scholarly journals Can the Markov Switching Model Forecast Exchange Rates?

10.3386/w4210 ◽  
1992 ◽  
Author(s):  
Charles Engel
Keyword(s):  
Exchange Rates ◽  
Markov Switching ◽  
Model Forecast ◽  
Markov Switching Model ◽  
Switching Model

scholarly journals A Three-State Markov-Modulated Switching Model for Exchange Rates

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Idowu Oluwasayo Ayodeji
Keyword(s):  
Exchange Rate ◽  
Exchange Rates ◽  
Markov Switching ◽  
The Other ◽  
Markov Switching Model ◽  
Switching Model ◽  
Empirical Results ◽  
Proposed Model ◽  
Markov Modulated ◽  
Japanese Yen

Several authors have examined the long swings hypothesis in exchange rates using a two-state Markov switching model. This study developed a model to investigate long swings hypothesis in currencies which may exhibit ak-state(k≥2)pattern. The proposed model was then applied to euros, British pounds, Japanese yen, and Nigerian naira. Specification measures such as AIC, BIC, and HIC favoured a three-state pattern in Nigerian naira but a two-state one in the other three currencies. For the period January 2004 to May 2016, empirical results suggested the presence of asymmetric swings in naira and yen and long swings in euros and pounds. In addition, taking0.5as the benchmark for smoothing probabilities, choice models provided a clear reading of the cycle in a manner that is consistent with the realities of the movements in corresponding exchange rate series.

scholarly journals Modelling foreign exchange rates: a comparison between markov-switching and markov-switching GARCH

2020 ◽  
Vol 20 (2) ◽  
pp. 917
Author(s):  
Mohd Azizi Amin Nunian ◽  
Siti Meriam Zahari ◽  
S.Sarifah Radiah Shariff
Keyword(s):  
Exchange Rates ◽  
Foreign Exchange ◽  
Markov Switching ◽  
Foreign Exchange Rates ◽  
Markov Switching Model ◽  
Switching Model ◽  
The Us ◽  
Log Likelihood ◽  
High Volatility ◽  
Japanese Yen

Foreign exchange rate is important as it determines a country's economic condition. It is used to carry out transfers of purchasing power between two or more countries. Volatility in exchange rates may result in difficulty in decision making especially, in financial sectors as high volatility could increase the risk in exchange rates. Thus, Markov switching model is employed in this study as it is believed to be efficient in handling not only volatilility but also nonlinearity characteristics in exchange rates. The aims of this study are to model the foreign exchange rates using two models; Markov Switching (M-S) models and Markov Switching Generalized Autoregressive Conditional Heteroscedasticity (M-S GARCH) and to compare these two models based on log-likelihood, AIC and BIC criteria. This study used the quarterly data of foreign exchange rates for Singapore Dollar (SGD), Korean Won (KRW), China Yuan Renminbi (CNY), Japanese Yen (JPY) and the US Dollar (USD) against Malaysia Ringgit (MYR) which were collected from Quarter 4, 2006 to Quarter 1, 2018. The findings indicate that Markov Switching is the best model since it has the highest log-likelihood value, and the lowest AIC and BIC values. The results show that JPY and SGD have highly persistent trends on regime 1 with probability values 0.96 and 0.84, respectively as compared to CNY, KRW and USD, while the latter have high persistent trends on regime 2 with probability values, 0.99, 0.95, 0.82, respectively.

A Markov-Switching model for building occupant activity estimation

2019 ◽  
Vol 183 ◽  
pp. 672-683 ◽  
Author(s):  
Sebastian Wolf ◽  
Jan Kloppenborg Møller ◽  
Magnus Alexander Bitsch ◽  
John Krogstie ◽  
Henrik Madsen
Keyword(s):  
Markov Switching ◽  
Markov Switching Model ◽  
Switching Model ◽  
Activity Estimation
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