Modeling and forecasting the oil volatility index
| Autor: | Helena Veiga, João Henrique Gonçalves Mazzeu, Massimo B. Mariti |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Heteroscedasticity
Leverage (finance) Strategy and Management Autoregressive conditional heteroskedasticity Management Science and Operations Research Ciências Sociais::Outras Ciências Sociais [Domínio/Área Científica] Conditional expectation Forecasting oil volatility Ciências Naturais::Matemáticas [Domínio/Área Científica] 0502 economics and business Econometrics 050207 economics Leverage Mathematics Measure (data warehouse) 050208 finance Scaled oil price changes 05 social sciences Ciências Naturais::Ciências da Computação e da Informação [Domínio/Área Científica] Variance (accounting) Ciências Sociais::Economia e Gestão [Domínio/Área Científica] Computer Science Applications Net oil price changes Autoregressive model Modeling and Simulation Heterogeneous autoregression Statistics Probability and Uncertainty Volatility (finance) |
| Zdroj: | Repositório Científico de Acesso Aberto de Portugal Repositório Científico de Acesso Aberto de Portugal (RCAAP) instacron:RCAAP |
| Popis: | The increase in oil price volatility in recent years has raised the importance of forecasting it accurately for valuing and hedging investments. The paper models and forecasts the crude oil exchange-traded funds (ETF) volatility index, which has been used in the last years as an important alternative measure to track and analyze the volatility of future oil prices. Analysis of the oil volatility index suggests that it presents features similar to those of the daily market volatility index, such as long memory, which is modeled using well-known heterogeneous autoregressive (HAR) specifications and new extensions that are based on net and scaled measures of oil price changes. The aim is to improve the forecasting performance of the traditional HAR models by including predictors that capture the impact of oil price changes on the economy. The performance of the new proposals and benchmarks is evaluated with the model confidence set (MCS) and the Generalized-AutoContouR (G-ACR) tests in terms of point forecasts and density forecasting, respectively. We find that including the leverage in the conditional mean or variance of the basic HAR model increases its predictive ability. Furthermore, when considering density forecasting, the best models are a conditional heteroskedastic HAR model that includes a scaled measure of oil price changes, and a HAR model with errors following an exponential generalized autoregressive conditional heteroskedasticity specification. In both cases, we consider a flexible distribution for the errors of the conditional heteroskedastic process. info:eu-repo/semantics/publishedVersion |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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