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Hi, I want to fit a mean and variance model jointly. For example I might want to fit an AR(2)-GARCH(1,1) model i.e. r_t = constant_term1 + b*r_t-1 + c*r_t-2 + a_t where a_t = sigma_t*epsilon_t where sigma^2_t = constant_term2 + p*sigma^2_t-1 + q*a^2_t-1 i.e. R estimates a constant_term1, b, c, constant_term2, p, q TIA Aditya

...ide the capabilities of the arima function in R. I'd like to fit an ARMA model where the variance of the disturbances is a function of some exogenous variable. So something like: Y_t = a_0 + a_1 * Y_(t-1) +...+ a_p * Y_(t-p) + b_1 * e_(t-1) +...+ b_q * e_(t-q) + e_t, where e_t ~ N(0, sigma^2_t), and with the variance specified by something like sigma^2_t = exp(beta_t * X_t), where X_t is my exogenous variable. I would be very grateful if somebody could point me in the direction of a library that could fit this (or a similar) model. Thanks, James Kirkby Actuarial Maths and Stats Her...

...520-%2520Technical%2520Document.pdf&ei=RSJhUd7YJIbktQaQ-YCAAw&usg=AFQjCNGpCXUdLSVHQtYJMl7MccLGQtdkDw&sig2=HBxWDrRTMN7rVqWu-Yp1zQ&bvm=bv.44770516,d.Yms Especially page 238 is interesting: "According to this model, returns are generated as follows" r_t=sigma_t xi_t sigma^2_t is calculated by EWMA xi is distributed according to the generalized error distribution. So they do not assume the returns to follow a certain distribution, but they assume the returns condition on the volatility to follow a certain distribution, right? Now my question is, how can one calculate...

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...''s,@KeeP@,,g'' \ -e ''s,_t\([^[:alnum:]_]\|$$\),_compat_t\1,g'' \ -e ''s,\(8\|16\|32\|64\)_compat_t\([^[:alnum:]_]\|$$\),\1_t\2,g'' \ -e ''s,\(^\|[^[:alnum:]_]\)xen_\?\([[:alnum:]_]*\)_compat_t\([^[:alnum:]_]\|$$\),\1compat_\2_t\3,g'' \ @@ -43,13 +44,15 @@ compat/%.h: compat/%.i Makefile compat/%.i: compat/%.c Makefile $(CPP) $(CFLAGS) $(cppflags-y) -o $@ $< -compat/%.c: public/%.h Makefile +compat/%.c: public/%.h xlat.lst Makefile mkdir -p $(@D) grep -v ''DEFINE_XEN_GUEST_HANDLE(long)''...

...2010 at 5:59 AM, Aditya Damani wrote: > Hi, > > I want to fit a mean and variance model jointly. > > For example I might want to fit an AR(2)-GARCH(1,1) model i.e. > > r_t = constant_term1 + b*r_t-1 + c*r_t-2 + a_t > > where a_t = sigma_t*epsilon_t > > where sigma^2_t = constant_term2 + p*sigma^2_t-1 + q*a^2_t-1 > > i.e. R estimates a constant_term1, b, c, constant_term2, p, q > > TIA > Aditya > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PL...