it in practice. NSA was detected serendipitously in recalcitrant empirical analyses containing a sizeable amount of global positive spatial autocorrelation (PSA) unaccounted for by standard spatial statistical models ...
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