Results 41 to 50 of about 27,670 (236)

ARCHModels.jl: Estimating ARCH Models in Julia

Journal of Statistical Software, 2023
This paper introduces ARCHModels.jl, a package for the Julia programming language that implements a number of univariate and multivariate autoregressive conditional heteroskedasticity models.
Simon A. Broda, Marc S. Paolella
doaj   +1 more source

Contemporaneous-threshold smooth transition GARCH models [PDF]

, 2011
This paper proposes a contemporaneous-threshold smooth transition GARCH (or C-STGARCH)model for dynamic conditional heteroskedasticity. The C-STGARCH model is a generalization tosecond conditional moments of the contemporaneous smooth transition ...
Dueker, M.J., Psaradakis, Zacharias, Sola, Martin, Spagnolo, F.   +3 more
core   +2 more sources

Spatial extension of generalized autoregressive conditional heteroskedasticity models

Spatial Economic Analysis, 2020
This paper proposes an extension of generalized autoregressive conditional heteroskedasticity (GARCH) models for a time series to those for spatial data, which are called here spatial GARCH (S-GARCH) models. S-GARCH models are re-expressed as spatial autoregressive moving-average (SARMA) models and a two-step procedure based on quasi-likelihood ...
Takaki Sato, Matsuda, Yasumasa
openaire   +1 more source

Electricity Price Prediction Using Multikernel Gaussian Process Regression Combined With Kernel‐Based Support Vector Regression

Journal of Forecasting, EarlyView.
ABSTRACT This paper presents a new hybrid model for predicting German electricity prices. The algorithm is based on a combination of Gaussian process regression (GPR) and support vector regression (SVR). Although GPR is a competent model for learning stochastic patterns within data and for interpolation, its performance for out‐of‐sample data is not ...
Abhinav Das, Stephan Schlüter, Lorenz Schneider   +2 more
wiley   +1 more source

Probabilistic Graph Models (PGMs) for Feature Selection in Time Series Analysis and Forecasting

JISR on Computing, 2021
Time series or longitudinal analysis has a very important aspect in the field of research. Day by day new and better analyses are getting developed in this field.
Syed Ali Raza Naqvi
doaj   +1 more source

Systematic Features of High-Frequency Volatility in Australian Electricity Markets: Intraday Patterns, Information Arrival and Calendar Effects [PDF]

, 2004
This paper investigates the intraday price volatility process in four Australian wholesale electricity markets; namely New South Wales, Queensland, South Australia and Victoria.
Higgs, Helen, Worthington, Andrew C.
core   +4 more sources

Intraday Functional PCA Forecasting of Cryptocurrency Returns

Journal of Forecasting, EarlyView.
ABSTRACT We study the functional PCA (FPCA) forecasting method in application to functions of intraday returns on Bitcoin. We show that improved interval forecasts of future return functions are obtained when the conditional heteroscedasticity of return functions is taken into account.
Joann Jasiak, Cheng Zhong
wiley   +1 more source

Enhanced state-space estimation of long-memory commodity volatility using the Unscented Kalman Filter and variational Bayes method for non-linear modeling

Frontiers in Applied Mathematics and Statistics
This study addresses the limitations of the Kalman Filter (KF) by extending the application of the Unscented Kalman Filter (UKF) and the variational Bayes method (VBM) for estimating long-memory (LM) volatility models.
Kisswell Basira, Lawrence Dhliwayo, Florance Matarise   +2 more
doaj   +1 more source

Timing Method for the Isoland Signalized Intersection Considering the Traffic Uncertainty

Nantong Daxue xuebao. Ziran kexue ban, 2020
To deal with the stochastic nature of the traffic flow, a timing method for the isolated signalized intersection is proposed considering traffic uncertainty.
LING Mo;WU Zhen;GUO Jianhua
doaj   +1 more source

Application of a Modified Generalized Regression Neural Networks Algorithm in Economics and Finance [PDF]

, 2011
In this paper we propose an alternative and modified Generalized Regression Neural Networks Autoregressive model (GRNN-AR) in S&P 500 and FTSE 100 index returns, as also in Gross domestic product growth rate of Italy, USA and UK. We compare the forecasts
Giovanis, Eleftherios
core