Combining density forecasts using focused scoring rules
By A. Opschoor, Dick Van Dijk, Michel van der Wel. 2017
51 citations recorded by the source corpus.
We investigate the added value of combining density forecasts focused on a specific region of support. We develop forecast combination schemes that assign weights to individual predictive densities based on the censored likelihood scoring rule and the continuous ranked probability scoring rule (CRPS) and compare these to weighting schemes based on the log score and the equally weighted scheme. We apply this approach in the context of measuring downside risk in equity markets using recently developed volatility models, including HEAVY, realized GARCH and GAS models, applied to daily returns on the S&P 500, DJIA, FTSE and Nikkei indexes from 2000 until 2013. The results show that combined density forecasts based on optimizing the censored likelihood scoring rule significantly outperform pooling based on equal weights, optimizing the CRPS or log scoring rule. In addition, 99% Value-at-Risk estimates improve when weights are based on the censored likelihood scoring rule.
Subfields: Economics and Econometrics, Management of Technology and Innovation
View Paper: https://www.semanticscholar.org/paper/d1044b3ea9ba0880ddb6bd20212ed4656fb15405
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Francesco Ravazzolo
- H. V. Dijk
- Florian Huber
- K. Aastveit
- Keven Bluteau
- Kris Boudt
- John M. Maheu
- Anastasios Panagiotelis
In Management of Technology and Innovation
Citations in the corpus
- The Evolution of Forecast Density Combinations in Economics (2018, background): “Opschoor et al. (2017) extend the framework to consider combining density forecasts to maximize performance in a specific region of the density so as to, for example, best measure downside risk (VaR) in equity markets, based on use of a censored scoring rule and the CRPS; and Raftery et al. (2005)…”
- Bayesian forecasting in economics and finance: A modern review (2022, background): “Relevant contributions here, which include specific treatments of the ubiquitous stochastic volatility (SV) model, are Polson et al. (1992), Jacquier et al.”
- Forecast combinations for value at risk and expected shortfall (2020, methodology): “9 of Taylor (2019) and Patton et al. (2019), who use the AL score to estimate dynamic models, and Dimitriadis and Bayer (2017), who present a regression framework for VaR and ES.”
- Forecasting risk with Markov-switching GARCH models:A large-scale performance study (2018, background): “, 2011), or even combinations of these (Opschoor et al., 2017).”