Introduction
The question that nearly all investors who have invested or are contemplating investing in risky assets ask at some point in time is, “how much can they lose on a particular investment?” Value at Risk (VAR) attempts to provide a reasonable answer to this question (Tarantino & Cernauskas, 2010, p. 8).
It is deceptive to regard Value at Risk to be an alternative to risk adjusted values and other probabilistic approaches. Nevertheless, VAR borrows a great a lot from them. There has been an extensive use of Value at Risk as a risk assessment tool, particularly in financial institutions and a broad literature that has developed around it (Leung, 2009, p. 20).
Generally, the Value at Risk measures the probable loss in value of a risky asset or a group of assets over a given period of time. For example, if the Value at Risk on an asset is 200 million pounds at a one-month, 90 percent confidential level, there is only a 10 percent chance that the value of the asset will drop more than 200 million pound over any given month. In its modified form, Value at Risk is sometimes defined more narrowly as a probable loss in value in standard market risk as opposed to the entire the risks (Tarantino & Cernauskas, 2010, p. 9).
Even though VAR can be used by any entity to assess its risk exposure, it is commonly used by mercantile and asset banks to determine the probable loss in value of their traded portfolios from undesirable market movement over a given period of time. This is normally compared with the existing capital and liquidity reserves to make sure that the incurred losses can be covered without putting the company/organization at risk (Tarantino & Cernauskas, 2010, p. 9).
Value at Risks has become a risk assessment tool for many financial institutions, for instance banks and insurance companies among others. Its usage in these institutions has been prompted by the letdown of the risk tracking system that was being used before. Risk tracking system was used to assess capital risk under severe conditions in business portfolios that could be revised continuously. Value at risk simply refers to the highest amount that a business can lose on an investment over a given period of time with a particular probability (Tarantino & Cernauskas, 2010, p. 10).
Value at risk can be measured in three ways. The first method assumes that the returns created by exposure to numerous market risks are generally distributed. Thus, it uses variance-covariance matrix of the whole harmonized instruments representing a variety of market risks to work out the standard deviation in portfolio returns and VAR is computed from these standard deviations.
In the second method, portfolio is run through a chronological data (historical simulation) and the probability of losses exceeding given values is estimated. The third method of calculating VAR assumes the return distributions for very individual market risk and runs Monte Carlo Simulations to arrive at the values (Leung, 2009, p. 20).
Each method used to calculate VAR has their merits and demerits. The variance-covariance method is easy to apply but the assumptions made here can be very difficult to keep up. The historical simulations method assumes that the historical periods used reflects the future and Monte Carlo Simulations wastes a lot of time since it requires a lot of calculations. In a nutshell, all the three measures of VAR only give estimates and are subject to judgement (Leung, 2009, p. 21).
According to Taleb (2007b) there are three categories of risks namely: known risks, emerging risks and black swans. Known risks are risks that have been identified by the company and planned for, in an attempt to avert or alleviate them. On the other hand, emerging risks are the type of risks that have been detected by companies but whose full extent and implications are not yet absolutely clear. Lastly, Black Swans are risks that are can not be predicted or avoided.
These types of risks always strike businesses and even society as a whole without warning (Taleb, 2007b, p. 198). Risks can also be classified as market risks, credit risks, liquidity risks, operational risks, legal risks, and environmental risks among others (Leung, 2009, p. 22).
Market risks originate from the dynamics in the level or volatility of market price. Credit risks arise from counterparties reluctance or incapability to meet their contractual obligation.
These parties can be individuals, corporations, or governments. Low frequency of default among corporations and governments has made it very hard to measure and manage credit risks from the historical perspective. It is difficult to come up with adequate amount of data to model such behaviour. Liquidity Risk is associated with business transactions. It can further be classified into market liquidity and funding liquidity risk (Leung, 2009, p. 22).
Market risk refers to lack of ability to carry out a transaction at current market prices because of the size of the position in relation to the common trading lots. Funding liquidity risk, also known as cash flow liquidity risk, refers to the lack of ability to fulfil payment obligations. Operational risk originates from human and technological faults or mishaps.
This type of risk is very hard to model and manage. However, considerable progress is being made each and everyday in the management of this type of risk. Management and models of market risks are unquestionably the most established among all because of the availability of data that are used in modelling the risks (Leung, 2009, p. 23).
Fundamental Shifts in Risks
Whatever the vocabularies that have been used to describe risks, it is apparent that the risk scenario facing organizations is changing. Experts and managers all over the world are now are aware of the fact that new risk landscape is emerging. However, they have often found it very difficult to establish the source of these risks and how to respond to them (Tarantino & Cernauskas, 2010, p. 4).
According to Leung (2009) companies can be in a better position to pursue their strategy if they understand and manage risks better. Company strategy includes strategy for growth, with the assurance that they have business flexibility to handle well-known risks and respond to the unforeseen.
Many managers feel that the risk frameworks and processes that are in place are not giving them adequate protection. In addition, the speed with which these risk events are taking place and the level at which they are impacting businesses are contagious. In other words, these risks are spreading faster across different classes of risk. The speed and contagion of some of these risks are even threatening the very existence of their organization and in extreme cases undermines the whole industry (Leung, 2009, p. 15).
A number of managers are frustrated by the fact that they are spending huge sums of money managing the current risks instead of moving swiftly and amenably to establish and tackle the emerging risks. Majority of them are not convinced that their return on enterprise risk management is entirely substantiated by the degree of protection accrued from them (Leung, 2009, p. 17).
Organizations at the moment face an environment where the known risks are bounded by ignorance or indecision. They face an environment where their sustainability and permission to operate are under regular and critical scrutiny. In addition, they face an environment where global risks are emerging very fast and are spreading rapidly across the traditional risk categories, and even becoming very difficult to control (Tarantino & Cernauskas, 2010, p. 5). According to the existing risk management methods, risks that can not be identified can not be controlled or managed. This kind of environment is causing problems to many companies.
To tackle this, companies have developed more advanced approaches in collecting and analysing risk data, to capture, forecast and tackle the broad range of risk they encounter. However, availability of adequate data does not necessarily mean protection. It is argued that too much risk information in some cases makes it very hard for the managers to identify the core of their risk issues. Therefore, under the current dynamic environment the value of the historical data needs to be enhanced (Tarantino & Cernauskas, 2010, p. 7).
Historical Accounts on Consequences of lack of Risk Management
In the early 80s, a vibrant hedging technology was developed in which the principle originated from option pricing theory. The principle of this hedging technology was to hedge stock portfolio against market risk by trading on the stock index features short or stock index put options. The former is sold and the latter bought.
The hedging technology worked until the market crashed in the late 80s. Critics blamed the terrible results of the crisis on the technology arguing that it aggravated the situation. This crisis illustrates how even the most sophisticated theoretical models may face market risk and have to be closely observed by the users as well as state agencies to avert disastrous results (Leung, 2009, p. 36).
In the mid 1994, hedge fund Long Term Capital Management which comprises of world celebrated team of financial experts and academicians was formed to pull funds that would benefit from the mishmash of the quantitative models developed by academicians, dealers’ market judgement and implementation possibilities.
Long Term Capital Management fundamentally employs trading approach of convergence to profit from negotiated opportunities (Leung, 2009, p. 36). Needless to say, it resulted in stellar performance until swap spreads began escalating unpredictably after Russia and other countries in the world defaulted on their government obligations. Towards the late 90s failure of Long Term Capital Management caused a major chaos in the global financial system.
A lot of lessons were learned from the Long Term Capital Management situation at a major cost. On a fundamental level, when assessing risks of any investment portfolios, overdependence on mathematical models should be avoided, instead judgement and common sense should also prevail. In addition, unanticipated correlations or the breakdown of chronological correlations should be kept in mind (Leung, 2009, p. 37).
Case Study: Global Financial Crisis (2007-2009)
The crisis started when home owners in U.S failed to pay back their mortgage debts. Effects of the crisis include regional banks failure and collapse of several financial institutions. In general, many European financial institutions saw enormously decline in capital associated bad debts and plunging values of collateralized debt repayments.
The massive losses led to escalation of interest rates in risk management for the banks and reduced their capability and willingness to take risk. This was evident in stringent lending conditions, withdrawn lines of credit bonds and increased loan spreads (Greenspan, 2008, p. 6).
Generally, the crisis generated challenges at all levels of the economy. Governments in Europe and emerging markets faced an urgent need to act concurrently in different fronts. Systematically and politically sensitive economic sectors had to be bailed out. The general downfall in economic activities had to be counteracted and vulnerable population groups had to be protected from declining incomes.
The European construction market had to be protected. These costly actions were taken in a context of falling government revenues and shrinking domestic and foreign financing, with medium to long term consequences for budgets and debt (Abadie, 2008, p.45).
The severity of the crisis sparked passionate debates among financial experts, politicians, and the public as whole on the real factors that led to the downturn. Amidst all these debates, the general sentiment that echoed in the mind of many is whether the crisis could have been avoided (Abadie, 2008, p.43). A number of investors have argued that the global financial crisis was unpredictable, thus labelled it a “Black Swan”. The Black Swan idea was introduced by Nicholas Nassim Taleb in 2007.
He argued that people have the general tendency of reflecting on historical events to predict the future and this limits their understanding of the world and increases their susceptibility to unpredicted catastrophic events. He further emphasized that chance plays a significant role in global affairs than most people would care to admit, and that Black Swans are worsened by the fact they can not be predicted (Taleb, 2007b, p. 9).
Regardless of the hullabaloo surrounding this theory, Taleb maintains that the global financial crisis was not a Black Swan event, and accuses a number of economic and financial experts as well as investors for misusing his theory.
Taleb argues that the global financial crisis was as a result of execution of flawed numerical models that relied mostly on risk assessment as a stabilizing tool in the financial system and that the global financial crisis was a predictable event, though only a few people saw it coming. He is bold in his critique, generally degrading market economists by referring to them as fools. He referred to Value at Risk as false knowledge or charlatanism (Skidmore, 2008, p. 1).
Nonetheless, some have seized Taleb’s Black Swan theory and are trying to relate it to the present global financial crisis. For instance, Belray Asset Management published a statement in the mid 2008 acknowledging the intrinsic worth of the Taleb’s theory, describing the financial crisis as a Black Swan and stressing that investors should anticipate negative returns on their returns.
Similarly, financial Advisory company Deloitte Touche Tohmatsu described the global crisis as a Black Swan and called for risk intelligence among investors and financial/economic experts. However, the statements issued by these companies did not talk about Taleb’s views on the crisis, nor his disapproval of the current risk evaluation models used by the numerous investors and financial advisors (Taleb, 2007a, p.10).
To a certain extent, investors across Wall Street have taken advantage of the idea of “Black Swan investing” and are developing unique portfolios or funds to profit from the next crisis. These special portfolios or funds slackly followed Taleb’s investment model of putting most of the funds in low risk investment and only using a small portion of the funds (about 10 percent) in high risk investment.
Even though the idea of special portfolio borrows a lot from Taleb’s investment model, it disregards Taleb’s argument on the flaws in the investor risk computation, and assumes that the present global financial crisis was indeed a black Swan event (Skidmore, 2008, p.5).
The level of the crisis and data issues
One thing that investors and financial experts have agreed on is that the financial crisis had catastrophic effect on the U.S. and global economies. According to the IMF global financial stability report, financial institutions have reduced their assets by roughly $3 trillion since the inception of the crisis. The Federal Reserve in 2009 estimated that U.S. had lost about $1.5 trillion in wealth in the first quarter of the year only, as a result of the falling stock and domestic prices.
At that particular moment, the growth rate, one of fundamental drivers of economic recovery, was struggling to return to its pre-crisis level. Even more evident to the average citizens in U.S and most of the European nations unemployment rates carried on to drift persistently at just below 10 percent and in the region of 5-7 million home mortgage was still facing foreclosure (Barker et al.,2009, p. 20).
Extensive analysis of the crisis has yielded numerous theories as to why the crisis occurred and why it was so severe. Although these theories differ in conclusion, there is a universal trend that can be picked up from the diverse arguments. These developments lend themselves to Taleb’s argument that the crisis was not unanticipated or a “Black Swan” event.
There were a number of distinct signs of flaws in the financial system which had been noticed by analysts. A New York professor Nouriel Roubini warned of the looming crisis in 2006 when he attended an IMF meeting. The accuracy of his prediction supported the idea that there were indication all along but was ignored (Skidmore, 2008, p. 6; Taleb, 2007b, p. 11).
However, some experts argue that there was shortage of consistent, usable data for both the regulators to keep a close watch on the systemic risks and for investors to assess probable risks for their activities. According to the IMF financial analysts, Adelheid Burgi-Schmelz, all-inclusive information underlining the risky activities of a number of investors, in addition to how interlocked national and global financial institutions have become, would have helped to detect the looming crisis and to take appropriate step to avert it.
He describes the global financial crisis as a letdown of the data systems to dig out the integration of economies and markets. The IMF department of statistics noted an increase in demand for extra global comparable, timely, and frequent data after the crisis had escalated. The IMF committee on capital market regulation, a group of top class financial experts and representatives from the private sector, seconded the idea that the current crisis could have been as a result of lack of vital information, and in some cases, half-truths or misinformation (Calomiris, 2009, p. 56).
For instance, Financial Stability Board, a consortium of national banks, treasuries, and other financial authorities, reported that there was lack of enough data to oversee and gauge risk distribution among financial products and the mechanism of credit risk transfer.
Equally, Financial Stability Board stated in a report to the G20 that data exhibiting the explosive nature of certain indicators could have helped watchdogs to spot probable economic weaknesses. The IMF report also noted some other gaps in data, for instance, data segments encompassing non-bank financial institutions, international banking flows, homogenized statutory finance statistics, and real property pricing. A number of analysts have also argued that there has been lack of efficient data flow between government and institutions (Taylor, 2009, p. 3).
An economist by the name Arnold Kling argued that it is not all about data organization and carrying out analysis but how we work with the results created. In other words, there should not be any inconsistency between knowledge and power. He emphasized that knowledge is becoming more and more focused and diffused but power is increasingly becoming more concentrated.
This tendency has resulted in many managers acting with defective knowledge of their businesses and the conceivable cause of their actions (Taylor, 2009, p. 3). This Columbian University professor criticized state regulations, which he says limit the ability of large institutional investors to own financial institutions such as banks. This normally causes distributed ownership which allows companies to operate with less accountability and information, leading to increased discretion towards risk.
According to this school of thought, shareholders increase and distributed rights does not dilute power as normally anticipated, but instead concentrates power among the managers. Kling further stressed that knowledge and rights are becoming more and more dispersed, while power remains in a confined cell Taleb, 2007b, p. 11.
Leverage and Debt issues
Lack of transparency and sufficient data in the global financial system became apparent when the speculative crisis hit a couple of years ago and financial institutions were serious faced by liquidity crisis. The crisis also caused asset devaluation and this means companies’ revenue stream that they could have used to pay liabilities dried up.
Failure to pay debts led to public outcry and the governments were forced to intervene to stabilize the financial system. The U.S. government intervened by enacting a law that authorized the treasury to buy all the distressed assets from large financial institutions and other conglomerates. This prompted instantaneous hostile response to the so called “bailout plan”, momentarily eclipsing imminent issues that might have created the eventual need for such backing (Posner, 2009, p. 20).
The assets held by most financial institutions were intrinsically full of risks. In addition, the ratio of debt to equity, or the leverage ratio, rose to tremulous level. According to the IMF report of 2009 some banks were borrowing at a ratio of 32:1 (corresponding to investing 1000 dollars with only 30 dollars of one’s funds). Besides, the disparity of risk to leverage resulted into a crisis itself (Posner, 2009, p. 21).
According to the statement released by Government Accountability Office, a watchdog organization in U.S., regulatory measures did not adequately limit these activities and this made a number of financial institutions to hold capital that did not commensurate with their risks, therefore facing capital shortfall when the crisis started. The banks were in a very unstable position, and it was not clear until the crisis exposed these bad debts (Calomiris, 2009, p. 58).
William Poole, former president of St. Louis Federal Reserve, argued that the fault was more on the financial institutions than with the allegations from critics that laid-back state regulation permitted leverage to get out of hand. According to him developing portfolios with unsecured long-maturity assets funded with less equity capital and short-maturity liabilities is an inexcusable blunder.
He asserts that even if the government was doing a perfect job in regulating businesses poor decisions by the individual financial institutions will result into crisis. This notion is in alignment with Kling’s concept of ethical versus cognitive crisis. Ethical failures take place when the compensation framework for the bank’s management encourages them to put their organization and other organizations at risk for short term benefit (Poole, 2010, p. 438).
On the other hand, cognitive failures take place when the management and the regulators overvalue the risk-mitigation results of statistical modelling and financial architect. Ethical failures are related to greed, while cognitive failure relates to Taleb’s assertion that risk cannot be gauged ingeniously. However, for Poole and a number of other experts, the financial institutions were vulnerable to ethical failure, proven by their indefensible actions (Poole, 2010, p. 439).
Government policies are also blamed for these leverage issues. Many discussions on this subject focus on the trumped-up ratings that were being awarded by credible agencies. Some government regulations translate ratings into leverage. For instance, in many cases businesses that seek to increase their leverage ratio are always restricted by regulations that are based on quality of assets they own.
If such businesses chose AAA over AA- rating, they are allowed by the government to raise their debts to equity level on the assumption that low risk is involved. While these ratings have good intention, there are high possibilities that they may be optimistically deceptive (Posner, 2009, p. 25).
Another key trend accredited to the global financial crisis is the devastating level of debt dependency by economies. In many countries like U.S. debts have been treated as income by consumers to maintain unsustainable level of consumption. Before the crisis, national debts had increased in many countries and there was high demand for external capital. When the crisis hit, many countries like U.S. already had budget deficit, therefore the stimulus package, which was meant to stabilize the economy, increased the debt further (Poole, 2010, p. 438).
According to the IMF, this was the first time the public had held more than half of the federal debt since the Second World War. The real estate accounted for the majority of the debt held by the public. Government have also been spending beyond their means, and therefore forced to borrow domestically and internationally.
This has put a lot of pressure on their reserves and increased the debt burden. The increasing deficit resulted into balance of payment problems. In addition, normally debt from one sector has a considerable impact on another sector, thus debt crisis in U.S. and Europe had a reverberating effect in the international system (Poole, 2010, p. 440).
Conclusion
Value at Risks has become a risk assessment tool for many financial institutions, for instance banks and insurance companies among others. Value at risk simply refers to the highest amount that a business can lose on an investment over a given period of time with a particular probability.
This means VAR depends on historical data to assess the current and predict future risks. However, a number of investors have argued that certain risks are unpredictable and are beyond VAR, thus labelled them “Black Swan”. The Black Swan idea was introduced by Nicholas Nassim Taleb in 2007. Hew argued that people have the general tendency of reflecting on historical events to predict the future and this limits their understanding of the world and increases their susceptibility to unpredicted catastrophic events.
He further emphasized that chance plays a significant role in global affairs than most people would care to admit. A number of investors and financial analysts believed that the global financial crisis was a “black swan”. For instance, financial Advisory Company Deloitte Touche Tohmatsu described the global crisis as a Black Swan and called for risk intelligence among investors and financial/economic experts.
However, Taleb and some financial experts have disputed this notion. They attributed the crisis to a number of factors including flawed models used by the financial institutions to assess and measure risks, data and leverage issues, and debts.
Preferences
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