CHAPTER II LITERATURE STUDY


 Gervase Morris
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1 CHAPTER II LITERATURE STUDY 2.1. Risk Management Monetary crisis that strike Indonesia during 1998 and 1999 has caused bad impact to numerous government s and commercial s bank. Most of those banks eventually liquidated or even shutdown from their business. At that time, banks in Indonesia experience a very tight selection and very decisive for its own existence in the future. Monetary crisis that happens also evoked a radical change in banking sector. Public bank that previously in the amount of 238 (year of 1997) has down to number of 149 (year of 2001) with a merger between private and government banks. The expert in banking sector analyze the caused of monetary crisis, and it was a weakness in bank management system, especially for risk management in every process of activity. Therefore, Bank of Indonesia keep trying with establishing a strict rules for every bank to apply risk management such as operational risk management, market risk management, liquidity risk management, financial risk management, etc. Nowadays, every bank has applied risk management in every process of its business. In accordance with the development of utilization of risk management and furthermore study of risk management as well as the present of more specific explanation, therefore risk management will be easily use, measure and manage to produce banking industry with a better endurance to its fundamental power. The development is characterized with the emerging of modern portfolio theory by Harry Markowitz, that divide risk into 2 type based on risk for individual returns: 1. Systematic Risk Systematic risk is a market risk that can not be diversified, for example interest rate, recessions, and war. 2. Unsystematic Risk Also known as specific risk, this risk is more specific to the risk that bear by individual for investment in stock and can be diversified as increasing stock amount in portfolio. It represent the returns component from stock that indirectly influenced by market movement. 6
2 In this process of writing, this research will set the focus of research limited to the market risk only Market Risk Management There are several opinions that explain about market risk. First opinion come from Robert Tampubolon in his book titled Risk Management. He said market risk is: Exposure that appear because the movement of market variable (interest rate and exchange value) from bank portfolio, that having an adverse movement. Can caused loss for bank This risk is also called systematic risk or correlation risk because of changing in market value of bank s assets that connected with systemic factors (the correlation between instrument, product, money or market). Risk that can t be diversified but only can be hedged. Another source, Carl Olsson in his book, Risk Management in Emerging Market, state that market risk is: A risk of loss caused by the change in fluctuating market price It was supported and dominated by the change in interest rate, exchange value, change in price of equity and change in price of commodity. Market risk is potentially to appear in time of contract between bank and client, and it will need a long time to implement it, in time of contract there might be a change in interest rate, cost of goods sold and also a change in value of money exchange, hence it needs a strategy to minimize the risk. One way to do it is with hedge. Hedge or hedging is a way to minimize risk with determining a buy or sell price for the future to use the price that determined at the time of contract. After identify the risk that might occur the investors will take an action to measure risk level in their investment. Every risk by a product will enable the investors to measure how much that risk can affect the loss so that the investors can minimize the possibility of risk occurrence. There are a lot of ways to measure and to minimize the possibility risk, one of the common methods is using portfolio. 7
3 2.2. Portfolio Theory Portfolio is a set of investment that composed to minimize the possibility of risk occurrence and to get a maximum expected return. The type of portfolio in every security company is different one to another, depends on how big product diversification that offered. Portfolio will be composed and controlled by a fund manager. Diversification for every portfolio is arranged based on its expected return level and risk. That is why it ll need an understanding for investment process that begins with investment policy formulation to the evaluation of investment yield. According to Suad Husnan (47:2005) explanation, there are several steps in investment process, which are: 1. Investment policy formulation The first step is capitalist should define what is his investment objective and how much of amount needed to invest. Capitalist should aware that to get a high level of returns, high level of risk exist since the two components have a positive correlation. There are 3 type of capitalist attitude to the risk: 1. Capitalist that willing to bear higher risk or should we say love the risk, also known as risk seeker. 2. Risk Averse, a capitalist that doesn t like and tend to avoid every possibility of risk no matter how small that is. 3. Risk neutral, a neutral capitalist, tend to act balance to the possibility of risk occurrences. 2. Security Analysis There are two approach in identify and analyze how the security company manages the fund from investor: 1. Technical Analysis Using historical price and calculate the actual return, to predict the present price and expected return. 2. Fundamental Analysis Analyze and identify the external factors such as interest rates, foreign exchange, etc to predict present stock price. 3. Construct the Portfolio In this process, the capitalist will divide their investment into some stocks. The diversification is depending on risk factor, and amount of investment. 8
4 4. Revise the Portfolio After construct the portfolio, we should reevaluate the performance of our portfolio, to observe whether it is well performance or not. If the portfolio doesn t give optimal return, so we must modify and revise the component of our portfolio. 5. ReEvaluate the Portfolio After construct the portfolio, we should reevaluate the performance of our portfolio, to observe whether it is well performance or not. Before construct the portfolio, the investor must be considering about measurement of return and risk, also determine the expected return for the present. Expected return is notated by E(R), and return is notated by R, so we get the equation: E( R ) i N i= = 1 N R ij N= Time period for one investment expected According To Drs. Abdul Halim, MM, AK ( 2005 : 35) Expected Return (ER) is average deliberated from various historical return. Rij = actual rate of return from investment i in the situation j N = Total of time period And to measure potential risk, we can use standard deviation equation, which is: N [( Rij E( Ri )] 2 σ i = N σ = j= 1 2 σ 2 σ = Variance 2 σ = Standard Deviation (estimate the potential risk appeared) For Instance: The fund manager of Security Company X is deciding a policy to give an alternative for investor. The alternative is to choose form of investment. This is obligation, foreign exchange, or stock. Every form of investment has different proportion of presentation in each sector. The breakdown of investment called diversification. The Security Company X will be dividing the capital into portfolio proportion show by the picture below: 9
5 Investment Breakdown Others, 34.56% Astra Group (saham), 13.76% Banking, 18.32% Resources, 24.05% Telcom, 9.31% Graph 2.1. Investment Breakdown From the illustration above, so the definition of the portfolio risk is sum of the risk from each stock based on the proportion or contribution of each stock to establish portfolio. The objective of calculate the portfolio risk is to anticipate, prepare and manage the maximize risk that could be arise during the investment. Portfolio risk also can be defining the risk that the fund manager performs inadequately, because of poor choice of investments to hold within the fund. Before identifying the portfolio risk, risk of each stock should have been getting first Value at Risk (VaR) Philippe Jorion in his book defined the Value at Risk (VaR): A method of assessing risk that uses standard statistical techniques used routinely in other technical fields. VaR summarizes the worst loss over a target horizon that will not be exceeded with a given level of confidence. According to the explanation above, Philippe Jorion make a conclusion that VaR can be using by owner and company management division to managing the risk in their company. The differences of using VaR as a tool to measure the risk, with other tools is VaR provide many point of view of expected risk occur to get the accurate outcome using forward looking. The calculation using VaR approach, not only can be used for derivatives calculation. But also can be implementing in every financial institution. Philippe Jorion gives opinion, VaR method can be classified: 1. Passive : Information report 2. Defensive: Risk control, to limit and compare the risk from the different activities in company. 10
6 3. Active: Risk Management Another source state the definition of VaR is: Penza and Bansal, 2001:65 VaR is Given probability of W% and a holding period of t days, an entity s VaR is the loss that is expected to be exceeded with a probability of only x percent n the t day holding period. Schwartz, Rober J, and Smith Jr Is that dollar amount such that the likelihood of experiencing a loss in the market value of a financial instrument or a portfolio of in excess of that amount, due to an adverse in market risk factors over a specified risk horizon is loss than a specified tolerance level. From several definitions, we can get conclusion about Value at Risk (VaR), VaR is the expected maximum risk (measure by currency) within one asset or portfolio in the determined time period and level of confidence inside normal distribution. In generally, the potential risks can be distributed normally. The picture below show how the normal distribution is: Graph 2.2. Normal Distribution Curve P(X) Mean Median Mode Symetrical arround a vertical line Symetrical arround a vertical line But, according to Situngkir H and Surya Y (2004) actually the financial data such as historical price of stock can t normally distribute, they get the tendency skewed to the left or to the right. And the curves of normal distribution charts tend to narrow or width. So, the first step is to determine and check using normal distribution approach to find the skewness and kurtosis of the distribution. 11
7 Skewness Skewness is a parameter that describes asymmetry in a random variable s probability distribution ( The graph below is show the illustration of positive skewness and negative skewness. The one on the right is negatively skewed and the one in the left is positively skewed. Graph2. 3. Positive and negative skewness, source: riskglosarry.com The skewness of random variable X is donated as skew(x) and defines: Where is the mean, is the standard deviation, and N is the number of data points. The skewness for a normal distribution is zero, and any symmetric data should have a skewness near zero. Negative values for the skewness indicate data that are skewed left and positive values for the skewness indicate data that are skewed right. By skewed left, we mean that the left tail is long relative to the right tail. Similarly, skewed right means that the right tail is long relative to the left tail. Some measurements have a lower bound and are skewed right Kurtosis Kurtosis is a parameter that describes the shape of a random variable s probability density function (PDF) ( Consider the two PDFs in Graph below: Graph2.4 Low and High kurtosis Source: 12
8 The greater standard deviation is shown by the tail and the bell of each graph. The PDF on the right is more peaked at the center, which might lead us to believe that it has a lower standard deviation. It has fatter tails, which might lead us to believe that it has a higher standard deviation. If the effect of the peaked ness exactly offsets that of the fat tails, the two PDFs will have the same standard deviation. The different shapes of the two PDFs illustrate kurtosis. The PDF on the right has a greater kurtosis than the one on the left. The kurtosis of a random variable X is denoted or kurt(x). It is defined as Where and are the mean and standard deviation of X. A normal random variable has a kurtosis of 3 irrespective of its mean or standard deviation. If a random variable s kurtosis is greater than 3, it is said to be leptokurtic. If its kurtosis is less than 3, it is said to be platykurtic. Leptokurtosis is associated with PDFs that are simultaneously peaked and have fat tails. Platykurtosis is associated with PDFs that are simultaneously less peaked and have thinner tails. They are said to have "shoulders." In Graph 2.4, the PDF on the left is platykurtic. The one on the right is leptokurtic Chi Square and JarqueBera Chi square and Jarque Bera are components to find the normal distribution curve from financial data. And to search JarqueBera test, the equation used is: n JB = 6 s + 2 k ( 3) 4 2 n= total of stock data s= skewness of each stock k= kurtosis of each stock For Chi Square, level degree of freedom determined in the level of 2, which is Cornish Fisher Expansion Cornish fisher expansion used to substitute standard deviation in not normal distribution. The equation to find the value is: 13
9 2.4 VaR Methodologies Commonly, there are three basic methods in wide use for calculating VaR and two levels at which these methods can be applied. The different methods of calculating VaR have varying abilities to handle optionally (nonlinear relations), stress testing, complex fixed income and derivative structures, risk causality, fat tails, nonsymmetrical distributions, aggregation of risk from multiple systems, etc. The three methodologies are discussed below: 1. Historical Simulation The historical simulation methodology repeatedly values current holdings based on the market conditions that existed over a specific historical period of time. This has the advantage of being very intuitive. In Historical Simulation, no assumption on the distribution of changes in market factors is required (the other two assume normally distributed market returns and therefore historical simulation better handles fat tails (kurtosis), i.e., extreme event risk, and asymmetric distributions (skewness), as are experienced in relatively illiquid markets such as emerging markets. Furthermore, the historical simulation methodology explicitly understands the characteristics of instruments with nonlinear behavior and analyzes based on historic market performance. ( (3.1) 2. Monte Carlo Simulation Monte Carlo simulation can be viewed as a hybrid of the parametric approach and the historical simulation approach (it is technically a parametric approach). It uses the variance/covariance matrix that the parametric approach uses to calculate an analytic solution to drive a simulation. The simulation works similar to the historical simulation, but rather than simply using history as it unfolded, Monte Carlo creates the history (called the path) based on the variance/covariance matrix derived from the actual historic market data. For linear instruments, the results of the Monte Carlo will be almost exactly the same as those of the historical simulation if the variance/covariance matrix driving the Monte Carlo was created from the same historical period that is used in the historical simulation. However, for 14
10 instruments that display nonlinear behavior (optionally), the Monte Carlo approach will appropriate measure the imbedded risk while the parametric approach will not. Monte Carlo simulation has the advantage that the parameterized history is significantly easier to modify (e.g., a 10% decline in the S&P with all other risks moving based on their historical correlation to the S&P) than the actual price and rate histories used in historical simulation. ( 3. Variance and Covariance Approach Assumes that distribution of changes in the portfolio value is normal. Since the normal distribution is completely characterized by its first two moments, the analyst must simply derive the mean and the variance of this normal distribution from: The multivariate distribution of the risk factors; The composition of the portfolio. ( In this discussion, writer will use two method, which are historical simulation and variance and covariance method. A VaR measure is the procedure by which we arrive at a VaR measurement. It is some computational algorithm, which is typically coded on a computer. A VaR metric is our interpretation of the VaR measurement. In our examples, the VaR metric was oneday 90% USD VaR. Other example of VaR metrics are: Two week 99% VaR Oneyear standard deviation of IDR return One day semivarianve of stock portfolio value 15
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