Composite likelihood estimation of stationary Gaussian processes with a view toward stochastic volatility
By Mikkel Bennedsen, Kim Christensen, Peter Christensen
Rating
1704
Battle Count: 75
Relevance
8/10
Highly relevant for modeling and forecasting volatility in financial markets, particularly cryptocurrencies
Implementation Complexity
7/10
Requires advanced statistical knowledge and computational resources for implementation
Reproducibility
4/5
The paper provides detailed methodology and simulation studies, enhancing reproducibility
About this paper
Methodology: Composite likelihood estimation. Problem types: Parameter estimation, Time Series Analysis, Stochastic Volatility Modeling.
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