fit_to_option_market_df function

Calibrate volatilities and equity-linked default intensity making many assumptions

This is a convenience function for calibrating variance cumulation (the at-the-money volatility of the continuous process) and equity linked default intensity of the form $h(s + (1-s)(S0/S_t)^p)$, using a data.frame of option market data.

fit_to_option_market_df(
  S0 = ragtop::TSLAMarket$S0,
  discount_factor_fcn = spot_to_df_fcn(ragtop::TSLAMarket$risk_free_rates),
  options_df = ragtop::TSLAMarket$options,
  min_maturity = 1/12,
  min_moneyness = 0.8,
  max_moneyness = 1.2,
  base_default_intensity = 0.05
)

Arguments

• S0: Current underlying price
• discount_factor_fcn: A function for computing present values to time t of various cashflows occurring during this timestep, with arguments T, t
• options_df: A data frame of American option details. It should have columns callput, K, time, mid, bid, and ask,
• min_maturity: Minimum option maturity to allow in calibration
• min_moneyness: Maximum option strike as a proportion of S0 to allow in calibration
• max_moneyness: Maximum option strike as a proportion of S0 to allow in calibration
• base_default_intensity: Overall default intensity (in natural units)

See Also

fit_to_option_market the underlying fit algorithm

Other Equity Dependent Default Intensity: find_present_value(), fit_variance_cumulation(), form_present_value_grid(), implied_jump_process_volatility()