Results 81 to 90 of about 12,191 (249)
Enhancing real estate investment trust return forecasts using machine learning
Real Estate Economics, Volume 53, Issue 3, Page 574-606, May 2025.Abstract We extend the emerging literature on machine learning empirical asset pricing by analyzing a comprehensive set of return prediction factors for real estate investment trusts (REITs). We show that machine learning models are superior to traditional ordinary least squares models and find that REIT investors experience significant economic gains ...
Kahshin Leow, Thies Lindenthal
wiley +1 more source
Quantitative Finance and EconomicsThe blockchain ecosystem has seen a huge growth since 2009, with the introduction of Bitcoin, driven by conceptual and algorithmic innovations, along with the emergence of numerous new cryptocurrencies.
David Alaminos +2 more
doaj +1 more source
Modeling the Interactions between Volatility and Returns using EGARCH‐M
Journal of Time Series Analysis, 2018 An EGARCH‐M model, in which the logarithm of scale is driven by the score of the conditional distribution, is shown to be theoretically tractable as well as practically useful. A two‐component extension makes it possible to distinguish between the short‐ and long‐run effects of returns on volatility, and the resulting short‐ and long‐run volatility ...
Lange, Rutger-Jan, Harvey, AC
openaire +3 more sources
The dynamic impact of uncertainty in causing and forecasting the distribution of oil returns and risk [PDF]
, 2018 The aim of this study is to analyze the relevance of recently developed news-based measures of economic policy and equity market uncertainty in causing and predicting the conditional quantiles of crude oil returns and risk.
Bonaccolto, G. +2 more
core +2 more sources
Multivariate range-based EGARCH models
International Review of Financial Analysis, 2022 Lili Yan +2 more
openaire +1 more source
Tail risk forecasting using Bayesian realized EGARCH models
, 2020 This paper develops a Bayesian framework for the realized exponential generalized autoregressive conditional heteroskedasticity (realized EGARCH) model, which can incorporate multiple realized volatility measures for the modelling of a return series. The realized EGARCH model is extended by adopting a standardized Student-t and a standardized skewed ...
Tendenan, Vica +2 more
openaire +2 more sources
MEMBANDINGKAN RISIKO SISTEMATIS MENGGUNAKAN CAPM-GARCH DAN CAPM-EGARCH
E-Jurnal Matematika, 2017 In making stock investments, investors usually pay attention to the rate of return and risk of the stock investment. To calculate risk using capital asset pricing model (CAPM), GARCH, and EGARCH.
VIKY AMELIAH +2 more
doaj +1 more source
European sovereign debt crisis and linkage of long-term government bond yields [PDF]
Based on the robust cross-correlation function approach developed by Hong (2001),　this paper investigates the causality-in-mean and the causality-in-variance of long-term bond yields in seven countries including “PIIGS†(Portugal, Ireland, Italy ...
Go Tamakoshi
core
The Effect of Congressional Sessions on the Stock Market in Emerging Democracy: the Case of Taiwan [PDF]
Political uncertainty, Congressional effect, Volatility asymmetry, EGARCH ...
Lin, Chin-Tsai, Wang, Yi-Hsien
core
Modelos ARCH, GARCH y EGARCH: aplicaciones a series financieras
Cuadernos de Economía, 2008 En este artículo se incluye una descripción de los modelos<br />ARCH, GARCH y EGARCH, y de los procesos de estimación de sus<br />parámetros usando máxima verosimilitud. Se propone un modelo<br />alternativo para el análisis de series financieras y se estudian<br />las series de precios y de retornos de las acciones de<br ...
Casas Monsegny, Marta +1 more
openaire +3 more sources