Thoughts on Extreme Risk in Indonesia
Portfolios are commonly optimised using return standard deviation. This article explores a variety of alternate methods for optimising the industry mix of Indonesian portfolios. These include value at risk (VaR) and conditional value at risk (CVaR) using both parametric and nonparametric methods. VaR captures risk at a higher threshold than standard deviation, usually at 95 % or 99 % confidence. CVaR captures extreme risks beyond VaR. The study is unique in its application and comparison of both parametric (normally distributed) and nonparametric VaR and CVaR methods to sectoral portfolio optimization. To capture a range of economic circumstances, the data period incorporates the Global Financial Crisis (GFC) as well as pre-GFC and post-GFC years. The study identifies a fairly narrow band of industries that feature as industries of choice in optimised portfolios across our range of metrics, although their optimal proportions differ across metrics. Using these extreme risk optimizers can assist investors in avoiding high-risk stocks and minimising risk for given return levels. Extreme volatility is also an indicator of underlying problems in an industry, and this comprehensive study on sectoral risk provides important information to lenders, regulators, economic policymakers and governments on the performance and risk of the Indonesian sectoral market and in reviewing sector-based investment and economic policies.
KeywordsPortfolios Value at risk (VaR) Conditional value at risk (CVaR) Extreme risk Indonesia
The authors thank Edith Cowan University Faculty of Business and Law Strategic Research Fund for funding assistance.
- Akyuwen R, Allen DE, Boffey RR, Kramadibrata A, Powell RJ, Singh AK, & Wijaya K (2014) Sectoral risk analysis in Indonesia using extreme metrics: Special focus on primary industries, Working PaperGoogle Scholar
- Allen DE, Powell RJ (2011) Measuring and optimising extreme sectoral risk in Australia. Asia Pac J Econ Bus 15(1):1–14Google Scholar
- Allen DE, Powell RJ, Boffey RR, Kramadibrata AR, Singh AK (2012) Thumbs up to parametric measures of relative VaR and CVaR in Indonesian sectors. Int J Bus Stud 20(1):27–42Google Scholar
- Bank for International Settlements (2009) Findings on the interaction of market and credit risk, wp 0916Google Scholar
- Crosbie P, Bohn J (2003) Modelling default risk. www.moodysanalytics.com. Accessed 12 Feb 2014
- Eko U (2008) Analisis dan Penilaian Kinerja Portofolio Optimal Saham-Saham LQ-45. Jurnal Ilmu Administrasi dan Organisasi 13(3):178–187Google Scholar
- Jadhav D, Ramanathan TV (2009) Parametric and non-parametric estimation of Value-at-Risk. J Risk Model Validation 3(1):51–71Google Scholar
- Markowitz H (1952) Portfolio selection. J Financ 7(1):77–91Google Scholar
- Merton R (1974) On the pricing of corporate debt: the risk structure of interest rates. J Finane 29:449–470Google Scholar
- Siregar H, Hasanah H, Noer AA (2012) Impact of the global financial crisis on the Indonesian economy: further analysis using export and investment channels. Eur J Soc Sci 30(3):438–450Google Scholar
- Sitinjak ELM (2011) Faktor Makro Ekonomi (Variabel CRR) pada Return Portofolio Pasar Saham di Indonesia Saat Bullish dan Bearish. Jurnal Organisasi dan Manajemen 7(2):117–139Google Scholar
- Wiksuana IGB (2009) Kinerja Portofolio Saham Berdasarkan Strategi Investasi Momentum di Pasar Modal Indonesia. Jurnal Manajemen dan Kewirausahaan 11(1):73–84Google Scholar