Introduction
In October 1994, the risk management group at J.P. Morgan took
the bold step of revealing its internal risk management methodology
through a fifty page technical document and a free data set
providing volatility and correlation information for roughly twenty
markets. At the time, there was little standardization in the
marketplace, and the RiskMetrics model took hold as the benchmark
for measuring financial risk. In the subsequent years, as the model
became a standard for educating the industry as well, the demands
for enhancements and advice grew. We continued to develop the
model, and by mid-1998, the Technical Document had been updated
three times, with the last release (the fourth edition, or
RiskMetrics Classic) tipping the scales at almost 300 pages, more
timely updates and advances had come in the form of thirteen
RiskMetrics Monitors, and the free dataset had expanded to cover
foreign exchange, equity, fixed income, and commodities in 33
countries. Demand for a straightforward implementation of the model
arose as well, leading to the development of our first software
product, FourFifteen.
In 1998, as client demand for the group's risk management
expertise far exceeded the firm's internal risk management
resources, RiskMetrics was spun off from J.P. Morgan. We have
continued in our commitment to transparency, and have continued to
publish enhancements to the RiskMetrics methodology, most recently
in two issues of the RiskMetrics Journal in 2000. In total, we have
now distributed approximately 100,000 physical copies of the
various versions of the Technical Document, and still consistently
provide over 1,000 electronic versions each month through our
website. Meanwhile, the RiskMetrics datasets are still downloaded
over 6,000 times each month.
Clearly, standards do not remain static as theoretical and
technological advances allow for techniques that were unpractical
or unknown previously and as new markets and financial products
require new data sources and methods. We have faced these issues;
the methodology employed in our second and third generation market
risk applications represents a significant enhancement of the
RiskMetrics model as documented in RiskMetrics Classic.
Additionally, our experience, and the experience of the industry as
a whole, has taught that a single risk statistic derived from a
single model is inadequate, and as such, we have emphasized the use
of alternative risk measures and stress tests in our software. So,
while our model has evolved, and now represents a standard for the
year 2001, the basic documentation still represents a standard for
the year 1996, and a good deal has changed since then.
Looking back, we can divide the material covered in RiskMetrics
Classic into three major pieces. The first of these, covered in
Part One, contains the applications of the measures, or the "why"
of risk measurement. In this area, regulatory standards have
changed, as have disclosure and management practices. To address
these changes, and to provide insight into risk management
practices without delving into modeling details, we published Risk
Management: A Practical Guide in 1999.
A second area, covered in Part Four of RiskMetrics Classic,
concerns the market data that serves as the key input to the model.
As we have covered more and broader markets, the data aspect of
RiskMetrics has perhaps expanded more than any other area. We have
formed a separate data service, DataMetrics, which now warehouses
close to 500,000 series. Acknowledging the critical nature of this
service, and its status as a product in itself, we will soon
publish the DataMetrics Technical Document. This document covers
market data sources used by DataMetrics, methods used to enhance
the quality of the data, such as outlier identification, fitting of
missing data, and synchronization, and analytics employed for
derived data such as bootstrapped yield curves.
The third area, covered in Parts Two and Three of RiskMetrics
Classic, is the mathematical assumptions used in the model itself.
Although we have made significant enhancements to the models as
represented in our software, our documentation has lagged this
innovation and, unfortunately, RiskMetrics Classic,as a
representation of our software, is slightly underwhelming. In other
words, a self contained statement of the standard risk model does
not exist today. The first goal of Return to RiskMetrics, then, is
to rectify this problem by documenting the updated market-standard
risk methodology that we have actually already implemented.
As well as this update, we have seen the need to clarify a
number of misconceptions that have arisen as a result of the
acceptance of RiskMetrics Classic. Practitioners have come to
equate Value-at-Risk (VaR), the variance-covariance method, and
RiskMetrics. Thus, it is common that pundits will criticize
RiskMetrics by demonstrating that VaR is not an appropriate measure
of risk. This is really a criticism of the use of a percentile to
measure risk, but not a criticism of the model used to compute the
measure. At the same time, we hear critics of VaR who claim the
method is deficient because it captures only linear positions. This
is not a criticism of the risk measure, but rather of the classic
RiskMetrics variance-covariance method used to compute the measure.
To be clear, we state that VaR is not RiskMetrics, and, in fact, is
a risk measure that could even be an output of a model at odds with
our assumptions. By the same token, RiskMetrics is not VaR, but
rather a model that can be used to calculate a variety of risk
measures. Finally, RiskMetrics is not a single set of computational
techniques and approximations, such as the linear portfolio
assumption or the Monte Carlo procedure. Rather, RiskMetrics
encompasses all of these within a hierarchy of solution techniques
for the fundamental model.
A final goal to this exercise is one of introspection. We have
spoken of clarifying what RiskMetrics is not; there also lies the
more difficult task of illuminating what RiskMetrics is. In a very
strict sense, RiskMetrics is two fundamental and battle-tested
modeling assumptions: that returns on risk factors are normally
distributed and that volatilities of risk factors are best
estimated using an exponentially weighted moving average of past
returns. These two assumptions carry over from RiskMetrics Classic.
Since the volatility estimation procedure has not changed, and
since its explanation in RiskMetrics Classic is clear, we will not
repeat the discussion in this document. On the other hand, though
the normality assumption has not changed, we have seen the need to
present it differently for clarity. In Chapter 2, we state the
assumptions more technically and discuss two frameworks to
calculate risk measures within the model: the closed-form approach,
which is simpler but requires more approximations, and the Monte
Carlo approach, which is more exact but also more burdensome. Of
course, two assumptions do not make a risk model, and even with
these assumptions stated, the model is not complete. For instance,
it is still necessary to specify the risk factors, to which we have
devoted Chapter 1, and the instrument pricing functions, to which
we have devoted Chapter 5.
More generally, a risk model does not make a risk management
practice. This brings us to a broader definition of RiskMetrics: a
commitment to the education of all those who apply the model
through clear assumptions and transparency of methods. Only by
understanding the foundation of a model, and by knowing which
assumptions are driven by practical needs and which by modeling
exactitude, can the user know the realm of situations in which the
model can be expected to perform well. This philosophy has
motivated our restatement and clarification of the RiskMetrics
modeling assumptions. Additionally, it has motivated us to discuss
complementary modeling frameworks that may uncover sources of risk
not revealed by the standard model. Chapters 3 (historical
simulation) and 4 (stress testing) are thus included not as an
obligatory nod to alternate approaches, but rather as necessary
complements to the standard statistical model. Only through a
combination of these is a complete picture of risk possible.
Throughout this document, our goal is the communication of our
fundamental risk-modeling framework. However, in the interest of
brevity, and to avoid overly taxing the patience of our readers, we
have stayed away from delving into details that do not add to the
basic understanding of our approach. For instance, in Chapter 5, we
have chosen not to catalogue all of the instruments that we cover
in our software application, but rather have provided a detailed
look at a representative set of instruments that illustrate a broad
range of pricing approaches: fixed cash flows, floating rate cash
flows, options with closed-form pricing solutions, and options
requiring Monte Carlo or tree-based pricing methods.
We recognize that following on RiskMetrics Classic, even if only
in a focused treatment as we have written here, is a humbling task.
We hope that this document is as useful in the year 2001 as
RiskMetrics Classic was in 1996. To the readers of the old
document, welcome back. We appreciate your continued interest, and
thank you in particular for the feedback and questions over the
last five years that have helped mold this new document. To those
of you who are new to RiskMetrics in particular and risk modeling
in general, we hope that this document gives you a solid
understanding of the field, and happily invite questions, comments,
and criticisms.
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