This study utilizes a panel data vector autoregression (PVAR) model to examine the relationships between Gold, Nifty, Crude Oil, and USD/INR, drawing from 3105 observations sourced from Yahoo Finance. Descriptive statistics reveal notable volatility, particularly in Gold and Crude Oil. Unit root tests confirm stationarity, crucial for time series analysis. Optimal lag selection recommends a lag order of 2, balancing model accuracy and complexity. Granger causality tests indicate limited predictive power, with gold influencing USD/INR unidirectionally. Impulse response function analysis and variance decomposition underscore Gold’s relative independence. Robustness tests affirm stability, highlighting USD/INR’s endogeneity. This study enhances understanding of financial dynamics, offering insights for risk management, portfolio diversification, and monetary policy.

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Interconnected Dynamics of Gold, Nifty, Crude Oil, and USD/INR: Insights from a Panel Data VAR Analysis

  • J. Shashidhar Yadav,
  • D. Saiprasad,
  • Madhu Druvakumar,
  • S. N. Venkatesh,
  • G. T. Jagadeesha,
  • H. B. Mahanthesh

摘要

This study utilizes a panel data vector autoregression (PVAR) model to examine the relationships between Gold, Nifty, Crude Oil, and USD/INR, drawing from 3105 observations sourced from Yahoo Finance. Descriptive statistics reveal notable volatility, particularly in Gold and Crude Oil. Unit root tests confirm stationarity, crucial for time series analysis. Optimal lag selection recommends a lag order of 2, balancing model accuracy and complexity. Granger causality tests indicate limited predictive power, with gold influencing USD/INR unidirectionally. Impulse response function analysis and variance decomposition underscore Gold’s relative independence. Robustness tests affirm stability, highlighting USD/INR’s endogeneity. This study enhances understanding of financial dynamics, offering insights for risk management, portfolio diversification, and monetary policy.