Archive for August, 2016

This can be proved by Chern classes using the fact that the complexified tangent bundle of all spheres are trivial and the normal bundle of an oriented hypersurface is trivial.  Thankfully the details are worked out here:  The Triviality of the Complexified Tangent Bundles of Some Manifolds.  Of course the nontrivial claim is that the actual universe is such a hypersurface but this is my hypothesis based on such issues as a thermal equilibrium for the cosmic background which defied Gaussian bounds for heat kernels and also the fact that the redshift slope is perfectly explained by an S4 geometry (predicted by it) and the fact that quasicrystals are parsimoniously explained as four-dimensional crystals.

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Volatility is modeled as a mean-reverting process employing the argument that if not and there is a law that it had followed for the past century then it would be much larger.  This is the same argument used to justify interest rates as mean-reverting.  The ‘classical’ models of volatility and interest rates had been the Cox-Ingersoll-Ross (1985)/Feller (1951) process and the Ornstein-Uhlenbeck process, i.e. the models dv = \kappa(\theta-v)dt + \sigma\sqrt{v}dw and dv=-\kappa v dt + \sigma dw.  The former has become much more popular for volatility models because of the closed-form option pricing by Heston (1993) model.  Eraker (2003) determined that jumps in volatility are necessary in addition to jumps in the price process, so volatility models must have jumps in addition to the mean reverting diffusion but this had not become standardized and in fact I was totally confused about this aspect simply because Heston and Bates models had been well-known.  An affine class closed form model with jumps in volatility was published by Duffie-Pan-Singleton in 2000 that I have used to test which has excellent fits to the volatility surface and whose implementation is a minor variation of the Heston/Bates models.  I had shown recently that in addition to  jumps time-change by the inverse stable subordinator are necessary to fit volatility.   Now we take the next step, which is to consider the problem of multivariate volatility models with jumps and inverse stable time change.  The method I had used to fit the univariate volatility models is by minimizing the error of fit of the characteristic function of the theoretical model with the empirical characteristic function.  Recall that for a series X_1,X_2,\dots the empirical characteristic function is simply \phi_e(t,\xi) = 1/n \sum_j e^{it\xi X_j}.  This definition is valid for X_j being vector quantities so we have to work with the theoretical multivariate characteristic function.  The multidimensional analogue of the square root process is the Wishart process.  It is the process governed by

dX_t = \sqrt{X_t} dB_t Q + Q^T dB_t^T \sqrt{X_t} + (MX_t + X_t M + \kappa Q^T Q) dt

A formula for the Laplace transform (and therefore the characteristic function) was derived for this matrix process by Bru (1991).  More details for this process can be found here:  AlessandroGnoatto and an option pricing model where the volatility follows the Wishart process can be found here: FonsecaWishartVolatilityModel.  Under time change by an absolutely continuous time change T_t =\int_0^s \dot{T}_s ds of volatility the process satisfies

dX_t = S_{\alpha}(t) (\sqrt{X_t} dB_t Q + Q^T dB_t^T \sqrt{X_t}) + S_{\alpha}(t) (MX_t + X_tM) dt + T_{\alpha}(t) \kappa Q^T Q dt

Now the Bru (1991) Laplace transform formula is given in laplacewishart Screenshot 2016-08-11 17.13.55

Now for the time-change we just insert the stopping time S_\alpha(t) and integrate against the density of the inverse stable subordinator.

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