Dynamic Volatility Modelling of Bitcoin Using Time-Varying Transition Probability Markov-Switching GARCH Model

2020 ◽  
Author(s):  
Chia Yen Tan ◽  
you beng koh ◽  
Kok Haur Ng ◽  
Kooi Huat Ng
Keyword(s):  
Transition Probability ◽  
Markov Switching ◽  
Garch Model ◽  
Time Varying

scholarly journals Cyclical causalities between the U.S. wholesale beef and feed prices: A Markov-switching approach

2020 ◽  
Vol 9 (2) ◽  
pp. 135
Author(s):  
Dicle Ozdemir
Keyword(s):  
Regime Switching ◽  
Transition Probability ◽  
Markov Switching ◽  
Nonlinear Effects ◽  
Time Varying ◽  
Leading Role ◽  
Livestock Feed ◽  
Switching Dynamics ◽  
Meat Market ◽  
The U.S

Abstract: This paper examines whether some major livestock feed prices as corn, sorghum, hay and barley play a leading role in the regime switching dynamics between two states of the beef price cycles and have nonlinear effects on wholesale beef market in the U.S. using time-varying transition probability Markov-switching autoregressive model (TVTP).  The study reveals that real wholesale beef price movement in the U.S. red meat market exhibits a nonlinear two regimes pattern. This evidence indicates that livestock feed prices provides some predicted power to the model of beef price regime switching and supports livestock feed prices contributing to whether the beef price levels remains in high-mean regime.

SYNCHRONIZATION IN AN ARRAY OF HYBRID COUPLED NEURAL NETWORKS WITH MODE-DEPENDENT MIXED DELAYS AND MARKOVIAN SWITCHING

2009 ◽  
Vol 23 (09) ◽  
pp. 1171-1187 ◽  
Author(s):  
YANG TANG ◽  
RUNHE QIU ◽  
JIAN-AN FANG
Keyword(s):  
Neural Networks ◽  
Transition Probability ◽  
Distributed Delay ◽  
Mode Switching ◽  
Mixed Delays ◽  
Time Varying ◽  
Chaotic Neural Networks ◽  
Coupled Neural Networks ◽  
Hybrid Coupling ◽  
Coupling Time

In this letter, a general model of an array of N linearly coupled chaotic neural networks with hybrid coupling is proposed, which is composed of constant coupling, time-varying delay coupling and distributed delay coupling. The complex network jumps from one mode to another according to a Markovian chain with known transition probability. Both the coupling time-varying delays and the coupling distributed delays terms are mode-dependent. By the adaptive feedback technique, several sufficient criteria have been proposed to ensure the synchronization in an array of jump chaotic neural networks with mode-dependent hybrid coupling and mixed delays in mean square. Finally, numerical simulations illustrated by mode switching between two complex networks of different structure dependent on mode switching verify the effectiveness of the proposed results.

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