FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3192412192> ?p ?o ?g. }

• W3192412192 endingPage "606" @default.
• W3192412192 startingPage "584" @default.
• W3192412192 abstract "Crude oil prices are of vital importance for market participants and governments to make energy policies and decisions. In this paper, we apply a newly proposed autoregressive conditional interval (ACI) model to forecast crude oil prices. Compared with the existing point-based forecasting models, the interval-based ACI model can capture the dynamics of oil prices in both level and range of variation in a unified framework. Rich information contained in interval-valued observations can be simultaneously utilized, thus enhancing parameter estimation efficiency and model forecasting accuracy. In forecasting the monthly West Texas Intermediate (WTI) crude oil prices, we document that the ACI models outperform the popular point-based time series models. In particular, ACI models deliver better forecasts than univariate ARMA models and the vector error correction model (VECM). The gain of ACI models is found in out-of-sample monthly price interval forecasts as well as forecasts for point-valued highs, lows, and ranges. Compared with GARCH and conditional autoregressive range (CARR) models, ACI models are also superior in volatility (conditional variance) forecasts of oil prices. A trading strategy that makes use of the monthly high and low forecasts is further developed. This trading strategy generally yields more profitable trading returns under the ACI models than the point-based VECM." @default.
• W3192412192 created "2021-08-16" @default.
• W3192412192 creator A5008429712 @default.
• W3192412192 creator A5050690906 @default.
• W3192412192 creator A5077071027 @default.
• W3192412192 creator A5077118418 @default.
• W3192412192 creator A5078558986 @default.
• W3192412192 date "2021-07-03" @default.
• W3192412192 modified "2023-10-18" @default.
• W3192412192 title "Forecasting crude oil price intervals and return volatility via autoregressive conditional interval models" @default.
• W3192412192 cites W1144081696 @default.
• W3192412192 cites W1201152205 @default.
• W3192412192 cites W1572591772 @default.
• W3192412192 cites W1927391910 @default.
• W3192412192 cites W1966674204 @default.
• W3192412192 cites W1967873618 @default.
• W3192412192 cites W1968914532 @default.
• W3192412192 cites W1970694313 @default.
• W3192412192 cites W1993887071 @default.
• W3192412192 cites W2002071019 @default.
• W3192412192 cites W2010925068 @default.
• W3192412192 cites W2039480584 @default.
• W3192412192 cites W2039853520 @default.
• W3192412192 cites W2042759796 @default.
• W3192412192 cites W2042883439 @default.
• W3192412192 cites W2045564168 @default.
• W3192412192 cites W2046364652 @default.
• W3192412192 cites W2061160212 @default.
• W3192412192 cites W2062776468 @default.
• W3192412192 cites W2068138154 @default.
• W3192412192 cites W2069964730 @default.
• W3192412192 cites W2073002835 @default.
• W3192412192 cites W2076847025 @default.
• W3192412192 cites W2078301133 @default.
• W3192412192 cites W2107592233 @default.
• W3192412192 cites W2119649214 @default.
• W3192412192 cites W2130426550 @default.
• W3192412192 cites W2150259063 @default.
• W3192412192 cites W2156963701 @default.
• W3192412192 cites W2268207689 @default.
• W3192412192 cites W2520300531 @default.
• W3192412192 cites W2560261639 @default.
• W3192412192 cites W2593666313 @default.
• W3192412192 cites W2602598932 @default.
• W3192412192 cites W2763263157 @default.
• W3192412192 cites W2804432385 @default.
• W3192412192 cites W2886842078 @default.
• W3192412192 cites W2893284817 @default.
• W3192412192 cites W2900075613 @default.
• W3192412192 cites W2900863199 @default.
• W3192412192 cites W2924701191 @default.
• W3192412192 cites W2944627537 @default.
• W3192412192 cites W3025096317 @default.
• W3192412192 cites W3083553495 @default.
• W3192412192 cites W3120491849 @default.
• W3192412192 cites W3121346496 @default.
• W3192412192 cites W3121875849 @default.
• W3192412192 cites W3122175640 @default.
• W3192412192 cites W3124259626 @default.
• W3192412192 cites W3124577930 @default.
• W3192412192 cites W3125172379 @default.
• W3192412192 cites W4235826538 @default.
• W3192412192 doi "https://doi.org/10.1080/07474938.2021.1889202" @default.
• W3192412192 hasPublicationYear "2021" @default.
• W3192412192 type Work @default.
• W3192412192 sameAs 3192412192 @default.
• W3192412192 citedByCount "8" @default.
• W3192412192 countsByYear W31924121922021 @default.
• W3192412192 countsByYear W31924121922022 @default.
• W3192412192 countsByYear W31924121922023 @default.
• W3192412192 crossrefType "journal-article" @default.
• W3192412192 hasAuthorship W3192412192A5008429712 @default.
• W3192412192 hasAuthorship W3192412192A5050690906 @default.
• W3192412192 hasAuthorship W3192412192A5077071027 @default.
• W3192412192 hasAuthorship W3192412192A5077118418 @default.
• W3192412192 hasAuthorship W3192412192A5078558986 @default.
• W3192412192 hasConcept C103402496 @default.
• W3192412192 hasConcept C105795698 @default.
• W3192412192 hasConcept C127413603 @default.
• W3192412192 hasConcept C1297061 @default.
• W3192412192 hasConcept C149782125 @default.
• W3192412192 hasConcept C151406439 @default.
• W3192412192 hasConcept C159877910 @default.
• W3192412192 hasConcept C161584116 @default.
• W3192412192 hasConcept C162324750 @default.
• W3192412192 hasConcept C174303752 @default.
• W3192412192 hasConcept C199163554 @default.
• W3192412192 hasConcept C21430997 @default.
• W3192412192 hasConcept C23922673 @default.
• W3192412192 hasConcept C24338571 @default.
• W3192412192 hasConcept C2986394398 @default.
• W3192412192 hasConcept C2987168347 @default.
• W3192412192 hasConcept C33923547 @default.
• W3192412192 hasConcept C70784835 @default.
• W3192412192 hasConcept C78762247 @default.
• W3192412192 hasConcept C91602232 @default.
• W3192412192 hasConceptScore W3192412192C103402496 @default.
• W3192412192 hasConceptScore W3192412192C105795698 @default.

• next