calibrate: Calibrate vols (SABR or quadratic smile fit) from market...

In prodipta/bsoption: Black-Scholes implementation for equity index options

Description

The first input is option chain data (dataframe) for a single maturity, the expected columns include the forward, expiry (in date format), strike, price and type (either call or put).

Usage

calibrate(options, valuedate, model = "quadratic", atmvol = 0,
  precision = 2, type = "delta")

Arguments

options	dataframe with options chain details
valuedate	valuation date for the calibration
model	Volatility model, can be either sabr or quadratic
atmvol	ATM volatility, if provided used in sabr calibration (else ATM vol is estimated with a quadratic vol fit first)
precision	degree of precision
type	Type of fit in case of quadratic model (either delta or strike)

Value

Fitted volatility object. The object is either of class quadvol or sabrvol depending upon the fit. The object includes atm vol level, and model specific parameters. It also stores the valuation date of calibration. The sabr model implements calibration of a lognormal SABR model (ref Hagan(2002) "Managing smile risk" and West (2005) "Calibration of the SABR Model in Illiquid Markets"). The model assumes a beta of 0 (lognormal behaviour) and calibrates other parameters of SABR. The quadratic model is a simple two parameters model of volatility smile, fitted with least square regression. The calibration type delta fits this quadratic smile based on percentage offset from ATM, while strike fits absolute deviation from ATM forward.

prodipta/bsoption documentation built on May 29, 2019, 2:57 p.m.