Results 101 to 110 of about 10,202,946 (277)
On KPSS with GARCH errors [PDF]
In this paper we discuss the finite sample behavior of the KPSS test in the presence of conditionally heteroskedastic errors. We confirm that under stationary GARCH errors the asymptotics of the KPSS remains valid. However, in finite samples we observe a
Marco Barassi
core
Threshold Asymmetric Conditional Autoregressive Range (TACARR) Model
Journal of Forecasting, EarlyView.ABSTRACT This paper introduces a Threshold Asymmetric Conditional Autoregressive Range (TACARR) model for analyzing the daily price ranges of financial assets. The proposed formulation assumes that the conditional expected range switches between two regimes, representing upward and downward market states, with the disturbance distribution also allowed ...
Isuru Ratnayake, V. A. Samaranayake
wiley +1 more source
LONG-TERM VOLATILITY DYNAMICS OF THE GERMAN STOCK MARKET : INSIGHTS FROM TWO DECADES OF DAILY RETURNS [PDF]
Analele Universităţii Constantin Brâncuşi din Târgu Jiu : Seria EconomieThis study provides an empirical analysis of the volatility dynamics of the Deutscher Aktienindex (DAX) stock index over a 20-year period based on daily observations, specifically from January 2, 2006, to March 20, 2026.
SHAHIL RAZA +6 more
doaj
Evaluation of VaR Estimates based on ARCH type Models [PDF]
فصلنامه پژوهشهای اقتصادی ایران, 2011 This paper studies four ARCH type models including ARCH, GARCH, EGARCH and TGARCH at Value at Risk (VaR) estimation. The four models were applied to daily Tehran stock market data to assess each model in estimating one day Value at Risk at various ...
Naser Khiabani, Maryam Sarooghi
doaj
Multimodality in the GARCH Regression Model [PDF]
Several aspects of GARCH(p,q) models that are relevant for empirical applications are investigated. In particular, it is noted that the inclusion of dummy variables as regressors can lead to multimodality in the GARCH likelihood.
Jurgen A. Doornik, Marius Ooms
core
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
A Novel Text‐Based Framework for Forecasting Carbon Prices
Journal of Forecasting, EarlyView.ABSTRACT This study proposes a text‐based framework for predicting EU carbon prices. Using weekly data from 2020 to 2024, we construct a multivariate dataset combining financial indicators, commodity prices, Google Trends measures, and news‐based sentiment extracted using FinBERT.
Christian Oliver Ewald, Yaoyu Li
wiley +1 more source
Финансы: теория и практикаThe main purpose of this paper is to test the performance of GARCH models in estimating and forecasting VaR (value at risk) of the US Fintech stock market from July 20, 2016, to December 31, 2021.
O. Gharbi, M. Boujelbène, R. Zouari
doaj +1 more source
Beta Forecasting With Realized Beta Estimators and Machine Learning Algorithms
Journal of Forecasting, EarlyView.ABSTRACT This paper applies machine learning algorithms to the modeling of realized betas for the purposes of forecasting stock systematic risk. Higher levels of beta forecast accuracy are demonstrated, relative to other studies in the literature. These improvements are also highly significant, both statistically and economically.
Bao Doan +3 more
wiley +1 more source
EAI Endorsed Transactions on Energy WebINTRODUCTION: The increasing coupling between carbon emission trading and electricity markets creates significant joint risk challenging grid cost-effectiveness and stability. Existing approaches apply LSTM and Copula models separately, lacking a unified
Runxin Hua
doaj +1 more source