**Risk Measurement**

Rayenda Brahmana

*As Requested to make the bachelor degree’s lecture material*

The fact is my PhD topic is not about risk management, it is about valuation. I have been called to make a brief about risk measurement. Fyi, I do not have any experience in teaching or lecturing. I do not know where and how to start, especially, when they gave me a very specific topic about risk measurement. At least, I remember what my teacher in high school told me, “A Loser says: It looks easy, but it is impossible. A winner says: it looks difficult, but it still possible.”. So, I just tried my best and sketched the taught that passed by in my head. Then, I made mind mapping, and I want to describe and write my mind mapping. This is the result……….

Theoretically, risk can be defined as a model about the precise of probability. It is about the possibility of suffering loss. It means Risk concept, statistically, is a model of dispersion. Dispersion, itself, can be defined as variability in a probability distribution. The measurements of dispersion are (i) Standard Deviation, (ii) Interquartile range, (iii) Range, (iv) Mean difference, (v) Median Absolute Deviation, (vi) Average absolute deviation, (vii) Covariance, etc

In finance, everything about risk is must be closely related to volatility. Before you learn what volatility is, you have to know about **Normal Distribution**. Because risk is defined as the possibility of suffering loss, it means there is dispersion from normal condition. Perhaps, the most important distribution which represents adequately many random processes is Normal Distribution. If the set of events disperse from the normal distribution, it can be identified as the occurrence of risk.

Dispersion model, which proxy by Standard Deviation, also can be applied in finance. To measure the risk of stock price can use volatility. The proxy of volatility is standard deviation. So, to know how much the risk of the stock that you choose is, you can just model it from the standard deviation of the stock returns.

In finance, this dispersion model concept is applied to measure financial risk such as market risk, credit risk, and liquidity risk. Of course, according to Jorion (2006) there is another risk which is Operational Risk. Operational risk is more about insurance, mitigation, and contingency. Before we hop to financial risk, maybe I should explain in brief about operational risk.

Operational Risk is a risk arising from execution of a company’s operation. It includes the fraud risk, litigation risk, environmental risk, and contingency risk. Many cases shows that operational risk is one of the important risk that have to be managed by companies. Indorayon Case in Indonesia shows how environment and contingency risk play important role for company going concern. Other cases are Allied Irish Bank (2002), Natwest (1997), Morgan Grenfell Asset Management (1991), Sumitomo (1996), Daiwa (1995), Barings (1995), and Bankers Trust (1995). These cases show how the failure of supervision which created rogue traders and internal fraud cause problems to companies. The failure of supervision is the part of operational risk. Operational risk can create financial distress or default. Then, to indicate the financial distress or default, we can use Z-Score of Altman.

Back to market risk, credit risk, and liquidity risk. Market risk is the risk inherent to the entire market or entire market segment. It can not be reduced by diversification. It is also called un-diversifiable risk. There are four standard of market risk which are: (i) equity risk (occurs in stock prices changing), (ii) commodity risk (occurs when commodity prices change), (iii) currency risk (occurs in forex changes), and (iv) interest rate risk (occurs in interest rate changes). The changes can be measured also by standard deviation or variance.

Theoretically, primarily to measure market risk is by using Value at Risk (VaR). VaR attempts to quantify the risk of losses DUE MOVEMENTS in financial market variables. Standard Deviation or Variance is a measurement of dispersion only. But the concept of standard deviation will be brought to VaR model.

VaR quantifies the risk losses probabilities in certain confidence level such as 99% or 95%. The result of VaR will be not a notional amount such as percentage of probability of losses. But VaR will give “managers” the nominal amount in certain level of significance.

Jorion (2006) addresses the chronology of the Evolution of Analytical Risk Management Tools, which is:

1938 Bond duration

1952 Markowitz mean-variance framework

1963 Sharpe’s capital asset pricing model

1966 Multiple factor models

1973 Black-Scholes option pricing model,“Greeks”

1988 Risk-weighted assets for banks

1993 Value at Risk

1994 RiskMetrics

1997 CreditMetrics,CreditRisk+

1998 Integration of credit and market risk

1998 Risk budgeting

Credit risk is the risk of loss due to a debtor’s non-payment of a loan, or interest of the credit. In the past, the measurement of credit risk was done by credit officer professional judgment. It is very hard to measure the credit risk contribution at overall level. Nowadays, credit risk can be measured in several ways such as Distribution, Expected Credit Loss, Worst Credit Loss, Marginal Contribution to Risk, and Remuneration of Capital.

Liquidity Risk is including asset liquidity and financing liquidity. Asset liquidity is related to how quick the asset can be traded. Financing liquidity or funding liquidity is related to liabilities such as the inability to pay the liability in maturity date, etc. So, liquidity risk can be defined as the risk that an asset can be traded quickly enough in the market to acquire require returns or to prevent a loss.

In finance, there are also two important variables in risk measurement, which are beta and correlation. As we know, in portfolio management, beta is the concept that explaining how the expected return of a stock or a portfolio is correlated to the return of the market as a whole.

Beta is a combination of volatility and correlation. For example, if one stock has low volatility and high correlation, and the other stock has low correlation and high volatility, beta can decide which is more “risky”. In other words, beta sets a floor on volatility. For example, if market volatility is 10%, any stock with a beta of 1 must have volatility of at least 10%.

Another way of distinguishing between beta and correlation is to think about direction and magnitude. If the market is always up 10% and a stock is always up 20%, the correlation is one (correlation measures direction, not magnitude). However, beta takes into account both direction and magnitude, so in the same example the beta would be 2 (the stock is up twice as much as the market).

In the end, briefly, in risk measurement, normal distribution concept is very important. Many models in risk management established from this concept. To learn more about risk measurement is to learn more about normal distribution concept. Happy studying!

This blog’s great!! Thanks :).

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nice blog indeed. but, sustainable development?

don’t say that you get inspired by me.. hehehe..

well done job, broer! and thanks again for this article. at least, it helps me to make risk management mind-mapping generally (not always about that ‘banking’ stuffs).

GBU and good luck!

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