SABR volatility model

In mathematical finance, the SABR model is a stochastic volatility model, which attempts to capture the volatility smile in derivatives markets. The name stands for 'stochastic alpha, beta, rho', referring to the parameters of the model. The SABR model is widely used by practitioners in the financial industry, especially in the interest rate derivative markets. It was developed by Patrick S. Hagan, Deep Kumar, Andrew Lesniewski, and Diana Woodward. In mathematical finance, the SABR model is a stochastic volatility model, which attempts to capture the volatility smile in derivatives markets. The name stands for 'stochastic alpha, beta, rho', referring to the parameters of the model. The SABR model is widely used by practitioners in the financial industry, especially in the interest rate derivative markets. It was developed by Patrick S. Hagan, Deep Kumar, Andrew Lesniewski, and Diana Woodward. The SABR model describes a single forward F {displaystyle F} , such as a LIBOR forward rate, a forward swap rate, or a forward stock price. The volatility of the forward F {displaystyle F} is described by a parameter σ {displaystyle sigma } . SABR is a dynamic model in which both F {displaystyle F} and σ {displaystyle sigma } are represented by stochastic state variables whose time evolution is given by the following system of stochastic differential equations: with the prescribed time zero (currently observed) values F 0 {displaystyle F_{0}} and σ 0 {displaystyle sigma _{0}} . Here, W t {displaystyle W_{t}} and Z t {displaystyle Z_{t}} are two correlated Wiener processes with correlation coefficient − 1 < ρ < 1 {displaystyle -1< ho <1} : The constant parameters β , α {displaystyle eta ,;alpha } satisfy the conditions 0 ≤ β ≤ 1 , α ≥ 0 {displaystyle 0leq eta leq 1,;alpha geq 0} . The above dynamics is a stochastic version of the CEV model with the skewness parameter β {displaystyle eta } : in fact, it reduces to the CEV model if α = 0 {displaystyle alpha =0} The parameter α {displaystyle alpha } is often referred to as the volvol, and its meaning is that of the lognormal volatility of the volatility parameter σ {displaystyle sigma } . We consider a European option (say, a call) on the forward F {displaystyle F} strike at K {displaystyle K} , which expires T {displaystyle T} years from now. The value of this option is equal to the suitably discounted expected value of the payoff max ( F T − K , 0 ) {displaystyle max(F_{T}-K,;0)} under the probability distribution of the process F t {displaystyle F_{t}} . Except for the special cases of β = 0 {displaystyle eta =0} and β = 1 {displaystyle eta =1} , no closed form expression for this probability distribution is known. The general case can be solved approximately by means of an asymptotic expansion in the parameter ε = T α 2 {displaystyle varepsilon =Talpha ^{2}} . Under typical market conditions, this parameter is small and the approximate solution is actually quite accurate. Also significantly, this solution has a rather simple functional form, is very easy to implement in computer code, and lends itself well to risk management of large portfolios of options in real time. It is convenient to express the solution in terms of the implied volatility of the option. Namely, we force the SABR model price of the option into the form of the Black model valuation formula. Then the implied volatility, which is the value of the lognormal volatility parameter in Black's model that forces it to match the SABR price, is approximately given by: where, for clarity, we have set C ( F ) = F β {displaystyle Cleft(F ight)=F^{eta }} . The value F mid {displaystyle F_{ ext{mid}}} denotes a conveniently chosen midpoint between F 0 {displaystyle F_{0}} and K {displaystyle K} (such as the geometric average F 0 K {displaystyle {sqrt {F_{0}K}}} or the arithmetic average ( F 0 + K ) / 2 {displaystyle left(F_{0}+K ight)/2} ). We have also set