Forecasting aggregate stock market volatility using financial and macroeconomic predictors Which models forecast best, when and why?

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of empirical finance
1. Verfasser: Nonejad, Nima (VerfasserIn)
Format: UnknownFormat
Sprache:eng
Veröffentlicht: June 2017
Schlagworte:
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Titel Jahr Verfasser
Can we forecast better in periods of low uncertainty? : the role of technical indicators 2023 Ferrer Fernández, María
Option price implied information and REIT returns 2023 Cao, Jie (Jay)
Forecasting tail risk measures for financial time series : an extreme value approach with covariates 2023 James, Robert
The PhD origins of finance faculty 2023 Jones, Todd R.
Global political risk and international stock returns 2023 Gala, Vito D.
Director optimism and CEO equity compensation 2023 Cook, Douglas O.
Disagreement, speculation, and the idiosyncratic volatility 2023 Wang, Jianqiu
Expected returns and risk in the stock market 2023 Brennan, Michael J.
Cross-sectional uncertainty and expected stock returns 2023 Yu, Deshui
Burned by leverage? : flows and fragility in bond mutual funds 2023 Molestina Vivar, Luis
Income inequality, inflation and financial development 2023 Kim, Dong-Hyeon
Easy money and competitive industries' booms and busts 2023 Shang, Longfei
CEO personality traits and corporate value implication of acquisitions 2023 Aabo, Tom
Industry regulation and the comovement of stock returns 2023 Blau, Benjamin
The money-inflation nexus revisited 2023 Ringwald, Leopold
Ownership structure and the cost of debt : evidence from the Chinese corporate bond market 2023 Chatterjee, Sris
Individual investors' trading behavior and gender difference in tolerance of sex crimes : evidence from a natural experiment 2023 Gao, Huasheng
Macroeconomic news and price synchronicity 2023 Cheema, Arbab K.
The pricing of jump and diffusive risks in the cross-section of cryptocurrency returns 2023 Leong, Minhao
Estimation with mixed data frequencies : a bias-correction approach 2023 Ghosh, Anisha
Alle Artikel auflisten