FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2050143770> ?p ?o ?g. }

• W2050143770 endingPage "180" @default.
• W2050143770 startingPage "171" @default.
• W2050143770 abstract "In this paper, by taking the 5-min high frequency data of the Shanghai Composite Index as example, we compare the forecasting performance of HAR-RV and Multifractal volatility, Realized volatility, Realized Bipower Variation and their corresponding short memory model with rolling windows forecasting method and the Model Confidence Set which is proved superior to SPA test. The empirical results show that, for six loss functions, HAR-RV outperforms other models. Moreover, to make the conclusions more precise and robust, we use the MCS test to compare the performance of their logarithms form models, and find that the HAR-log(RV) has a better performance in predicting future volatility. Furthermore, by comparing the two models of HAR-RV and HAR-log(RV), we conclude that, in terms of performance forecasting, the HAR-log(RV) model is the best model among models we have discussed in this paper." @default.
• W2050143770 created "2016-06-24" @default.
• W2050143770 creator A5078826764 @default.
• W2050143770 creator A5087953918 @default.
• W2050143770 creator A5088507879 @default.
• W2050143770 creator A5089891264 @default.
• W2050143770 date "2014-07-01" @default.
• W2050143770 modified "2023-10-18" @default.
• W2050143770 title "Which is the better forecasting model? A comparison between HAR-RV and multifractality volatility" @default.
• W2050143770 cites W1594021427 @default.
• W2050143770 cites W1963787328 @default.
• W2050143770 cites W1968955467 @default.
• W2050143770 cites W1979575715 @default.
• W2050143770 cites W1983879282 @default.
• W2050143770 cites W1996274012 @default.
• W2050143770 cites W1999814123 @default.
• W2050143770 cites W1999996900 @default.
• W2050143770 cites W2001806364 @default.
• W2050143770 cites W2003677026 @default.
• W2050143770 cites W2005116958 @default.
• W2050143770 cites W2005424182 @default.
• W2050143770 cites W2006830613 @default.
• W2050143770 cites W2008361621 @default.
• W2050143770 cites W2018249206 @default.
• W2050143770 cites W2031817855 @default.
• W2050143770 cites W2031838071 @default.
• W2050143770 cites W2032484498 @default.
• W2050143770 cites W2036982624 @default.
• W2050143770 cites W2039497966 @default.
• W2050143770 cites W2039566874 @default.
• W2050143770 cites W2039838036 @default.
• W2050143770 cites W2042129823 @default.
• W2050143770 cites W2042674197 @default.
• W2050143770 cites W2050902901 @default.
• W2050143770 cites W2051235503 @default.
• W2050143770 cites W2061160212 @default.
• W2050143770 cites W2061705762 @default.
• W2050143770 cites W2062289616 @default.
• W2050143770 cites W2068138154 @default.
• W2050143770 cites W2071602085 @default.
• W2050143770 cites W2071642288 @default.
• W2050143770 cites W2078412593 @default.
• W2050143770 cites W2095537491 @default.
• W2050143770 cites W2097822957 @default.
• W2050143770 cites W2112797280 @default.
• W2050143770 cites W2115844007 @default.
• W2050143770 cites W2125536334 @default.
• W2050143770 cites W2135606128 @default.
• W2050143770 cites W2136215272 @default.
• W2050143770 cites W2140585983 @default.
• W2050143770 cites W2142752097 @default.
• W2050143770 cites W2146134639 @default.
• W2050143770 cites W2150679232 @default.
• W2050143770 cites W2176007373 @default.
• W2050143770 cites W3121364726 @default.
• W2050143770 cites W3122118888 @default.
• W2050143770 cites W3122175640 @default.
• W2050143770 cites W3122351404 @default.
• W2050143770 cites W3124026849 @default.
• W2050143770 cites W3125987794 @default.
• W2050143770 doi "https://doi.org/10.1016/j.physa.2014.03.007" @default.
• W2050143770 hasPublicationYear "2014" @default.
• W2050143770 type Work @default.
• W2050143770 sameAs 2050143770 @default.
• W2050143770 citedByCount "34" @default.
• W2050143770 countsByYear W20501437702015 @default.
• W2050143770 countsByYear W20501437702017 @default.
• W2050143770 countsByYear W20501437702018 @default.
• W2050143770 countsByYear W20501437702019 @default.
• W2050143770 countsByYear W20501437702020 @default.
• W2050143770 countsByYear W20501437702022 @default.
• W2050143770 countsByYear W20501437702023 @default.
• W2050143770 crossrefType "journal-article" @default.
• W2050143770 hasAuthorship W2050143770A5078826764 @default.
• W2050143770 hasAuthorship W2050143770A5087953918 @default.
• W2050143770 hasAuthorship W2050143770A5088507879 @default.
• W2050143770 hasAuthorship W2050143770A5089891264 @default.
• W2050143770 hasConcept C117996083 @default.
• W2050143770 hasConcept C133905733 @default.
• W2050143770 hasConcept C134306372 @default.
• W2050143770 hasConcept C149782125 @default.
• W2050143770 hasConcept C162324750 @default.
• W2050143770 hasConcept C24189920 @default.
• W2050143770 hasConcept C33923547 @default.
• W2050143770 hasConcept C39927690 @default.
• W2050143770 hasConcept C40636538 @default.
• W2050143770 hasConcept C41008148 @default.
• W2050143770 hasConcept C60092789 @default.
• W2050143770 hasConcept C91602232 @default.
• W2050143770 hasConceptScore W2050143770C117996083 @default.
• W2050143770 hasConceptScore W2050143770C133905733 @default.
• W2050143770 hasConceptScore W2050143770C134306372 @default.
• W2050143770 hasConceptScore W2050143770C149782125 @default.
• W2050143770 hasConceptScore W2050143770C162324750 @default.
• W2050143770 hasConceptScore W2050143770C24189920 @default.
• W2050143770 hasConceptScore W2050143770C33923547 @default.
• W2050143770 hasConceptScore W2050143770C39927690 @default.
• W2050143770 hasConceptScore W2050143770C40636538 @default.

• next