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ESTIMATING VOLATILITY CLUSTERING USING GJR-GARCH MODEL: A CASE STUDY FOR GERMAN STOCK MARKET

Authors: RACHANA BAID; CRISTI SPULBAR; JATIN TRIVEDI; RAMONA BIRAU; ANCA IOANA IACOB (TROTO);

ESTIMATING VOLATILITY CLUSTERING USING GJR-GARCH MODEL: A CASE STUDY FOR GERMAN STOCK MARKET

Abstract

The purpose of this article is to concentrate on the stylized data in the financial series of the major index DAX of the German stock market. Moreover, we investigated the effects of positive and negative news on the volatility of the stock market of Germany, such as DAX index. One of the most fascinating topics for investor research is the financial market volatility of an emerging financial market. Because of this, factorial risks and the likelihood of larger returns are increased. We take into account daily OBS (observations) in the number of 4037 for the sample period January 2007 to November 2022. The study used the GJR-GARCH, or Generalized Autoregressive Conditional Heteroskedisticity type model. We discovered that the DAX index financial series feature a dynamic volatility scale. The GJR-GARCH model was fitted and the stronger impact of innovations was discovered.

Keywords

positive news, gjr-garch model, Commercial geography. Economic geography, Economics as a science, stock returns, negative news, volatility clustering, developed stock market, HF1021-1027, HB71-74

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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