**1. Introduction**

The members of the Association of Southeast Asian Nations (ASEAN)<sup>1</sup> already produce 3.43% of the worldwide Gross Domestic Product (GDP) in 2016 and even the economically smaller countries such as Vietnam are on the rise. The ASEAN-6<sup>2</sup> have an annual GDP growth from 2016 to 2017 of 4.91% and share 4.48% of the world's average annual GDP growth of 3.6%.<sup>3</sup>

However, the crisis of Asian markets in 1997 shows that investors have to accept other risks than those in industrialized and developed western economies. The Asian crisis is characterized by an unparalleled contagion throughout the markets and extreme market and currency movements. While this crisis has almost only regional macroeconomic effects, consequences and lessons from it are drawn globally (Hunter et al. 1999). Another example for very high contagion in these markets are disruptions in the wake of the global financial crisis beginning in 2007, as Asian emerging markets do not offer diversification potential (Kenourgios and Dimitriou 2015).

During times of crises as well as during more moderate times of daily business, investors and portfolio managers face the challenge to properly estimate and model dispersion in market prices,

<sup>1</sup> The ASEAN consists of ten countries: Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, and Vietnam.

<sup>2</sup> ASEAN-6 are the six biggest contributor to GDP of the ASEAN region, i.e., Indonesia, Thailand, Philippines, Singapore, Malaysia, and Vietnam.

<sup>3</sup> Own calculations based on data.worldbank.org and www.imf.org.

formalized in its volatility or variance. Depending on the trading position, financial risk has to be determined for the long and short position. While the long trading position (e.g., having bought an asset to sell it at a later point) is concerned with falling prices or negative returns, the short trading position (e.g., short-selling an asset, i.e., borrow an asset and directly sell, to re-buy it at a later point to give it back to the owner) faces rising prices or positive returns (Giot and Laurent 2003). This is of particular importance if asymmetric distributions, such as the Skewed Student's-*t* distribution, are found to provide a better resemblance of the empirical price return distribution than symmetric distributions like the Normal or Student's-*t* distribution. In this work, we use the Value-at-Risk (VaR) as a measure for financial risk, which is determined by the volatility of an investment. Albeit the fact that VaR has been replaced by Expected Shortfall as the main tool to determine the minimum capital requirements for banks under the Basel framework, VaR is still in place for backtesting the internally used risk models (Basel Committee on Banking Supervision 2016). Here, we incorporate two competing classes of volatility models. Within the framework of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models (Bollerslev 1986; Engle 1982), we model volatility conditional on its past. This allows for including volatility clusters with periods of high and low market movements. In the next step, the Asymmetric Power ARCH (Ding et al. 1993) is applied to account to asymmetric news impact on volatility. Another so-called stylized fact is the long lasting dependence of shocks in a time series known as long memory. The Fractionally Integrated GARCH (FIGARCH, (Baillie et al. 1996)) model is able to depict this pattern. The Fractionally Integrated Asymmetric Power ARCH (FIAPARCH) of Tse (1998) combines both long memory and asymmetry. The second class of models is based on the stochastic volatility (SV) model introduced by Taylor (1986). In addition to the standard SV model, we implement specifications that are able to depict the leverage effect as well as heavy tails. We use both classes to forecast the volatility over specific horizons based on estimates of a training window. With these variance forecasts, we then predict the VaR. These VaR predictions are evaluated and compared over different markets against standard approaches such as the non-parametric Historical Simulation (HS).

In this work, we focus on six major ASEAN stock market indices. Given the regional proximity and general similarity of these markets, we aim to understand if this also yields comparable variance properties. This would imply that methods of modeling and forecasting volatility as well as the VaR have comparable performances across the markets and that these markets could be grouped. While there is a plethora of literature on variance modeling for commodities, stock markets, and exchange rates of developed countries, academic advances on Asian stock indices and their comparison is relatively sparse. Walther (2017) identifies a sufficient performance of GARCH with a symmetric Student's-*t* distribution as well as FIAPARCH with a skewed Student's-*t* distribution in terms of variance and VaR forecasting for Vietnamese stock indices. Brooks and Persand (2003) show that asymmetric approaches work well to forecast the VaR for the Singapore and Thailand equity indices. So and Yu (2006) use different GARCH models to estimate the VaR in twelve different stock markets including Indonesia, Malaysia, Thailand, and Singapore. Su and Knowles (2006) perform a VaR analysis of Mixture Normal models on stock indices including Malaysia, Singapore, and Thailand. Lastly, McMillan and Kambouroudis (2009) and Sharma and Vipul (2015) provide large studies of different equity indices (including many ASEAN countries) for VaR forecasting.

Our results sugges<sup>t</sup> that the volatility structures in the ASEAN markets is heterogeneous and include various so-called stylized facts. Hence, we observe that more sophisticated models provide better forecasts than standard approaches. However, given the different dynamics in the markets, we cannot conclude with one explicit model choice over all ASEAN equity markets.

The remainder of the paper is structured as follows: Section 2 presents methods of estimation of the volatility and forecasting and assessing the VaR. Section 3 provides the data basis. Section 4 presents the results and their discussion. Section 5 provides the conclusions.
