Academic journal article International Journal of Business Studies


Academic journal article International Journal of Business Studies


Article excerpt

We examine relative share market risk between Indonesian sectors and how this changes during extreme market fluctuations. Ten sectors comprising the IDX Composite Index are examined over an eight-year period spanning the pre-GFC, GFC and post-GFC. Risk is measured using parametric and nonparametric Value at Risk (VaR) and Conditional Value at Risk (CVaR), which measures risk beyond VaR. In contrast to studies on most global markets, and due to relative stability in the Indonesian market, no significant differences are found in relative portfolio risk between the conditional and non-conditional measures, or between parametric and nonparametric measures. The insights are important to investors in choosing the sectoral mix of their portfolio.

Keywords: Indonesia Stock Exchange, Value at Risk, Conditional Value at Risk, parametric, nonparametric


Indonesia is a vitally important market in the Asia Pacific region. The country has the world's fourth largest population of 241 million people, is a member of the G20 major economies in the world, has the largest economy in South East Asia, and has an annual average GDP growth rate exceeding 6% over the five years to 2011 (Statistics Indonesia, 2012). The World Federation of Exchanges (2011) rated the performance of the Indonesia Stock Exchange (IDX) as the world's fifth best in 2010 and second best in 2011.

The IDX at March 2012 comprised 442 entities with a market capitalisation of nearly 4000 trillion rupiah (approximately USD 400 billion). The IDX was formed through the merger of the Jakarta and Surabaya Stock Exchanges in 2007, and has a daily average of just over four thousand trades.

Indonesia is an emerging economy, which was a Dutch colony prior to receiving independence in 1945. Initially highly reliant on agricultural activity, as well as the oil and gas sectors, the economy has significantly expanded its other industries over the past few decades, with the IDX now having a good spread across a range of sectors including Agriculture, Energy, Banks and Insurance, Consumer Discretionary, Consumer Staples, Industrials, Mining, Materials, Real Estate, and IT and Telecommunications (see Table 1 in our data section).

While the strong performance of the IDX makes it an attractive market for investors, it is of course a fundamental truth that higher returns are usually associated with higher risks, and it is therefore important to investors to understand those risks. This paper examines the relative risk of investing in those industry sectors making up the IDX.

Value at Risk (VaR) is a popular volatility metric for measuring market risk, and we use VaR to measure sectoral risk in this paper. A major criticism of VaR, particularly since the GFC, is that it only measures risk up to a selected threshold, and says nothing of the risk beyond that threshold. We thus also use Conditional Value at Risk (CVaR), which does measure extreme risk (the risk beyond VaR). For robustness, we use both parametric VaR as introduced by RiskMetrics (JP Morgan and Reuters, 1996), as well as non-parametric VaR. CVaR is the average of returns beyond these measures. Parametric methods have the advantage of being quick and easy to use as they require only the mean and standard deviation, but if returns are not normally distributed they can be inaccurate as compared to the historical approach which measures actual returns. Similarity in outcomes between the measures will indicate that parametric measures can be confidently applied in the Indonesian market, whereas large differences will indicate their unsuitability.

There are three research questions addressed by this study. Firstly, we explore which are the least (most) risky Indonesian sectors to invest in. Secondly, we examine whether the relative risk changes between sectors using CVaR as compared to VaR. Thirdly, we determine whether the outcome is consistent using both parametric and nonparametric methods. …

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