SearcharxivSearch

arXiv · 2310.01123

Impact of Economic Uncertainty, Geopolitical Risk, Pandemic, Financial & Macroeconomic Factors on Crude Oil Returns -- An Empirical Investigation

Abstract

This study aims to use simultaneous quantile regression (SQR) to examine the impact of macroeconomic and financial uncertainty including global pandemic, geopolitical risk on the futures returns of crude oil (ROC). The data for this study is sourced from the FRED (Federal Reserve Economic Database) economic dataset; the importance of the factors have been validated by using variation inflation factor (VIF) and principal component analysis (PCA). To fully understand the combined effect of these factors on WTI, study includes interaction terms in the multi-factor model. Empirical results suggest that changes in ROC can have varying impacts depending on the specific period and market conditions. The results can be used for informed investment decisions and to construct portfolios that are well-balanced in terms of risk and return. Structural breaks, such as changes in global economic conditions or shifts in demand for crude oil, can cause return on crude oil to be sensitive to changes in different time periods. The unique aspect ness of this study also lies in its inclusion of explanatory factors related to the pandemic, geopolitical risk, and inflation.

Explore related subjects

Keep this discovery

BibTeXRIS

Sarit Maitra. 2023-10-02. Impact of Economic Uncertainty, Geopolitical Risk, Pandemic, Financial & Macroeconomic Factors on Crude Oil Returns -- An Empirical Investigation. https://arxiv.org/abs/2310.01123

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Identification in Linear Quantile Panel Models

This paper studies identification in linear quantile panel models with unrestricted individual heterogeneity when the number of time periods is fixed and small. We impose strict exogeneity, whereby the conditional quantile restriction holds given the individual's complete regressor history and latent individual effect, but otherwise allow the disturbances to be arbitrarily dependent over time.

econ.EM

Experimental Design for Policy Choice

We show how to optimally design experiments when the resulting data will be used to choose a welfare-maximizing policy subject to constraints. A decision maker seeks to maximize Bayes expected welfare by choosing a policy whose effects depend on an unknown finite-dimensional parameter. The decision maker has access to a first wave of experimental data with a fixed design but may choose the design of a second wave that will be collected before choosing the policy. The resulting experimental design--policy choice problem is a very high-dimensional dynamic program that is generally intractable in finite samples. We propose a tractable approximation based on the limit experiment and show it is asymptotically optimal using a new asymptotic representation theorem for adaptive experiments with continuous treatments. We apply the method to a conditional cash transfer experiment and demonstrate the potential for large gains from tailoring the experiment to the policy choice.

econ.EM

Designing Spatial Treatments

Spatial treatments are interventions assigned to locations potentially distinct from those of the responding units. We study their optimal design under a general model in which a unit's response diminishes with distance to a treated site. Our estimand of interest is an ``uncontaminated'' effect equal to the average impact of a single intervention site over all hypothetical sites. We propose a novel design based on a Mat\'{e}rn point process which separates treatments by a distance of at least $r$. A larger choice of $r$ reduces bias by separating interventions but increases variance by reducing their numerosity. We choose $r$ to maximize the rate of convergence of a Horvitz-Thompson estimator and prove that this is minimax rate-optimal. We provide weak conditions under which the estimator is asymptotically normal and propose a variance estimator.

econ.EM