Paper: arXiv 2409.08355

Abstract

This paper examines the influence of low-frequency macroeconomic variables on the high-frequency returns of copper futures and the long-term correlation with the S&P 500 index, employing GARCH-MIDAS and DCC-MIDAS modeling frameworks. The estimated results of GARCH-MIDAS show that realized volatility (RV), level of interest rates (IR), industrial production (IP) and producer price index (PPI), volatility of Slope, PPI, consumer sentiment index (CSI), and dollar index (DI) have significant impacts on Copper futures returns, among which PPI is the most efficient macroeconomic variable. From comparison among DCC-GARCH and DCC-MIDAS model, the added MIDAS filter of PPI improves the model fitness and have better performance than RV in effecting the long-run relationship between Copper futures and S&P 500.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 6.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced econometric models like GARCH-MIDAS and DCC-MIDAS, which involve complex time-series decompositions and mixed-frequency frameworks (high math). It uses real-world financial and macroeconomic data (copper futures, S&P 500, macro indicators) for empirical analysis with model comparisons, indicating significant data and implementation efforts (moderate-high rigor).

Research Flowchart

  flowchart TD
  A["Research Goal<br>Examine low-frequency macroeconomic<br>influence on copper futures returns<br>and S&P 500 correlation"] --> B{"Data & Methodology"}
  
  B --> C1["High-Frequency Data<br>Copper Futures Returns"]
  B --> C2["Low-Frequency Data<br>Macro Variables: RV, IR, IP, PPI, CSI, DI"]
  
  C1 & C2 --> D["Modeling Process<br>GARCH-MIDAS & DCC-MIDAS"]
  
  D --> E{"Key Findings & Outcomes"}
  
  E --> F1["GARCH-MIDAS Results<br>PPI most significant macro variable<br>RV, IR, IP also significant"]
  E --> F2["DCC-MIDAS Results<br>PPI-MIDAS improves model fitness<br>Superior to RV for long-run<br>Copper-S&P 500 relationship"]

Cited References (20)

  1. Forecasting copper prices with dynamic averaging and selection models (2015)
  2. A component model for dynamic correlations (2011)
  3. Two are better than one: Volatility forecasting using multiplicative component GARCH-MIDAS models (2020)
  4. Volatility of copper prices and the effect of real interest rate changes (2018)
  5. Economic drivers of commodity volatility: The case of copper (2021)
  6. Impact of macroeconomic news on metal futures (2012)
  7. On the economic sources of stock market volatility (2008)
  8. Stock market volatility and macroeconomic fundamentals (2013)
and 12 more
  1. Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional hetroscedasticity models (2002)
  2. The MIDAS touch: Mixed data sampling regression models (2004)
  3. Copper price determination: fundamentals versus non-fundamentals (2018)
  4. Metal volatility in presence of oil and interest rate shocks (2008)
  5. Asymmetric effects of oil shocks on carbon allowance price: evidence from China (2021)
  6. The impact of macroeconomic news on Chinese futures (2019)
  7. Modeling volatility and correlations between emerging market stock prices and the prices of copper, oil and wheat (2014)
  8. Forecasting changes in copper futures volatility with GARCH models using an iterated algorithm (2003)
  9. The macroeconomic variables impact on commodity futures volatility: A study on Indian markets (2021)
  10. Research on the Influence of Economic Policy Uncertainty on the Supply Chain Finance (2020)
  11. Economic policy uncertainty and commodity market volatility: implications for economic recovery (2022)
  12. COVID-19 impact on commodity futures volatilities (2022)

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