Document Type

Conference Proceeding

Publisher

The Modelling and Simulation Society of Australia and New Zealand Inc.

Faculty

Faculty of Business and Law

School

School of Business/Marketing and Services Research Centre

RAS ID

16981

Comments

This article was originally published as: Allen, D. E., Boffey, R. R., Kramadibrata, A. R., Powell, R. , & Singh, A. (2013). Primary sector volatility and default risk in Indonesia. In MODSIM2013, 20th International Congress on Modelling and Simulation (pp. 1298-1304). Canberra, Australia: The Modelling and Simulation Society of Australia and New Zealand Inc. Original article available here

Abstract

The Indonesian market is a critical market to the South East Asian region, being that region’s largest economy. The primary sectors of the Indonesian economy, incorporating Agriculture and Mining, are of critical importance to the country, representing approximately one quarter of GDP and providing nearly 40% of the nation’s employment. Mining and Agriculture stock returns significantly outperformed the Indonesian Stock Exchange (IDX) composite index in the five years leading up to Global Financial Crisis (GFC), and experienced savage falls during the GFC. Against this background, we examine the market and credit risk of these sectors during the pre-GFC, GFC and post-GFC periods. Market risk is measured using Value at Risk (VaR) and Conditional Value at Risk (CVaR). VaR is a popular metric which measures potential losses over a specific time period, up to a selected threshold. A key downside of this metric is that it says nothing of the extreme risk beyond VaR, which is a major limitation for this study, given the extreme volatility experienced by the primary sectors in Indonesia over the studied period. We therefore also use CVaR, which measures the extreme risk beyond VaR. For credit risk, we use the Merton-KMV Distance to Default (DD) metric, as well as our own Conditional DD (CDD) metric to measure extreme default risk. The key advantage that the Merton-KMV model has over other credit models, it that it incorporates fluctuating asset values. This makes it more responsive to changes in market conditions than most other credit models which remain static between rating periods. The importance of fluctuating asset values in measuring credit risk has been raised by the Bank of England (2008), who make makes the point that not only do asset values fall in times of uncertainty, but rising probabilities of default make it more likely that assets will have to be liquidated at market values. Similar to VaR, the Merton-KMV model has deficiencies in that it uses the standard deviation of asset value fluctuations, which tends to smooth the volatility and does not capture tail risk over that period. Our CDD model is able to measure risk at the most extreme times of the economic cycle, which is precisely when firms are most likely to fail, and when banks are most likely to experience high credit losses. We find that market risk for the primary industries is significantly higher than the broader market, and that there is a relatively higher difference between VaR and CVaR, indicating a higher tail risk. Mining, in particular has a higher market risk than other Indonesian sectors. Interestingly, this is not the case with credit risk, where the risk for Agriculture is lower than the overall market, and the risk for Mining is not significantly different to the overall market. This is because the leverage of a firm is a key component of the Merton-KMV model and we find the leverage for the Agriculture and Mining industries to be far more conservative than the broader market. This means that these primary sectors are able to withstand relatively higher levels of asset volatility. These findings can benefit both lenders and investors when considering the inclusion of these sectors in their investment or loan portfolio mix.

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