Multi-regime Forecasting Model for the Impact of COVID-19 Pandemic on Volatility in Global Equity Markets

2020 ◽  
Author(s):  
Nazli Sila Alan ◽  
Robert F. Engle ◽  
Ahmet K Karagozoglu
Keyword(s):  
Equity Markets ◽  
Forecasting Model ◽  
Global Equity Markets ◽  
The Impact

Unanticipated Shocks and Systemic Influences: The Impact of Contagion in Global Equity Markets in 1998

2003 ◽  
Author(s):  
Mardi Dungey ◽  
Renee Fry ◽  
Brenda González-Hermosillo ◽  
Vance L. Martin
Keyword(s):  
Equity Markets ◽  
Global Equity Markets ◽  
The Impact

Unanticipated Shocks and Systemic Influences: The Impact of Contagion in Global Equity Markets in 1998

2003 ◽  
Vol 03 (84) ◽  
pp. 1 ◽  
Author(s):  
Brenda González-Hermosillo ◽  
Vance Martin ◽  
Renee Fry ◽  
Mardi Dungey ◽  
◽  
...  
Keyword(s):  
Equity Markets ◽  
Global Equity Markets ◽  
The Impact

Up- and Downside Variance Risk Premia in Global Equity Markets

2018 ◽  
Author(s):  
Matthias Held ◽  
Julia Kapraun ◽  
Marcel Omachel ◽  
Julian Thimme
Keyword(s):  
Equity Markets ◽  
Risk Premia ◽  
Global Equity Markets ◽  
Variance Risk

scholarly journals Container Throughput Forecasting Using Dynamic Factor Analysis and ARIMAX Model

2017 ◽  
Vol 29 (5) ◽  
pp. 529-542 ◽  
Author(s):  
Marko Intihar ◽  
Tomaž Kramberger ◽  
Dejan Dragan
Keyword(s):  
Factor Analysis ◽  
Goodness Of Fit ◽  
Moving Average ◽  
Real Data ◽  
Information Criteria ◽  
Dynamic Factor Analysis ◽  
Forecasting Model ◽  
Dynamic Factor ◽  
Macroeconomic Indicators ◽  
The Impact

The paper examines the impact of integration of macroeconomic indicators on the accuracy of container throughput time series forecasting model. For this purpose, a Dynamic factor analysis and AutoRegressive Integrated Moving-Average model with eXogenous inputs (ARIMAX) are used. Both methodologies are integrated into a novel four-stage heuristic procedure. Firstly, dynamic factors are extracted from external macroeconomic indicators influencing the observed throughput. Secondly, the family of ARIMAX models of different orders is generated based on the derived factors. In the third stage, the diagnostic and goodness-of-fit testing is applied, which includes statistical criteria such as fit performance, information criteria, and parsimony. Finally, the best model is heuristically selected and tested on the real data of the Port of Koper. The results show that by applying macroeconomic indicators into the forecasting model, more accurate future throughput forecasts can be achieved. The model is also used to produce future forecasts for the next four years indicating a more oscillatory behaviour in (2018-2020). Hence, care must be taken concerning any bigger investment decisions initiated from the management side. It is believed that the proposed model might be a useful reinforcement of the existing forecasting module in the observed port.

Volatility linkage across global equity markets

2014 ◽  
Vol 25 (2) ◽  
pp. 71-89 ◽  
Author(s):  
Liang Ding ◽  
Yirong Huang ◽  
Xiaoling Pu
Keyword(s):  
Equity Markets ◽  
Global Equity Markets
Sign in / Sign up

Export Citation Format

Share Document