A Markov-switching spatio-temporal ARCH model Article Swipe
Tzung Hsuen Khoo
,
Dharini Pathmanathan
,
Philipp Otto
,
Sophie Dabo‐Niang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2310.02630
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2310.02630
Stock market indices are volatile by nature, and sudden shocks are known to affect volatility patterns. The autoregressive conditional heteroskedasticity (ARCH) and generalized ARCH (GARCH) models neglect structural breaks triggered by sudden shocks that may lead to an overestimation of persistence, causing an upward bias in the estimates. Different regime-switching models that have abrupt regime-switching governed by a Markov chain were developed to model volatility in financial time series data. Volatility modelling was also extended to spatially interconnected time series, resulting in spatial variants of ARCH models. This inspired us to propose a Markov switching framework of the spatio-temporal log-ARCH model. In this article, we discuss the Markov-switching extension of the model, the estimation procedure and the smooth inferences of the regimes. The Monte-Carlo simulation studies show that the maximum likelihood estimation method for our proposed model has good finite sample properties. The proposed model was applied to 28 stock indices data that were presumably affected by the 2015-2016 Chinese stock market crash. The results showed that our model is a better fit compared to that of the one-regime counterpart. Furthermore, the smoothed inference of the data indicated the approximate periods where structural breaks occurred. This model can capture structural breaks that simultaneously occur in nearby locations.
Related Topics
Concepts
Arch
Econometrics
Volatility (finance)
Heteroscedasticity
Autoregressive model
Autoregressive conditional heteroskedasticity
Markov chain
Stock market
Markov chain Monte Carlo
Structural break
Stock market index
Computer science
Mathematics
Economics
Monte Carlo method
Statistics
Engineering
Geography
Context (archaeology)
Archaeology
Civil engineering
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2310.02630
- https://arxiv.org/pdf/2310.02630
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387390013
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4387390013Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2310.02630Digital Object Identifier
- Title
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A Markov-switching spatio-temporal ARCH modelWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-10-04Full publication date if available
- Authors
-
Tzung Hsuen Khoo, Dharini Pathmanathan, Philipp Otto, Sophie Dabo‐NiangList of authors in order
- Landing page
-
https://arxiv.org/abs/2310.02630Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2310.02630Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2310.02630Direct OA link when available
- Concepts
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Arch, Econometrics, Volatility (finance), Heteroscedasticity, Autoregressive model, Autoregressive conditional heteroskedasticity, Markov chain, Stock market, Markov chain Monte Carlo, Structural break, Stock market index, Computer science, Mathematics, Economics, Monte Carlo method, Statistics, Engineering, Geography, Context (archaeology), Archaeology, Civil engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.(ARCH) | 20 |
| abstract_inverted_index.Markov | 58, 93 |
| abstract_inverted_index.abrupt | 53 |
| abstract_inverted_index.affect | 13 |
| abstract_inverted_index.better | 171 |
| abstract_inverted_index.breaks | 28, 193, 200 |
| abstract_inverted_index.crash. | 162 |
| abstract_inverted_index.finite | 139 |
| abstract_inverted_index.market | 1, 161 |
| abstract_inverted_index.method | 132 |
| abstract_inverted_index.model, | 111 |
| abstract_inverted_index.model. | 100 |
| abstract_inverted_index.models | 25, 50 |
| abstract_inverted_index.nearby | 205 |
| abstract_inverted_index.sample | 140 |
| abstract_inverted_index.series | 68 |
| abstract_inverted_index.shocks | 9, 32 |
| abstract_inverted_index.showed | 165 |
| abstract_inverted_index.smooth | 117 |
| abstract_inverted_index.sudden | 8, 31 |
| abstract_inverted_index.upward | 43 |
| abstract_inverted_index.(GARCH) | 24 |
| abstract_inverted_index.Chinese | 159 |
| abstract_inverted_index.applied | 146 |
| abstract_inverted_index.capture | 198 |
| abstract_inverted_index.causing | 41 |
| abstract_inverted_index.discuss | 105 |
| abstract_inverted_index.indices | 2, 150 |
| abstract_inverted_index.maximum | 129 |
| abstract_inverted_index.models. | 86 |
| abstract_inverted_index.nature, | 6 |
| abstract_inverted_index.neglect | 26 |
| abstract_inverted_index.periods | 190 |
| abstract_inverted_index.propose | 91 |
| abstract_inverted_index.results | 164 |
| abstract_inverted_index.series, | 79 |
| abstract_inverted_index.spatial | 82 |
| abstract_inverted_index.studies | 125 |
| abstract_inverted_index.affected | 155 |
| abstract_inverted_index.article, | 103 |
| abstract_inverted_index.compared | 173 |
| abstract_inverted_index.extended | 74 |
| abstract_inverted_index.governed | 55 |
| abstract_inverted_index.inspired | 88 |
| abstract_inverted_index.log-ARCH | 99 |
| abstract_inverted_index.proposed | 135, 143 |
| abstract_inverted_index.regimes. | 121 |
| abstract_inverted_index.smoothed | 182 |
| abstract_inverted_index.variants | 83 |
| abstract_inverted_index.volatile | 4 |
| abstract_inverted_index.2015-2016 | 158 |
| abstract_inverted_index.Different | 48 |
| abstract_inverted_index.developed | 61 |
| abstract_inverted_index.extension | 108 |
| abstract_inverted_index.financial | 66 |
| abstract_inverted_index.framework | 95 |
| abstract_inverted_index.indicated | 187 |
| abstract_inverted_index.inference | 183 |
| abstract_inverted_index.modelling | 71 |
| abstract_inverted_index.occurred. | 194 |
| abstract_inverted_index.patterns. | 15 |
| abstract_inverted_index.procedure | 114 |
| abstract_inverted_index.resulting | 80 |
| abstract_inverted_index.spatially | 76 |
| abstract_inverted_index.switching | 94 |
| abstract_inverted_index.triggered | 29 |
| abstract_inverted_index.Volatility | 70 |
| abstract_inverted_index.estimates. | 47 |
| abstract_inverted_index.estimation | 113, 131 |
| abstract_inverted_index.inferences | 118 |
| abstract_inverted_index.likelihood | 130 |
| abstract_inverted_index.locations. | 206 |
| abstract_inverted_index.one-regime | 178 |
| abstract_inverted_index.presumably | 154 |
| abstract_inverted_index.simulation | 124 |
| abstract_inverted_index.structural | 27, 192, 199 |
| abstract_inverted_index.volatility | 14, 64 |
| abstract_inverted_index.Monte-Carlo | 123 |
| abstract_inverted_index.approximate | 189 |
| abstract_inverted_index.conditional | 18 |
| abstract_inverted_index.generalized | 22 |
| abstract_inverted_index.properties. | 141 |
| abstract_inverted_index.Furthermore, | 180 |
| abstract_inverted_index.counterpart. | 179 |
| abstract_inverted_index.persistence, | 40 |
| abstract_inverted_index.autoregressive | 17 |
| abstract_inverted_index.interconnected | 77 |
| abstract_inverted_index.overestimation | 38 |
| abstract_inverted_index.simultaneously | 202 |
| abstract_inverted_index.spatio-temporal | 98 |
| abstract_inverted_index.Markov-switching | 107 |
| abstract_inverted_index.regime-switching | 49, 54 |
| abstract_inverted_index.heteroskedasticity | 19 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |