Macroeconomic Influences on Exchange Rates, NSE Nifty, and Gold: Evaluating Linear and Quantile Regression Models

Abstract

This study addresses the confusion between Ordinary Least Squares (OLS) regression and Quantile Regression (QR) in analyzing the impact of macroeconomic variables on financial markets, specifically exchange rates, NSE Nifty returns, and gold prices. It also seeks to identify the key economic factors that influence these financial indicators, as the relationship between economic variables and market performance remains complex and multifaceted. To tackle these challenges, the study compares the effectiveness of both OLS and QR in capturing the impact of macroeconomic variables like inflation, interest rates, and foreign reserves on financial markets. The analysis proceeds by utilizing data from January 2019 to December 2023, sourced from the Reserve Bank of India (RBI), to assess monthly returns (MOM) of the selected financial indicators. A combination of descriptive statistics, Pearson correlation, and regression models is employed to explore the relationships between the variables. The study reveals that QR provides more nuanced insights, outperforming OLS by capturing the heterogeneity of effects across different market conditions, while OLS offers a more generalized view. Overall, the findings underscore the advantages of using Quantile Regression to better understand the conditional relationships between macroeconomic variables and financial market outcomes.