The application of expectation maximization to manage missing data, biases value‐at‐risk and volatility models in financial time series
Multivariate time series analysis requires synchronized and continuous data for its models. However, there can be special occasions when one or some data is missing due to lack of trading activity. This paper focuses on the impact of different missing data handling methods on GARCH and Value‐at‐R...
Elmentve itt :
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| Dokumentumtípus: | Könyv része |
| Megjelent: |
Tomas Bata University in Zlín, Faculty of Management and Economics
Zlin
2016
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| Sorozat: | Conference Proceedings DOKBAT 12th Annual International Bata Conference for Ph.D. Students and Young Researchers
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| mtmt: | 3107568 |
| Online Access: | http://publicatio.bibl.u-szeged.hu/21838 |
| Tartalmi kivonat: | Multivariate time series analysis requires synchronized and continuous data for its models. However, there can be special occasions when one or some data is missing due to lack of trading activity. This paper focuses on the impact of different missing data handling methods on GARCH and Value‐at‐Risk model parameters, namely the volatility persistence and asymmetry and the fat‐tailness of the corrected data. The main added value of current paper is the comparison of the impact of different methods (like listwise deletion, mean‐substitution and maximum‐likelihood‐ based Expectation Maximization) on daily financial time series, because this subject has insufficient literature. Current study tested daily closing data of floating currencies from Kenya (KES), Ghana (GHS), South Africa (ZAR), Tanzania (TZS), Uganda (UGX), Gambia (GMD), Madagascar (MGA) and Mozambique (MZN) in USD denomination against EUR/USD rate between March 8 2000 and March 6 2015 acquired from Bloomberg database. Current paper suggest the usage of mean ubstitution or listwise deletion for daily financial time series due to their tendency to have a close‐to‐zero first momentum. |
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| Terjedelem/Fizikai jellemzők: | 17 202-219 |
| ISBN: | 9788074545924 |