Cause Categories in Operational Risk Management
The definition of operational risk has been narrowed down. This has helped organizations better manage their operational risks. All operational risks can now be traced to four basic causes viz. people, processes, systems, and external events. However, merely stating a risk as a systems risk does not provide too much information. There is no actionable […]
What Should Corporates and Business Leaders do in Low Growth and Recessionary Times
The World Economy is in a Perfect Storm of Economic Events The world economy is stagnating and while the stock market is booming, the real economy that consists of manufacturing and agriculture is in dire straits. While the United States is being nudged towards recession due to the effects of the US-China Trade War, Europe, […]
How to Spot Signs of Sexual Harassment and Act on Them
Ever since the allegations against the famous (now infamous) Hollywood celebrity, Harvey Weinstein, about sexual harassment broke out, there has been a flurry of women across the political, economic, social, and entertainment world who have come forward detailing their own experiences with sexual harassment. Also, with noted Silicon Valley firms being accused of covering up […]
Ethics in Human Resource Management
Human resource management deals with manpower planning and development related activities in an organization. Arguably it is that branch of management where ethics really matter, since it concerns human issues specially those of compensation, development, industrial relations and health and safety issues. There is however sufficient disagreement from various quarters. There are different schools of […]
What is Market Risk? – How its Measured and its Sources
The global financial system is connected to markets. Markets are where companies go to if they want to raise funds. They are also the place where current investors go when they want to liquidate their existing investments. Hence, it would be fair to say that the financial system of the entire world is closely intertwined […]
Expected default frequency (EDF) is an important metric to be considered during the mitigation of credit risk. It is commonly used in many formulas used to predict future credit risks and default rates.
The term expected default frequency (EDF) actually refers to the KMV model which was developed by credit rating agency Moody’s. The model is also called the KMV model since it represents the initials of the three researchers who developed the model viz. Kealhofer, McQuown, and Vasicek. In this article, we will understand what the expected default frequency (EDF) model is and how it helps firms make better decisions when it comes to credit risk management.
The expected default frequency (EDF) is a method to gauge the probability that the firm will default on its debts. For the purposes of this model, default is actually defined as the point wherein the market value of all the assets of the firm goes below the outstanding value of the debt obligations that need to be paid. This is in contrast with the other credit risk models wherein the firm is said to be in default if it does not have enough money to make scheduled interest or principal payments.
The results of the expected default frequency (EDF) models are time-specific. The most common time period used in this model is of one year. However, it is not uncommon to use this model with a time horizon as long as 5 years.
From the above definition of the expected default frequency (EDF) model, it is relatively easy to guess the three components of the model. The three components have been explained below:
The current market value of the assets is estimated using the market capitalization of the shares of the firm which has been listed on the stock exchange. However, values on the stock exchange can vary relatively quickly. This is the reason that an average value is used while computing the market value of the assets of the firm. It is for this reason that the expected default frequency (EDF) model is best applied to publically listed companies since estimating the fair market value of companies that are privately held can be a difficult task and any valuation number may be subjective, biased, and open to debate.
The underlying assumption of the expected default frequency (EDF) model is that the equity of the firm can be seen as a call option on the debt of the firm. This is the reason that the Black Scholes model, which is a model for pricing options is used to determine the value of the assets of the firm using the value of equity as an input.
The expected default frequency (EDF) model does not only take into account the market value of the firm. It also takes into account how stable this market value is. This is what helps the model weed out temporarily inflated higher values which may be the result of stock market bubbles. The idea is to get a feel about what the valuation of the firm is on a consistent basis.
The expected default frequency (EDF) model uses the most basic measure to determine the volatility of the market value. This measure is called the standard deviation. The assumption is that if the market value of a firm is highly volatile, then the firm is more likely to end up in default.
The default point is the minimum expected value of the firm’s total assets to avoid missing scheduled interest and principal payments. Now, we can see that the default point is actually the function of the debt that a firm has.
The expected default frequency (EDF) model generally calculates the default point as a sum of 100% of the short-term liabilities as well as 50% of the long-term liabilities. The percentage of long-term debt is often changed by analysts. However, the percentage of short-term debt stays fixed at 100%.
The more debt it has, the more payments it has to make and hence a higher probability of default. This is why a standard definition of the default point is difficult since the value is firm-specific. Also, the expected default frequency (EDF) makes some oversimplifications. For instance, it does not take into account that different debts have different maturities. Instead, the assumption is that all the debt matures at the same time!
The expected default frequency (EDF) model is used to calculate what is known as distance to default. This is a popular ratio that is known to accurately predict the probability of default.
The distance to default metric is simply derived by dividing the net worth of the firm by its own volatility. Both these values are derived from the market data and hence this is considered to be a superior alternative to other bookish models.
The bottom line is that the expected default frequency (EDF) model is an important arrow in the quiver of any credit analyst. It is important to know and fully understand the results given by this model before making a decision about the mitigation of credit risk of any counterparty.
Your email address will not be published. Required fields are marked *
