Bio
Opiribo Olaniyo is a researcher affiliated with WORLDQUANT University in the United States. He holds a Master of Science in Financial Engineering from WorldQuant University, with a specialization in Computer Science and Machine Learning. His research interests lie at the intersection of computing and financial engineering, as demonstrated by his work on assessing price relationships and weather impacts on closely related commodities. Opiribo is an active contributor to the academic community, serving as a fellow and author, and his work reflects a commitment to applying computational methods to real-world economic and financial challenges.
Educational Journey
WorldQuant University
Masters of Science in Financial Engineering • Computer Science, Machine Learning Specialization
Experience
Research
Assessing the Price Relationship and Weather Impact on Selected Pairs of Closely Related Commodities Assessing the Price Relationship and Weather Impact on Selected Pairs of Closely Related Commodities
As indicated by various works of literature, climate change has a significant impact on agricultural commodities resulting in variation between demand and supply. The research study adopted quantitative analysis for comparative analysis of price relationships for three pairs of agricultural commodities against closely related products and how weather impacts them. As an interesting comparison, we also selected a pair of non-agricultural commodities for analysis. Downloaded data for the analysis were daily historical price data for the commodities, and daily summary of weather data for precipitation and temperature for the regions were the selected commodities are most produced. Using programming languages like Python and R, we carried out exploratory data analysis using the following statistics, such as graphs, scatter plots of returns, QQ plots for normality, time series diagnostics (AC, PAC) ARIMA, correlation. An exciting part of our work is our model selection, where we used SARIMAX for regressing endogenous data, i.e., commodity prices and exogenous data weather data.
