# Modeling the Price Volatility of Cassava Chips in Thailand: Evidence from Bayesian GARCH-X Estimates

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Research Methodology

#### 2.1. The Unit Root Test Using Bayesian Estimation

#### 2.2. The GARCH-X Using Bayesian Estimation

## 3. Empirical Results

#### 3.1. Data Descriptive

#### 3.2. Stationary Testing

#### 3.3. The Estimation of GARCH-X(1,1) Using Bayesian Inference

## 4. Discussion

## 5. Conclusions and Policy Recommendation

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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Items | Interpretation |
---|---|

BF < 1/10 | Strong evidence for ${M}_{j}$ |

1/10 < BF < 1/3 | Moderate evidence for ${M}_{j}$ |

1/3 < BF < 1 | Weak evidence for ${M}_{j}$ |

1 < BF < 3 | Weak evidence for ${M}_{i}$ |

3 < BF < 10 | Moderate evidence for ${M}_{i}$ |

10 < BF | Strong evidence for ${M}_{i}$ |

Statistics | Y | X1 | X2 | X3 | X4 | X5 | X6 |
---|---|---|---|---|---|---|---|

Mean | 0.0019 | 0.0042 | 0.0016 | 0.0031 | 0.0019 | 0.0028 | 0.0019 |

Median | 0.0110 | −0.0172 | 0.0000 | −0.0093 | 0.0010 | 0.0000 | −0.0317 |

Max. | 0.2943 | 3.4250 | 0.2877 | 3.3891 | 0.3236 | 0.2395 | 3.1231 |

Min. | −0.3044 | −2.5720 | −0.2881 | −2.5388 | −0.3332 | −0.2332 | −2.2706 |

Std.Dev. | 0.1931 | 0.6841 | 0.1894 | 0.6777 | 0.1956 | 0.1886 | 0.6254 |

Skewness | −0.0578 | 0.7161 | −0.0364 | 0.6982 | −0.0516 | −0.0121 | 0.7895 |

Kurtosis | 1.5083 | 9.3457 | 1.5001 | 9.3080 | 1.5821 | 1.4137 | 9.1423 |

Jarque-Bera | 9.6073 | 181.6222 | 9.6779 | 179.1358 | 8.6737 | 10.8018 | 172.6151 |

Probability | 0.0082 | 0.0000 | 0.0079 | 0.0000 | 0.0131 | 0.0045 | 0.0000 |

Variables | Bayesian Factor Ratios (M1/M2) | Implication | Result |
---|---|---|---|

Cassava chip price of Thailand (Y) | 1.65 × 10^{−31} | Strong evidence for Mj | I(0) |

China’s cassava import volume from Thailand (X1) | 2.2 × 10^{−17} | Strong evidence for Mj | I(0) |

China’s cassava chips import price from Thailand (X2) | 5.3 × 10^{−32} | Strong evidence for Mj | I(0) |

China’s cassava starch import price from Thailand (X3) | 1.14 × 10^{−17} | Strong evidence for Mj | I(0) |

Substitute crop price: maize (X4) | 3.33 × 10^{−29} | Strong evidence for Mj | I(0) |

Substitute crop price: wheat (X5) | 3.97 × 10^{−39} | Strong evidence for Mj | I(0) |

Thailand’s cassava products export volume (X6) | 2.24 × 10^{−16} | Strong evidence for Mj | I(0) |

Variables | X1 | X2 | X3 | |||
---|---|---|---|---|---|---|

Coefficient | 95%CI | Coefficient | 95%CI | Coefficient | 95%CI | |

$\omega $ | −0.0002 (0.000473) | (−0.0006, 0.0012) | 0.0002 (0.0004) | (−0.0005, 0.0012) | 0.0003 (0.0040) | (−0.0006, 0.0210) |

$\alpha $ | 0.0023 (0.100100) | (0.0021, 0.1927) | 0.0025 (0.0990) | (0.0021, 0.1922) | 0.0024 (0.0091) | (0.0021, 0.1920) |

$\beta $ | 0.0326 (0.090000) | (0.0100, 0.6900) | 0.0234 (0.0900) | (0.0960, 0.7100) | 0.3618 (0.0910) | (0.0396, 0.9071) |

$\gamma $ | 0.0006 (0.000494) | (0.0001, 0.0009) | 0.0010 (0.0045) | (0.0009, 0.0015) | 0.0001 (0.0005) | (0.00009, 0.0096) |

Sigma2 | 0.0001 (0.000002) | (0.00009, 0.00024) | 0.0002 (0.0010) | (0.00008, 0.0010) | 0.0035 (0.0001) | (0.0006, 0.0140) |

Variables | X4 | X5 | X6 | |||
---|---|---|---|---|---|---|

Coefficient | 95%CI | Coefficient | 95%CI | Coefficient | 95%CI | |

$\omega $ | −0.0029 (0.0005) | (−0.0065, 0.0013) | 0.0037 (0.0050) | (−0.0067, 0.0062) | −0.0062 (0.0054) | (−0.0560, 0.0122) |

$\alpha $ | 0.0032 (0.0104) | (0.0020, 0.1935) | 0.0025 (0.1004) | (0.0020, 0.1931) | 0.0029 (0.0900) | (0.0019, 0.0640) |

$\beta $ | 0.0249 (0.0800) | (0.0003, 0.7690) | 0.0240 (0.0800) | (0.0090, 0.7000) | 0.0233 (0.9990) | (0.0037, 0.9071) |

$\gamma $ | 0.0600 (0.0019) | (0.0003, 0.0786) | −0.0210 (0.0207) | (−0.0411, 0.0030() | 0.0102 (0.0006) | (0.0090, 0.0102) |

Sigma2 | 0.0010 (0.0002) | (0.0076, 0.0035) | 0.0100 (0.0100) | (0.00008, 0.0014) | 0.0004 (0.0080) | (0.0008, 0.0078) |

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**MDPI and ACS Style**

Singvejsakul, J.; Chaovanapoonphol, Y.; Limnirankul, B.
Modeling the Price Volatility of Cassava Chips in Thailand: Evidence from Bayesian GARCH-X Estimates. *Economies* **2021**, *9*, 132.
https://doi.org/10.3390/economies9030132

**AMA Style**

Singvejsakul J, Chaovanapoonphol Y, Limnirankul B.
Modeling the Price Volatility of Cassava Chips in Thailand: Evidence from Bayesian GARCH-X Estimates. *Economies*. 2021; 9(3):132.
https://doi.org/10.3390/economies9030132

**Chicago/Turabian Style**

Singvejsakul, Jittima, Yaovarate Chaovanapoonphol, and Budsara Limnirankul.
2021. "Modeling the Price Volatility of Cassava Chips in Thailand: Evidence from Bayesian GARCH-X Estimates" *Economies* 9, no. 3: 132.
https://doi.org/10.3390/economies9030132