Results 111 to 120 of about 27,670 (236)
Indonesian Journal on Computing (Indo-JC), 2018 Gold is a one of high selling value items in the market, and it can be used as an investment item. The price of gold in the market tends to be stable and not undergoing too significant changes which makes gold be a very valuable item. The aim of this research is to predict gold price using AR (1) and ARCH (1) model which are the part of time series ...
Ni Luh Ketut Dwi Murniati +2 more
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Financial Time Series Uncertainty: A Review of Probabilistic AI Applications
Journal of Economic Surveys, Volume 40, Issue 2, Page 915-953, April 2026.ABSTRACT Probabilistic machine learning models offer a distinct advantage over traditional deterministic approaches by quantifying both epistemic uncertainty (stemming from limited data or model knowledge) and aleatoric uncertainty (due to inherent randomness in the data), along with full distributional forecasts.
Sivert Eggen +4 more
wiley +1 more source
AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTICY UNDER ERROR-TERM NON-NORMALITY [PDF]
This paper explores the impact of error-term non-normality on the performance of the normal-error Generalized Autoregressive Conditional Heteroskedastic (GARCH) model under small and moderate sample sizes.
Ramirez, Octavio A.
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Portfolio optimization with mixture vector autoregressive models
, 2020 Obtaining reliable estimates of conditional covariance matrices is an important task of heteroskedastic multivariate time series. In portfolio optimization and financial risk management, it is crucial to provide measures of uncertainty and risk as ...
Boshnakov, Georgi N., Ravagli, Davide
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The Monetary Policy–Commodities Nexus: A Survey
Journal of Economic Surveys, Volume 40, Issue 2, Page 1050-1082, April 2026.ABSTRACT This survey synthesizes evidence on the bidirectional links between commodity markets and monetary policy. On the commodities‐to‐policy side, we review how shocks to energy, food, and metals pass through to inflation, inflation expectations, economic activity, and financial stability in state‐dependent ways that vary by shock type, exposure ...
Martin T. Bohl +2 more
wiley +1 more source
Chaos in Fractionally Integrated Generalized Autoregressive Conditional Heteroskedastic Processes
, 2016 Fractionally integrated generalized autoregressive conditional heteroskedasticity (FIGARCH) arises in modeling of financial time series. FIGARCH is essentially governed by a system of nonlinear stochastic difference equations ${u_t}$ = ${z_t}$ $(1-\sum\limits_{j=1}^q _j L^j) _{t}^2 = +(1-\sum\limits_{j=1}^q _j L^j - (\sum\limits_{k=1}^p _k L^k)
Yilmaz, Adil, Unal, Gazanfer
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Contemporaneous aggregation of GARCH processes
, 2007 In this article, the effect of contemporaneous aggregation of heterogeneous generalized autoregressive conditionally heteroskedastic (GARCH) processes, as the cross-sectional size diverges to infinity is studied.
Zaffaroni, P
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Introduction to prediction in classical time series models (in Russian) [PDF]
This essay discusses basic notions of time series prediction and states traditional approaches to prediction in classical Box-Jenkins models, vector autoregressions, and autoregressive models with conditional heteroskedasticity.
Alexander Tsyplakov
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, 1995 In this dissertation, three essays are presented that apply recent advances in time-series methods to the analysis of inflation and stock market index data for Taiwan. Specifically, ARCH and GARCH methodologies are used to investigate claims of increased volatility in economic time-series data since 1980.
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Threshold quantile autoregressive models [PDF]
, 2009 We study in this article threshold quantile autoregressive processes. In particular we propose estimation and inference of the parameters in nonlinear quantile processes when the threshold parameter defining nonlinearities is known for each quantile, and
Galvao Jr, A. F. +2 more
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