Prediction of risk
Decoding the Serial Dependence of Stock Return Volatility with Copula
Bibliographic Data
| ID | 21652503 |
|---|---|
| Authors | Liang Zhu (0009-0004-6147-0863, Shenzhen University), Christine Lim (0000-0002-7547-8725), Jianlun Zhang (0000-0002-9937-1607, University of Macau) |
| Year | 2021 |
| Volume | 45 |
| Issue | 1 |
| Pages | 6-27 |
| Publication date | 2021-01-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Hospitality & Tourism Research (JOURNAL) |
| Journal identifiers | ISSN: 1096-3480 • E-ISSN: 1557-7554 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/1096348020919490 |
| OpenAlex | W3033800082 |
| Language | EN |
| References cited | 51 |
In this article, a new approach based on the copula theory is employed in the analysis and forecasting of hospitality and tourism-related stock return volatility (HTSRV). The application of copula-based models for univariate time series is state-of the-art methodologies with new perspectives for economic analysis in tourism and hospitality. This flexible method provides numerous functions for serial dependence specification of volatility series. Eight hospitality–tourism stocks are analyzed for their serial dependence structures to provide insight for forecasting stock return volatility. While the forecasting performance between our chosen copulas and benchmark models is inconclusive, the empirical results show that copulas well specify both linear and nonlinear serial dependence structures, which lead to forecasting results as good as or even better than those of the benchmark models. This property allows us to use copulas for HTSRV forecasting without the concern of model misspecification
Bivariate analysis · Econometrics · Economics · Financial economics · Machine learning · Multivariate statistics · Realized variance · Tourism · Univariate · Computer Science · Engineering · Financial Risk and Volatility Modeling · Market Dynamics and Volatility · Monetary Policy and Economic Impact
Real-Time Macroeconomic Forecasting With a Heteroscedastic Inversion Copula
Asymmetric Forecast Densities for U.S. Macroeconomic Variables from a Gaussian Copula Model of Cross-Sectional and Serial Dependence
A review of research on tourism demand forecasting
Testing dependence between GDP and tourism's growth rates
Introduction
| Highly cited | No |
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