Forecasting oil price volatility: Forecast combination versus shrinkage method

By Yaojie Zhang, Yu Wei, Yi Zhang, Daxiang Jin. Energy Economics, 2019

151 citations recorded by the source corpus.

Abstract In this paper, we compare the predictive ability between forecast combination and shrinkage method in the prediction of oil price volatility. Our investigation is based on the heterogeneous autoregressive (HAR) framework. Five combination approaches combine the individual forecasts generated by the HAR model and its various extensions, while two prevailing shrinkage methods, the elastic net and lasso, employ all the predictors in our HAR framework to generate the forecast of oil price volatility. The model confidence set (MCS) test shows that the elastic net and lasso have significantly better out-of-sample forecasting performance than not only the individual extended HAR models but also the combination approaches. This result is robust across a wide range of checks. In addition, we document that the elastic net and lasso also exhibit substantially higher directional accuracy. Furthermore, a mean-variance investor can realize sizeable economic gains by using the volatility forecasts based on the shrinkage methods to allocate her portfolio.

Subfields: Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/e1783a435d22ba4bbe45ba7bce57d9675672528e

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

1. Chao Liang
2. Xiafei Li
3. Yifeng Zhang
4. Xuhui Zhang
5. Yu Wei

In Management Science and Operations Research

1. Yu Wei
2. Xiafei Li
3. Yifeng Zhang
4. Xuhui Zhang
5. Chao Liang

Citations in the corpus

• Uncertainty and crude oil market volatility: new evidence (2020, methodology): “…two shrinkage methods, namely, least absolute shrinkage and selection operator (Lasso) proposed by Tibshirani (1996) and the elastic net proposed by Zou and Hastie (2005), which are proven to be an effective way to solve overfitting problem by various studies (Li and Chen 2014; Zhang et al. 2019b).”