Climate Risks and Stock Market Volatility over a Century in an Emerging Market Economy: The Case of South Africa
Abstract
:1. Introduction
2. Brief Discussion of Stock Return Volatility Literature of South Africa
3. Forecasting Models
3.1. Classical Models
3.2. NoVaS-Type Models
3.2.1. GARCH-NoVaS Model
3.2.2. GARCHX-NoVaS Model
4. Model Evaluation
5. Data
6. Empirical Results
- Stage-1 model: we apply the GARCH and GARCH-NoVaS models to compute predictions. These two models are the benchmark for classical and model-free type methods.
- Stage-2 model: we add OR and GS to the model. This results in GARCHX and GARCHX-NoVaS models with two covariates.
- Stage-3 model: we take DTA or DYTA data into account based on Stage 2 models. Meanwhile, we keep including OR and GS as exogenous variables.
- Stage-4 model: we estimate the volatilities of DTA and DYTA by means of GARCH or NoVaS models and then use the estimates as additional covariates. In order to simplify notation, we denote the volatility of DTA/DYTA estimated by a GARCH model as DTAV1/DYTAV1, while we use DTAV2/DYTAV2 to denote the volatility of DTA/DYTA as estimated by means of a NoVaS model.
- The effects of OR and GS: the role of fundamentals- and sentiments-based information is revealed by the comparison of the Stage-1 and -2 models in Table 1. Taking the GARCH model as the benchmark, the Stage-2 GARCH model performs better when we use the SSPE statistic to evaluate 6- and 12-step-ahead predictions (moving window of size 500). The results of the CW test corroborate that the MSPE of the GARCH Stage-2 model is significantly smaller in a statistical sense than that of the benchmark model. However, for the moving window with 240 observations, the benchmark model beats the Stage-2 GARCH model. One reason may be that the sample size is not large enough to obtain a satisfactory estimation of the GARCHX model. However, OR and GS are also statistically beneficial to the predictions when we study the NoVaS method. Moreover, this improvement can also be observed for the 240-moving window.
- The effects of DTA/DYTA: the results that we report in Table 2 show that, for GARCH-type models, with a 500- or 240-moving window, the improvement in SSPE brought about by including DTA or DYTA in the models is negligible. Actually, the Stage-2 GARCH model outperforms the Stage-3 GARCH model, irrespective of whether we study DTA or DYTA, for 1-, 3-, and 6-step-ahead predictions. The corresponding CW tests are not significant. The NoVaS-type models, however, can utilize climate information to yield more accurate forecasts. For example, the GARCHX-NoVaS-3-DTA model is better than the corresponding Stage-2 NoVaS model when we use a 500-moving window. The corresponding CW test also implies that we can reject the null hypothesis. However, the gain in forecast accuracy is hardly visible for predictions based on a 240-moving window, but it is still statistically significant at a significance level of 0.05. According to our results, DTA is more useful when the moving window size is 500, and DYTA is more useful for a 240-moving window.
- The effects of volatilities of DTA/DYTA: according to Table 3, the volatility of DTA and DYTA is almost useless in improving the forecast accuracy of the GARCHX models, and almost all CW tests when applied to the corresponding Stage-3 and -4 models cannot reject the null hypothesis. Interestingly, the NoVaS-type models produce some forecasting benefits after including the volatility of DTA or DYTA, especially for long prediction horizons and a short moving window. For two types of volatility, DTAV1 and DYTAV2, the forecasts are slightly more accurate than their counterparts estimated by the NoVaS model.
- The effects of applying the model-free NoVaS prediction technique: it is evident from Table 1, Table 2 and Table 3 that the NoVaS-type models are much better than the corresponding GARCH models for all four stages, and, hence, our work adds to the general literature on stock return volatility forecasting in South Africa that has primarily relied on the GARCH framework. More importantly, when we add climate risks to the NoVaS model, we observe that forecasting performance improves. The classical GARCH model, however, fails to take advantage of the information embedded in these covariates. All in all, the combination of the temperate anomaly and its volatility captured by a GARCH model gives the best model (Stage-4 NoVaS) due to its large MSE accuracy and robustness. Our findings thus corroborate the importance of climate risks in driving historical second-moment movements of an emerging stock market, i.e., South Africa, just like what was detected for the US and other advanced economies by [14,64]. In the process, we confirm that the role of physical risks due to changes in the temperature anomaly and its volatility acting as proxies of rare disaster events can be associated with the theoretical idea of the predictive relationship between asset market volatility and disaster risks.
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Ratio of Squared Errors | p-Value of CW-Test | |||||||
---|---|---|---|---|---|---|---|---|
Prediction Step | 1 | 3 | 6 | 12 | 1 | 3 | 6 | 12 |
Moving window size 500: | ||||||||
GARCH (Benchmark) | 1.000 | 1.000 | 1.000 | 1.000 | ||||
GARCH-NoVaS | 0.998 | 0.957 | 0.883 | 0.746 | ||||
GARCHX-2 | 1.000 | 1.004 | 0.998 | 0.987 | 0.087 | 0.036 | 0.014 | 0.003 |
GARCHX-NoVaS-2 | 0.995 | 0.959 | 0.881 | 0.732 | 0.061 | 0.032 | 0.000 | 0.000 |
Moving window size 240: | ||||||||
GARCH (Benchmark) | 1.000 | 1.000 | 1.000 | 1.000 | ||||
GARCH-NoVaS | 1.070 | 1.025 | 0.908 | 0.684 | ||||
GARCHX-2 | 1.023 | 1.060 | 1.092 | 1.059 | 0.449 | 0.401 | 0.349 | 0.030 |
GARCHX-NoVaS-2 | 0.990 | 0.934 | 0.842 | 0.644 | 0.041 | 0.000 | 0.000 | 0.000 |
Ratio of Squared Errors | p-Value of CW-Test | |||||||
---|---|---|---|---|---|---|---|---|
Prediction Step | 1 | 3 | 6 | 12 | 1 | 3 | 6 | 12 |
Moving window size 500: | ||||||||
GARCHX-2 (Benchmark) | 1.000 | 1.000 | 1.000 | 1.000 | ||||
GARCHX-NoVaS-2 | 0.994 | 0.956 | 0.882 | 0.742 | ||||
GARCHX-3-DTA | 1.000 | 1.000 | 1.005 | 0.996 | 0.386 | 0.309 | 0.986 | 0.102 |
GARCHX-NoVaS-3-DTA | 1.004 | 0.947 | 0.878 | 0.732 | 0.894 | 0.001 | 0.013 | 0.000 |
GARCHX-3-DYTA | 1.000 | 1.001 | 1.003 | 1.004 | 0.940 | 0.917 | 0.943 | 0.806 |
GARCHX-NoVaS-3-DYTA | 0.997 | 0.955 | 0.882 | 0.734 | 0.525 | 0.143 | 0.187 | 0.001 |
Moving window size 240: | ||||||||
GARCHX-2 (Benchmark) | 1.000 | 1.000 | 1.000 | 1.000 | ||||
GARCHX-NoVaS-2 | 0.967 | 0.882 | 0.771 | 0.608 | ||||
GARCHX-3-DTA | 1.002 | 1.003 | 1.006 | 0.996 | 0.837 | 0.787 | 0.929 | 0.154 |
GARCHX-NoVaS-3-DTA | 0.968 | 0.887 | 0.772 | 0.602 | 0.234 | 0.473 | 0.240 | 0.042 |
GARCHX-3-DYTA | 1.004 | 1.009 | 1.009 | 1.010 | 0.994 | 0.998 | 0.976 | 0.847 |
GARCHX-NoVaS-3-DYTA | 0.963 | 0.888 | 0.770 | 0.601 | 0.070 | 0.723 | 0.192 | 0.023 |
Ratio of Squared Errors | p-Value of CW-Test | |||||||
---|---|---|---|---|---|---|---|---|
Prediction Step | 1 | 3 | 6 | 12 | 1 | 3 | 6 | 12 |
Moving window size 500: | ||||||||
GARCHX-3-DTA (Benchmark) | 1.000 | 1.000 | 1.000 | 1.000 | ||||
GARCHX-NoVaS-3-DTA | 1.004 | 0.948 | 0.874 | 0.734 | ||||
GARCHX-4-DTAV1 | 1.001 | 1.000 | 0.995 | 0.992 | 0.576 | 0.298 | 0.037 | 0.014 |
GARCHX-NoVaS-4-DTAV1 | 1.001 | 0.960 | 0.881 | 0.749 | 0.156 | 0.991 | 0.721 | 0.960 |
GARCHX-4-DTAV2 | 1.000 | 0.999 | 0.993 | 0.990 | 0.419 | 0.103 | 0.001 | 0.002 |
GARCHX-NoVaS-4-DTAV2 | 0.999 | 0.962 | 0.876 | 0.743 | 0.081 | 0.993 | 0.404 | 0.808 |
GARCHX-3-DYTA (Benchmark) | 1.000 | 1.000 | 1.000 | 1.000 | ||||
GARCHX-NoVaS-3-DYTA | 0.997 | 0.953 | 0.879 | 0.731 | ||||
GARCHX-4-DYTAV1 | 1.000 | 1.000 | 0.998 | 0.999 | 0.974 | 0.371 | 0.144 | 0.357 |
GARCHX-NoVaS-4-DYTAV1 | 1.005 | 0.957 | 0.885 | 0.740 | 0.961 | 0.647 | 0.797 | 0.801 |
GARCHX-4-DYTAV2 | 1.000 | 0.999 | 1.002 | 0.997 | 0.891 | 0.205 | 0.823 | 0.133 |
GARCHX-NoVaS-4-DYTAV2 | 1.003 | 0.957 | 0.887 | 0.740 | 0.926 | 0.581 | 0.882 | 0.808 |
Moving window size 240: | ||||||||
GARCHX-3-DTA (Benchmark) | 1.000 | 1.000 | 1.000 | 1.000 | ||||
GARCHX-NoVaS-3-DTA | 0.966 | 0.884 | 0.766 | 0.603 | ||||
GARCHX-4-DTAV1 | 1.003 | 1.003 | 0.999 | 1.015 | 0.789 | 0.811 | 0.269 | 0.923 |
GARCHX-NoVaS-4-DTAV1 | 0.973 | 0.880 | 0.756 | 0.585 | 0.762 | 0.091 | 0.008 | 0.005 |
GARCHX-4-DTAV2 | 1.001 | 1.000 | 1.001 | 1.003 | 0.700 | 0.481 | 0.477 | 0.516 |
GARCHX-NoVaS-4-DTAV2 | 0.977 | 0.882 | 0.761 | 0.585 | 0.941 | 0.176 | 0.066 | 0.008 |
GARCHX-3-DYTA (Benchmark) | 1.000 | 1.000 | 1.000 | 1.000 | ||||
GARCHX-NoVaS-3-DYTA | 0.960 | 0.880 | 0.763 | 0.593 | ||||
GARCHX-4-DYTAV1 | 1.000 | 1.001 | 0.997 | 0.994 | 0.879 | 0.691 | 0.069 | 0.184 |
GARCHX-NoVaS-4-DYTAV1 | 0.965 | 0.867 | 0.752 | 0.562 | 0.777 | 0.002 | 0.053 | 0.001 |
GARCHX-4-DYTAV2 | 1.001 | 1.001 | 1.004 | 1.005 | 0.945 | 0.864 | 0.861 | 0.640 |
GARCHX-NoVaS-4-DYTAV2 | 0.967 | 0.872 | 0.752 | 0.580 | 0.853 | 0.012 | 0.026 | 0.020 |
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Wu, K.; Karmakar, S.; Gupta, R.; Pierdzioch, C. Climate Risks and Stock Market Volatility over a Century in an Emerging Market Economy: The Case of South Africa. Climate 2024, 12, 68. https://doi.org/10.3390/cli12050068
Wu K, Karmakar S, Gupta R, Pierdzioch C. Climate Risks and Stock Market Volatility over a Century in an Emerging Market Economy: The Case of South Africa. Climate. 2024; 12(5):68. https://doi.org/10.3390/cli12050068
Chicago/Turabian StyleWu, Kejin, Sayar Karmakar, Rangan Gupta, and Christian Pierdzioch. 2024. "Climate Risks and Stock Market Volatility over a Century in an Emerging Market Economy: The Case of South Africa" Climate 12, no. 5: 68. https://doi.org/10.3390/cli12050068