I am doing a project where I look for seasonalities in stock prices. E.g. we have created dummies for “turn of month effect”, the last day of every month and the first day of the next month will have the value 1.
We need to find out whether to use OLS or GARCH methods due to heteroskedasticity.
We are analysing one effect at the time.

So first we have the day of week effect, where we regress the days of week dummies on the Log return of the index.
The data is from 1996 to 2020.

First I use the tsset command and create the times series.

Then I run the regression:
reg IndexReturn dayOfMonthDummy

and this will by the output. We cant reject the H0 of heteroskedasticity,

chi2(1) = 0.00
Prob > chi2 = 0.9776

But when I run you test from this article:
estat archlm

I get this, which means there are ARCH effect.
We dont know what to do here. Are the time-series heteroskedastitic or not?
Thank you so much in advance.

Thanks for your reply. I have read that the GARCH approach is often used with stock returns. Can you explain to me why I would consider the GARCH approach instead of only the ARCH approach (if this is the case)?

Hi
Yeah, you can use GARCH approach instead of ARCH in financial data because firstly GARCH is extension of ARCH model only but after having inclusion of moving average. As financial data changes every second, thus with incorporation of conditional change in variance over time, more adequate results could be derived.

3 years & 11 months ago

Hi Divya, thank you for informative articles!

I am doing a project where I look for seasonalities in stock prices. E.g. we have created dummies for “turn of month effect”, the last day of every month and the first day of the next month will have the value 1.

We need to find out whether to use OLS or GARCH methods due to heteroskedasticity.

We are analysing one effect at the time.

So first we have the day of week effect, where we regress the days of week dummies on the Log return of the index.

The data is from 1996 to 2020.

First I use the tsset command and create the times series.

Then I run the regression:

reg IndexReturn dayOfMonthDummy

and this will by the output. We cant reject the H0 of heteroskedasticity,

chi2(1) = 0.00

Prob > chi2 = 0.9776

But when I run you test from this article:

estat archlm

lags(p) | chi2 df Prob > chi2

————-+————————————————————-

1 | 297.097 1 0.0000

I get this, which means there are ARCH effect.

We dont know what to do here. Are the time-series heteroskedastitic or not?

Thank you so much in advance.

Mathias

^{}3 years & 10 months ago

Hi Mathias,

As per your query, the series is heteroskedastic and you have to fit ARCH model for addressing seasonality in stock prices.

3 years & 10 months ago

Thanks for your reply. I have read that the GARCH approach is often used with stock returns. Can you explain to me why I would consider the GARCH approach instead of only the ARCH approach (if this is the case)?

^{}3 years & 7 months ago

Hi

Yeah, you can use GARCH approach instead of ARCH in financial data because firstly GARCH is extension of ARCH model only but after having inclusion of moving average. As financial data changes every second, thus with incorporation of conditional change in variance over time, more adequate results could be derived.