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| From | tzygmund mcfarlane <tzygmund@googlemail.com> |
| To | statalist@hsphsun2.harvard.edu |
| Subject | Re: st: calculate volatility in different ways |
| Date | Tue, 26 Jul 2011 09:58:07 -0700 |
If you would like to compute variously smoothed squared returns: /****************************************/ clear* sysuse sp500, clear tsset date // generate returns g ret = log(open/L.open) g retsq = ret^2 su ret retsq // EWMA filter (sq. returns) scalar lambda = 0.95 // smoothing parameter qui su retsq local double var_retsq = r(Var) tssmooth exponential ewma_retsq = retsq, /// parms(`lambda') samp0(`var_retsq') label variable ewma_retsq "Sq. Ret. (EWMA Filt.)" // MA filter (sq. returns) tssmooth ma ma_retsq = retsq, window(59 1 0) label variable ma_retsq "Sq. Ret. (MA filt.)" // MA filter (variance) preserve tempfile rollvar_ret rolling ma_var = r(Var), window(60) saving(`rollvar_ret', replace): /// summarize ret, d use `rollvar_ret', clear rename end date label variable ma_var "Filt. Ret. Variance" save `rollvar_ret', replace restore merge 1:1 date using `rollvar_ret' li *_retsq ma_var in 1/10 tsline *_retsq ma_var /***************************************/ Note the virtually imperceptible difference between filtering the variance and the squared returns. On Tue, Jul 26, 2011 at 8:48 AM, Oliver Jones <ojones@wiwi.uni-bielefeld.de> wrote: > Hi, > dose this help you? > > ********** Begin example **************** > sysuse sp500, clear > tsset date > > rolling Var = r(Var), window(60) step(1): summarize open, d > > list in 1/10 > ********** End example **************** > > > Best > Oliver > > Am 25.07.2011 13:00, schrieb Nadine R: >> >> Dear Statalisters! >> >> I have a new problem: I want to calculate the volatility of some stock >> returns using Equally Weighted Moving Averages and Exponential >> Weighted Moving Averages in Stata 11.0. I have daily return data for >> the stocks. I did not use the standard deviation measures as I need >> the volatilities for different periods (if I could calculate the >> 60-day-volatilities for 10 years with this formula I would do it like >> this). >> >> formula for Equally Weighted (for a 60-day-window: M=60): >> variance at day t = 1/M * sum of the 60 preceding squared returns >> variance at day 60 = 1/60 * Sum(from (return t=1)^2 to (return t=60)^2) >> variance at day 61 = 1/60 * Sum(from (return t=2)^2 to (return t=61)^2) >> ... >> >> I tried it with a formula like this: >> generate var = 1/60 * sum((return[_n-60])^2 - (return[_n])^2) >> >> where the "-" should indicate a "until/till" but Stata interpreted it >> as a minus. >> >> formula for the Exponential Weighted Moving Averages: >> variance at day t = lambda*variance(t-1) + (1 - lambda)*squared >> return(t-1) >> >> I tried it with a formula like this with lambda = 0.95 but it seems >> that Stata does not calculate var(t=0) and then var(t=1) and therefore >> the formula did not work: >> generate var = 0.95*(var[_n-1]) + 0.05*return[_n]^2 >> >> Thanks for yur help. >> >> Kind regards, >> >> Nadine >> * >> * For searches and help try: >> * http://www.stata.com/help.cgi?search >> * http://www.stata.com/support/statalist/faq >> * http://www.ats.ucla.edu/stat/stata/ > > -- > Universität Bielefeld > Fakultät für Wirtschaftswissenschaften > Lehrstuhl für Ökonometrie und Statistik > - - > Bielefeld University > Faculty of Business Administration and Economics > Chair of Econometrics and Statistics > - - > Raum / room: V9-110 > Tel / phone: +49 (0)521 106 4871 > --- > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/