Results 41 to 50 of about 1,495 (237)

Volatility Modeling and Spillover: The Turkish and Russian Stock Markets

Istanbul Business Research
This study investigates the internal and external (spillover) characteristics of the volatility of the Turkish and Russian stock market indices. To this end, generalized autoregressive conditional heteroskedasticity models that are classified as short ...
Ahmet Galip Gençyürek
doaj   +1 more source

Forecasting gains by using extreme value theory with realised GARCH filter

IIMB Management Review, 2021
Early empirical evidence suggests that the realised generalised autoregressive conditional heteroskedasticity (GARCH) model provides significant forecasting gains over the standard GARCH models in volatility forecasting.
Samit Paul, Prateek Sharma
doaj   +1 more source

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

A Novel Approach to Forecasting After Large Forecast Errors

Journal of Forecasting, EarlyView.
ABSTRACT A sequence of increasingly large same‐sign 1‐step‐ahead forecast errors are most likely due to a sudden unexpected shift. Large forecast errors can be expensive, but also contain valuable information. Impulse indicators acting as intercept corrections to set forecasts back on track can be quickly tested for replacing outliers, a location shift
Jennifer L. Castle, Jurgen A. Doornik, David F. Hendry   +2 more
wiley   +1 more source

Optimal Variance Forecasting in a Trading Context

Journal of Forecasting, EarlyView.
ABSTRACT In financial trading, the economic value of return and variance forecasts arises from three key components: an investor's risk preference, the quality of return predictions, and the accuracy of risk estimates. This study isolates the third component—risk knowledge—and demonstrates that its contribution is a non‐linear function of realized and ...
Nick Taylor
wiley   +1 more source

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

Machine Learning Approaches to Forecast the Realized Volatility of Crude Oil Prices

Journal of Forecasting, EarlyView.
ABSTRACT This paper presents an evaluation of the accuracy of machine learning (ML) techniques in forecasting the realized volatility of West Texas Intermediate (WTI) crude oil prices. We compare several ML algorithms, including regularization, regression trees, random forests, and neural networks, to several heterogeneous autoregressive (HAR) models ...
Talha Omer, Kristofer Månsson, Pär Sjölander, Gazi Salah Uddin   +3 more
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

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