Parameter learning with particle filters
Common applications of asset-pricing models in practice rely on recalibrating model parameters periodically for effective risk management. Yet, these model parameters are often assumed to be constant over time, thereby countering the notion of readjusting these values. A possible solution to this pr...
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| Format: | Dissertation |
| Language: | English |
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Faculty of Commerce
2021
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| Online Access: | http://hdl.handle.net/11427/32913 |