**3. Data**

For our analysis of the main ASEAN financial markets, we include six country stock market indices. We choose the Indonesian Jakarta Stock Exchange Composite Index (JCI), the Kuala Lumpur Stock Exchange (KLSE) of Malaysia, the Philippines Stock Exchange PSEI Index (PCOMP), the Stock Exchange of Thailand (SET), the Singapore Strait's Time Index (STI), and the Vietnam Ho Chi Minh Stock Index (VNI). Hence, we exclude the smaller stock markets of Myanmar, Cambodia, Laos, and Brunei from our analysis. The data is retrieved from Bloomberg in USD denominations for the period from 1 July 2006 to 30 June 2017. The period is chosen such that we obtain an equal number of observations of around *n* = 2700 for all indices accounting for individual holidays. We note that the VNI has some zero volume trading days before our chosen period. We calculate the daily returns of the stock indices by logarithmic price differences. For the forecasting exercise, we use the in-sample data from 1 July 2006 to 30 June 2017. This leaves us with an out-of-sample period from 1 July 2006 to 30 June 2017 and six years of 1-, 5-, and 20-days ahead forecasts for each index.

Descriptive statistics, provided in Table 1, show evidence that the empirical distributions of the index returns are of leptokurtic shape, indicated by an increased excess kurtosis. The JCI has the highest kurtosis of 12.54 while the VNI features the lowest at 4.46. Moreover, all return series are skewed to the left; the series' distributions have large negative returns with a higher probability compared to their positive counterpart. In comparison to indices of developed countries and global benchmarks, the empirical moments are quite extreme for indices, in particular the kurtosis, suggesting less diversification effects within each index. This highlights the relatively high risks associated with investing in these markets. In addition to the non-normal appearance of moments, we test for autocorrelation in the return series. The Ljung–Box (LB) test and the ARCH test both reject the hypothesis of no autocorrelation in the returns. The Augmented Dickey-Fuller (ADF) test rejects the hypothesis of a unit root in the return series. Based on these results, we assume the underlying distribution for the GARCH framework as Skewed Student's-*t*. This distribution choice over alternatives such as the Normal or symmetric Student's-*t* distribution ensures that we cover heavy tails and skewness found in the series, which impacts the parameter estimation and forecasting exercise. The series are depicted in Figure 1.

**Figure 1.** Log-returns for the period 1 July 2006–30 June 2017.



Note: Obs. is the number of observations, Min. and Max. are the minimum and maximum return in the sample, Std. Dev. is the standard deviation, LB(12) refers to the Ljung-Box test with 12 lags, ARCH(12) refers to the ARCH test for heteroskedasticity at 12 lags and ADF is the augmented Dickey-Fuller test.
