optimize_df_for_students
- MonteCarlo.optimize_df_for_students(var_level)
Find degrees of freedom for the t-distribution that best match empirical VaR and CVaR.
The method minimizes the squared error between theoretical and empirical VaR/CVaR using scipy.optimize.minimize_scalar with bounds (2.1, 50).
- Parameters:
- var_levelint
Confidence level in percent for Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR). Must be in [1, 99].
- Returns:
- float
Estimated degrees of freedom for Student’s t-distribution.
- Raises:
- ValueError
If var_level is outside [1, 99].