scholarly journals Modelling the Relationship Between Agricultural Commodity Prices, Energy Prices, And Exchange Rate: Evidence from A Three Stage Markov-Switching Model

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Yegnanew A Shiferaw
Keyword(s):  
Exchange Rate ◽  
Markov Switching ◽  
Commodity Prices ◽  
Energy Prices ◽  
Markov Switching Model ◽  
Agricultural Commodity ◽  
Switching Model ◽  
The Relationship

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 Analysis of the Relationship between World Oil Price and Exchange Rate on Agricultural Commodity Prices in Indonesia

2018 ◽  
Vol 8 (12) ◽  
Author(s):  
Cut Endang Kurniasih ◽  
Hizir . ◽  
Muhammad Nasir ◽  
Mohd Sadad Mahmud ◽  
Norfadzilah Rashid ◽  
...  
Keyword(s):  
Exchange Rate ◽  
Commodity Prices ◽  
Oil Price ◽  
Agricultural Commodity ◽  
The Relationship

scholarly journals Oil Price Shock and Regime Switching Behaviour of Exchange Rate in Nigeria

2020 ◽  
Author(s):  
Taofeek Olusola AYINDE ◽  
Farouq A. ADEYEMI
Keyword(s):  
Exchange Rate ◽  
Regime Switching ◽  
Exchange Rate Regime ◽  
Oil Price ◽  
Markov Switching Model ◽  
Switching Model ◽  
Regime Changes ◽  
Switching Characteristics ◽  
The Relationship ◽  
Model Technique

Abstract This study examines the relationship between oil prices and exchange rate in Nigeria by viewing this relationship in another dimension which is the ability of exchange rate to move from one regime to the other. The period of investigation spanned the period 1980–2017. The regime switching characteristics of exchange rate were examined with the Markov Switching Model technique. The regime switching test suggests that there are two exchange rate regimes of managed float and fixed regimes. It was found that the probability of exchange rate to move from a managed float regime to a fixed regime was very low as compared to switching from fixed to managed float regimes. In fact, the expected duration of transitions is about 4 years for managed float but 9 years for fixed exchange rate regime. Both oil price and exchange rate move in opposite direction; despite the regime changes.

scholarly journals Stock Return and Trading Volume - An Application of the Markov Switching Model

2016 ◽  
Vol 1 (1) ◽  
pp. 55
Author(s):  
Jing-Tung Wu
Keyword(s):  
Linear Relation ◽  
Trading Volume ◽  
Stock Return ◽  
Markov Switching ◽  
Markov Switching Model ◽  
Financial Decisions ◽  
Switching Model ◽  
Non Linear ◽  
Stock Effect ◽  
The Relationship

<p>The relationship between stock return and trading volume has been extensively documented by earlier studies. Financial academics unveiled some potential reasons of this phenomenon, but it still left for several inconclusive evidences. This paper employs the Markov switching model (MSM) to investigate the relationship. The results identify that second regime exist and persist, which is consistent with earlier study indicating that there are complex influences in stock return and trading volume. One possible explanation would be that people are not always rational; their financial decisions are influenced by behavioral biases. If people change their beliefs or preferences, then the regime switches. This leads to the conclusion that, the results are strongly in favor of a non-linear relation between stock return and trading volume. Also, it is interesting to find that industry play an important role. The deviations of the regime parameters are different across industries, which stands for risk-varying and is fit to the conventional wisdoms. This paper further tests the glamour (value) stock effect by the MSM; the result shows that the factor does affect the probabilities of the expected regime duration periods.</p>

FORECASTING OF OIL AND AGRICULTURAL COMMODITY PRICES: VARMA VERSUS ARMA

2017 ◽  
Vol 12 (03) ◽  
pp. 1750012 ◽  
Author(s):  
MUSTAFA GÜLERCE ◽  
GAZANFER ÜNAL
Keyword(s):  
Moving Average ◽  
Commodity Prices ◽  
Wavelet Coherence ◽  
Autoregressive Moving Average ◽  
Multiple Time ◽  
Energy Prices ◽  
Agricultural Commodity ◽  
Vector Autoregressive ◽  
Dynamic Correlations ◽  
Model Free

The aim of this paper is to show that the estimates made with vector autoregressive–moving-average (ARMA) models based on the coherent time intervals of the multiple time series give more precise results than the univariate case. The previous literature on dynamic correlations (co-movement) in between food and energy prices has mixed results and mainly based on parametric approaches. Therefore, partial wavelet coherence (PWC) and multiple wavelet coherence (MWC) methods are used, respectively, to uncover the coherency simultaneously for time and frequency domains. In our study; world oil, corn, soybeans, wheat and sugar prices are examined instead of the return and volatility relationship between oil and agricultural commodities due to model-free approach of wavelet analysis.

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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