Primary sector volatility and default risk in Indonesia

D. E. Allen, R. R. Boffey, A. R. Kramadibrata, R. J. Powell, A. K. Singh

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationMODSIM 2013
Subtitle of host publicationProceedings of the 20th International Congress On Modelling And Simulation
EditorsJulia Piantadosi, Robert Anderssen, John Boland
Place of PublicationCanberra, ACT
PublisherModelling and Simulation Society of Australia and New Zealand Inc. (MSSANZ)
Pages1298-1304
Number of pages7
ISBN (Electronic)9780987214331
Publication statusPublished - 1 Jan 2013
Externally publishedYes
Event20th International Congress on Modelling and Simulation - Adapting to Change: The Multiple Roles of Modelling, MODSIM 2013 - Held jointly with the 22nd National Conference of the Australian Society for Operations Research, ASOR 2013 and the DSTO led Defence Operations Research Symposium, DORS 2013 - Adelaide, Australia
Duration: 1 Dec 20136 Dec 2013

Publication series

NameProceedings - 20th International Congress on Modelling and Simulation, MODSIM 2013

Conference

Conference20th International Congress on Modelling and Simulation - Adapting to Change: The Multiple Roles of Modelling, MODSIM 2013 - Held jointly with the 22nd National Conference of the Australian Society for Operations Research, ASOR 2013 and the DSTO led Defence Operations Research Symposium, DORS 2013
CountryAustralia
CityAdelaide
Period1/12/136/12/13

Keywords

  • Agriculture
  • Distance to default
  • Mining
  • Value at risk

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