Browsing by Author Rungrapee Phadkantha

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Showing results 1 to 14 of 14
Issue DateTitleAuthor(s)
1-Jan-2019Analysis of herding behavior using bayesian quantile regressionRungrapee Phadkantha; Woraphon Yamaka; Songsak Sriboonchitta
1-Jan-2018Analysis of risk, rate of return and dependency of REITs in ASIA with capital asset pricing modelRungrapee Phadkantha; Woraphon Yamaka; Roengchai Tansuchat
1-Jan-2022A Bayesian Approach to Quantile Regression for Interval-Valued Data: Application to CAPMRungrapee Phadkantha; Woraphon Yamaka; Songsak Sriboonchitta
1-May-2021A convex combination approach for artificial neural network of interval dataWoraphon Yamaka; Rungrapee Phadkantha; Paravee Maneejuk
1-Jun-2021A convex combination approach for Markov switching CAPM of interval dataWoraphon Yamaka; Rungrapee Phadkantha
1-Sep-2021Economic and energy impacts on greenhouse gas emissions: A case study of China and the USAWoraphon Yamaka; Rungrapee Phadkantha; Pichayakone Rakpho
1-Jan-2021Herding Behavior Existence in MSCI Far East Ex Japan Index: A Markov Switching ApproachWoraphon Yamaka; Rungrapee Phadkantha; Paravee Maneejuk
1-Jan-2019Modeling dependence of agricultural commodity futures through markov switching copula with mixture distribution regimesWoraphon Yamaka; Rungrapee Phadkantha; Songsak Sriboonchitta
1-Jan-2022The Nonlinear Connectedness Among Cryptocurrencies Using Markov-Switching VAR ModelNamchok Chimprang; Rungrapee Phadkantha; Woraphon Yamaka
1-Aug-2022The nonlinear impact of electricity consumption on economic growth: Evidence from ThailandRungrapee Phadkantha; Woraphon Yamaka
26-Jul-2018A nonlinear time-varying copula using kink approachRungrapee Phadkantha; Woraphon Yamaka; Songsak Sriboonchitta
1-Jan-2022Predicting Energy Price Volatility Using Hybrid Artificial Neural Networks with GARCH-Type ModelsPichayakone Rakpho; Woraphon Yamaka; Rungrapee Phadkantha
1-Jan-2022Revisiting the Determinants of Thai Economic Growth: A Mixed Frequency ApproachRungrapee Phadkantha; Wiranya Puntoon; Kongliang Zhu
1-Sep-2020Why the use of convex combinations works well for interval data: A theoretical explanationRungrapee Phadkantha; Woraphon Yamaka