Using Multivariate Dynamic Conditional Correlation GARCH model to analysis financial market data

Alshenawy, Fatma; Abdo, Doaa  A.

doi:10.21608/zcom.2023.213791.1258

Using Multivariate Dynamic Conditional Correlation GARCH model to analysis financial market data

نوع المستند : تجاریة کل ما یتعلق بالعلوم التجاریة

المؤلفون

Fatma Alshenawy ¹
Doaa A. Abdo ²

¹ Mansoura- Egypt

² كلية التجارة -جامعة المنصورة

10.21608/zcom.2023.213791.1258

المستخلص

In financial markets, understanding the dynamic relationships between assets is crucial for effective portfolio management. This study highlights the importance of using the DCC-GARCH (Dynamic Conditional Correlation - Generalized Autoregressive Conditional Heteroskedasticity) model as a powerful multivariate analysis tool to capture the dynamic correlations between the S&P 500, Crude Oil Price, Natural Gas Price, and Gold Price. The DCC-GARCH model provides a flexible framework for modeling time-varying correlations, allowing investors to account for the changing relationships between assets over time. The study estimates the correlations and forecasts their evolution over the next 365 days, providing valuable insights for portfolio optimization and risk management. The results demonstrate the potential diversification benefits offered by these assets and emphasize the need for adaptive portfolio management based on the dynamic correlations. By employing the DCC-GARCH model, investors can better understand the complex interactions between assets and make more informed decisions about asset allocation, ultimately leading to improved risk-adjusted returns. This study underscores the significance of incorporating advanced multivariate techniques, such as DCC-GARCH, in financial analysis and portfolio management.

الكلمات الرئيسية