Loan advice

Prices motivate economic players

admin — Fri, 16 Oct 2009 16:43:04 +0000

Market prices establish a reward-penalty (profit-loss) structure that encourages people to work, cooperate with others, use efficient production methods, supply goods that are intensely desired by others, and invest for the future. Self-interested entrepreneurs will seek to produce only the goods consumers value enough to pay a price sufficient to cover production cost. Self-interest will also encourage producers to use efficient production methods and adopt cost-saving technologies because lower costs will mean greater profits. Firms that fail to do so will be unable to compete successfully in the marketplace.
We asked you to reflect on why the grocery stores in your local community generally have on hand about the right amount of milk, bread, vegetables, and other goods. Likewise, how is it that refrigerators, automobiles, and CD players, produced at different places around the world, make their way to stores near you in approximately the same numbers that they are demanded by consumers? The invisible hand principle provides the answer, and it works without political direction. No government agency needs to tell decision makers to keep costs low or produce those goods most intensely desired by consumers. Similarly, no one has to tell individuals that they should
- develop skills that are highly valued by others. Once again the profit motive
- higher earnings in this case will do the job. Many of the things we take for granted in our ordinary lives reflect the invisible hand at work.

Nature of Liabilities

admin — Fri, 09 Oct 2009 16:41:38 +0000

The nature of an institutional investor’s liabilities will dictate the general investment strategy to pursue. Depository institutions, for example, seek to generate income by the spread between the return that they earn on their assets and the cost of their funds. Life insurance companies are in the spread business. Pension funds are not in the spread business, in that they themselves do not raise funds in the market. Certain types of pension funds seek to cover the cost of pension obligations at a minimum cost to the plan sponsor. Most investment companies face no explicit costs for the funds they acquire and must satisfy no specific liability obligations, the exception being target-term trusts.
A liability is a cash outlay that must be made at a specific time to satisfy the contractual terms of an obligation. An institutional investor is concerned with both the amount and timing of liabilities, because its assets must produce the cash flow to meet any payments it has promised to make in a timely way. In fact, liabilities are classified according to the degree of certainty of their amount and timing.
The descriptions of cash outlays as either known or uncertain are undoubtedly broad. When we refer to a cash outlay as being uncertain, we do not mean that it cannot be predicted. There are some liabilities where the “law of large numbers” makes it easier to predict the timing and/or amount of cash outlays. This work is typically done by actuaries, but even actuaries have difficulty predicting natural catastrophes such as floods and earthquakes.
In our description of each type of risk category, it is important to note that, just like assets, there are risks associated with liabilities. Some of these risks are affected by the same factors that affect asset risks.
A Type I liability is one for which both the amount and timing of the liabilities are known with certainty. An example would be when an institution knows that it must pay $8 million six months from now. Banks and thrifts know the amount that they are committed to pay (principal plus interest) on the maturity date of a fixed-rate certificate of deposit (CD), assuming that the depositor does not withdraw funds prior to the maturity date. Type I liabilities, however, are not limited to depository institutions. A product sold by life insurance companies is a guaranteed investment contract, popularly referred to as a GIC. The obligation of the life insurance company under this contract is that, for a sum of money (called a premium), it will guarantee an interest rate up to some specified maturity date.

Institutional Investors

admin — Thu, 01 Oct 2009 16:40:41 +0000

Managers of the funds of financial entities manage those funds to meet specified investment objectives. For many institutional investors (insurance companies, pension funds, investment companies, depository institutions, and endowments and foundations), those objectives are dictated by the nature of their liabilities. It is within the context of the asset/liability problem faced by managers of institutional funds that investment vehicles and investment strategies make any sense. Therefore, we provide an overview of the investment objectives of institutional investors and the constraints imposed on managers of the funds of these entities.

Role of Financial Intermediaries

admin — Fri, 25 Sep 2009 16:39:02 +0000

Financial intermediaries obtain funds by issuing financial claims against themselves to market participants and then investing those funds. The investments made by financial intermediaries—their assets—can be in loans and/or securities. These investments are referred to as direct investments. As just noted, financial intermediaries play the basic role of transforming financial assets that are less desirable for a large part of the public into other financial assets—their own liabilities—which are preferred more by the public. This transformation involves at least one of four economic functions: (1) providing maturity intermediation; (2) risk reduction via diversification; (3) reducing the costs of contracting and information processing; and (4) providing a payments mechanism.
Maturity intermediation involves a financial intermediary issuing liabilities against itself that have a maturity different from the assets it acquires with the fund raised. An example is a commercial bank that issues short-term liabilities (i.e., deposits) and invests in assets with a longer maturity than those liabilities. Maturity intermediation has two implications for financial markets. First, investors have more choices concerning maturity for their investments; borrowers have more choices for the length of their debt obligations. Second, because investors are reluctant to commit funds for a long period of time, they will require that long-term borrowers pay a higher interest rate than on short-term borrowing. In contrast, a financial intermediary will be willing to make longer-term loans, and at a lower cost to the borrower than an individual investor would, by counting on successive deposits providing the funds until maturity (although at some risk as discussed below). Thus, the second implication is that the cost of longer-term borrowing is likely to be reduced.
To illustrate the economic function of risk reduction via diversification, consider an investor who invests in a mutual fund. Suppose that the mutual fund invests the funds received in the stock of a large number of companies. By doing so, the mutual fund has diversified and reduced its risk. Investors who have a small sum to invest would find it difficult to achieve the same degree of diversification because they would not have sufficient funds to buy shares of a large number of companies. Yet by investing in the investment company for the same sum of money, investors can accomplish this diversification, thereby reducing risk. This economic function of financial intermediaries—transforming more risky assets into less risky ones—is called diversification. While individual investors can do it on their own, they may not be able to do it as cost effectively as a financial intermediary, depending on the amount of funds they have to invest. Attaining cost-effective diversification in order to reduce risk by purchasing the financial assets of a financial intermediary is an important economic benefit for financial markets.
Investors purchasing financial assets should develop skills necessary to understand how to evaluate an investment. Once those skills are developed, investors should apply them to the analysis of specific financial assets that are candidates for purchase (or subsequent sale). Investors who want to make a loan to a consumer or business will need to write the loan contract (or hire an attorney to do so). While there are some people who enjoy devoting leisure time to this task, most of us find that leisure time is in short supply, so to sacrifice it, we have to be compensated. The form of compensation could be a higher return obtained from an investment. In addition to the opportunity cost of the time to process the information about the financial asset and its issuer, there is the cost of acquiring that information. All these costs are called information processing costs. The costs of writing loan contracts are referred to as contracting costs. Another dimension to contracting costs is the cost of enforcing the terms of the loan agreement. There are economies of scale in contracting and processing information about financial assets, because of the amount of funds managed by financial intermediaries. The lower costs accrue to the benefit of the investor who purchases a financial claim of the financial intermediary and to the issuers of financial assets, who benefit from a lower borrowing cost.
While the previous three economic functions may not have been immediately obvious, this last function should be. Most transactions made today are not done with cash. Instead, payments are made using checks, credit cards, debit cards, and electronic transfers of funds. These methods for making payments are provided by certain financial intermediaries. The ability to make payments without the use of cash is critical for the functioning of a financial market. In short, depository institutions transform assets that cannot be used to make payments into other assets that offer that property.

INDUSTRY’S EVALUATION OF MODELING TOOLS

admin — Mon, 24 Aug 2009 15:51:20 +0000

A recent study by The Intertek Group tried to assess how the use of financial modeling in asset management had changed over the highly volatile period from 2000 to 2002. Participants in the study included 44 heads of asset management firms in Europe and North America; more than half were from the biggest firms in their home markets.
The study found that the role of quantitative methods in the investment decision-making process had increased at almost 75% of the firms while it had remained stable at about 15% of the firms; five reported that their process was already essentially quantitative. Demand pull and management push were among the reasons cited for the growing role of models. The head of risk management and product control at an international firm said, “There is genuinely a portfolio manager demand pull plus a top-down management push for a more systematic, robust process.” Many reported that fund managers have become more eager consumers of modeling. “Fund managers now perceive that they gain increased insights from the models,” the head of quantitative research at a large northern European firm commented.
In another finding, over one half of the participants evaluated that models had performed better in 2002 than two years ago; some 20% evaluated 2002 model performance to be stable with respect to the previous two years while another 20% considered that performance worsened. Performance was widely considered to be model-dependent. Among those that believed that model performance had improved, many attributed better performance to a better understanding of models and the modeling process at asset management firms. Some firms reported having in place a formal process in which management was systematically trained in modeling and mathematical methods.
The search for a silver bullet typical of the early days of “rocket science” in finance has passed; modeling is now widely perceived as an approximation, with the various models shedding different light on the same phenomena. Just under 60% of the participants in the 2002 study indicated having made significant changes to their modeling approach from 2000 to 2002; for many others, it was a question of continuously recalibrating and adapting the models to the changing environment.
Much of the recent attention on quantitative methods has been focused on risk management—a relatively new function at asset management firms. More than 80% of the firms participating in the Intertek study reported a significant evolution of the role of risk management from 2000 to 2002. Some of the trends revealed by the study included daily or real-time risk measurement and the splitting of the role of risk management into two separate functions, one a support function to the fund managers, the other a central control function reporting to top management.
In another area which is a measure of an increasingly systematic process, more than 60% of the firms in the 2002 study reported having formalized procedures for integrating quantitative and qualitative input, though half mentioned that the process had not gone very far and 30% reported no formalization at all. One way the integration is being handled is through management structures for decision-making. A source at a large player in the bond market said, “We have regularly scheduled meetings where views are expressed. There is a good combination of views and numbers crunched. The mix between quantitative and qualitative input will depend on the particular situation. For example, if models are showing a 4 or 5 standard deviation event, fundamental analysis would have to be very strong before overriding the models.”
Many firms have cast integration in a quantitative framework. The head of research at a large European firm said, “One year ago, the integration was totally fuzzy, but during the past year we have made the integration extremely rigorous. All managers now need to justify their statements and methods in a quantitative sense.” Some firms are prioritizing the inputs from various sources. A business manager at a Swiss firm said, “We have recently put in place a scoring framework which pulls together the gut feeling of the fund manager and the quantitative models. We will be taking this further. The objective is to more tightly link the various inputs, be they judgmental or model results.”
Some firms see the problem as one of model performance evaluation. “The integration process is becoming more and more institutionalized,” said the head of quantitative research at a big northern European firm. “Models are weighted in terms of their performance: if a model has not performed so well, its output is less influential than that of mod- els which have performed better.”
In some cases, it is the portfolio manager himself who assigns weights to the various inputs. A source at a large firm active in the bond markets said, “Portfolio managers weight the relative importance of quantitative and qualitative input in function of the security. The more complex the security, the greater the quantitative weighting; the more macro, long-term, the less the quantitative input counts: Models don’t really help here.” Other firms have a fixed percentage, such as 50/50, as corporate policy. Outside of quantitatively run funds, the feeling is that there is a weight limit in the range of 60–80% for quantitative input. “There will always be a technical and a tactical element,” said one source.
Virtually all firms reported a partial automation in the handling of qualitative information, with some 30% planning to add functionality over and above the filtering and search functionality now typically provided by the suppliers of analyst research, consensus data and news. About 25% of the participants said that they would further automate the handling of information in 2003. The automatic summarization and analysis of news and other information available electronically was the next step for several firms that had already largely automated the investment process.

THE ROLE OF INFORMATION TECHNOLOGY

admin — Sat, 15 Aug 2009 15:49:26 +0000

Advances in information technology are behind the widespread adoption of modeling in finance. The most important advance has been the enormous increase in the amount of computing power, concurrent with a steep fall in prices. Government agencies have long been using computers for economic modeling, but private firms found it economically justifiable only as of the 1980s. Back then, economic modeling was considered one of the “Grand Challenges” of computational science.
In the late 1980s, firms such as Merrill Lynch began to acquire super- computers to perform derivative pricing computations. The overall cost of these supercomputing facilities, in the range of several million dollars, limited their diffusion to the largest firms. Today, computational facilities ten times more powerful cost only of a few thousand dollars.
To place today’s computing power in perspective, consider that a 1990 run-of-the-mill Cray supercomputer cost several million U.S. dollars and had a clock cycle of 4 nanoseconds (i.e., 4 billionths of a second or 250 million cycles per second, notated as 250 MHz). Today’s fast laptop computers are 10 times faster with a clock cycle of 2.5 GHz and, at a few thousand dollars, cost only a fraction of the price. Supercomputer performance has itself improved significantly, with top computing speed in the range of several teraflops7 compared to the several mega- flops of a Cray supercomputer in the 1990s. In the space of 15 years, sheer performance has increased 1,000 times while the price-performance ratio has decreased by a factor of 10,000. Storage capacity has followed similar dynamics.
The diffusion of low-cost high-performance computers has allowed the broad use of numerical methods. Computations that were once per- formed by supercomputers in air-conditioned rooms are now routinely performed on desk-top machines. This has changed the landscape of financial modeling. The importance of finding closed-form solutions and the consequent search for simple models has been dramatically reduced. Computationally-intensive methods such as Monte Carlo simulations and the numerical solution of differential equations are now widely used. As a consequence, it has become feasible to represent prices and returns with relatively complex models. Nonnormal probability distributions have become commonplace in many sectors of financial modeling. It is fair to say that the key limitation of financial econometrics is now the size of available data samples or training sets, not the computations; it is the data that limits the complexity of estimates.
Mathematical modeling has also undergone major changes. Techniques such as equivalent martingale methods are being used in derivative pricing and cointegration , the theory of fat-tailed processes, and state-space modeling (including ARCH/GARCH and stochastic volatility models) are being used in econometrics.
Powerful specialized mathematical languages and vast statistical software libraries have been developed. The ability to program sequences of statistical operations within a single programming language has been a big step forward. Software firms such as Mathematica and Math- works, and major suppliers of statistical tools such as SAS, have created simple computer languages for the programming of complex sequences of statistical operations. This ability is key to financial econometrics which entails the analysis of large portfolios.8
Presently only large or specialized firms write complex applications from scratch; this is typically done to solve specific problems, often in the derivatives area. The majority of financial modelers make use of high-level software programming tools and statistical libraries. It is difficult to overestimate the advantage brought by these software tools; they cut development time and costs by orders of magnitude.
In addition, there is a wide range of off-the-shelf financial applications that can be used directly by operators who have a general under- standing of the problem but no advanced statistical or mathematical training. For example, powerful complete applications from firms such as Barra and component applications from firms such as FEA make sophisticated analytical methods available to a large number of professionals.
Data have, however, remained a significant expense. The diffusion of electronic transactions has made available large amounts of data, including high-frequency data (HFD) which gives us information at the transaction level. As a result, in budgeting for financial modeling, data have become an important factor in deciding whether or not to under- take a new modeling effort.
A lot of data are now available free on the Internet. If the required granularity of data is not high, these data allow one to study the viability of models and to perform rough tuning. However, real-life applications, especially applications based on finely grained data, require data streams of a higher quality than those typically available free on the Internet.

FINANCIAL ENGINEERING IN HISTORICAL PERSPECTIVE

admin — Wed, 05 Aug 2009 15:45:57 +0000

In its modern sense, financial engineering is the design (or engineering) of contracts and portfolios of contracts that result in predetermined cash flows contingent to different events. Broadly speaking, financial engineering is used to manage investments and risk. The objective is the transfer of risk from one entity to another via appropriate contracts. Though the aggregate risk is a quantity that cannot be altered, risk can be transferred if there is a willing counterparty.
Financial engineering came to the forefront of finance in the 1980s, with the broad diffusion of derivative instruments. However the concept and practice of financial engineering are quite old. Evidence of the use of sophisticated cross-border instruments of credit and payment dating from the time of the First Crusade (1095–1099) has come down to us from the letters of Jewish merchants in Cairo. The notion of the diversification of risk (central to modern risk management) and the quantification of insurance risk (a requisite for pricing insurance policies) were already understood, at least in practical terms, in the 14th century. The rich epistolary of Francesco Datini, a 14th century merchant, banker and insurer from Prato (Tuscany, Italy), contains detailed instructions to his agents on how to diversify risk and insure cargo.5 It also gives us an idea of insurance costs: Datini charged 3.5% to insure a cargo of wool from Malaga to Pisa and 8% to insure a cargo of malmsey (sweet wine) from Genoa to Southampton, England. These, according to one of Datini’s agents, were low rates: He considered 12–15% a fair insurance premium for similar cargo.
What is specific to modern financial engineering is the quantitative management of uncertainty. Both the pricing of contracts and the optimization of investments require some basic capabilities of statistical modeling of financial contingencies. It is the size, diversity, and efficiency of modern competitive markets that makes the use of modeling imperative.

Measuring and Evaluating Performance

admin — Sun, 26 Jul 2009 15:45:14 +0000

The measurement and evaluation of investment performance is the last step in the investment management process. Actually, it is misleading to say that it is the last step since the investment management process is an ongoing process. This step involves measuring the performance of the portfolio and then evaluating that performance relative to some benchmark.
Although a portfolio manager may have performed better than a benchmark, this does not necessarily mean that the portfolio manager satisfied the client’s investment objective. For example, suppose that a financial institution established as its investment objective the maximization of portfolio return and allocated 75% of its funds to common stock and the balance to bonds. Suppose further that the manager responsible for the common stock portfolio realized a 1-year return that was 150 basis points greater than the benchmark. Assuming that the risk of the portfolio was similar to that of the benchmark, it would appear that the manager outperformed the benchmark. However, sup- pose that in spite of this performance, the financial institution cannot meet its liabilities. Then the failure was in establishing the investment objectives and setting policy, not the failure of the manager.

Approaches to Portfolio Construction

admin — Sun, 19 Jul 2009 15:44:14 +0000

Constructing an efficient portfolio based on the expected return for a portfolio (which depends on the expected return of all the asset returns in the portfolio) and the variance of the portfolio’s return (which depends on the variance of the return of all of the assets in the portfolio and the covariance of returns between all pairs of assets in the portfolio) are referred to as “mean-variance” portfolio management. The term “mean” is used because the expected return is equivalent to the “mean” or “average value” of returns. This approach also allows for the inclusion of constraints such as lower and upper bounds on particular assets or assets in particular industries or sectors. The end result of the analysis is a set of efficient portfolios—alternative portfolios from which the investor can select—that offer the maximum expected portfolio return for a given level of portfolio risk.
There are variations on this approach to portfolio construction. Mean-variance analysis can be employed by estimating risk factors that historically have explained the variance of asset returns. The basic principle is that the value of an asset is driven by a number of systematic factors (or, equivalently, risk exposures) plus a component unique to a particular company or industry. A set of efficient portfolios can be identified based on the risk factors and the sensitivity of assets to these risk factors.
With either the full mean-variance approach or the multifactor risk approach there are two variations. First, the analysis can be performed by investors using individual assets (or securities) or the analysis can be performed on asset classes.
The second variation is one in which the input used to measure risk is the tracking error of a portfolio relative to a benchmark index, rather than the variance of the portfolio return. By a benchmark index it is meant the benchmark that the investor’s performance is compared against. Tracking error is the variance of the difference in the return on the portfolio and the return on the benchmark index. When this “tracking error multifactor risk approach” to portfolio construction is applied to individual assets, the investor can identify the set of efficient portfolios in terms of a portfolio that matches the risk profile of the benchmark index for each level of tracking error. Selecting assets that intentionally cause the portfolio’s risk profile to differ from that of the benchmark index is the way a manager actively manages a portfolio. In contrast, indexing means matching the risk profile. “Enhanced” indexing basically means that the assets selected for the portfolio do not cause the risk profile of the portfolio constructed to depart materially from the risk profile of the benchmark.
At the other extreme of the full mean-variance approach to portfolio management is the assembling of a portfolio in which investors ignore all of the inputs—expected returns, variance of asset returns, and covariance of asset returns—and use their intuition to construct a portfolio. We refer to this approach as the “seat-of-the-pants approach” to portfolio construction. In a rising stock market, for example, this approach is too often confused with investment skill. It is not an approach we recommend.

Inputs Required

admin — Sun, 12 Jul 2009 15:42:31 +0000

To construct an efficient portfolio, the investor must be able to quantify risk and provide the necessary inputs. As will be explained in the next series of posts, there are three key inputs that are needed: future expected return (or simply expected return), variance of asset returns, and correlation (or covariance) of asset returns.
There are a wide range of approaches to obtain the expected return of assets. Investors can employ various analytical tools that will be discussed throughout this blog to derive the future expected return of an asset. For example, we will see that there are various asset pricing models that provide expected return estimates based on factors that historically have been found to systematically affect the return on all assets. Investors can use historical average returns as their estimate of future expected returns. Investors can modify historical average returns with their judgment of the future to obtain a future expected return. Another approach is for investors to simply use their intuition without any formal analysis to come up with the future expected return.
This input can be obtained for each asset by calculating the historical variance of asset returns. There are sophisticated time series statistical techniques that can be used to improve the estimated variance of asset returns. Some investors calculate the historical variance of asset returns and adjust them based on their intuition.
The covariance (or correlation) of returns is a measure of how the return of two assets vary together. Typically, investors use historical covariances of asset returns as an estimate of future covariances. But why is a covariance of asset returns needed? As will be explained, the covariance is important because the variance of a portfolio’s return depends on it and the key to diversification is the covariance of asset returns.