You have not told us what software you used to get the results you
present. My first question is whether you are working with prices or
log(prices)? If the former, I suggest you consider the latter; price
changes tend to be much better behaved, more nearly normal, etc., on the
log scale than in dollars, Euros, Rupias, or whatever.
Secondly, have you made a normal probability plot of the residuals,
preferably using "studres" in library(MASS)? (If you don't have
Venables and Ripley 2002 Modern Applied Statistis with S, Springer, I
recommend you get it and spend some time with it. In addition to
"studres", it has an excellent chapter devoted to an introductory
discussion of time series analysis.) Outliers suggest you may need to
be working with some of the more sophisticated Rmetrics tools, but I'm
not sufficiently familiar with those to say much more about that at the
present time.
If I had outliers, I might just delete them initially. However, I
would definitely want to come back to them later, because the outliers
could provide more information than other observations to predict, for
example, a structural change in the market. Modeling and reacting
properly to such signals could make the difference between stellar
performance and disaster in managing a hedge fund.
Thirdly, have you made acf and pacf of the residuals? Also, have you
computed the Box-Ljung statistic (function Box.test)? If no, I suggest
you do that as a next step. If they indicate some kind of
autocorrelation structure, I might then try to model and estimate that
along with your regression model using function "arima".
If you still have questions (which I suspect), then feel free to ask
another question. However, before you do that, PLEASE do read the
posting guide prior! "http://www.R-project.org/posting-guide.html".
Many people find answers to their own questions in the process of
working through the posting guide. Questions posted following that
process tend to be clearer, easier for others to understand and respond
to. On average, this tends to increase the speed, volume and utility of
replies.
spencer graves
Krishna wrote:
> Hi everyone
>
> I am trying to estimate the optimal hedge variance ratio for cross
> hedging two commodities. the price levels are used (compared to price
> change and % price change) and used the OLS with dummy variable for
> estimating the co-efficients. the equation looks like this
>
> Y = B + B1*D1 + B2*X + B3*(X*D1)
>
> Where Y = Daily Cash market price
> D1 = Dummy variable taking value 1 for period Oct-Mar and 0 for Apr-Sep
> X = Daily futures market price on which cross hedging is done.
> B,B1,B2,B3 are the slope co-efficients.
>
> The results look like this
> Regression Statistics
> Multiple R 0.948702709
> R Square 0.900036831
> Adjusted R Square 0.89981135
> Standard Error 25.52050965
> Observations 1334
>
>
> Coefficients Standard Error t Stat P-value
> Intercept 53.817 4.375 12.300 0.000
> X 0.986 0.012 80.283 0.000
> D1 27.399 6.106 4.487 0.000
> D1 * X -0.100 0.017 -5.820 0.000
>
> It is understood the slope co-efficients for different periods are
> significant as indicated by t-table value. But I feel suspicious on
> the reliability of this values.
>
> I have used 5 years of daily price data for running the regression,
> and I feel suscpicious becasue, the monthly correlations (pearson
> correlation co-efficient) are highly varying between spot and futures
> and some times even negative.
>
> Can someone suggest me
> a) the tests to judge the reliability of hedge-variance values
> b) Is there any other better method than described here for estimating
> the hedge-variance values
>
> Thank you for the attention and look forward for an early reply
>
> rgds
>
> snvk
>
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