Equity Vol Surface
financepy.market.volatility.equity_vol_surface
Classes
EquityVolSurface
EquityVolSurface(anchor_dt: financepy.utils.date.Date, stock_price: float, discount_curve: financepy.market.curves.discount_curve.DiscountCurve, dividend_curve: financepy.market.curves.discount_curve.DiscountCurve, expiry_dts: List[financepy.utils.date.Date], strikes: numpy.ndarray, volatility_grid: numpy.ndarray, vol_func_type: financepy.utils.global_types.VolFuncTypes = <VolFuncTypes.CLARK: 0>, fin_solver_type: financepy.utils.global_types.SolverTypes = <SolverTypes.NELDER_MEAD: 1>) -> None
Class to perform a calibration of a chosen parametrised surface to the
prices of equity options at different strikes and expiry tenors. There is
a choice of volatility function from cubic in delta to full SABR and SSVI.
Check out VolFuncTypes. Visualising the volatility curve is useful.
Also, there is no guarantee that the implied pdf will be positive.
Methods
vol_from_strike_dt
vol_from_strike_dt(self, k: float, expiry_dt: financepy.utils.date.Date) -> float
Interpolates the Black-Scholes volatility from the volatility
surface given call option strike and expiry date. Linear interpolation
is done in variance space. The smile strikes at bracketed dates are
determined by determining the strike that reproduces the provided delta
value. This uses the calibration delta convention, but it can be
overriden by a provided delta convention. The resulting volatilities
are then determined for each bracketing expiry time and linear
interpolation is done in variance space and then converted back to a
lognormal volatility.
vol_from_delta_date
vol_from_delta_date(self, call_delta: float, expiry_dt: financepy.utils.date.Date, delta_method=None) -> Tuple[float, float]
Interpolates the Black-Scholes volatility from the volatility
surface given a call option delta and expiry date. Linear interpolation
is done in variance space. The smile strikes at bracketed dates are
determined by determining the strike that reproduces the provided delta
value. This uses the calibration delta convention, but it can be
overriden by a provided delta convention. The resulting volatilities
are then determined for each bracketing expiry time and linear
interpolation is done in variance space and then converted back to a
lognormal volatility.
check_calibration
check_calibration(self, verbose: bool) -> None
Compare calibrated vol surface with market and output a report
which sets out the quality of fit to the ATM and 10 and 25 delta market
strangles and risk reversals.
implied_dbns
implied_dbns(self, low_s: float, high_s: float, num_intervals: int) -> List[financepy.utils.distribution.FinDistribution]
Calculate the pdf for each tenor horizon. Returns a list of
FinDistribution objects, one for each tenor horizon.
plot_vol_curves
plot_vol_curves(self) -> None
Generates a plot of each of the vol discount implied by the market
and fitted.
Generated automatically from the FinancePy source code.
Do not edit this file manually.