scholarly journals Efficient Gibbs Sampling for Markov Switching GARCH Models

2012 ◽  
Author(s):  
Monica Billio ◽  
Roberto Casarin ◽  
Ayokunle Anthony Osuntuyi
Keyword(s):  
Gibbs Sampling ◽  
Markov Switching ◽  
Garch Models

scholarly journals Efficient Gibbs sampling for Markov switching GARCH models

2016 ◽  
Vol 100 ◽  
pp. 37-57 ◽  
Author(s):  
Monica Billio ◽  
Roberto Casarin ◽  
Anthony Osuntuyi
Keyword(s):  
Gibbs Sampling ◽  
Markov Switching ◽  
Garch Models

scholarly journals A Two-Regime Markov-Switching GARCH Active Trading Algorithm for Coffee, Cocoa, and Sugar Futures

Mathematics ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 1001 ◽  
Author(s):  
Oscar V. De la Torre-Torres ◽  
Dora Aguilasocho-Montoya ◽  
María de la Cruz del Río-Rama
Keyword(s):  
Markov Switching ◽  
Garch Models ◽  
Arch Model ◽  
Conditional Heteroscedasticity ◽  
Arch Models ◽  
Trading Decisions ◽  
Portfolio Managers ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Potential Use ◽  
The U.S

In the present paper we tested the use of Markov-switching Generalized AutoRegressive Conditional Heteroscedasticity (MS-GARCH) models and their not generalized (MS-ARCH) version. This, for active trading decisions in the coffee, cocoa, and sugar future markets. With weekly data from 7 January 2000 to 3 April 2020, we simulated the performance that a futures’ trader would have had, had she used the next trading algorithm: To invest in the security if the probability of being in a distress regime is less or equal to 50% or to invest in the U.S. three-month Treasury bill otherwise. Our results suggest that the use of t-student Markov Switching Component ARCH Model (MS-ARCH) models is appropriate for active trading in the cocoa futures and the Gaussian MS-GARCH is appropriate for sugar. For the specific case of the coffee market, we did not find evidence in favor of the use of MS-GARCH models. This is so by the fact that the trading algorithm led to inaccurate trading signs. Our results are of potential use for futures’ position traders or portfolio managers who want a quantitative trading algorithm for active trading in these commodity futures.

scholarly journals A Test of Using Markov-Switching GARCH Models in Oil and Natural Gas Trading

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 129 ◽  
Author(s):  
Oscar V. De la Torre-Torres ◽  
Evaristo Galeana-Figueroa ◽  
José Álvarez-García
Keyword(s):  
Natural Gas ◽  
Markov Switching ◽  
Gaussian Model ◽  
Garch Models ◽  
Trading Rule ◽  
Treasury Bills ◽  
Oil Market ◽  
Oil And Natural Gas

In this paper, we test the use of Markov-switching (MS) GARCH (MSGARCH) models for trading either oil or natural gas futures. Using weekly data from 7 January 1994 to 31 May 2019, we tested the next trading rule: to invest in the simulated commodity if the investor expects to be in the low-volatility regime at t + 1 or to otherwise hold the risk-free asset. Assumptions for our simulations included the following: (1) we assumed that the investors trade in a homogeneous (Gaussian or t-Student) two regime context and (2) the investor used a time-fixed, ARCH, or GARCH variance in each regime. Our results suggest that the use of the MS Gaussian model, with time-fixed variance, leads to the best performance in the oil market. For the case of natural gas, we found no benefit of using our trading rule against a buy-and-hold strategy in the three-month U.S. Treasury bills.

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