Applying the Laplace approximation to the Generalized Kalman Filter -- with an application to Stochastic Volatility Models}

Let be an dimensional multivariate time series for where is a random vector with probability density function . For each , the are random vectors which satisfy the condition

. where and .

Let be the probability density function for before is oberved. After observing we want to calculate the probability distribution of given . This is given by

where

let Let . Approximate by its second order taylor expansion in at .

so that

Making a change of variables and integrating we obtain

This is the Laplace approximation to the integral in (0.2).

To calculate (0.4) it is necessary to maximize with respect to and to calculate its hessian matrix with respect to .

For the maximization we employ the Newton-Raphson algorithm. Let

This operation is carried out a fixed number times and then . For ``well behaved'' problems the sequence converges quadratically to . We approximate by a multivariate normal with

and approximate by a multivariate normal with

Now

As above we maximize the integrand of (0.5) with respect to and use the Laplace approximation to the integral. This produces the sequence of conditional probabilities, . The log-likelihood function for the observed sequence is given by

Parameter estimation

Although we have not explicitly shown them the conditional likelihood functions depend on a number of parameters. These parameters include the specification of , other parameters in the probability density and parameters which determine . If we denote these parameters by and write to indicate this dependence the log-likelihood function becomes

the maximum likelihood estimates for the parameter vector are found by maximizing (0.7) with respect to .

The stochastic volatility model

The version of the stochastic volatility model presented here is from the paper Multivariate Stochastic Volatility Models: Estimation and a comparison with VGARCH Models by Danielsson.

It is assumed that has a multivariate normal distribution with and covariance matrix where is an diagonal matrix whose 'th element on the diagonal is given by where the satisfy the relationship

where is a multivariate normal random variable with and . If and are two vectors with 'th component and is the vector with 'th componment . is an postive definite matrix satisfying , that is a corellation matrix. Then

and the distribution of is multivariate normal with mean vector and covariance matrix given by

is the diagonal matrix whose diagonal is equal to the vector .

To perform the Newton-Raphson calculations it is necessary to calculate the first and second derivatives of expression (0.8) with respect to the parameter vector . This is the most involved part of the calculations and will depend on the particular form of the model. In the present case the calculations are simplified by the fact that only depends on through the diagonal matrix .

The probability density function is assumed to be multivariate normal with and .

The Data

The data consist of the daily Mark/Dollar and Yen/dollar exchange rates and the US and Japaneese stock index data. There are 1301 time periods with some missing data. The missing data which are denoted by the impossibly large value of 10,000 were replaced with the average from the period before and after. They can howerever easily be estimated in the model is desired.

The Results

The model was fit with various combinations of the parameters and the log-likelihood was examined to investigate the improvement in fit due to the addition of the parameters.

The parameters and did not produce a significant improvement to the fit. measures the asymmetry in the response of the variance to positive and negative shocks.

Here are the parameter estimates and their standard deviations for the model with , and .


 index   name    value      std dev   
    1   w(1)       -1.3749e-001 4.9434e-002
    2   w(2)       -6.5649e-001 1.6161e-001
    3   w(3)        3.1693e-002 1.0574e-002
    4   w(4)       -1.2973e-002 1.5375e-002
    5   lambda1(1)  1.5564e-001 4.9688e-002
    6   lambda1(2)  1.8647e-001 6.9525e-002
    7   lambda1(3)  -6.9265e-002 1.4158e-002
    8   lambda1(4)  -1.6689e-001 3.1626e-002
    9   delta(1)    8.2229e-001 4.6074e-002
   10   delta(1)    5.0848e-001 1.0785e-001
   11   delta(1)    9.5763e-001 1.4602e-002
   12   delta(1)    9.3610e-001 1.8812e-002
   29   R(1,1)      1.0000e+000 0.0000e+000
   30   R(1,2)      5.3821e-001 2.2883e-002
   31   R(1,3)      -7.1704e-002 2.9477e-002
   32   R(1,4)      -3.8796e-002 2.9278e-002
   33   R(2,1)      5.3821e-001 2.2883e-002
   34   R(2,2)      1.0000e+000  0.0000e+000
   35   R(2,3)      -1.2932e-001 2.9111e-002
   36   R(2,3)      -4.1466e-002 2.9468e-002
   37   R(3,1)      -7.1704e-002 2.9477e-002
   38   R(3,2)      -1.2932e-001 2.9111e-002
   39   R(3,3)      1.0000e+000  0.0000e+000
   40   R(1,4)      8.8811e-002 2.9085e-002
   41   R(4,1)      -3.8796e-002 2.9278e-002
   42   R(4,2)      -4.1466e-002 2.9468e-002
   43   R(4,3)      8.8811e-002 2.9085e-002
   44   R(4,4)      1.0000e+000  0.0000e+000
   45   Omega(1,1)  6.5973e-001 6.3099e-002
   46   Omega(1,2)  1.9827e-001 1.6129e-002
   47   Omega(1,3)  -1.3395e-001 5.4982e-002
   48   Omega(1,4)  -3.5161e-002 2.6676e-002
   49   Omega(2,1)  1.9827e-001 1.6129e-002
   50   Omega(2,2)  2.0570e-001 2.3994e-002
   51   Omega(2,3)  -1.3489e-001 3.2608e-002
   52   Omega(2,4)  -2.0985e-002 1.5016e-002
   53   Omega(3,1)  -1.3395e-001 5.4982e-002
   54   Omega(3,2)  -1.3489e-001 3.2608e-002
   55   Omega(3,3)  5.2895e+000 5.7872e-001
   56   Omega(3,4)  2.2791e-001 7.9318e-002
   57   Omega(4,1)  -3.5161e-002 2.6676e-002
   58   Omega(4,2)  -2.0985e-002 1.5016e-002
   59   Omega(4,3)  2.2791e-001 7.9318e-002
   60   Omega(4,4)  1.2451e+000 1.7043e-001
   61   Z(1,1)      2.3967e-001 7.4268e-002
   62   Z(1,2)      2.0711e-001 5.5599e-002
   63   Z(1,3)      3.8832e-002 1.8505e-002
   64   Z(1,4)      2.4097e-002 2.0344e-002
   65   Z(2,1)      2.0711e-001 5.5599e-002
   66   Z(2,2)      4.6309e-001 1.1143e-001
   67   Z(2,3)      3.4298e-002 2.3017e-002
   68   Z(2,4)      9.6831e-003 2.9999e-002
   69   Z(3,1)      3.8832e-002 1.8505e-002
   70   Z(3,2)      3.4298e-002 2.3017e-002
   71   Z(3,3)      3.9101e-002 1.6885e-002
   72   Z(3,4)      2.4602e-002 1.1053e-002
   73   Z(4,1)      2.4097e-002 2.0344e-002
   74   Z(4,2)      9.6831e-003 2.9999e-002
   75   Z(4,3)      2.4602e-002 1.1053e-002
   76   Z(4,4)      9.6109e-002 3.4268e-002

The AD Model Builder TPL file for the model is given below.


DATA_SECTION
  init_int ndim
  init_int nobs
  int ndim1
  int ndim2
 !! ndim1=ndim*(ndim+1)/2;
 !! ndim2=ndim*(ndim-1)/2;
  init_matrix Y(1,nobs,1,ndim)
 LOC_CALCS
  // replace missing values (10000) with the average of before and after.
  for (int i=2;i100.0)     // did this work
          cerr << " Y(i,j) too big " << Y(i,j) << endl; 
      }      
 END_CALCS

PARAMETER_SECTION matrix h_mean(1,nobs,1,ndim) 3darray h_var(1,nobs,1,ndim,1,ndim) number ldR; init_vector theta0(1,ndim,3); vector lmin(1,nobs) init_bounded_vector w(1,ndim,-10,10) vector w1(1,ndim) init_vector lambda(1,ndim,2) init_vector lambda2(1,ndim,-1) init_bounded_vector delta(1,ndim,0,.98) sdreport_matrix R(1,ndim,1,ndim) sdreport_matrix Omega(1,ndim,1,ndim) matrix ch_R(1,ndim,1,ndim) matrix Rinv(1,ndim,1,ndim) init_bounded_vector v_R(1,ndim2,-1.0,1.0) sdreport_matrix Z(1,ndim,1,ndim) matrix ch_Z(1,ndim,1,ndim) init_bounded_vector v_Z(1,ndim1,-1.0,1.0) matrix S(1,ndim,1,ndim); objective_function_value f INITIALIZATION_SECTION delta 0.9 PROCEDURE_SECTION

fill_the_matrices(); int sgn; ldR=ln_det(R,sgn); Rinv=inv(R); dvar_vector tmp(1,ndim); dvar_matrix sh(1,ndim,1,ndim); h_mean(1)=theta0; h_var(1)=0; for (int i=2;i<=nobs;i++) { dvar_vector tmean=update_the_means(w,h_mean(i-1),Y(i-1)); dvar_matrix v=update_the_variances(h_var(i-1)); tmp=tmean; dvar_vector h(1,ndim); dvar_vector gr(1,ndim); for (int ii=1;ii<=4;ii++) // do the Newton-Raphson 4 times { xfp12(tmp, Y(i),tmean,v,gr,sh); // get 1st and 2nd derivatives h=-solve(sh,gr); //sh is hessian and gr is the gradient tmp+=h; // add new step h } double nh=norm2(value(h)); // check size of h for convergence if (nh>1.e-1) cout << "No convergence in NR " << nh << endl; if (nh>1.e+02) { f+=1.e+7; // this ensures that the function minimizer will take a return; // smaller step } h_mean(i)=tmp; h_var(i)=inv(sh); lmin(i)=fp(tmp,Y(i),tmean,v); int sgn; f+=lmin(i)+0.5*ln_det(sh,sgn); // Laplace approximation } f-=0.5*nobs*ndim*log(2.*3.14159); Omega=S;

FUNCTION dvar_vector update_the_means(dvar_vector& w,dvar_vector& m,dvector& e) dvar_vector tmp= w+elem_prod(delta,m)+elem_prod(lambda,e); if (active(lambda2)) tmp+=elem_prod(lambda2,fabs(e)); return tmp;

FUNCTION dvar_matrix update_the_variances(dvar_matrix& v) dvar_matrix tmp(1,ndim,1,ndim); for (int i=1;i<=ndim;i++) { for (int j=1;j<=i;j++) { tmp(i,j)=delta(i)*delta(j)*v(i,j); if (i!=j) tmp(j,i)=tmp(i,j); } } tmp+=Z; return tmp;

FUNCTION dvariable fp(dvar_vector& h, dvector& y, dvar_vector& m,dvar_matrix& v) dvar_vector eh=exp(.5*h); for (int i=1;i<=ndim;i++) { for (int j=1;j<=i;j++) { S(i,j)= eh(i)*eh(j)*R(i,j); if (i!=j) S(j,i)=S(i,j); } }

dvariable lndet; dvariable sgn; dvar_vector u=solve(S,y,lndet,sgn); dvariable l; l=.5*lndet+.5*(y*u); dvar_vector hm=h-m; w1=solve(v,hm,lndet,sgn); l+=.5*lndet+.5*(w1*hm); return l;

FUNCTION void xfp12(dvar_vector& h, dvector& y,dvar_vector& m,dvar_matrix& v, dvar_vector gr,dvar_matrix& hess) dvar_vector ehinv=exp(-.5*h); dvariable lndet; dvariable sgn; dvar_vector ys=elem_prod(ehinv,y); dvar_vector u=Rinv*ys; gr=0.5; dvar_vector vv=elem_prod(ys,u); gr-=.5*vv; dvar_vector hm=h-m; dvar_vector w=solve(v,hm,lndet,sgn); gr+=w; for (int i=1;i<=ndim;i++) { for (int j=1;j<=i;j++) { hess(i,j)=0.25*ys(i)*ys(j)*Rinv(i,j); if (i!=j) hess(j,i)=hess(i,j); } } for (i=1;i<=ndim;i++) { hess(i,i)+=.25*vv(i); } hess+=inv(v);

FUNCTION fill_the_matrices int ii=1; ch_Z.initialize(); for (int i=1;i<=ndim;i++) { for (int j=1;j<=i;j++) ch_Z(i,j)=v_Z(ii++); ch_Z(i,i)+=0.5; } Z=ch_Z*trans(ch_Z); ch_R.initialize(); ii=1; for (i=1;i<=ndim;i++) { for (int j=1;j REPORT_SECTION report<<"observed"< TOP_OF_MAIN_SECTION arrmblsize=20000000; gradient_structure::set_CMPDIF_BUFFER_SIZE(25000000); gradient_structure::set_GRADSTACK_BUFFER_SIZE(1000000);