Market risk case studies
April was a great months for putting together case studies around market risk. We managed to do three before we ran out of steam and had to include our review of Michael Lewis’ Flash boys in the list.
Technically speaking Flash boys is not a market risk case study but it covers so many inter connecting themes and has raised such a fuss in markets and communities with its coverage of the HFT industry that we had to include it in our list of cases below.
Without further ado, here is a list of our weekend read. Four market risk case studies.
China Aviation Oil – Jet Fuel Hedging leads to Speculative options trading leads to the path down south. The China Aviation Oil (CAO) disaster made headlines in the Far East. The case raised eye brows because it originated out of conservative Singapore and no one expected a State owned firm to drop US$ 550 million in speculative trading. Timeline 2004.
JP Morgan rewrites Moby Dick – The London Whale story. The London Whale story has been retold many times. Here is our version with a link to the official JP Morgan investigation. There are bit of oversights, some conflict of interest, cut and paste in Excel gone wrong (supposedly) and lot of egg on the face on the bank that had supposedly managed to steer clear of ginormous losses.
Ceylon Petroleum Corporation (CPC) oil hedging attempts lead to court. The case that all airlines love to talk about when it comes to fuel hedging in the region and regional bank don’t. They wish that it would simply go away, which is possibly the reason why there is so little information available about the Ceylon Petroleum scandal. Despite its relative size and the impact it had on the Sri Lankan economy.
The juiciest bit deal with speculations around why Citi finally lost its claim in the courts in Singapore while Standard Chartered and Deutsch Bank emerged victorious in their court cases against Ceylon Petroleum.
Lewis makes it official – The markets are rigged. Flash boys is a must read for all equity market traders. And if you have ever wondered what the fuss is about HFT, Lewis presents an easy to understand review of the HFT industry. The industry understandably is not very pleased.
Of the four it’s the Lewis piece that we had the most fun writing and we suspect the most fun you will have reading too. While the cumulative losses on the first three cases added to US$ 7 billion and change, the Lewis story is significantly more interesting.
And here is a bonus fifth. Ever wondered about how Economic Capital is calculated? Here is our guide on Calculating economic capital for the banking industry. An outline of an alternate model, followed by a indepth case study featuring Goldman, JP Morgan, Barclays, Wells Fargo and Citibank.
Tagged with:Case Studies, Case Study, market risk, Risk
Nowadays, risk management becomes an important module of every industry. However, its magnitude in banking industry is much more obvious because, usually, the profit of every bank is directly related to the amount of risk it takes. It means that the more risk it takes, the more profit it can earn. However, this huge amount of risk should be carefully managed in order to reduce the possibility of loss or bankruptcy. Therefore, the Bank for International Settlements (BIS) has founded the Basel Committee on Banking Supervision (hereafter Basel Committee), which has developed several documents containing basic standards, guidelines, and consultative papers for risk management and banking supervision. One of the most recent and well-known documents of BIS is Basel II accord. It includes the most popular and trusted guidelines in banking supervision and risk management, which are generally acquiesced by central banks all over the world including the Central Bank of Iran.a
Basel II accord has classified major banking risks into three different types: credit risk, market risk, and operational risk. Credit risk is an investor's risk of loss arising from a borrower who does not make payments as promised. Market risk is the risk that the value of a portfolio, either an investment portfolio or a trading portfolio, will decrease due to the change in value of the market risk factors. The four standard market risk factors are stock prices, interest rates, foreign exchange rates, and commodity prices. Operational risk, which is the main focus of this study based on Basel II accord, has been defined as the risk of loss resulting from inadequate or failed internal processes, people and systems, or from external events. This definition includes legal risk, but excludes strategic and reputational risk (Basel Committee on Banking Supervision 2006).
In the last two decades, a significant number of financial institutions have experienced loss or bankruptcy due to the mismanagement of operational risks. Some famous instances are as follows: First, Societe Generale Bank, alleged fraud by a trader, lost 4.9 billion € in 2008. Second, Former currency trader was accused of hiding US $691 million in losses at Allfirst Bank of Baltimore in 2002. Third, UK's Barings Bank collapsed after trader Nick Leeson lost £860 million (US $1.28 billion at the time) on futures trades in 1995 (BBC News 2008). For more related cases, go to Gallati (2003). In the case of Iran, most of the banks have been state-owned up to a few years ago, and the government has prevented them from insolvency. However, emerging of private banks, along with service development of both private and state-owned banks in recent years, led in to a more competitive market, which encounters banks with more complex operational risks that need to be considered. Since the operational risk has greatly affected a large number of banks globally, as seen in non-Iranian cases above, and due to the lack of attention to the subject in Iranian banks and legislators, a new trend of research in this area is indispensable.
Measuring is one of the main steps in operational risk management. Basel II accord introduces three different ways for measuring operational risk in financial institution: the first method is Basic Indicator Approach (BIA), which calculates Capital-at-Risk (CaR) as a fraction of the bank's gross income; the second proposed method is called Standardized Approach (SA), which divides the institution into eight specified business lines and, in each one, computes the business line-specific CaRs as a percentage of their relevant gross incomes then adds these eight CaRs to obtain the bank's total CaR; and finally, Basel II suggests Advanced Measurement Approaches (AMA) in which banks are permitted to develop their own methodology to assess yearly operational risk exposure within a confidence interval of 99.9% or more. The first two methods are easy to apply but undesirable among banks because, as a consequence of their conceptual simplicity, BIA and SA models do not provide any insights into drivers of ethods in Iran, refer to Karafarin Bank (2009) and Erfanian and Sharbatoghli (2006). However, the third category of methods (i.e., AMA) has not been implemented in any bank in Iran, which is much more sensitive to risk; therefore, it is recommended by Basel Committee and widely applied by international banks.
Among the eligible variants of AMA, over the last few years, a statistical model widely used in the insurance sector and often referred to as the Loss Distribution Approach (LDA) has become a standard in the banking industry around the world (for two examples see Chapelle et al. (2007) and Aue and Kalkbrener (2006)). Anyway, to our knowledge, it is not employed by any bank in Iran. When applying the LDA in Iranian banking circumstance, some issues arise: First, operational loss events have not been recorded thoroughly, so available loss data are rare and inferences of their related distributions need special concern. Second, because there is no bankruptcy reported, there are no data available for extreme losses. Third, the previous methods implemented in Iran (BIA and SA) do not explicitly account for dependence structure of risks. Therefore, the objective of this study is to present the comprehensive LDA framework for the measurement of operational risk of banks in Iran, whereas we try to provide recommendations to resolve Iran's specific issues by utilizing available statistical and mathematical techniques.
The methodology of this study has been applied in Karafarin Bank, which is an Iranian private bank. For more information about Karafarin Bank, visit its home page (Karafarin Bank 2010).
This paper is organized as follows: in ‘Methodology’, a comprehensive methodology of measuring operational risk is discussed; then in ‘Empirical analysis’, we apply the methodology to loss data of Karafarin Bank, and results are reported. Finally, concluding remarks will be presented in the last section.