Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 4, 1065-1076 2024 Publisher: Learning Gate DOI: 10.55214/25768484.v8i4.1482 © 2024 by the authors; licensee Learning Gate © 2024 by the authors; licensee Learning Gate * Correspondence: agustina@mikroskil.ac.id Investment volatility during red sea crisis: Study in ASEAN country Agustina1*, Andreani Caroline Barus2, Syafira Ulya Firza3, Fandi Halim4, Litka Tiadoraria Br Ginting5 1,2,3,4,5Universitas Mikroskil, Medan, North Sumatera, Indonesia; agustina@mikroskil.ac.id (A.) andreani@mikroskil.ac.id (A.C.B.) syafira.firza@mikroskil.ac.id (S.U.F.) fandi@mikroskil.ac.id (F.H.) litka.ginting@mikroskil.ac.id (L.T.B.G.) Abstract: The objective of this research is to investigate the volatility of investment instruments, with the aim of providing the best model for predicting investment value. In this topic, the main concepts and theories that underpin the research are presented. Efficient market hypothesis and rational expectations theory stand out, providing a solid basis for understanding the context of the investigation. The methodology adopted for this research GARCH modelling to analysis investment instrument and the impact of its volatility. Data collection was carried out through yahoo finance website and investing website during red sea war event. The research identified optimal GARCH models for different indexes and assets: GARCH (1,1) for JCI, STI, SET, and Bitcoin, E-GARCH (1,1) for KLSE, PSE, and VSE, and GJR-GARCH (1,1) for Gold. It discussed volatility persistence and leverage effects during the Red Sea War, considering model sensitivities, geopolitical impacts, and research limitations. This research's practical and theoretical implications inform financial risk management, highlighting the importance of robust volatility modeling. The findings can enhance portfolio optimization and investment strategies during geopolitical instability. These insights underscore the need for sophisticated approaches to manage financial risks effectively. This research uses advanced GARCH models to analyze volatility during the Red Sea Crisis, revealing asset-specific behaviors under geopolitical stress. The findings improve financial risk management and investment strategies in unstable conditions, enhancing volatility predictions and professional practices. Keywords: ASEAN, GARCH, Investment, Red sea crisis, Volatility. 1. Introduction Investment plays a critical role in a country's economic growth. Investment market stability and predictability are essential to attract and retain investors, both domestic and international, as it will provide the necessary certainty and confidence for investors to make sustainable and profitable investment decisions. However, global geopolitical conditions often pose challenges which potentially disrupt such stability. One example of a geopolitical crisis that occurred was the Red Sea Crisis, which had a wide impact on global economy, including ASEAN region. The Red Sea crisis is a geopolitical conflict involving several countries in the region, with implications that go far beyond regional boundaries. This conflict not only disrupts international trade, but also creates uncertainty for investors. Political instability and increasing security threats lead to high market volatility, which in turn influences investment decisions in various sectors of the economy. This condition not only affects countries directly involved in the conflict, but also has an impact on other regions with close economic ties, including ASEAN countries ASEAN countries with interconnected economies are highly dependent on international trade and are therefore particularly vulnerable to external shocks such as the Red Sea Crisis. Fluctuations in investment flows, exchange rates, and stock markets are some of the impacts of this crisis on the https://orcid.org/https:/0009-0004-8987-6646 https://orcid.org/0009-0008-9556-7419 https://orcid.org/0009-0000-3941-927X https://orcid.org/0009-0004-9090-0877 https://orcid.org/0009-0003-8065-5938 1066 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 4: 1065-1076, 2024 DOI: 10.55214/25768484.v8i4.1482 © 2024 by the authors; licensee Learning Gate ASEAN economy. The following figure explains how stock market fluctuations and gold prices occurred because of the Red Sea War. Figure 1. Performance before and after the red sea crisis. Source: Yahoo Finance. Figure 1 shows volatility of stock market in ASEAN countries due to Red Sea Crisis. This chart shows the movement of stock indices from various ASEAN countries during the period before and after the Red Sea Crisis. In the chart, the stock indices such as JCI Index (Indonesia), KLSE Index (Malaysia), PSEi (Philippines), STI Index (Singapore), SET Index (Thailand), and VN-Index (Vietnam), experienced significant up-and-down movements, especially at the end of October after the first eruption of Red Sea Crisis. Stock market performance in ASEAN-6 countries often experiences high volatility when events that threaten the global economy occur. In addition to stock market, global commodities such as gold are also very sensitive to global economic conditions and will fluctuate along with geopolitical uncertainty. When the global economy faces uncertainty or crisis, the price of gold tends to rise because it is considered a safe haven asset by investors. Gold is a commodity that is in demand as an investment even in the conditions of the Red Sea conflict. Likewise, in several events that hit the global economy, gold prices are a commodity whose value has high volatility. Figure 2. Bitcoin performance before and after the red sea crisis Source: Yahoo Finance. 1067 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 4: 1065-1076, 2024 DOI: 10.55214/25768484.v8i4.1482 © 2024 by the authors; licensee Learning Gate In addition to the stock market and gold price, Cryptocurrency is also one of the investment assets that is in great demand by investors and is very vulnerable to changes in geopolitical conditions. It can be seen in Figure 2 that one of the cryptocurrency assets, Bitcoin, experienced a sharp increase after the Red Sea Crisis around the end of October. At the end of October 2023 Cryptocurrencies experienced an upswing, with fluctuations experienced until early January 2024. The significant rise in late October 2020 indicates an increase in investor interest and confidence in Bitcoin as a hedge asset during times of crisis. Based on these phenomena and issues, research was conducted to test whether the red sea crisis had an impact on stock value indices in ASEAN-6 countries, gold prices, and Bitcoin. In addition, this research also analyzed the volatility of stock index values in ASEAN-6 countries, gold prices, and Bitcoin before and after the crisis. Thus, this research is expected to provide valuable insights for readers, especially investors in making investments. This research is expected to enrich the academic literature on the relationship between geopolitical crises and investment volatility in the ASEAN region. 2. Theoretical Framework Geopolitical events such as the red sea crisis can affect economic conditions globally and trigger unexpected market reactions. The volatile global stock market is characterized by high volatility and increased uncertainty for investors. This turmoil often impacts investor confidence, which can lead to fluctuations in stock indices. In such a situation, it is important for stakeholders to understand the ongoing dynamics to make informed decisions and mitigate the risks. As such, an in-depth analysis of the impact of geopolitical crisis on stock market is essential to assist investors in determining investment strategies. Several previous studies have conducted research related to the impact of geopolitical conditions on a country's stock index(Agustina & Barus, 2023) (Olayungbo et al., 2024)(Mgadmi et al., 2023)(Guo et al., 2021)(Hamil et al., 2023)(Nguyen & Nguyen, 2022)(Truong & Van Vo, 2023)(Hoang Tien et al., 2021)(Alghifary et al., 2023)(Puspitasari, 2024)(Arisandhi & Robiyanto, 2022). The price of commodities such as gold is one of the investments that is quite sensitive to geopolitical instability. Gold is often considered a safe haven asset, which means investors are likely to turn to gold during times of economic or political uncertainty. When geopolitical tensions arise, demand for gold is likely to increase as investors seek refuge from volatile stock and currency market risks. Gold prices will fluctuate due to the geopolitical crisis due to increasing demand as a safer asset. Therefore, understanding gold price volatility in the context of geopolitical crises is important for investors to make informed decisions and manage the risks associated with gold price volatility. Several previous studies have conducted research related to the impact of geopolitical conditions on global commodity prices such as gold (Agustina & Barus, 2023)(Chemkha et al., 2021)(Kayral et al., 2023)(Daskalakis & Daglis, 2023)(Tetteh & Ntsiful, 2023)(Taera et al., 2023)(Zhang & Mani, 2021)(Nguyen & Nguyen, 2022)(Tunnisa & Darmawan, 2023)(Salikin & Wahab, 2024). Cryptocurrencies Prices such as Bitcoin are also extremely sensitive to geopolitical instability conditions. Like gold, Bitcoin is often considered a safe haven asset, although it is still relatively new compared to traditional commodities. When geopolitical tensions arise, many investors are looking for alternatives to store their wealth value, and Bitcoin is often acts as a choice due to its decentralized nature and not tied to a particular country's monetary policy. During periods of geopolitical crisis, the demand for Bitcoin can increase significantly. Uncertainty about the stability of a country can encourage investors to shift their assets to Bitcoin as a form of diversification and hedging. This can cause Bitcoin transactions to increase, which leads to bitcoin price fluctuations. Several previous studies have conducted research related to the impact of geopolitical conditions on global commodity prices such as gold(Agustina & Barus, 2023)(Chemkha et al., 2021)(Kayral et al., 2023)(Umar et al., 2021)(Mgadmi et al., 2023)(Guo et al., 2021)(Sarkodie et al., 2022)(Taera et al., 2023)(Zhang & Mani, 2021)(Tunnisa & Darmawan, 2023)(Almeida et al., 2024). 1068 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 4: 1065-1076, 2024 DOI: 10.55214/25768484.v8i4.1482 © 2024 by the authors; licensee Learning Gate Thus, an analyse of whether the investment instruments included in safe-haven investment are seen in Figure 3. Figure 3. Theoretical framework. Based on figure 3, the framework of this research describes differences analysis at stock indices performance in six ASEAN countries (Indonesia-JCI, Malaysia-KLSE, Singapore-STI, Thailand-SET, Philippines-PSE, Vietnam-VSE) on the price of gold and cryptocurrency (Bitcoin) during three periods: before the crisis, during the crisis (referred to as the Red Sea Crisis), and after the crisis. This research uses a difference test and the GARCH (Generalized Autoregressive Conditional Heteroskedasticity) model to analyze the volatility of each of these variables during the red sea war period. The results of this analysis are then used to formulate an optimal investment strategy. 3. Method This research is quantitative descriptive research with a GARCH analysis model based on impact of the red sea crisis. This research model is used to predict stock index volatility in ASEAN-6 and manage investment portfolios to control investment risks resulting from changes in uncertain economic conditions. The objects of this research are 6 ASEAN countries which have more advanced and larger economies compared to the others, namely Indonesia, Malaysia, the Philippines, Singapore, Thailand, and Vietnam. This research uses secondary data from www.tradingeconomics.com and http://www.finance.yahoo.com The data used in this research was taken 90 days before and after the red sea crisis event, starting from July 21, 2023 until then. January 17, 2024. The data analysis techniques used in analyzing the data in this research include: 1. Descriptive statistics This test is used to show the characteristics of the data from this research. This test is to see the research data's mean, median, maximum, and minimum. Descriptive statistics help in looking at the distribution of data and the distribution of data used in this research. 2. Difference Test This test was carried out to see if there was a significant difference between the conditions before and after the Red Sea crisis. Before conducting a differential test, the researcher conducted a normality http://www.tradingeconomics.com/ http://www.finance.yahoo.com/ 1069 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 4: 1065-1076, 2024 DOI: 10.55214/25768484.v8i4.1482 © 2024 by the authors; licensee Learning Gate test first. The normality test aims to find out whether each variable has a normal distribution or not. As for the test, it was carried out by looking at the distribution of data before and after the war. The normality test used Kolmogorov-Smirnov (K-S) statistical analysis. After that, from results of normality test, the type of hypothesis test to be used will be decided. Hypothesis tests are carried out to answer the research question. If the data is distributed normally, One sample t-test will be used. However, if the data is distributed abnormally, one-sample Wilcoxon signed rank test will be used (Agustina & Barus, 2023). 3. GARCH Model Financial time series often exhibit periods of low volatility followed by periods of high volatility; a phenomenon known as volatility clustering. To model this characteristic in economic and financial time series, ARCH and GARCH models are commonly employed (Bollerslev, 1986). a. GARCH Model In financial price volatility modeling, the GARCH family is chosen for its ability to capture dynamic volatility changes and clustering of volatility. Among these models, the one commonly selected is referred to as the GARCH (1,1) model (Karmakar, 2005). b. GJR-GARCH Model The GJR-GARCH model was employed to examine asymmetric characteristics in financial market returns. According to this model, it suggests that investors are more anxious about negative returns than they are about positive returns, leading to what is known as the leverage effect (Karmakar, 2005). c. E-GARCH Model This model is the exponential GARCH model to account for the asymmetry in the fundamental GARCH model. The E-GARCH model could account for more lags in conditional variance (Nelson1, 1991). 4. Result and Discussion Based on the closing rates for Stock Indices (JCI, KLSE, SET, STI, PSE, VSE), World Gold, and Crypto Currency (Bitcoin), 90 days before, 90 days after and during the Red Sea War, the research results are as follows: Table 1. Descriptive statistic for selected variables. Statistic/V ariables Bitcoin Gold JCI KLSE PSE STI SET VSE Entire period Mean 33514.17 1962.036 6986.537 1451.285 6310.842 3180.722 1458.910 57.30315 Median 29908.74 1961.800 6939.890 1451.000 6265.140 3184.300 1431.720 56.97000 Maximum 46970.50 2081.900 7359.760 1501.110 6680.450 3373.980 1576.670 65.47000 Minimum 25162.65 1816.600 6642.420 1413.520 5961.990 3053.360 1357.970 48.47000 Std. Dev. 6821.974 61.98186 154.1577 16.05858 188.4041 75.93907 65.71474 3.907515 Skewness 0.421270 -0.141929 0.681192 0.373443 0.308800 0.433292 0.323565 0.173960 Kurtosis 1.642276 2.450279 2.674506 4.580923 2.234884 2.626672 1.458367 2.004413 Jarque- Bera 19.25605 2.886714 14.79703 23.05607 7.291527 6.714653 21.08203 8.388152 Probability 0.000066 0.236134 0.000612 0.000010 0.026101 0.034828 0.000026 0.015085 Before red sea war Mean 27481.11 1912.194 6923.572 1445.229 6315.812 3234.993 1517.117 54.51489 Median 27076.78 1918.450 6922.995 1448.095 6266.340 3218.485 1529.250 53.74000 Maximum 30084.54 1970.500 7016.840 1463.510 6679.130 3373.980 1576.670 58.82000 Minimum 25162.65 1816.600 6852.840 1413.520 6041.040 3136.620 1427.110 48.47000 1070 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 4: 1065-1076, 2024 DOI: 10.55214/25768484.v8i4.1482 © 2024 by the authors; licensee Learning Gate Statistic/V ariables Bitcoin Gold JCI KLSE PSE STI SET VSE Std. Dev. 1397.813 37.77119 43.28175 13.85425 165.3575 57.75065 40.64449 2.204941 Skewness 0.350923 -0.910337 0.392261 -0.992254 0.796269 0.729105 -0.913505 -0.112417 Kurtosis 1.629148 3.222891 2.334073 3.068806 2.639861 2.815348 2.559433 2.530073 Jarque- Bera 8.894336 12.61700 3.971000 14.78628 9.997043 8.101777 13.24524 1.017682 Probability 0.011712 0.001821 0.137312 0.000615 0.006748 0.017407 0.001330 0.601192 After red sea war Mean 39600.51 2011.808 7051.060 1457.436 6306.890 3127.352 1401.103 60.11033 Median 40611.11 2007.200 7078.040 1454.380 6260.440 3114.075 1401.560 60.89000 Maximum 46970.50 2081.900 7359.760 1501.110 6680.450 3240.270 1434.590 65.47000 Minimum 29682.95 1932.600 6642.420 1435.330 5961.990 3053.360 1357.970 52.80000 Std. dev. 4200.694 36.40822 194.2941 15.91958 210.5032 48.94090 16.19179 3.186774 Skewness -0.308301 -0.071936 -0.265987 1.133928 0.076667 0.700155 -0.085054 -0.718397 Kurtosis 2.086262 2.185847 1.783406 3.507667 1.876149 2.554552 2.291383 2.902355 Jarque- Bera 4.556681 2.563289 6.611619 20.25336 4.824574 8.097355 1.991532 7.777165 Probability 0.102454 0.277580 0.036670 0.000040 0.089610 0.017445 0.369440 0.020474 The results of descriptive statistics for selected variables over the entire period (from 21 July 2023 to 17 January 2024) are presented in Table 1. According to standard deviation, Bitcoin was the most volatile with a value of 6821.974, while VSE was the least volatile, with a value of 3.907515. This indicates that Bitcoin was more likely to experience large swings in price than the VSE index. The price distributions of all the variables except Gold were positively skewed, indicating that the probability of price increases was higher than the probability of price decreases. Overall, the VSE Index did not experience much volatility during this period, suggesting that investing in this index has a moderate to low risk. However, the results from the period before the Red Sea Crisis (from 21 July 2023 to 18 October 2023) show that although the VSE index was the least volatile, its price distribution experienced negative skewness. This indicates that before the Red Sea Crisis, the probability of a price decrease was higher than the probability of a price increase. Overall, in the period before the Red Sea Crisis, it is evident that the JCI Index was more stable than the other variables. Nevertheless, the results from the period after the Red Sea Crisis (20 October 2023 to 17 January 2024) show that the KLSE index was the second least volatile variable after the VSE index. The low volatility of this index corresponded with positive skewness, meaning that after the Red Sea Crisis, the probability of a price increase was higher than the probability of a price decrease. Thus, in other words, after this Crisis, investing in the KLSE index became a safe choice for investors with moderate to low risk. Table 2. Independent samples Test for difference testing. Variable Normality test Difference testing Before crisis After crisis Test Sig. Result JCI index 0,010 0,000 Wilcoxon test 0,000 Significant KLSE index 0,000 0,000 Wilcoxon test 0,000 Significant PSE index 0,000 0,001 Wilcoxon test 0,777 No Significant STI index 0,000 0,000 Wilcoxon test 0,000 Significant SET index 0,000 0,056 T-test 0,752 No Significant VSE index 0,006 0,000 Wilcoxon test 0,000 Significant Gold 0,000 0,018 Wilcoxon test 0,000 Significant 1071 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 4: 1065-1076, 2024 DOI: 10.55214/25768484.v8i4.1482 © 2024 by the authors; licensee Learning Gate Variable Normality test Difference testing Before crisis After crisis Test Sig. Result Bitcoin 0,000 0,000 Wilcoxon test 0,000 Significant Based on Table 2, if the Shapiro-Wilk sig value for either pre and post is above 0.05, then the data is normally distributed, and hypothesis testing is carried out using the independent sample T-Test. Meanwhile, if the Shapiro-Wilk sig value for either pre and post is below 0.05, then the data is not normally distributed, and hypothesis testing is carried out using the Wilcoxon Sign Rank Test. From table 2, it can be explained that the JCI Index, KLSE Index, PSE Index, STI Index, VSE Index, gold, and bitcoin are affected by the wilcoxon test. Meanwhile, the SET Index variable uses a t-test. Based on the results of difference testing, the variables JCI Index, KLSE index, STI Index, VSE index, gold and bitcoin show the difference between before and after the red sea crisis. Meanwhile, the PSE index and SET Index show that there is no difference between before and after the red sea crisis. From these results, stock indices in Indonesia, Malaysia, Singapore, and Vietnam have experienced a change in sentiment due to the occurrence of the red sea crisis. Similarly, gold and bitcoin also experienced a change in sentiment during the red sea crisis. This shows that the red sea crisis affects global economic conditions, resulting in significant economic disruption due to political and economic uncertainty. However, the PSE index and SET index do not show a difference between before and after the red sea crisis. This happened because investors believed that the impact of the crisis was temporary so that the crisis recovery would take place quickly so that investors remained optimistic and considered that the crisis would not have a long-term impact. Time series data underwent analysis to detect unit roots. The premise that statistical properties remain consistent over time underpins most statistical tests and methodologies. A stationary time series is essential for modelling and predicting the relationships between variables. Table 3. Augmented Dickey–Fuller test results for the selected variables. Variables Before red sea war After red sea war Entire period ADF t ADF prob Trend prob ADF t ADF prob Trend prob ADF t ADF prob Trend prob JCI index -7.023 0.000* 0.996 -13.937 0.000* 0.537 -9.449 0.000* 0.836 KLSE index -8.930 0.000* 0.285 -11.890 0.000* 0.869 -12.725 0.000* 0.699 PSE index -8.126 0.000* 0.741 -11.876 0.000* 0.496 -14.611 0.000* 0.863 STI index -10.261 0.000* 0.656 -11.088 0.000* 0.445 -15.408 0.000* 0.673 SET index -11.449 0.000* 0.975 -8.470 0.000* 0.492 -10.326 0.000* 0.907 VSE index -8.180 0.000* 0.670s -6.832 0.000* 0.601 -11.616 0.000* 0.992 Gold -9.059 0.000* 0.545 -8.409 0.000* 0.605 -12.642 0.000* 0.717 Bitcoin -11.797 0.000* 0.921 -12.099 0.000* 0.640 -11.329 0.000* 0.932 Note: *shows the 1% significance level. Table 3 shows the results of the augmented Dickey–Fuller (ADF) test for daily closing price returns for selected variables from 21 July 2023 to 17 January 2024. The ADF test was used to determine whether a time series is stationary. A stationary time series is one whose statistical properties do not change over time. The variables taken for this research, included Stock Indices such as JCI, KLSE, SET, STI, PSE, VSE and World Gold & Bitcoin. The probability values of all variables were near zero during the study period. This indicated that all the variables’ prices remained stationary during the study period. A unit root null hypothesis was rejected for all log-returns of stock indices because all underlying variables were stationary at the level. 1072 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 4: 1065-1076, 2024 DOI: 10.55214/25768484.v8i4.1482 © 2024 by the authors; licensee Learning Gate Figure 4. Price Trends in the Financial Markets over the period of 21 July 2023 to 17 January 2024. 1073 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 4: 1065-1076, 2024 DOI: 10.55214/25768484.v8i4.1482 © 2024 by the authors; licensee Learning Gate Figure 4. Price Fluctuations in the Financial Markets over the period of 21 July 2023 to 17 January 2024. Table 4. Results based on the GARCH models for entire study period. Variables Model Log AIC α (ARCH) β (GARCH) γ (Gamma) JCI index GARCH (1,1) -875.012 9.778 -0.049 * 1.043 * - GJR-GARCH (1,1) -880.314 9.848 -0.093’ * 0.720 * 0.435 * EGARCH (1,1) -885.079 9.901 0.289 * -0.821 * -0.060 KLSE index GARCH (1,1) 38.983 -0.378 -0.204 * -0.846 * - GJR-GARCH (1,1) -527.641 5.952 -0.065 * 0.584 * -0.056 * EGARCH (1,1) 376.250 -4.114 -0.680 * 0.816 * 0.205 * PSE index GARCH (1,1) -304.368 3.437 0.165 * -0.631 * - GJR-GARCH (1,1) -929.260 10.414 0.154 * 0.551 ** -0.144 * EGARCH (1,1) 7604.813 -84.431 0.088 -0.859 ** 0.110 * STI index GARCH (1,1) -756.038 8.467 -0.079 * 0.550 * - GJR-GARCH (1,1) -758.570 8.506 -0.001 0.777 * -0.083 EGARCH (1,1) -757.699 8.497 -0.251 ** 0.693 * 0.076 SET index GARCH (1,1) 32.844 -0.299 0.144 * -0.850 * - GJR-GARCH (1,1) -635.860 7.143 -0.091 * 0.601 * 0.132 ** EGARCH (1,1) -639.233 7.180 0.298 * -0.762 * 0.077 VSE index GARCH (1,1) -257.336 2.915 -0.014 0.592 * - GJR-GARCH (1,1) -257.999 2.933 0.083 0.594 * -0.102 EGARCH (1,1) -230.315 2.626 1.160 * -0.131 * 0.113 Gold GARCH -706.628 7.907 -0.057 * 0.595 * - GJR-GARCH -703.429 7.894 -0.033 0.560 * -0.054 EGARCHS -705.561 7.906 0.460 * -0.450 ** 0.003 Bitcoin GARCH (1,1) 1244.113 -13.768 0.021 ** -0.996 * - GJR-GARCH (1,1) -955.122 10.701 0.441 * 0.759 * -0.175 * EGARCH (1,1) 805.283 -8.881 0.308 * -0.940 * -0.055 * Note: ** refers to 10% significance level, and * refers to 5% significance level. The empirical results with respect to the different GARCH models for study period (21 July 2023 to 17 January 2024) are illustrated in Table 4. Based on Table 4, the AIC (Akaike Information Criteria) values for each selected variables indicated that GARCH (1,1) model was the best-fitted for simulating the price volatility of JCI Index, STI Index, SET Index, and Bitcoin. For KLSE Index, PSE Index, and VSE Index E-GARCH (1,) was the best model for modelling the volatility, while the best model for modelling Gold’s volatility was GJR-GARCH (1,1). The outcomes of the GARCH models, used to analyze the effect of the Red Sea Crisis Event on the prices of chosen variables, are shown in Tables 4. Based on the E-GARCH (1, 1) model, JCI Index, KLSE Index, PSE Index, SET Index, and Bitcoin exhibited asymmetric effects at various levels of significance throughout the study period. With respect 1074 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 4: 1065-1076, 2024 DOI: 10.55214/25768484.v8i4.1482 © 2024 by the authors; licensee Learning Gate to the EGARCH model clearly indicated that the prices of JCI Index, KLSE Index, PSE Index, SET Index, and Bitcoin demonstrated significant long-term memory effect and asymmetric behavior during the entire study period with the highest volatility persistence in JCI Index (β = 0.435). This high persistence was likely due to the global financial instability. However, the leverage effect was observed in KLSE Index, PSE Index, and Bitcoin throughout this study period with the highest leverage coefficient in KLSE Index (β = 0.205). The Red Sea cris, a significant geopolitical conflict, has had profound implications on global financial markets. Geopolitical events often lead to heightened uncertainty, causing substantial fluctuations in asset prices. This increased volatility can be attributed to various factors, including changes in investor sentiment, shifts in economic fundamentals, and alterations in risk perceptions. The influence of price fluctuations in financial assets during economic instability causing by global conflict of various regions has been widely examined in academic research (Agustina & Barus, 2023)(Umar et al., 2021)(Mgadmi et al., 2023)(Hamil et al., 2023)(Truong & Van Vo, 2023)(Puspitasari, 2024). Volatility, a statistical measure of the dispersion of returns for a given security or market index, serves as a crucial indicator of market stability and investor confidence. During periods of geopolitical tension, such as the Red Sea Crisis, the anticipation of potential economic and political repercussions can lead to abrupt market movements. Accurate volatility predictions can aid in developing effective risk management strategies, ensuring market stability, and making informed investment decisions. The GARCH model and its variants, such as GJR-GARCH and EGARCH, are widely used to model and forecast financial market volatility. These models account for time-varying volatility and can capture the persistence and clustering of volatility observed in financial markets. Our study focuses on predicting investment volatility during the Red Sea Crisis event using the GARCH family of models. After analyzing the results, it can be concluded the best-fitting models for each variable, as determined by the lowest AIC values, vary. The GARCH(1,1) model is most suitable for the JCI Index, STI Index, SET Index, and Bitcoin. In contrast, the EGARCH(1,1) model is the best fit for the KLSE Index, PSE Index, and VSE Index. Meanwhile, the GJR-GARCH(1,1) model is the most appropriate for Gold. This indicates that different indices and assets exhibit unique volatility characteristics, necessitating the use of various GARCH-type models to capture their specific behaviors accurately. The results of this analysis offer valuable insights into the behavior of financial markets during periods of geopolitical instability, highlighting the importance of robust volatility modeling in managing financial risk. The research on predicting investment volatility during the Red Sea Crisis using the GARCH family models has several important implications. It underscores the necessity for sophisticated volatility modeling, like GARCH, EGARCH, and GJR-GARCH, in enhancing risk management and crafting tailored investment strategies. By recognizing the unique volatility characteristics of different assets, investors can optimize their portfolios more effectively during volatile periods. The study also highlights the profound impact of geopolitical events on financial markets, emphasizing the need for policymakers and investors to consider these factors in decision-making processes. Accurate volatility predictions foster market stability and investor confidence, contributing to more informed and resilient financial markets. Additionally, the research advances the field of volatility modeling and offers practical applications for financial institutions, improving their portfolio management practices. Overall, this study provides valuable insights for risk management, investment strategies, and understanding the effects of geopolitical events on market volatility. 5. Conclusion Based on the research results and discussion, the optimal investment strategy for investors during periods of heightened volatility, such as the Red Sea Crisis, involves a combination of diversification, dynamic asset allocation, and robust risk management techniques. Investors should diversify their portfolios across various asset classes and regions to mitigate risks, while employing hedging instruments like options and futures. Dynamic asset allocation is essential, shifting towards more stable assets such as bonds or gold, especially during increased volatility periods as indicated by the EGARCH 1075 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 4: 1065-1076, 2024 DOI: 10.55214/25768484.v8i4.1482 © 2024 by the authors; licensee Learning Gate model. Conversely, during lower volatility, assets like Bitcoin and the JCI Index, identified by the GARCH(1,1) model, can be prioritized. Risk management through stop-loss orders and adjusting position sizes based on volatility helps protect investments. Tactical investments informed by volatility models, such as favoring gold during geopolitical tensions, can enhance performance. Maintaining a long-term perspective and continuously monitoring and adjusting strategies ensure alignment with market conditions and risk tolerance, enabling investors to effectively navigate and capitalize on market opportunities. Acknowledgements We are immensely thankful to the Direktorat Jenderal Pendidikan Tinggi, Riset dan Teknologi (DRTPM) of the Ministry of Education, Culture, Research, and Technology for their generous funding through the fundamental research grant. This support has been crucial in facilitating the completion of our research project. We would also like to express our heartfelt appreciation to Universitas Mikroskil for their provision of essential resources, facilities, and an inspiring environment that has contributed to the progress of this research. Copyright: © 2024 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). References Agustina, A., & Barus, A. C. (2023). Investasi Safe Haven: Dampak Perang Rusia - Ukraina. Owner, 7(3), 2330–2339. https://doi.org/10.33395/owner.v7i3.1523 Alghifary, M. S., Kadji, D., & Hafizah, I. (2023). 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