Credit Risk Quantifying and Managing

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Table of Contents

Credit Risk concerning Interests and Bonds
Duration Analysis in context with Scenario Analysis
Value-at-risk Analysis
Portfolios Risk and Return
Interest Rate Gap Analysis
Interest Rate Gap Analysis Consideration
Hedging against Interest Rate Risk
Works Cited

Credit Risk enables banks and bondholders to recover some portion of their bond value when there is a default. Different seniority bonds are issued by the asset value, for example, secured bonds are issued on the value of the land or building, and in case they are not paid, the bondholder can claim security ownership, however, in the event of typical bankruptcy unsecured bonds uphold the highest priority.

The more senior the bondholder, the more serious would be the recovery. Recovery in terms of credit requires several years liquidation before a full payout to bondholders and bank lenders. Restructuring takes some time to recover therefore recovery values are measured first from the market price of a bond after a default. However measuring recovery risk this way is not always accurate for the lesser liquid bonds and bank loans are, the lesser would be the risk of default market.

Credit bonds are secured through credit ratings which companies like Moodys, Standard and Poors, and Fitch-IBCA perform by evaluating the financial strength of the borrower. Different ratings like BBB, Baa, Ba, and BB determine the ratings for which the bonds are issued differentiate between higher and lower ratings. On the other hand, banks secure their loans through internal ratings which vary from numeric rate 5 to 20. Credit rating agencies issue external ratings which are not adjustable unless and until there is a permanent amelioration in terms of financial stability of the company, whereas banks internal ratings are easily adjustable to detect and demonstrate default over the period.

Credit Risk concerning Interests and Bonds

Bank assets are dependant upon the market interest rates and are affected by the hedging interest rates, for which to measure we must analyze the relationship between interest rate and securities. Bonds interest rates are measured through coupon rate, yield to maturity rate, and zero-coupon yield method. A coupon rate is used whenever there is a need to signify the relationship between interest payment and the bonds face value. Though unable to find bond value based on the market rate, the market interest rate evaluates the face value of the bond. Credit risk is measured through rating transactions by which a bond is rated by migrating from one class to another. Interest rate risk is usually measured through duration analysis, interest rate gap analysis, and value-at-risk (VAR).

Duration Analysis in context with Scenario Analysis

Duration changes whenever there is some risk factor in interest rates fluctuation. Risk management is best measured by sensitivity tools as such tools or measures allow a portfolio manager to analyze any upcoming scenario in terms of interest rates fluctuations. For example the impact of the rise or fall of interest rate on an individuals portfolio and so on. For this reason, risk managers keep a close eye even on minor rises and falls of interest rates and chose such scenarios for interest rates that are easier to detect changes in fixed portfolios.

The goal of investors is to maximize the expected return on wealth and minimize wealth risk. To achieve this goal, it is useful to consider that the expected return on wealth is a linear function of the expected returns on the individual assets in the investors portfolio, the goal of maximizing expected returns on wealth can be achieved by investing in assets with high expected returns. The standard deviation of return on wealth is not, however, a linear function of the standard deviations of the returns on the assets in the portfolio.

Instead, the standard deviation of wealth is less than the average standard deviation of the assets in which the wealth is invested, as long as the returns on all the investments held are not perfectly positively correlated with one another; i.e., as long as the return on at least one asset is not always proportional to that of all the other assets. Investment into more than one asset whose returns are not perfectly correlated with the returns on other assets held is called diversification.

Value-at-risk Analysis

While using the VAR equation to determine the amount to put into the risky portfolio still leaves a 0.1% chance of losing more than H% of an investors wealth. However, to put the possibility of such an unlikely disaster in perspective, it can be noted that the likelihood of a far worse disaster of dying in an auto accident in the next 5 years in the U.S. is about as high. Statistics can also be used to develop confidence intervals for returns under different distributional assumptions.

For instance, in a 1997 study, Fong and Vasicek developed a model for determining the maximum amount that can be lost (under specified confidence intervals) for a portfolio with a skewed distribution. Models that attempt to measure the maximum feasible loss to a portfolio are widely referred to as estimating value at risk (VAR). Since even very complex assumptions on the distribution of asset returns cannot fully characterize all the future possible events that can occur, many advocates that VAR analysis should be supplemented by stress tests that simulate the prices of assets or entire portfolios under various extreme conditions. Only thereby can the complete risk of a portfolio be evaluated.

Because investors often have more complex, multiperiod financial problems than a 1-period VAR can solve, more involved financial solutions must frequently be found for determining the optimal amount of leverage. Although statistical concepts similar to those used in the previous subsection may be applied to such problems, the multiperiod complexity typically necessitates computerization. An example of computerized multiperiod portfolio analysis is the SIA software that not only evaluates multiperiod portfolio situations but also values individual assets in the portfolio.

Long-term multi-period analysis of portfolios must take into consideration one very important statistical property. In particular, over the long term, higher expected returns sometimes more than offset higher standard deviations when returns are compounded over long periods. As a result, portfolios with higher expected returns are sometimes less risky long-term despite having higher volatility over short horizons. The reason is that a volatile portfolio with a higher mean expected return can have a range of feasible returns that are higher under virtually all cases than for a stable portfolio with lower expected returns (i.e., the difference in the means of the return distributions can be so high as to exceed a feasible number of large standard deviations away from the mean). When the returns on one portfolio are greater or equal to the returns on another portfolio in all feasible scenarios, the superior portfolio is said to exhibit stochastic dominance.

Portfolios Risk and Return

Portfolios the combination of two or more securities or assets is different from single investments which are held in isolation. Investors rarely place their entire wealth into a single asset or investment and prefer to construct a portfolio or group of investments. Portfolios return and risk are measured through a weighted average of the expected returns of the securities that comprise that portfolio and whose weights are equal to the proportion of total funds invested in each security.

Portfolio risk can be minimized by combining various securities that are not necessarily correlated. The process continues as portfolio risk stocks combine to form equally weighted portfolios and with a single stock the portfolio risk acts as the standard deviation of that one stock. As soon as the randomly selected stocks held in the portfolio increase, the total risk of the portfolio reduces.

A portfolio asset allocation that emphasizes money market securities is most desirable when both common stock and long-term bond prices are falling as most of the investors do not want to be in common stocks or long-term bonds in later stages of portfolio assets. Both securities lose value at their most rapid rate in these periods of the economic/stock price cycle. Money market securities offer the only positive return to be acquired over this relatively short span in the cycle where such securities do not lose value and still have a positive return. Whereas other common stocks and long-term bonds have negative returns.

The lower interest rates reflect slowing and/or declining economic activity, higher Fed money supply, lower consumer and business demand for money, and less inflation. Lower-quality bonds may not feel the upward price pressure, therefore the higher-default lower quality risk bonds are viewed with apprehension. The default risk of such lower quality risk bonds is often underappreciated by investors during the economic expansion of the Initial Stages in the economic/stock price cycle. Their default risk is often overappreciated with the prices of lower-quality bonds continue to decline even after the incipient earnings recovery and despite lower interest rates. Bond investors remain fearful these issuers may not revive in time, if at all, to meet looming debt service as the price reaction of lower-quality bonds varies directly with the severity of their default risk (Bolten, 2000, p. 55).

Interest Rate Gap Analysis

As commercial banks are mostly concerned by cash flows and not by the standard tools like Value-at-Risk, therefore the impact of interest rate changes on the bank is based on accrual income. For this reason, interest rate analysis is used after measuring the sensitivity of financial-based institution measures annually, quarterly and monthly income statements according to changes in interest rates. The banking interest rate in contrast to the efficient markets theory is risky and therefore limited. The interest rate gap model relies on the availability of close substitutes of income statements for securities whose price is potentially affected by noise trading.

Interest Rate Gap Analysis Consideration

The gap analysis in context with commercial banks considers the following:

The effect of point rise on the interest rate by net interest income after a specific period.
Interest rate scenario and its usage in gap analysis.
Scenarios where interest rates are below market rates.
Credit card receivables that not always move along with the market rates.
Portfolios on fixed-rate mortgages and equity holdings.

Example

In the early 1990s, ECA along with the U.S. Eximbank adopted a new method of assessing risks in the corporate bond sector and establishing reserves, called the market yield approach. Based upon the risk evaluation of foreign countries by private capital markets, this method provided the opportunity of analyzing various yields of non-payment probabilities (Gianturco, 2001, p. 143). Eximbank looks specifically at the yields on different sovereign country bonds and how Moodys and Standard & Poor (S&P) rate those bonds. Some of Eximbanks categories had no counterpart in Moodys and S&P because they were very risky markets where international bond issues are not usually possible (Gianturco, 2001, p. 143).

The main factor behind quantifying such risks starts with the time and credibility issues which are addressed through pre-commitments and graduated responses with the possibility of overrides. In this case, many financial analysts believe that general policy like Structured Early Intervention and Resolution Programs makes a difference in pre-determining interventions. However, there is no sign of placing a particular framework through which banks or financial institutions may categorize them as magical capital ratio. On the other hand, there is a threat of increasing potential danger as the capital ratio declines.

Financials like Goldstein and Turner (1996) argued that there is always a threat to commercial banks from that of private bondholders that they are likely to impose on banks in the absence of government insurance or guarantees (Brealey et al, 2001, p. 130).

Hedging against Interest Rate Risk

Low-cost hedging financial risks necessarily impose a restriction on the integrands of the time integrals and stochastic integrals which says that although they can vary randomly, they can do so only by depending at each point in time on information that is available at that time. Virtually all of the processes of hedging, including price processes, interest rate processes, and trading strategies, have to satisfy this restriction. So it is necessary to model the information available in the economy at each point which in the real world causes interest rates to take only positive values, although in some models they follow processes that can take negative values.

In the language of Interest, outstanding is a stock variable displaying the state of affairs at a moment, while issuance and trading are flow variables displaying activity during a period. These variables are larger than that of the underlying banking world as it is customary to give global exports as a yardstick. It was $6.3tr in 2000, a mere 6.3 percent of the financial assets and still less of equity trading, not to speak of major interbank payments flows, roughly $1,400tr in 1999, or the $1.2tr of daily foreign exchange trading in April 2001.

The scale of such activity based upon hedging easily raises negative comments about a financial economy that fears the destabilizing potential of financial markets. The professional bankers are well aware of most of the activity that originates from portfolio trimming by institutional investors and the balancing of books by bankers (Laulajainen, 2003, p. 93).

Bankers, therefore, follow safety rules set by regulators and the accounting community, on the other hand, institutional investors also have guidelines to follow, to beat an equity index or outperform peers, for example. The financials consider it risky to wait for the stabilization of constantly changing markets, and expensive to implement large corrections when they have become necessary.

In this context it is much better to adjust the portfolio continuously, this escorts to very large trading figures. The hedging element takes place not in situations where trading originates from trimming but a hedging element is there where there is a desire to insulate core activity from market oscillation. To achieve such risk minimization through hedging, counterparts are needed where speculators who have the insight and courage to take an opposite view of the markets development and shoulder the risk against a fee.

Works Cited

Bolten E. Steven, (2000) Stock Market Cycles: A Practical Explanation: Quorum Books: Westport, CT.

Brealey A. Richard, Alastair Clark, Charles Goodhart, Juliette Healey, Glenn Hoggarth, David T. Llewellyn, Chang Shu, Peter Sinclair & Farouk Soussa. (2001) Financial Stability and Central Banks: A Global Perspective: Routledge: London.

Gianturco E. Delio, (2001) Export Credit Agencies: The Unsung Giants of International Trade and Finance: Quorum Books: Westport, CT.

Laulajainen Risto, (2003) Financial Geography: A Bankers View: Routledge: New York.

Murphy Austin, (2000) Scientific Investment Analysis: Quorum Books: Westport, CT.

Yago Glenn & Susanne Trimbath, (2003) Beyond Junk Bonds: Expanding High Yield Markets: Oxford University Press: New York.

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