[R-SIG-Finance] Demean or not to demean
alexios ghalanos
alexios at 4dscape.com
Wed Aug 12 10:54:45 CEST 2015
The mean is estimated using maximum likelihood when include.mean=TRUE,
else it is zero (that should be clear from the vignette).
As to the assumptions on zero mean residuals, any
econometric/statistical reference should
answer that.
Alexios
On 12/08/2015 09:48, Gareth McEwan wrote:
> If one specifies "include.mean = TRUE" in "ugarchspec", does the
> "ugarchfit" function then use that (conditional mean) estimate to
> center/demean the innovations before subsequently fitting the GARCH
> portion of the model?
>
> Moreover, I'm trying to
> (1) understand the consequences/disadvantages of not demeaning the data
> prior to passing through to the GARCH routine, i.e.,
>
> (2) what effects take place when setting "include.mean = FALSE" in the
> "mean.model" specification
>
> Many thanks
> Gareth
>
>
>
> Refer:
> On 11/08/2015 10:33, Gareth McEwan wrote:
>
> Hi all
>
> I was hoping someone could shed light or direct me to a resource (or
> two)
> regarding a "demean" question.
>
> As I understand, QMLEs estimated on "demeaned" log return data vs
> straight
> "log return" data behave quite differently in finite samples
> (particularly
> for nonlinear MA models where the MA parameter is of interest).
> Apparently,
> for linear AR models, demeaning data does not seriously affect
> estimation
> of non-intercept parameters (refer: Yong Bao "Should We Demean Data?").
>
> For monthly financial log return data, I find ARMA specifications
> are not
> significant, but some sample *means *ARE significant, while others
> are not.
> In either case, I add the GARCH model specification with various error
> distributions from the "rugarch" package.
>
> Code example:
> x.log.ret = diff(log(price.x) #i.e. not "demeaned"
> spec <- ugarchspec(variance.model=list(model="sGARCH",garchOrder=c(1,1),
>
> submodel=NULL,external.regressors=NULL,variance.targeting=F),
> mean.model=list(*armaOrder = c(0,0)*,* include.mean = T*,
> external.regressors=NULL),
> distribution.model="std")
> tempgarch <- ugarchfit(spec=spec,data=x.log.ret,solver="hybrid")
>
> I work through the steps necessary to fitting a *t*Copula from which to
> simulate and ultimately work my way back to simulated returns.
>
> The goal here is to extract from the matrix of simulated returns those
> groups of returns coinciding with certain pre-determined
> "scenarios". These
> are then used for portfolio optimization.
>
> In the "global respect" of the methodology, can anyone shed light on the
> merits/demerits of not first demeaning the data? I haven't found any
> glaring problems, but it bothers me that the "rugarch" package
> operates on
> demeaned data.
>
>
> The rugarch package does NOT operate on demeaned data. It offers the
> option (default=TRUE) through "include.mean" on whether to demean the
> data or not. In case you are not demeaning, then the data are passed
> straight to the GARCH routine and assumed as the zero-mean residuals.
>
> On Wed, Aug 12, 2015 at 10:37 AM, Gareth McEwan <mcewan.gareth at gmail.com
> <mailto:mcewan.gareth at gmail.com>> wrote:
>
> Hmmm, I did, initially (to R-sig-finance email address).
>
> You then replied (which I thought would address the R-sig-finance
> group), and me to your reply (also thinking it would address the group).
>
> I'll double check..
>
> On Wed, Aug 12, 2015 at 10:34 AM, alexios ghalanos
> <alexios at 4dscape.com <mailto:alexios at 4dscape.com>> wrote:
>
> You should post your question to the mailing list.
>
> A.
>
> On 12/08/2015 09:30, Gareth McEwan wrote:
> > Sorry to keep bothering you Alexios (I know you're busy).
> >
> > Referring to my most recent response: if one specifies "include.mean =
> > TRUE" in "ugarchspec", does the "ugarchfit" function use that
> > (unconditional mean?) estimate to center/demean the innovations?
> >
> > I'm trying to understand how the "include.mean = T" is used in fitting
> > the model.
> >
> > Many thanks
> > Gareth
> >
> >
> > On Tue, Aug 11, 2015 at 1:22 PM, Gareth McEwan <mcewan.gareth at gmail.com <mailto:mcewan.gareth at gmail.com>
> > <mailto:mcewan.gareth at gmail.com <mailto:mcewan.gareth at gmail.com>>> wrote:
> >
> > Hi Alexios
> >
> > Is it correct then to say that by specifying "include.mean = TRUE"
> > in "ugarchspec", the "ugarchfit" function uses that (unconditional
> > mean) estimate to demean the data before subsequently fitting the
> > GARCH portion of the model?
> >
> > Many thanks
> > Gareth
> >
> >
> >
> > On Tue, Aug 11, 2015 at 12:02 PM, alexios <alexios at 4dscape.com <mailto:alexios at 4dscape.com>
> > <mailto:alexios at 4dscape.com <mailto:alexios at 4dscape.com>>>
> wrote:
> >
> > On 11/08/2015 10:33, Gareth McEwan wrote:
> >
> > Hi all
> >
> > I was hoping someone could shed light or direct me
> to a
> > resource (or two)
> > regarding a "demean" question.
> >
> > As I understand, QMLEs estimated on "demeaned" log
> return
> > data vs straight
> > "log return" data behave quite differently in
> finite samples
> > (particularly
> > for nonlinear MA models where the MA parameter is of
> > interest). Apparently,
> > for linear AR models, demeaning data does not
> seriously
> > affect estimation
> > of non-intercept parameters (refer: Yong Bao
> "Should We
> > Demean Data?").
> >
> > For monthly financial log return data, I find ARMA
> > specifications are not
> > significant, but some sample *means *ARE
> significant, while
> > others are not.
> > In either case, I add the GARCH model
> specification with
> > various error
> > distributions from the "rugarch" package.
> >
> > Code example:
> > x.log.ret = diff(log(price.x) #i.e. not "demeaned"
> > spec <-
> >
> ugarchspec(variance.model=list(model="sGARCH",garchOrder=c(1,1),
> >
> >
> submodel=NULL,external.regressors=NULL,variance.targeting=F),
> > mean.model=list(*armaOrder = c(0,0)*,*
> > include.mean = T*,
> > external.regressors=NULL),
> > distribution.model="std")
> > tempgarch <-
> ugarchfit(spec=spec,data=x.log.ret,solver="hybrid")
> >
> > I work through the steps necessary to fitting a
> *t*Copula
> > from which to
> > simulate and ultimately work my way back to
> simulated returns.
> >
> > The goal here is to extract from the matrix of
> simulated
> > returns those
> > groups of returns coinciding with certain
> pre-determined
> > "scenarios". These
> > are then used for portfolio optimization.
> >
> > In the "global respect" of the methodology, can
> anyone shed
> > light on the
> > merits/demerits of not first demeaning the data? I
> haven't
> > found any
> > glaring problems, but it bothers me that the "rugarch"
> > package operates on
> > demeaned data.
> >
> >
> > The rugarch package does NOT operate on demeaned data.
> It offers
> > the option (default=TRUE) through "include.mean" on
> whether to
> > demean the data or not. In case you are not demeaning,
> then the
> > data are passed straight to the GARCH routine and
> assumed as the
> > zero-mean residuals.
> >
> >
> >
> > Thank you very much for the help
> > Gareth
> >
> >
> > Alexios
> >
> >
> >
>
>
>
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