Dynamic Interactions Between Monetary Indicators and Stock Market Behavior in Nepal

Abstract

Background: The relationship between monetary indicators and equity market performance is a critical area of study, particularly in emerging economies like Nepal, where financial markets are still developing. Understanding how monetary variables influence the Nepal Stock Exchange Index (NEPSE) can provide key insights for investors, policymakers, and financial institutions.

Objectives: This study aims to examine the dynamic short-run and long-run effects of monetary indicators—reserve money, deposits, liquidity, and lending interest rates—on NEPSE performance. It seeks to uncover the complex transmission mechanisms between monetary policy and stock market behavior in Nepal.

Methods: A quantitative research design is employed, utilizing the Autoregressive Distributed Lag (ARDL) model on monthly data from 2005 to 2024. Stationarity tests confirm mixed-order integration of variables, validating the ARDL approach. The model assesses both immediate and lagged effects while ensuring robustness through diagnostic checks for serial correlation and heteroskedasticity.

Findings: The results reveal time-dependent effects—reserve money and deposits exhibit varying positive and negative impacts across different lags, reflecting delayed transmission mechanisms. Liquidity positively influences NEPSE at a three-month lag, while the lending interest rate has a consistent and significant negative effect. The model demonstrates strong diagnostic validity, supporting the reliability of the findings.

Conclusion: The study underscores the importance of macroeconomic stability and well-timed policy interventions in fostering stock market growth. It provides empirical evidence on the nuanced relationship between monetary policy and equity markets in Nepal, offering actionable insights for stakeholders.

Novelty: This research contributes to the limited literature on Nepal’s stock market by applying the ARDL framework to capture both short-run and long-run dynamics. Future studies could extend this work by incorporating nonlinear models or additional macroeconomic variables.