# Investigating Spillover Effects between Foreign Exchange Rate Volatility and Commodity Price Volatility in Uganda

## Abstract

**:**

## 1. Introduction

## 2. Overview of Financial Sector Stability

## 3. Literature Review

## 4. Methodology

## 5. Results and Discussion

#### 5.1. Data and Descriptive Statistics

#### 5.2. Discussion of Results

#### 5.2.1. Multivariate GARCH Analysis

_{1}and θ

_{2}at the 10% level. The estimated parameters for θ

_{1}and θ

_{2}sum to a value close to one, implying that volatility exhibits a highly persistent behavior. Nevertheless, since the sum is a value less than one, the dynamic conditional correlations are mean reverting. The magnitudes of parameters θ

_{1}and θ

_{2}indicate that the evolution of the conditional covariance depends more on its past values than on lagged residuals’ innovations, such that shock persistence has a greater impact in the long run (see coefficients for θ

_{2}in all of the models) as compared to the short-run (see coefficients for θ

_{1}in all of the models). In addition, only the DCC model provides statistically significant evidence for the existence of short-run volatility shock spillover effects. The significance of θ

_{1}and θ

_{2}in the DCC model suggests that conditional correlations are highly dynamic and time varying, which is an indication that the assumptions of CCC do not hold, which is consistent with evidence in the literature.

_{1}= θ

_{2}= 0. In Table 2, the results for both the DCC and VCC show that their respective Wald test results reject the null hypothesis that θ

_{1}= θ

_{2}= 0 at all of the conventional significance levels, which is an indication that the assumption of time-invariant conditional correlations maintained in the CCC model is too restrictive for the data. According to the information criterion tests in Table 1, the DCC model, which consistently has the lowest estimated coefficients for the AIC and BIC criteria, is the best model of the three MGARCH models.

#### 5.2.2. Spillover Analysis in a Generalized VAR Framework

#### 5.3. Sensitivity and Robustness Analysis

#### 5.4. Limitation and Suggestions for Future Research

## 6. Conclusions and Recommendations

## Funding

## Conflicts of Interest

## References

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1 | The detailed Cholesky factorization analyses are not reported in this paper, but can be requested from the author. |

**Figure 2.**Evolution of dynamic conditional correlations. Notes: lrt, dlcpa and dlcfp denote the Ugandan foreign exchange rate return volatility, food price index volatility, and crude oil price index volatility, respectively.

DLCFP | DLCPA | LRT | |

Mean | 0.000 | 0.004 | 0.005 |

Maximum | 0.070 | 0.203 | 0.163 |

Minimum | −0.129 | −0.316 | −0.083 |

Std. Dev. | 0.026 | 0.082 | 0.025 |

Skewness | −0.531 | −0.787 | 0.916 |

Kurtosis | 5.422 | 4.499 | 9.957 |

Jarque-Bera | 88.573 [0.000] | 59.864 [0.000] | 655.669 [0.000] |

N | 304.000 | 304.000 | 304.000 |

Returns correlations | |||

Ljung–Box (2) | 33.239 [0.000] | 23.369 [0.000] | 29.326 [0.000] |

Ljung–Box (6) | 41.665 [0.000] | 27.225 [0.000] | 30.063 [0.000] |

Ljung–Box (12) | 44.634 [0.000] | 40.666 [0.000] | 35.145 [0.000] |

Tests for stationarity | |||

ADF test | −13.130 [0.000] | −13.305 [0.000] | −13.147 [0.000] |

PP Test | −13.306 [0.000] | −13.341 [0.000] | −13.120 [0.000] |

KPSS Test | 0.076 | 0.096 | 0.061 |

Test critical values: | ADF | PP | KPSS |

1% level | −3.452 | −3.452 | 0.739 |

5% level | −2.871 | −2.871 | 0.463 |

10% level | −2.572 | −2.572 | 0.347 |

CCC | DCC | VCC | ||||
---|---|---|---|---|---|---|

Coefficient | p-Value | Coefficient | p-Value | Coefficient | p-Value | |

Panel A—GARCH Results | ||||||

ω _{Exchange_rate_return} | 0.000 *** | 0.001 | 0.000 *** | 0.001 | 0.000 *** | 0.001 |

α _{Exchange_rate_return} | 0.460 *** | 0.000 | 0.468 *** | 0.000 | 0.468 *** | 0.000 |

β _{Exchange_rate_return} | 0.423 *** | 0.000 | 0.431 *** | 0.000 | 0.431 *** | 0.000 |

ω _{Food_price_index} | 0.000 | 0.126 | 0.000 | 0.137 | 0.000 | 0.137 |

α _{Food_price_index} | 0.088 ** | 0.030 | 0.093 ** | 0.014 | 0.093 ** | 0.014 |

β _{Food_price_index} | 0.864 *** | 0.000 | 0.871 *** | 0.000 | 0.871 *** | 0.000 |

ω _{Crude_oil_price_index} | 0.001 | 0.184 | 0.001 | 0.152 | 0.001 | 0.152 |

α _{Crude_oil_price_index} | 0.191 *** | 0.009 | 0.182 *** | 0.004 | 0.182 *** | 0.004 |

β _{Crude_oil_price_index} | 0.703 *** | 0.000 | 0.734 *** | 0.000 | 0.734 *** | 0.000 |

Panel B—Conditional correlation results | ||||||

ρ_{(Exchange_rate_return, Food_price_index)} | −0.209 *** | 0.000 | −0.134 | 0.321 | −0.183 ** | 0.046 |

ρ_{(Exchange_rate_return, Crude_oil_price_index)} | −0.093 | 0.132 | −0.081 | 0.583 | −0.069 | 0.469 |

ρ_{(Food_price_index, Crude_oil_price_index)} | 0.175 *** | 0.005 | 0.279 * | 0.091 | 0.242 ** | 0.024 |

Panel C—Diagnostics | ||||||

df | 7.867 | 0.000 | 8.176 | 0.000 | 8.184 | 0.000 |

θ_{1} | 0.034 | 0.050 | 0.046 | 0.163 | ||

θ_{2} | 0.931 | 0.000 | 0.873 | 0.000 | ||

Log-likelihood | 1816 | 1821 | 1820 | |||

Wald test | 1973.74 | 0.000 | 359.5 | 0.000 | ||

AIC | 4800 | 4793 | 4796 | |||

BIC | 4860 | 4860 | 4863 |

_{1}= θ

_{2}= 0; AIC and BIC are the Akaike Information Criterion and Schwartz Criteria, respectively. The model that yields the lowest AIC (BIC) value is considered to generate the data best. Using AIC and BIC, the CCC model has slightly smaller AIC and BIC values, and thus is the preferred model according to these criteria. The asterisks ***, **, and * represent 1%, 5%, and 10% significance level, respectively.

lrt | dlcpa | dlcfp | $\mathbf{ALL}\text{}\mathbf{to}\mathbf{i}$ | |
---|---|---|---|---|

lrt | 97.68 | 0.61 | 1.71 | 2.32 |

dlcpa | 9.99 | 89.35 | 0.66 | 10.65 |

dlcfp | 8.01 | 15.20 | 76.79 | 23.21 |

$\mathrm{i}$ to ALL | 18.00 | 15.81 | 2.37 | 12.06 |

Net $\mathrm{i}$ to ALL | 15.68 | 5.16 | −20.84 | 0.00 |

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## Share and Cite

**MDPI and ACS Style**

Katusiime, L.
Investigating Spillover Effects between Foreign Exchange Rate Volatility and Commodity Price Volatility in Uganda. *Economies* **2019**, *7*, 1.
https://doi.org/10.3390/economies7010001

**AMA Style**

Katusiime L.
Investigating Spillover Effects between Foreign Exchange Rate Volatility and Commodity Price Volatility in Uganda. *Economies*. 2019; 7(1):1.
https://doi.org/10.3390/economies7010001

**Chicago/Turabian Style**

Katusiime, Lorna.
2019. "Investigating Spillover Effects between Foreign Exchange Rate Volatility and Commodity Price Volatility in Uganda" *Economies* 7, no. 1: 1.
https://doi.org/10.3390/economies7010001