0.4 Notation

0.4 Notation

Good notation is more than a convention. It shapes how we think, streamlines problem solving, avoids misunderstandings and facilitates communication. Nowhere was this more evident than the introduction of Hindu-Arabic numerals into Europe during the Middle Ages. Because they made arithmetic computations so easy, the new numerals supplanted both Roman numerals and the abacus, which had been widely used in Europe. Another example comes from calculus. While Newton and Leibniz disputed who discovered the fundamental theorem of calculus, there is no dispute as to whose notation was superior. We still use Leibniz’s notation today.

One of the contributions of this book is consistent notation for expressing ideas related to value-at-risk. Once you master the notation reading the book, I encourage you to keep using it. The notation will guide your thinking and help you avoid pitfalls.

Value-at-risk draws on many branches of mathematics. Each offers its own notation conventions. Because these conflict, it is impossible to observe them all simultaneously. But the book’s notation consistently presents financial concepts related to calculus, linear algebra, probability, statistics, time series analysis, and numerical methods, drawing on existing conventions where possible.

Random quantities are indicated with capital English letters. If they are univariate random variables they are italic non-bold: QRSX, etc. If they are multivariate in some sense—random vectors, random matrices, stochastic processes—they are italic bold: QRSX, etc. Nonrandom quantities are indicated with lowercase italic letters. These are nonbold for scalars: qrsx, etc. They are bold for vectors, matrices, or time series: qrsx, etc.

With this notation, if a random variable is denoted X, a specific realization of that random variable may be denoted x. Such notational correspondence between random quantities and realizations of those random quantities is employed throughout the book.

Components of vectors or matrices are distinguished with subscripts. Consider the random vector

[0.1]

or the matrix

[0.2]

Time also enters the equation. To avoid confusion, I do not indicate time with subscripts. Instead, I use superscripts that precede the rest of the symbol. For example, the Australian dollar Libor curve evolves over time. We may represent its value at time t as

[0.3]

The value at time 3 of 1-month AUD Libor is The entire curve at time 1 is denoted 1R. The univariate stochastic process representing 1-week AUD Libor over time is represented R2. The 15-dimensional stochastic process representing the entire curve over time is denoted R. Time 0 is generally considered the current time. At time 0, current and past Libor curves are known. As nonrandom vectors, they are represented with lowercase bold letters: If time is measured in days, yesterday’s value of 12-month AUD Libor is denoted

The advantage of using preceding superscripts to denote time is clarity. By keeping time and component indices physically separate, my notation ensures one will never be confused for the other. Use of preceding superscripts is unconventional but not without precedent. Actuarial notation makes extensive use of preceding superscripts.

Much other notation is standardized, as will become evident as the book unfolds. Some frequently occurring notation is summarized below.

log natural logarithm
n! factorial of an integer n, which is given by the product n! = 1·2·3 ··· (n –1)·n, with 0! = 1
B(m, p) binomial distribution for the number of “successes” in m trials, each with probability p of success
U(a, b) uniform distribution on the interval (a, b)
Un(Ω) n-dimensional uniform distribution on the region Ω
N(μ, σ2) normal distribution with mean μ and variance σ2
Λ(μ, σ2) lognormal distribution with mean μ and variance σ2
χ2(ν, δ2) chi-squared distribution with ν degrees of freedom and
non-centrality parameter δ2
Nn(μ, Σ) joint-normal distribution with mean vector μ and covariance matrix Σ
1P random variable for a portfolio’s value at time 1
0p portfolio value at time 0
( 0p, 1P) a portfolio
1L random variable for portfolio loss: 0p1P
1R random vector of key factors (key vector)
0r vector of key factor values at time 0
1S random vector of asset values at time 1 (asset vector)
0s vector of asset values at time 0
Any of these might indicate a risk vector that is not a key vector.
E( ) unconditional expected value
tE( ) expected value conditional on information available at time t
std( ) unconditional standard deviation
tstd( ) standard deviation conditional on information available at time t
var( ) unconditional variance
tvar( ) variance conditional on information available at time t
tμ unconditional mean of the time t term of a stochastic process
t|t–kμ mean of the time t term of a stochastic process conditional on information available at time tk
tΣ unconditional covariance matrix of the time t term of a stochastic process
t|t–kΣ covariance matrix of the time t term of a stochastic process conditional on information available at time tk
θ frequently used to denote a portfolio mapping function
frequently used to denote a (non-portfolio) mapping function
ω portfolio holdings
tφ( ) unconditional PDF of the time t term of a stochastic process
t|t–kφ( ) PDF of the time t term of a stochastic process conditional on information available at time tk
tΦ( ) unconditional CDF of the time t term of a stochastic process
t|t–kΦ( ) CDF of the time t term of a stochastic process conditional on information available at time tk
Tildes can be placed above or between symbols. Placed above indicates a remapping; for example, denotes a remapping of 1P = θ(1R). Placed between indicates that a random variable or random vector has a particular distribution; for example, X ~ N(0,1) indicates that random variable X is standard normal.
indicates that a random variable or random vector has a particular distribution, conditional on information available at time t. For example, indicates that, conditional on information available at time 0, 1X is standard normal.
indicates that analytic software more sophisticated than a spreadsheet may be useful in solving an exercise

For more detailed explanations of notation, Section 2.2 addresses general mathematical notation used throughout the book. Section 4.6 elaborates on notation for time series analysis. Section 1.8 explains the notation of value-at-risk measures.

Currencies are indicated with standard codes:

Exhibit 0.1: currency codes

Where applicable, millions are indicated as MM. For example, 3.5 million Japanese yen is indicated JPY 3.5MM.

Exchange rates are indicated as fractions, so an exchange rate of 1.62 USD/GBP indicates that one British pound is worth 1.62 US dollars.

Acronyms used include those shown in Exhibit 0.2.

BBA British Bankers Association
CAD Capital Adequacy Directive
CBOT Chicago Board of Trade
CDF Cumulative Distribution Function
CME Chicago Mercantile Exchange
CSCE Coffee, Sugar and Cocoa Exchange
EICCG explicit inversive congruential generator
ETL expected tail loss
ICG inversive congruential generator
IID independent and identically distributed
IPE International Petroleum Exchange
LCG linear congruential generator
Libor London Interbank Offered Rate
LIFFE London International Financial Futures and Optons Exchange
LME London Metals Exchange
MGF moment generating function
ML maximum likelihood
MRG multiple recursive generator
MSE mean squared error
NYBOT New York Board of Trade
NYMEX New York Mercantile Exchange
NYSE New York Stock Exchange
OTC over the counter
P&L profit and loss
PDF probability density function
PF probability function
PMMR probabilistic metric of market risk
RAROC risk-adjusted return on capital
RORAC return on risk-adjusted capital
SEC Securities and exchange Commission
TSE Toronto Stock Exchange
UNCR Uniform Net Capital Rule
VaR value-at-risk
WCE Winnipeg Commodities Exchange
Exhibit 0.2: Acronyms