Using Garch-in-Mean Model to Investigate Volatility and Persistence at Different Frequencies for Bucharest Stock Exchange during 1997-2012
In our paper we use data mining to compare the volatility structure of high (daily) and low (weekly, monthly) frequencies for seven Romanian companies traded on Bucharest Stock Exchange and three market indices, during 1997-2012. For each of the 10 time series and three frequencies we fit a GARCH-in...
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General Association of Economists from Romania
2012-05-01
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doaj-a153163a857c45f3be4fb31bdc5d01642020-11-25T00:19:42ZengGeneral Association of Economists from RomaniaTheoretical and Applied Economics1841-86781844-00292012-05-01XIX5557618418678Using Garch-in-Mean Model to Investigate Volatility and Persistence at Different Frequencies for Bucharest Stock Exchange during 1997-2012Iulian PANAIT0Ecaterina Oana SLĂVESCU1 Bucharest Academy of Economic Studies Bucharest Academy of Economic Studies In our paper we use data mining to compare the volatility structure of high (daily) and low (weekly, monthly) frequencies for seven Romanian companies traded on Bucharest Stock Exchange and three market indices, during 1997-2012. For each of the 10 time series and three frequencies we fit a GARCH-in-mean model and we find that persistency is more present in the daily returns as compared with the weekly and monthly series. On the other hand, the GARCH-in-mean failed to confirm (on our data) the theoretical hypothesis that an increase in volatility leads to a rise in future returns, mainly because the variance coefficient from the mean equation of the model was not statistically significant for most of the time series analyzed and on most of the frequencies. The diagnosis that we ran in order the verify the goodness of fit for the model showed that GARCH-in-mean was well fitted on the weekly and monthly time series but behaved less well on the daily time series. http://store.ectap.ro/articole/721.pdf stock returnsvolatilitypersistenceGARCH modelemerging marketsdata mining |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Iulian PANAIT Ecaterina Oana SLĂVESCU |
spellingShingle |
Iulian PANAIT Ecaterina Oana SLĂVESCU Using Garch-in-Mean Model to Investigate Volatility and Persistence at Different Frequencies for Bucharest Stock Exchange during 1997-2012 Theoretical and Applied Economics stock returns volatility persistence GARCH model emerging markets data mining |
author_facet |
Iulian PANAIT Ecaterina Oana SLĂVESCU |
author_sort |
Iulian PANAIT |
title |
Using Garch-in-Mean Model to Investigate Volatility and Persistence at Different Frequencies for Bucharest Stock Exchange during 1997-2012 |
title_short |
Using Garch-in-Mean Model to Investigate Volatility and Persistence at Different Frequencies for Bucharest Stock Exchange during 1997-2012 |
title_full |
Using Garch-in-Mean Model to Investigate Volatility and Persistence at Different Frequencies for Bucharest Stock Exchange during 1997-2012 |
title_fullStr |
Using Garch-in-Mean Model to Investigate Volatility and Persistence at Different Frequencies for Bucharest Stock Exchange during 1997-2012 |
title_full_unstemmed |
Using Garch-in-Mean Model to Investigate Volatility and Persistence at Different Frequencies for Bucharest Stock Exchange during 1997-2012 |
title_sort |
using garch-in-mean model to investigate volatility and persistence at different frequencies for bucharest stock exchange during 1997-2012 |
publisher |
General Association of Economists from Romania |
series |
Theoretical and Applied Economics |
issn |
1841-8678 1844-0029 |
publishDate |
2012-05-01 |
description |
In our paper we use data mining to compare the
volatility structure of high (daily) and low (weekly, monthly) frequencies
for seven Romanian companies traded on Bucharest Stock Exchange and
three market indices, during 1997-2012. For each of the 10 time series
and three frequencies we fit a GARCH-in-mean model and we find that
persistency is more present in the daily returns as compared with the
weekly and monthly series. On the other hand, the GARCH-in-mean
failed to confirm (on our data) the theoretical hypothesis that an increase
in volatility leads to a rise in future returns, mainly because the variance
coefficient from the mean equation of the model was not statistically
significant for most of the time series analyzed and on most of the
frequencies. The diagnosis that we ran in order the verify the goodness of
fit for the model showed that GARCH-in-mean was well fitted on the
weekly and monthly time series but behaved less well on the daily time
series. |
topic |
stock returns volatility persistence GARCH model emerging markets data mining |
url |
http://store.ectap.ro/articole/721.pdf
|
work_keys_str_mv |
AT iulianpanait usinggarchinmeanmodeltoinvestigatevolatilityandpersistenceatdifferentfrequenciesforbuchareststockexchangeduring19972012 AT ecaterinaoanaslavescu usinggarchinmeanmodeltoinvestigatevolatilityandpersistenceatdifferentfrequenciesforbuchareststockexchangeduring19972012 |
_version_ |
1725370482634522624 |