A stochastic volatility inspired (SVI) model provides a parametric form for the implied volatility smile. A tool employing this model allows for the calibration of these parameters to market data, typically derived from option prices. This calibration process involves finding the optimal parameter set that minimizes the difference between the model-generated implied volatilities and the observed market volatilities. For instance, given a set of observed option prices for different strikes and maturities, the tool can determine the SVI parameters that best reproduce the market’s implied volatility surface.
Such tools are crucial in finance for tasks like option pricing, hedging, and risk management. By providing a smooth and consistent representation of the volatility surface, they facilitate interpolation and extrapolation of volatilities across different strikes and maturities. This is essential for valuing and hedging options with strike prices or maturities not directly observed in the market. Historically, managing the volatility smile has been challenging, and the development of parametric models like SVI represents a significant advancement, providing a more robust and manageable framework than earlier, less flexible approaches.
