Applied Modeling Techniques and Data Analysis 2. Группа авторов

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Applied Modeling Techniques and Data Analysis 2 - Группа авторов

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the other, the Black-Scholes price in logarithmic variables is

      [2.1]image

      and τ = Tt ∈ [0, T], x, k ∈ ℝ, image is the cumulative distribution function of the standard normal random variable.

      DEFINITION 2.1.- The implied volatility σ = σ(t,x, y, T, k) is the unique positive solution of the nonlinear equation

image

      REMARK 2.1.- In the literature on option pricing, there are concepts of model implied volatility and market implied volatility. If the right-hand side of the above equation, i.e. u(t,x,y,T,k), refers to the European option price under a given model, then σ = σ(t,x,y,T,k) is called the model implied volatility. If u(t,x, y,T,k) is replaced by the observed market option price, then we have the so-called market implied volatility. Here, we work with the (model) implied volatility.

      Pagliarani and Pascucci (2017) proved that under some mild conditions, the following limits exist:

image

      where the limit is taken as (T,k) approaches (t,x) within the parabolic region

image

      for an arbitrary positive real number λ and nonnegative integers m and q.

      Moreover, Pagliarani and Pascucci (2017) established an asymptotic expansion of the implied volatility in the following form:

      [2.2]image

      as (T,k) approaches (t,x) within image.

      We apply the above described theory to the double-mean-reverting model by Gatheral (2008) given by the following system of stochastic differential equations:

      [2.3]image

      where the Wiener processes image are correlated: image, and where parameters κ1,κ2, θ, ξ1, ξ2, α1, α2 are the positive real numbers. Note that while S0 is observable in the market, ν0 and image are usually not observable and may be calibrated from the market data on options.

      In this model, with rate κ1 the variance νt mean reverts to a level image which itself moves over time to the level θ at a (usually slower rate) κ2, hence the name double-mean-reverting. Here, parameters α1, α2 ∈ [1/2,1]. In the case of α1 = α2 = 1/2, we have the so-called double Heston model; in the case of α1 = α2 = 1, the double lognormal model; and finally, in the general case, the double CEV model (Gatheral 2008).

      In section 2.2, we formulate three theorems that give the asymptotic expansions of implied volatility of orders 0, 1 and 2. Detailed proof of Theorems 2.1 and 2.2 as well as a short proof of Theorem 2.3 without technicalities are given in section 2.3.

      Put xt = ln St.

      THEOREM 2.1.- The asymptotic expansion of order 0 of the implied volatility has the form

image

      THEOREM 2.2.- The asymptotic expansion of order 1 of the implied volatility has the form

image

      THEOREM 2.3.- The asymptotic expansion of order 2 of the implied volatility has the form

      [2.4]image

      PROOF OF THEOREM 2.1.- First, we perform the change of variable χt = ln St in the system [2.3]. Using the multidimensional Itô formula, we obtain

image image

      with z = (x,y,z)T. We have

image

      From Pagliarani and Pascucci (2017), Definition 3.4, we have

      Following Lorig et al. (2017), Appendix B, put

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