# Geopolitical Uncertainties and Malaysian Stock Market Returns: Do Market Conditions Matter?

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## Abstract

**:**

## 1. Introduction

## 2. Related Empirical Studies

## 3. Empirical Models

#### 3.1. Baseline Model

#### 3.2. Multi-Factor Model with Markov-Switching Dynamic Regression Approach

_{t}|S

_{t−1}}. In addition, the transition from one regime to another varies on the observation of a transition variable, m, so that P{S

_{t}|S

_{t−k}} = P{S

_{t}|S

_{t−k}, m

_{t}}. ${p}_{ij}\left({m}_{t-k}\right)$ is the probability of moving from regime h in period t to regime j in period t–k and conditional on the dynamics of the transition variable and k periods [5,16]. The transition probability ${p}_{ij}\left({z}_{t-k}\right)$ facilitates in estimating the expected duration ${D}_{ij}=(1/1-{p}_{ij})$] for each regime. The time varying probability matrix is as follows.

#### 3.3. Markov Switching Estimation Process

_{t}= (y

_{t}, y

_{t}

_{−1}, …, y

_{1}). With designating $\theta $, the study defines the conditional likelihood function of the observed data, $\xi $

_{t}, as follows.

#### 3.4. Multi-Factor Model with Quantile Regression Approach

#### 3.5. Data

## 4. Empirical Results

#### 4.1. Volatility-Dependent Effects of Geopolitical Uncertainties

#### 4.2. Market Condition Dependent Effects of Geopolitical Uncertainties

## 5. Discussion

## 6. Conclusions

#### Practical Implications

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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CGPR | CPI | EX | GPR | INT | ISLAMIC MKT | CONVENTIONAL MKT | OIL | |
---|---|---|---|---|---|---|---|---|

Mean | 0.084 | 0.001 | 0.001 | 0.058 | 0.001 | 0.005 | 0.003 | 0.004 |

Median | −0.022 | 0.002 | −0.002 | −0.019 | 0.000 | 0.009 | 0.006 | 0.016 |

Maximum | 2.295 | 0.099 | 0.068 | 1.976 | 0.117 | 0.144 | 0.135 | 0.216 |

Minimum | −0.538 | −0.103 | −0.041 | −0.584 | −0.319 | −0.153 | −0.152 | −0.267 |

Std. Dev. | 0.490 | 0.016 | 0.019 | 0.379 | 0.037 | 0.038 | 0.035 | 0.087 |

Skewness | 2.086 | −1.874 | 0.670 | 1.935 | −4.438 | −0.534 | −0.537 | −0.695 |

Kurtosis | 9.169 | 30.879 | 3.975 | 8.956 | 46.798 | 6.203 | 6.631 | 4.264 |

Jarque-Bera | 311.963 | 4450.830 | 15.450 | 283.799 | 11233.390 | 64.115 | 80.642 | 19.853 |

Probability | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |

PP | −15.77 *** | −19.30 *** | −7.68 *** | −14.74 *** | −11.80 *** | −9.80 *** | −10.33 *** | −8.21 *** |

ADF | −12.21 *** | −2.63 *** | −7.74 *** | −14.00 *** | −11.70 *** | −9.68 *** | −10.08 *** | −8.13 *** |

This Table Report BDS Statistics. “m” Represents Numbers of EMBEDDING dimension. ε, Epsilon, Presents Tolerance | |||||
---|---|---|---|---|---|

m | ε(1) | ε(2) | ε(3) | ε(4) | |

Conventional MKT | 2 | 0.081 *** | 0.027 *** | 0.025 *** | 0.012 *** |

3 | 0.070 *** | 0.036 *** | 0.049 *** | 0.025 *** | |

Islamic MKT | 2 | 0.004 * | 0.024 *** | 0.026 *** | 0.010 *** |

3 | 0.003 * | 0.029 *** | 0.048 *** | 0.020 *** |

Low Volatility Regime | High Volatility Regime | Extreme Volatility Regime | ||||
---|---|---|---|---|---|---|

Coefficient | t-Statistic | Coefficient | t-Statistic | Coefficient | t-Statistic | |

Constant | −0.002 | −0.33 | 0.016 | 11.64 *** | 0.010 | 4.71 *** |

MKT(-1) | 0.429 | 1.86 * | −0.216 | −5.20 *** | −0.138 | −2.28 ** |

CPI | −1.286 | −1.42 | 0.022 | 0.39 | −0.219 | −2.41 ** |

EX | −0.336 | −0.94 | −1.066 | −14.96 *** | −0.603 | −4.29 *** |

INT | −0.360 | −1.29 | 0.022 | 1.04 | −0.472 | −7.71 *** |

OIL | 0.194 | 3.02 *** | 0.102 | 6.08 *** | −0.271 | −10.43 *** |

OIL(-1) | −0.162 | −2.53 ** | −0.055 | −3.27 *** | 0.235 | 9.47 *** |

GPR | −0.049 | −2.36 ** | −0.005 | −2.05 ** | −0.075 | −3.04 *** |

GPR(-1) | 0.019 | 1.29 | −0.002 | −0.51 | −0.005 | −0.80 |

CGPR | −0.027 | −1.68 * | −0.004 | −1.54 | −0.006 | −1.52 |

CGPR(-1) | −0.058 | −3.14 *** | −0.000 | −0.16 | −0.006 | −2.17 ** |

Log Sigma | −3.477 | −27.01 *** | −5.200 | −30.32 ** | −4.996 | −29.33 *** |

Transition Matrix Parameters | ||||||

P11-CGPR | −1.70 | −1.99 ** | LR Statistic | 25.782 *** | ||

P12-CGPR | −4.09 | −1.92 * | LR (Prob) | 0.000 | ||

P21-CGPR | 2.12 | 1.84 * | Akaike info criterion | −4.179 | ||

P22-CGPR | −1.24 | −0.48 | LogLik | 321.40 | ||

P31-CGPR | −2.75 | −1.76 * | DW | 1.946 | ||

P32-CGPR | 9.04 | 1.04 | ||||

Timing Varying Transition Probabilities and Time Varying Expected Durations | ||||||

P11 | 0.3707 | D1 | 7.709 | |||

P22 | 0.4329 | D2 | 10.309 | |||

P33 | 0.3095 | D3 | 1.621 |

Islamic | Low Volatility Regime | High Volatility Regime | Extreme volatility Regime | |||
---|---|---|---|---|---|---|

Coefficient | t-Statistic | Coefficient | t-Statistic | Coefficient | t-Statistic | |

Constant | −0.004 | −0.37 | 0.009 | 3.45 *** | 0.019 | 3.90 *** |

MKT(-1) | 0.056 | 0.17 | 0.048 | 0.60 | −0.130 | −1.37 |

CPI | 0.134 | 0.23 | 0.053 | 0.34 | −0.586 | −2.71 *** |

EX | 0.088 | 0.17 | −0.245 | −1.36 | −1.192 | −6.81 *** |

INT | −0.103 | −0.29 | 0.026 | 0.64 | −0.319 | −3.34 *** |

OIL | 0.190 | 1.38 | 0.056 | 2.76 *** | −0.063 | −1.50 |

OIL(-1) | −0.223 | −1.88 * | −0.028 | −1.05 | 0.288 | 6.26 *** |

GPR | −0.021 | −1.15 | 0.005 | 1.05 | −0.019 | −2.55 ** |

GPR(-1) | 0.047 | 1.29 | −0.008 | −2.18 ** | −0.019 | −2.61 ** |

CGPR | 0.000 | 0.01 | −0.029 | −3.67 *** | −0.014 | −1.14 |

CGPR(-1) | −0.005 | −0.13 | 0.008 | 1.90 * | −0.010 | −1.04 |

Log Sigma | −3.163 | −22.18 *** | −4.813 | −22.24 *** | −4.364 | −20.64 *** |

Transition Matrix Parameters | ||||||

P11-CGPR | −1.90 | −2.03 ** | LR Statistic | 16.034 *** | ||

P12-CGPR | −3.79 | −1.30 | LR (Prob) | 0.000 | ||

P21-CGPR | 4.78 | 1.21 | Akaike info criterion | −3.854 | ||

P22-CGPR | −4.36 | −2.21 *** | LogLik | 300.24 | ||

P31-CGPR | 0.06 | 0.02 | DW | 1.846 | ||

P32-CGPR | 2.39 | 1.68 * | ||||

Timing Varying Transition Probabilities and Time Varying Expected Durations | ||||||

P11 | 0.4147 | D1 | 4.856 | |||

P22 | 0.3332 | D2 | 1.785 | |||

P33 | 0.3330 | D3 | 1.607 |

Bearish Market | Normal Market | Bullish Market | |||
---|---|---|---|---|---|

$\mathit{\tau}\text{}=\text{}0.10$ | $\mathit{\tau}\text{}=\text{}0.3$ | $\mathit{\tau}\text{}=\text{}0.50$ | $\mathit{\tau}\text{}=\text{}0.70$ | $\mathit{\tau}\text{}=\text{}0.90$ | |

Constant | −0.041 (−5.23) *** | −0.006 (−1.32) | 0.009 (2.82) *** | 0.020 (6.35) *** | 0.039 (8.70) *** |

MKT(-1) | 0.175 (1.01) | −0.091 (−0.80) | −0.107 (−1.07) | −0.051 (−0.33) | −0.045 (−0.54) |

CPI | −0.298 (−2.16) ** | −0.103 (−0.85) | −0.128 (−1.06) | −0.073 (−0.28) | −0.445 (−1.97) |

EX | −0.207 (−1.04) | −0.677 (−3.84) *** | −0.812 (−4.84) *** | −0.666 (−2.39) ** | −0.889 (−3.30) *** |

INT | −0.134 (−2.82) *** | −0.026 (−0.50) | 0.007 (0.14) | −0.036 (−0.12) | −0.125 (−0.59) |

OIL | 0.046 (0.88) | 0.052 (1.36) | 0.061 (1.98) ** | 0.050 (1.10) | −0.042 (−0.92) |

OIL(-1) | 0.082 (1.96) ** | 0.026 (0.64) | −0.022 (−0.67) | 0.006 (0.01) | 0.024 (0.48) |

GPR | −0.020 (−1.99) ** | −0.007 (−1.14) | −0.000 (−0.008) | −0.005 (−0.77) | −0.012 (−2.01) ** |

GPR(-1) | −0.016 (−1.15) | −0.001 (−0.18) | −0.002 (−0.37) | −0.007 (−1.11) | 0.000 (−0.01) |

CGPR | −0.011 (−2.36) ** | −0.001 (−0.16) | −0.004 (−0.62) | −0.002 (−0.37) | −0.005 (−0.78) |

CGPR(-1) | −0.011 (−1.27) | −0.004 (−0.52) | −0.004 (−0.64) | −0.003 (−0.56) | −0.007 (−0.48) |

Pseudo R-squared | 0.1800 | 0.1108 | 0.0889 | 0.1269 | 0.1308 |

Adjusted R-squared | 0.1136 | 0.0377 | 0.0139 | 0.0560 | 0.0602 |

Quasi-LR statistic | 25.88 *** | 20.45 *** | 18.63 ** | 25.45 *** | 20.40 ** |

Bearish Market | Normal Market | Bullish Market | |||
---|---|---|---|---|---|

$\mathit{\tau}\text{}=\text{}0.10$ | $\mathit{\tau}\text{}=\text{}0.3$ | $\mathit{\tau}\text{}=\text{}0.50$ | $\mathit{\tau}\text{}=\text{}0.70$ | $\mathit{\tau}\text{}=\text{}0.90$ | |

Constant | −0.037 (−5.74) *** | −0.013 (−2.43) *** | 0.009 (2.51) *** | 0.019 (5.92) *** | 0.045 (7.80) *** |

MKT(-1) | 0.094 (0.65) | 0.034 (0.21) | −0.007 (−0.007) | −0.078 (−0.92) | −0.018 (−0.16) |

CPI | −0.075 (−0.58) | −0.130 (−1.18) | −0.029 (−0.16) | −0.147 (−0.48) | −0.720 (−3.10) |

EX | −0.151 (−3.05) *** | −0.050 (−0.83) | 0.004 (0.007) | −0.020 (−0.25) | 0.058 (0.78) |

INT | 0.004 (0.10) | 0.062 (1.18) | 0.054 (1.48) | 0.028 (−0.83) | 0.004 (0.07) |

OIL | 0.119 (3.14) *** | 0.063 (1.16) | −0.010 (−0.27) | −0.013 (−0.33) | 0.055 (0.91) |

OIL(-1) | −0.016 (−2.49) ** | −0.007 (−0.99) | −0.003 (−0.45) | −0.001 (−0.21) | −0.010 (−1.22) |

GPR | 0.013 (1.43) | −0.009 (−1.29) | 0.001 (0.17) | 0.002 (−0.32) | −0.012 (−1.16) |

GPR(-1) | −0.018 (−2.43) ** | −0.006 (−0.84) | 0.001 (0.27) | −0.002 (−0.42) | −0.005 (−0.41) |

CGPR | 0.017 (2.43) ** | 0.005 (0.83) | 0.001 (0.26) | −0.002 (−0.42) | −0.046 (−0.40) |

CGPR(-1) | −0.015 (−2.47) ** | 0.003 (−0.41) | −0.006 (−1.06) | −0.009 (−1.84) | −0.006 (−0.42) |

Pseudo R-squared | 0.2106 | 0.1246 | 0.092 | 0.0757 | 0.0700 |

Adjusted R-squared | 0.1465 | 0.0535 | 0.0183 | 0.0003 | 0.0001 |

Quasi-LR statistic | 37.20 *** | 22.88 ** | 19.71 ** | 16.75 * | 8.88 |

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**MDPI and ACS Style**

Hoque, M.E.; Zaidi, M.A.S.; Hassan, M.K.
Geopolitical Uncertainties and Malaysian Stock Market Returns: Do Market Conditions Matter? *Mathematics* **2021**, *9*, 2393.
https://doi.org/10.3390/math9192393

**AMA Style**

Hoque ME, Zaidi MAS, Hassan MK.
Geopolitical Uncertainties and Malaysian Stock Market Returns: Do Market Conditions Matter? *Mathematics*. 2021; 9(19):2393.
https://doi.org/10.3390/math9192393

**Chicago/Turabian Style**

Hoque, Mohammad Enamul, Mohd Azlan Shah Zaidi, and M. Kabir Hassan.
2021. "Geopolitical Uncertainties and Malaysian Stock Market Returns: Do Market Conditions Matter?" *Mathematics* 9, no. 19: 2393.
https://doi.org/10.3390/math9192393