Financial Education

Using the Monte Carlo method to estimate Value at Risk (VaR) produces a set of random outcomes reflecting the effects of particular sets of risks. Each set of outcomes is based on a probability distribution for each variable of interest. The distributions for each variable can be normal or non-normal.

Monte Carlo simulations are frequently the only method that provides a practical means to generate necessary risk management information. However, it can become quite a hog of computer resources for large portfolios.

One way to estimate VaR is to use the historical method, which graphs the actual daily returns over a user-specified past period into a histogram. For a two-year observation period (500 trading days) the 1% VaR would be the loss on the fifth-worst day, and the 5% VaR would be the loss on the 25th-worst day.

The results reflect past results, not necessarily those that will be encountered in the future. It is also important to adjust for a moving investment horizon. For example, calculating the VaR for bonds expiring in 2020 from historical results of the prior year would be best done using bonds expiring in 2019.

An advantage of the historical method is that it is non-parametric, which means it does not require assumptions for probability distribution. The disadvantage is that the past may have very different risk characteristics from the future.

Investment managers often have a fiduciary duty to their clients, which means their investment actions must consider the portfolio’s appropriateness in terms of:

1. the needs and circumstances of the client
2. the basic characteristics of an investment
3. the basic characteristics of the overall portfolio

Since each of these factors can change over time, fiduciary duty requires actively monitoring each using a systematic process.

One way to estimate VaR is the analytical method, also called the variance-covariance method.

This method assumes a normal distribution of portfolio returns, which requires estimating the expected return and standard deviation of returns for each asset. As the number of securities in a portfolio increases, these calculations can become unwieldy. As a result, a simplifying assumption of zero expected return is sometimes made. This assumption has little effect on the outcome for short-term (daily) VaR calculations but is inappropriate for longer-term measures of VaR.

The advantage of this method is its simplicity. The disadvantage is that the assumption of a normal return distribution can be unrealistic.

Part of the responsibility of any investment manager is to seek the best possible execution for clients. Best execution is the trading strategy that maximizes the value of the client’s portfolio, subject to the investor’s objectives and constraints.

Some characteristics of best execution include:

• A tie to the investment decision (obtaining the right price or capitalizing on the information)
• Inability to know what the best execution will be prior to the actual execution, but an ability to measure and analyze the execution afterward
• A component of complex practices and relationships that are undergoing continuous refinement

To help achieve best execution, firms should establish processes around maximizing the asset value of client portfolios, and establish guidelines for measuring and managing execution. The compliance with these procedures should be documented and disclosed to clients.

Firms should also disclose general information about their trading techniques, venues and agents and also any potential conflicts of interest that may result.

Value at Risk (VaR) has come to be regarded as the premier risk management technique for the financial industry. It measures the probability-based measure of potential loss that can be measured for specific transactions, business units or the total enterprise.

VaR estimates the loss in money terms that could be exceeded (i.e. it represents the minimum loss) at a given level of probability. For example, a $5 million one-day VaR at 5% indicates a 5% chance that losses could exceed $5 million on a given day.

All else equal, a higher loss has a lower probability of occurrence. Likewise, reducing the probability level from 5% to 1% (the two most common levels in use) would result in a higher VaR at the lower probability level.

Portfolios and firms face a number of non-financial risks that must be identified, measured and monitored. These include:

• Operational risk - the loss caused by failures in systems and procedures, or from external events
• Model risk - incorrect or misspecified valuation models
• Settlement (Herstatt) risk - occurs when one counterparty to a transaction is settling the account, while the other party is declaring bankruptcy
• Regulatory risks - uncertainty regarding how transactions will be regulated or how regulations may change
• Legal/contract risk - the potential loss when a contract is not upheld
• Tax risk - uncertainty surrounding tax laws
• Accounting risk - uncertainty regarding proper way to record transactions or regarding potential rule changes
• Sovereign and political risks - regime changes that could affect business relationships, or the potential default of a sovereign borrower

Institutional investors are increasingly using their voting rights to influence the management teams at companies in which they invest. In the June 2007 Journal of Banking and Finance, Cornett, Marcus, Saunders and Tehranian examine whether such actions actually improve the operating performance of the investee companies.

Using institutional ownership data from 13-F statements and cash flow ROA as a measure of operating performance, the authors find that operating performance is related to the degree of institutional ownership.  (Higher institutional ownership indicates better operating performance.)

Financial risk exposures include market risk, credit risk and liquidity risk.

Market risk relates to interest rates, exchange rates, stock prices and commodity prices. How will changes in these factor affect the portfolio, particularly in the context of asset/liability management?

Credit risk is the loss caused when a debtor or the counterparty in an agreement fails to make a payment. It can be managed using credit derivatives, or simply by using traditional credit analysis techniques in order to screen counterparties.

Liquidity risk is an inability to efficiently buy or sell an asset. It is important to realize that a security’s liquidity can change during the investor’s time horizon. Changes in asset liquidity, particularly when liquidity declines, have an important impact on the overall ability of a portfolio to meet client objectives.

In the Fall/Winter 2006 Journal of Applied Finance, Jiraporn and Ning investigate the relationship between shareholder rights and dividend policy to determine the role of agency costs in dividend policy.

Under the free cash flow theory, higher dividends reduce the cash available to management. This theory suggests that companies with weak shareholder protection will offer lower payouts in order to provide more perks to management. This management opportunism hypothesis suggests a direct relationship between dividends and shareholder rights.

The substitution hypothesis says dividends are a substitute for shareholder rights, and that an inverse relationship exists.

Using the Governance Index as a proxy for shareholder rights, the authors find an inverse relationship between dividends and shareholder rights, supporting the substitution hypothesis that high dividend payments are a method companies use to compensate for having weak shareholder rights.