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Forecasting the Volatility in Financial Assets using Conditional Variance Models

Swanson, Jesper LU and Hultman, Hugo (2017) NEKN01 20171
Department of Economics
Abstract
This thesis examines multiple ARCH-family models' volatility forecasting performance on the London Bullion Market Gold price, the OMXS30, and the USD/EUR exchange rate. Further, this thesis uses two different time periods to exploit differences and similarities in the forecast accuracy among the conditional variance models. The models we examine are the ARCH, the GARCH, the IGARCH, the EGARCH, and the GJR-GARCH model. Furthermore, we divide each period into an in-sample and an out-of-sample period. The models are estimated in the in-sample period and then used to forecast the volatility in the out-of-sample period. This thesis uses the squared reutrns as an unbiased approximation of the latent volatility. The forecasts are evaluated using... (More)
This thesis examines multiple ARCH-family models' volatility forecasting performance on the London Bullion Market Gold price, the OMXS30, and the USD/EUR exchange rate. Further, this thesis uses two different time periods to exploit differences and similarities in the forecast accuracy among the conditional variance models. The models we examine are the ARCH, the GARCH, the IGARCH, the EGARCH, and the GJR-GARCH model. Furthermore, we divide each period into an in-sample and an out-of-sample period. The models are estimated in the in-sample period and then used to forecast the volatility in the out-of-sample period. This thesis uses the squared reutrns as an unbiased approximation of the latent volatility. The forecasts are evaluated using two loss functions, the mean absolute error and the mean squared error. The results indicate a very inconsistent ranking among the models. None of the models seem superior to the other models, based on both loss functions, when forecasting the conditional volatilit in the different assets and periods. However, the ARCH models seems to perform well relative the other models, when forecasting the volatility of the gold using only the mean absolute error as a tool to evaluate the forecasts. (Less)
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author
Swanson, Jesper LU and Hultman, Hugo
supervisor
organization
course
NEKN01 20171
year
type
H1 - Master's Degree (One Year)
subject
keywords
Volatility Forecasting, Conditional Variance, ARCH, GARCH, IGARCH, EGARCH, GJR-GARCH
language
English
id
8911242
date added to LUP
2017-07-10 13:53:00
date last changed
2017-07-10 13:53:00
@misc{8911242,
  abstract     = {This thesis examines multiple ARCH-family models' volatility forecasting performance on the London Bullion Market Gold price, the OMXS30, and the USD/EUR exchange rate. Further, this thesis uses two different time periods to exploit differences and similarities in the forecast accuracy among the conditional variance models. The models we examine are the ARCH, the GARCH, the IGARCH, the EGARCH, and the GJR-GARCH model. Furthermore, we divide each period into an in-sample and an out-of-sample period. The models are estimated in the in-sample period and then used to forecast the volatility in the out-of-sample period. This thesis uses the squared reutrns as an unbiased approximation of the latent volatility. The forecasts are evaluated using two loss functions, the mean absolute error and the mean squared error. The results indicate a very inconsistent ranking among the models. None of the models seem superior to the other models, based on both loss functions, when forecasting the conditional volatilit in the different assets and periods. However, the ARCH models seems to perform well relative the other models, when forecasting the volatility of the gold using only the mean absolute error as a tool to evaluate the forecasts.},
  author       = {Swanson, Jesper and Hultman, Hugo},
  keyword      = {Volatility Forecasting,Conditional Variance,ARCH,GARCH,IGARCH,EGARCH,GJR-GARCH},
  language     = {eng},
  note         = {Student Paper},
  title        = {Forecasting the Volatility in Financial Assets using Conditional Variance Models},
  year         = {2017},
}