Are benefits from oil - stocks diversification gone? New evidence from a dynamic copula and high frequency data
Oil is perceived as a good diversification tool for stock markets. To fully understand this potential, we propose a new empirical methodology that combines generalized autoregressive score copula functions with high frequency data and allows us to capture and forecast the conditional time-varying joint distribution of the oil -- stocks pair accurately. Our realized GARCH with time-varying copula yields statistically better forecasts of the dependence and quantiles of the distribution relative to competing models. Employing a recently proposed conditional diversification benefits measure that considers higher-order moments and nonlinear dependence from tail events, we document decreasing benefits from diversification over the past ten years. The diversification benefits implied by our empirical model are, moreover, strongly varied over time. These findings have important implications for asset allocation, as the benefits of including oil in stock portfolios may not be as large as perceived.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
On financial market correlation structures and diversification benefits across and within equity sectors
We study how to assess the potential benefit of diversifying an equity portfolio by investing within and across equity sectors. We analyse 20 years of US stock price data, which includes the global financial crisis (GFC)…
Co-benefits of Agricultural Diversification and Technology for Food and Nutrition Security in China
China is the leading crop producer and has successfully implemented sustainable development programs related to agriculture. Sustainable agriculture has been promoted to achieve national food security targets such as foo…
NutritionAsset Selection via Correlation Blockmodel Clustering
We aim to cluster financial assets in order to identify a small set of stocks to approximate the level of diversification of the whole universe of stocks. We develop a data-driven approach to clustering based on a correl…
ClusteringAdvanced LSTM Neural Networks for Predicting Directional Changes in Sector-Specific ETFs Using Machine Learning Techniques
Trading and investing in stocks for some is their full-time career, while for others, it's simply a supplementary income stream. Universal among all investors is the desire to turn a profit. The key to achieving this goa…
On the "mementum" of Meme Stocks
The meme stock phenomenon is yet to be explored. In this note, we provide evidence that these stocks display common stylized facts on the dynamics of price, trading volume, and social media activity. Using a regime-switc…