Credit Market Volatility Research

🟩 Completed
Quantitative Finance & Econometrics

Overview

This project investigates how credit market stress impacts the volatility of both Bitcoin and NASDAQ assets. Using a panel data framework and quantile regression, it explores nuanced, heterogeneous effects across different volatility regimes. The analysis highlights the role of credit spreads, Treasury yield curve, and implied volatility in driving asset volatility, with a focus on asset-specific risk transmission.

Objectives

  • Model the impact of credit market stress on asset volatility
  • Compare volatility dynamics between Bitcoin and NASDAQ
  • Visualize and interpret quantile regression results

Technologies

Stata (panel data prep, regression, quantile regression, table generation), Python (pandas, numpy, matplotlib for data visualization)

Stats_of_Key_Variables.png Stats_by_Asset.png Quintile_Regression_Bitcoin.png Quintile_Regression_Nasdaq.png py_plot_L_baa_aaa_spread.png py_plot_L_treasury_spread.png py_plot_L_implied_vol.png py_plot_L_neg_log_ret.png
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