Glossary

Symbols

(probabilistic) linear regression, 15.1.1

A

absence of arbitrage, 24a.1.3
abstract Bayes theorem, 24b.4.4
accounting signals, 46.5
accrued interest, 23.2.1
action, 37.1.1
activation function, 14.2.5
activity time, 1.8.2
actual cash-flow, 23.2.3
actual exchange rate, 23.2.3
actual value, 23.2.3
adaptive execution algorithm, 10.3.2
additive, 31.11.1
admissible, 38.2.2
algebraic multiplicity, 50.6.5
algebraic Riccati equation, 50.8.2
allocation policy, 6.5
alpha, 7.3.1
alternative beta, 9c
alternative hypothesis, 41.1
analysis of variance, 14.1.5
ancestor, 15.3.4
angle, 50.4.1
    canonical, 36.3.2
    covariance, 36.3.2
    expectation, 36.3.1
anti-monotonic, 34.2
    transformation, 51.5.2
arbitrage pricing theory, 24b.3.1
architecture, 14.2.5
area under curve (AUC), 14.4.4
arithmetic Brownian motion with drift, 44.1.2
Arrow-Debreu securities, 24b.1.3
Arrow-Pratt absolute risk aversion function, 7.4.1
ask size, 1.8.1
assets, 6.7.1
at the money, 1.4.2
    foreign exchange market, 1.4.4
augmented view variables, 16.5.3
autocorrelation
    function, 45.1
autocovariance
    function, 45.1
autoencoders, 14.5
autoregressive conditional duration, 2.6.2
autoregressive moving average process
    autoregressive
        multivariate (VAR), 45.3.6
        univariate (AR), 45.3.1
    integrated
        multivariate (VARIMA), 45.3.6
        univariate (ARIMA), 45.3.4
    moving average
        multivariate (VMA), 45.3.6
        univariate (MA), 45.3.2
        univariate infinite order, 45.3.2, 45.3.2
    multivariate (VARMA), 45.3.6
    univariate (ARMA), 45.3.3
autoregressive of order one, 45.2
autoregressive-fractionally integrated-moving average of order (p,d,q), 2.4
auxiliary measure, 31.9

B

Bachelier, 26.3.1
backpropagation, 48.1.1
backward cash-flow-adjusted value, 23.1.5
backward/forward exponential weighted moving average, 3.11.6
bag of words, 15.3.3
bagging, 40.4.1
balance sheet, 6.7.1
balanced, 40.1
bandpass filter, 45.7
bandwidth, 3.1.2
    kernel density estimate, 3.2.3
bandwidth matrix, 3.1.2
base case, 16.6.1
base distribution, 16.1.1
base measure, 31.9
basis, 28.4.3, 50.1.2
    canonical, 50.1.2
    orthonormal, 50.4.3
basis denominator, 28.4.3
basis instruments, 24b.1.1
Bayes classifier, 14.4.1, 14.4.3
Bayes error
    expected error, 39.1.2
    frequentist approach, 39.4.2
    frequentist prediction, 39.3.2
Bayes risk, 37.1.2
Bayes theorem, 38.2.1
Bayesian decision, 37.1.3
Bayesian networks, 15.3.6
benchmark, 28.5.1
best ask, 1.8.1
best bid, 1.8.1
best prediction, 50.4.4
beta-adjusted excess return, 28.5.2
bets, 8b.5
between-cluster/group variance, 14.1.5
Bhattacharyya coefficient, 40.4.4
bias, 36.6.4, 39.1.2
bid size, 1.8.1
bid-ask spread, 10.1
bilateral value adjustment, 6.2
binary classification, 14.4
binary matrix, 14.4.3
binning, 1.8.3
binomial inverse theorem, 50.5.4
binomial tree, 2.1.2
bins, 1.8.3, 49.3.2
bins width, 49.3.2
Black-Merton-Scholes model, 26.3.2
bond yield, 5.3.2
bootstrap aggregating, 40.4.1
boundedness, 16.1.7
breadth, 9c.5.2, 14.2.5
breakdown point, 3.7.2
Bregman divergence, 15.1.3, 32.1.4
Brownian motion, 2.1.1
Buhlmann pricing equation, ??
Buhlmann principle, 25.2.4
butterfly, 1.4.4

C

calendar signal, 46.5
calibrate, 26
call option, 1.4.1
canonical basis property, 31.8.2
canonical correlation matrix, 34.3.1
canonical parameters, 31.9
capital asset pricing model, 24b.2
capital gain, 23.1.6
carry signal, 46.1
CART, 14.2.3
cash-flow function, 5.1
cash-flow-adjusted value, 23.1.5
categorical distributions, 31.8.1
categories, 31.8.1
Cauchy distribution, 31.6.2
Cauchy-Schwartz inequality, 50.4.1
causal, 45.7
center of the bin, 49.3.2
central tendency, 36.5.1
    affine equivariant, 36.4.1, 36.4.2
certainty-equivalent, 7.4
certainty-equivalent principle, 25.2.2
chain rule, 15.3.6
    gradient, 51.1.2
    matrix-variate, 51.1.3
    multivariate, 51.1.2
    univariate, 51.1.1
change of variable formula
    multivariate, 51.4.3
    univariate, 51.4.1
characteristic equation, 50.6.1
characteristic function
    multivariate, 31.1
    univariate, 31.1
characteristic matrix, 9c.4
characteristic polynomial, 50.6.1
characteristic portfolio, 9c.2
characteristic portfolios, 9c.4
Chebyshev’s inequality, 36.2.7
chi-squared distribution, 31.5.2
child, 15.3.4
child orders, 10
Cholesky decomposition, 50.8.3
Cholesky root, 50.8.3
CIR, 44.2.2
claims, 1.6
classes, 31.8.1
classical-equivalent, 38.2.2
classification, 13.1.1, 14.4
classification and regression trees, 14.2.3
clean price, 23.2.1, 23.2.1
clique, 15.3.4
clique factorization, 15.3.5
clustering, 13.1.2, 14.5.6
co-monotonic, 7.2
    co-monotonicity, 34.2
    copula invariance, 33.2.4
    transformation, 51.5.2
co-monotonic additivity, 7.2
coarseness level, 49.3.2
codes, 14.5
coherent satisfaction measure, 7.7.1
cointegrated, 42.1
cointegrated space, 2.8.1
cointegration signal, 46.3.3
cointegration vector, 2.8.1
    multivariate process, 42.1
collateral, 6.9.1
commonalities, 12.4.5
comparable instruments, 26
complete, 24b.1.1
completeness pricing formula, 24b.1.2
compound distribution, 31.3.3
compound Poisson process, 44.1.3
compounded rate of return
    (instantaneous) compounded rate of return, 28.4.6
    average compounded rate of return, 28.4.6
compounded return, 28.4.1
concave down function
    univariate, 51.6.1
concave function
    multivariate, 51.6.2
    univariate, 51.6.1
concave up function
    univariate, 51.6.1
concavity, 7.2
condition number, 3.5.2
conditional, 38.2.1
conditional cdf, 31.3.1
conditional covariance, 31.3.2
conditional distribution of variable, 31.3.1
conditional excess distribution, 7.5.2
conditional expectation, 31.3.2
conditional independence, 15.3, 40.1
conditional pdf, 31.3.1
conditional principal component analysis, 12.6.14
conditional principal directions, 12.6.14
conditional principal variances, 12.6.14
conditional probability, 31.3.1
conditional statistical feature, 31.3.2
conditional value at risk, 7.6.2
conditioning-marginalization, 16.1.3
cone, 48.1.3
conic programming, 48.1.3
conjugate distribution, 38.2.4
consistency with weak dominance, 7.2
consistent with q-th order dominance, 7.2
constancy, 7.2
constant proportion portfolio insurance, 9d.4.1
constraint set, 48.1
constructed variables
    unsupervised learning, 13.1
continuous-state distributions, 2.1
convex function
    multivariate, 51.6.2
    univariate, 51.6.1
convex programming, 48.1.2
convex set, 48.1.3
convexity, 5.3.2
    risky investment, 9d.6
    satisfaction/risk measures, 7.2
convolution, 45.7
coordinate descent, 48.2.7
copula, 33.2.2, 33.2.2
copula-marginal combination, 33.4.2
copula-marginal distributions, 33.4.2
copula-marginal separation, 33.4.1
copula-pdf, 33.2.3, 33.2.3
Cornish-Fisher approximation, 7.5.2
Cornish-Fisher expansion, 7.5.2
correlation, 34.3
correlation function, 34.3
correlation matrix, 34.3
cost of equity, 26.2.1
cost of trading, 9d.6
counterparty credit risk, 6.2
coupon, 1.11.2
coupon bond, 1.3.1
CoVaR, 6.6.1
covariance matrix, 36.2.1
covariance principle, 24a.5
covariance stationary, 45.1
covariant, 32.1.1
covariates, 13.1
Cramer representation, 45.6
Cramer-Lundberg ruin model, 6.7.5
credit ratings, 1.5.3
credit structural model, 128
credit value adjustment , 6.2
critical point, 48.1
cross the spread, 10.1
cross-autocovariance
    function, 45.1
cross-sectional, 9c, 9c
cross-sectional LFM, 12.5
cross-sectional sample median of default probabilities, 1.5.3
cumulant , 4.7.1
cumulative cash-flow, 23.1.3
cumulative distribution function
    multivariate, 31.1
    univariate, 31.1
cumulative link, 31.13.1
cumulative monetary amount, 1.8.1
cumulative number of migrations, 1.5.3
cumulative P&L, 1.9
cumulative signed volume, 1.8.1
cumulative trade sign, 1.8.1
cumulative volume, 1.8.1
currency carry trade, 5.2.2
curvature, 1.3.5
curve/surface signals, 46.5

D

data, 37.1.1
debt, 6.7.1
debt value adjustment, 6.2
decay, 1.3.5
decision, 37.1.1
decoder, 14.5
decreasing function, 51.5.1
    strictly, 51.5.1
degree of reversal, 9c.2.3
delta, 1.4.4
dependent variables, 12.1, 13.1
depth, 14.2.5
derivative
    directional, 51.1.2
    first, 51.1.1
    higher order, 51.1.1
    partial, 51.1.2
    second, 51.1.1
    total, 51.1.2
    univariate, 51.1.1
descendant, 15.3.4
determinant, 50.2.3
deviation
    maximum, 36.5.3
    mean absolute, 36.5.3
    median absolute, 36.5.6
    semi, 36.5.4
    subquantile, 36.5.5
differentiable function
    multivariate, 51.1.2
    univariate, 51.1.1
dimension, 50.1.2
Dirac delta, 31.8.2
direct sum, 50.1.3
directed graph, 15.3.4
dirty price, 23.2.1
discount, 1.11.2
discount factor, 23.2.3
discount function, 5.1
discounted cash-flow, 26.2.1
discounted cash-flow adjusted value, 25.3.1
discounted payoff, 25.2
discrete Fourier transform, 49.3.1
discrete-state distributions, 2.1
discrete-state random walk, 2.1.2
discretized cf, 49.3.2
discretized pdf, 49.3.2
discriminant model
    probabilistic, 13.3.1
discriminant next-step model, 2.7.6
discriminant variables, 31.13.1
dispersion, 36.5.1
    affine equivariant, 36.4.1
    modal, 36.4.1
distance, 50.4.1
    covariance, 36.3.2
    expectation, 36.3.1
    Lp, 36.6.2
distance to default, 2.3.4
distorted cdf
    satisfaction indices/risk measures, 7.6.1
distorted ex-ante performance
    satisfaction indices/risk measures, 7.6.1
distorted pdf
    satisfaction indices/risk measures, 7.6.1
distortion expectation, 7.6.1
distortion function, 7.6.1
distortion principle, 25.2.3
distributional view, 16.1.3
diversification distribution, 8b.5
diversity, 40.4.4
dividend-adjusted value, 1.1
    stocks, 23.1.5
divisors, 31.11.2
dollar duration, 6.1.3
dollar-neutral constraint, 9c.2.2
dominant-residual LFM, 12.1.2
dot product, 50.4
drawdown, 28.6
    maximum (absolute) drawdown, 28.6
    maximum percentage drawdown, 28.6
    percentage drawdown, 28.6
dscrete-time Fourier transform, 49.3.1
dual Legendre, 32.1.3
dually flat, 32.1.3
duration, 5.3.2
DV01, 6.1.3
dynamic allocation, 9c
dynamic conditional correlation, 3.9.3
dynamic graphical models, 45.4
dynamic linear factor model, 17.2
dynamic models, 17
dynamic principal component, 17.6
dynamic regression model, 17.4

E

e-affine coordinates, 32.1.2
e-flat, 32.1.2
e-geodesic, 32.1.2
EBITDA, 6.7.2
economic capital, 7.10
economic net income, 6.7.2
edges, 15.3.4
effective convexity, 5.3.2
effective delta, 5.3.3
effective duration, 5.3.2
effective key rates durations, 5.3.2
effective number of bets, 8b.5
effective number of scenarios, 3.1.4
effective rank, 16.1.6
effective rho, 5.3.3
    key-rates, 5.3.3
effective volga, 5.3.3
efficient market hypothesis, 2.1
eigenvalue, 50.6.1
eigenvector, 50.6.1
elastic net, 40.2.2, 48.2.7
    constrained generalized elastic net, 48.2.7
ellipsoid, 36.2.4
    expectation-covariance, 36.2.4
    location-dispersion, 36.4.2
ellipsoid test for invariance, 43.1
elliptical distribution, 31.7.1
encoder, 14.5
energy, 38.1.2
ensemble approach, 37.1.4
enterprise value, 26.2.2
entropy, 38.1.2
equilibrium performance model
    Black-Litterman, 47.11
equilibrium returns
    Black-Litterman, 47.7
equity, 6.7.1
equity book value, 6.7.1
equivalent optimization problem, 48.3.1
ergodic (in mean), 42.1
Erlang process, 44.1.3
error, 36.6.4, 39.1.2
    multivariate, 36.5.7
    posterior error, 39.1.3
    univariate, 36.5.1
error correction, 45.2.9
Esscher principle, 25.2.4
estimable, 7.2
estimate, 39.1.1
    model risk, estimation risk, 37.1.4
estimation, 13.3
estimation model, 3.4
    Bayesian estimation, 38.2.2
estimation risk
    model risk, estimation risk, 37.1.4
estimation set, 40.1
    point prediction, 39.3.1
    predictive distribution, 39.4.1
estimation uncertainty, 38.2.2
estimator, 39.1.1
European-style derivatives, 1.4
evidence
    maximum likelihood, 38.1.2
evidence lower bound, 38.1.2
ex-dividend date, 23.1.2
exotic beta, 9c
expectation
    multivariate, 36.2.1
    univariate, 36.1.1
expectation rule, 31.8.3
expected drawdown, 7.3.1
expected overperformance, 7.3.1
expected shortfall
    sensu stricto, 7.6.2
expected utility, 7.4
expected value of the process variance (EVPV), 14.1.5
expectile, 7.7.2
expectile-VaR, 7.7.2
expiry, 1.4
explanatory variables, 13.1
exponential decay probabilities, 3.1.1
exponential family distribution, 31.9
exponential kernel, 3.1.2
exponential of the entropy, 3.1.4
exponential principle, 25.2.2
exponential tilting, 16.1.5
exponentially weighted moving average, 3.2.4
exponentially weighted moving correlation, 3.2.4
exponentially weighted moving covariance, 3.2.4
exponentially weighted moving quantile, 3.2.4
exponentially weighted moving standard deviation, 3.2.4
exposure, 6.1.3
    portfolio P&L, 9c.2
    risky investment, 9d.6
exposure at default, 1.5.2
extrema
    local, 48.1
    relative, 48.1
extreme value theory, 7.5.2

F

face value, 1.3.1
factor analysis, 3.5.6
factor analysis matrix, 3.5.6
factor loadings, 12.1
factor premia, 9c.4.2
factor premium, 9c.1
factor-replicating portfolios
    arbitrage pricing theory, 24b.3.1
factors, 9c, 13.1
    linear factor model, 12.1
fair value, 23.1.2
false negative rate, 14.4.3
false positive rate, 14.4.3
fast Fourier transform, 49.3
feature engineering, 14.1
features, 13.1
Feller condition, 44.5.1
filtering, 13.3
financial instrument, 23.1.1
finite difference
    backward first order, 51.2.1, 51.2.2
    central first order, 51.2.1, 51.2.2
    central second order, 51.2.1, 51.2.2
    forward first order, 51.2.1, 51.2.2
first in, 8b.1.1
first order criterion, 48.1
first order differential, 51.1.2
    matrix-variate, 51.1.3
Fisher consistent, 3.7.1
Fisher discriminant analysis (FDA), 14.4.6
Fisher information distance, 32.1.4
flexible probabilities, 3
    estimation, 31.8
forecast, 12.1
forecasting
    inference, 13.3
foreign exchange function, 5.1
foreign exchange rate, 1.2.1
forward, 1.2.2
forward cash-flow-adjusted value, 23.1.5
forward exchange rate, 1.2.1
forward rate, 1.11.2
forward swap, 1.3.1
forward variance swap rate, 1.4.6
fourier integral, 49.3.1
Fourier series, 49.3.1
fractional Brownian motion, 44.4
fractional integrated process, 2.4
Frechet-Hoeffding bounds, 34.1.1
frequency response function, 45.7
    gain, 45.7
    phase, 45.7
frequentist risk, 37.1.2
full-investment, 9c.2.2
fully constrained LFM, 12.6.15
fundamental accounting equation, 6.7.1
fundamental law of active management, 9c.5.2
fundamental LFM, 12.5
fundamental signals, 46.5
fundamental theorem of asset pricing, 24a.2.2
fundamental theorem of calculus
    first, 51.4.2
    second, 51.4.2
funding risk, 6.2
funding value adjustment, 6.2

G

gamma distribution, 31.5.2
Gateaux derivative, 14.2.8
Gaussian kernel, 3.1.2, 3.2.3
Gaussian process, 31.4.2
generalized autoregressive conditional heteroscedastic, 2.6.1
generalized excess return, 28.5.2
generalized linear models (GLM), 15.1.1
generalized linear return, 28.4.3
generalized method of moments with flexible probabilities (GMMFP) estimate, 3.6.2
    minimization, 3.6.3
generalized Pareto distribution, 7.5.2
generalized weight, 28.4.4
generative model, 13.3.1
generative next-step model, 2.7.6
generator, 44.3.1
generic position, 26.1.3
geometric Brownian motion, 5.1.1
geometric multiplicity, 50.6.5
Gibbs distribution, 15.3.5
Gini coefficient, 14.4.4
Gini impurity, 15.2.3
glasso, 3.5.5
    Tikhonov, 40.2.3
Glivenko-Cantelli theorem, 3.2.1
global minimum variance portfolio, ??
Gordon growth model, 26.2.1
grade, 33.1
grades, 33.2.1
gradient, 51.1.2
    matrix-variate, 51.1.3
    vector-valued function, 51.1.2
gradient descent, 48.1.1
    stochastic, 48.1.1
Gram matrix, 50.8.4
Gramian, 50.8.4
grand mean, 3.5.1
graph, 15.3.4
graphical lasso, 3.5.5
Greeks, 5.3
gross exposure, 6.1.3
growth stocks, 46.2.1

H

Hadamard product, 50.5.2
half-life, 3.1.1
Hamiltonians, 31.9
hazard function, 1.6
Hellinger distance, 40.4.4
Hermitian inner product, 49.3.1
Hessian, 51.1.2
    matrix-variate, 51.1.3, 51.1.3
Heston model, 26.3.3
hidden variables, 13.1
    maximum likelihood, 38.1.2
    point prediction, 39.3.1
high breakdown estimators, 3.7.2
high breakdown point with flexible probabilities, 3.7.2
high minus low, 9c.1
high water mark, 28.6
historical cdf, 3.2.1
historical cross-sectional, 12.5.7
historical distribution, 3.2.1
historical pdf, 3.2.1
historical principal component, 12.3.7
historical repricing, 5.5.2
historical with flexible probabilities (HFP) autoencoder, 39.3.4
historical with flexible probabilities (HFP) cdf, 3.2.1
historical with flexible probabilities (HFP) correlation matrix, 3.2.2
historical with flexible probabilities (HFP) covariance matrix, 3.2.2
historical with flexible probabilities (HFP) distribution, 3.2.1
historical with flexible probabilities (HFP) estimate, 3.2.1
historical with flexible probabilities (HFP) mean, 3.2.2
historical with flexible probabilities (HFP) median, 3.7.2
historical with flexible probabilities (HFP) pdf, 3.2.1
historical with flexible probabilities (HFP) predictor, 39.3.4
historical with flexible probabilities (HFP) quantile, 3.7.2
historical with flexible probabilities (HFP) standard deviation vector, 3.2.2
hold-out, 39.3.5
Hotelling statistic, 41.1.4
Hurst coefficient, 44.4
hybrid Monte Carlo-historical, 4.5.2

I

ice-cream cone, 48.1.5
identity transformation, 50.2.3
idiosyncratic, 12.1.3
ill-conditioned, 3.5.2
image space, 50.2
implementation shortfall, 28.3
implied returns
    Black-Litterman, 47.7
implied volatility, 1.4.3
implied volatility surface, 1.4.3
improper integral, 51.4.1
impulse response, 45.7
in the money, 1.4.2
in-sample error, 40.16
inception, 28.2.2
income, 23.1.6
income statement, 6.7.2
increasing function, 51.5.1
    strictly, 51.5.1
indefinite integral, 51.4.2
independent component analysis, 14.5.8
independent variables, 13.1
    linear factor model, 12.1
inefficiency, 36.6.4, 39.1.2
inference, 13.3
infinitely divisible, 31.11.2
inflator, 24a.2.2
influence function, 3.7.1
information, 2.10.1
    random time series, 3
information coefficient, 9c.5.2
information generator, 2.10.1
information ratio, 36.1.1
    conditional information ratio, 9c.5.2
    maximum (l2-mean unconditional) information ratio, 9c.5.2
    maximum conditional information ratio, 9c.5.2
information set, 2.10.1, 14.1.5
    linearized, 12.2.6
information/view, 16.6.1
inner product, 50.4
    covariance , 36.3.2
    expectation, 36.3.1
    linearity, 50.4
    positive definiteness, 50.4
    symmetry, 50.4
inner product space, 50.4
innovation
    weak, 36.3.4
inputs, 13.1
instantaneous exchange rate, 23.2.3
instantaneous forward curve, 1.11.2
instantaneous forward rate, 1.11.2
instantaneous spot rate, 1.11.2
integral Fourier transform, 49.3.1
integrated, 42.1
integration by parts, 51.4.1
integration operator, 35.4
intensity models, 26.4.2
interaction, 14.2.1
interest rate, 1.3.3
internal rate of return, 28.4.6
interquantile range, 36.4.1
intuitive r-squared, 12.5.5
invariance rule, 31.8.3
invariant, 36.4.2
invariants, 2
inverse, 50.2.3
inverse transform sampling, 33.1
inverse-call, 1.3.4
inverse-Wishart distribution, 31.6.7
invertible, 50.2.3
investment factor, 28.4.6
    reinvested instrument, 23.1.4
iso-contour, 36.6.1
isolated, 8b.1.1
iterated integral, 51.4.3

J

jackknife estimator, 3.7.1
Jacobian, 51.1.2
James-Stein estimator, 3.5.1
Jeffreys prior, 38.2.2
joint model, 37.1.2
joint scenario, 31.8
jump rule, 23.1.3

K

k-fold, 39.3.5
k-means clustering, 14.5.6
kappa ratio, 7.8.2
Karhunen-Loeve representation, 45.6
Kendall’s tau, 34.2.1
kernel, 3.2.3
kernel density estimate, 3.2.3
kernel stochastic discount factor, 24a.2.3
kernel trick, 14.2.2, 50.7
kernel with flexible probabilities (KFP) generalized mean, 3.2.3
kernel with flexible probabilities (KFP) pdf, 3.2.3
key rates, 1.3.5
Kolmogorov-Smirnov test, 43.1
Kronecker product, 50.5.2
Kullback-Leibler divergence, 32.1.4

L

L2-space, 36.3
labels, 13.1
Lagrange multiplier, 48.1.1
Laplace approximation, 36.4.1
large capitalization stocks, 46.5
lasso, 40.2.2, 48.2.7
lasso regression, 18.3.2
lasso shooting, 48.2.7
last in, 8b.1.2
last transaction price, 1.8.1
latent variables, 13.1
    maximum likelihood, 38.1.2
law invariant, 7.2
law of iterated expectations, 14.1.5
law of one price, 24a.1.2
law of total covariance, 14.1.5
law of total variance, 14.1.5
LDL-Cholesky decomposition, 50.8.3
learning, 13.3
least-squares residual, 14.1.1, 14.1.1
leave-1-out, 39.3.5
leave-p-out, 39.3.5
leaves, 14.2.3
Legendre transformation, 32.1.3
length, 50.4.1
    covariance, 36.3.2
    expectation , 36.3.1
    Lp, 36.6.2
level, 1.3.5
leverage, 6.1.4
leverage effect, 2.6.2
Levy process, 44.1.1
Levy-Khintchine, 44.1.4
liabilities, 6.7.1
Libor, 1.11.2
likelihood, 3.4
    Bayesian estimation, 38.2.2
    estimators as random variables, 39.1.2
    maximum likelihood, 38.1.1
limit order book, 1.8.1
limit order placement, 10.3
linear classification, 14.203
linear combination, 50.1.2
linear dependence, 50.1.2
linear discriminant analysis (LDA), 15.2.1
linear factor model, 12.1
linear independence, 50.1.2
Linear L2 error matrix, 36.3.4
linear L2 prediction, 12.2.7
linear L2 projection, 12.2.8
linear law of iterated L2 projections, 12.2.8
linear law of total covariance, 12.2.8
linear law of total variance, 12.2.8
linear observation equation, 45.4
linear pricing equation, 24a.2.1
linear programming, 48.1.8
linear return, 28.4.1
linear state space model, 45.4
linear time invariant filter, 45.7
linear transformation, 50.2
linear transition equation, 45.4
linearity, 24a.1.1
linearly constrained quadratic programming, 48.1.6
linearly separable, 14.205
link function, 31.9
linkage matrix, 9c.5.2
liquidation, 28.2.2
liquidation valuation, 26.1.5
liquidity curve, 10.1
    "market buy" liquidity curve, 10.1
    "market sell" liquidity curve, 10.1
local Markov property, 15.3.6
location, 36.5.1
    affine equivariant, 36.4.1, 36.4.2
    multivariate, 36.5.7
log-loss, 13.2
log-partition function
    exponential family distribution, 31.9
log-return, 28.4.6
log-sum-exp function, 31.9.2
logarithmic score, 13.2
logistic function, 31.388
logistic regression, 15.2.1
logit function, 31.9.2
lognormal distribution, 31.6.6
    shifted, 31.6.6
long holdings, 28.1
long memory, 2.4
long position, 26.1.1
longitudinal data, 40.1
Lorentz cone, 48.1.5
Lorenz curve, 31.13
loss, 36.6.4, 37.1.1
loss given default, 1.5.2
lower partial moment, 36.5.4
lower partial moment principle, 25.2.1
Lp-space, 36.6.2

M

m-affine coordinates, 32.1.2
m-flat, 32.1.2
m-geodesic, 32.1.2
m-square, 5.3.2
macro signals, 46.5
macroeconomic LFM, 12.2
Mahalanobis distance, 36.2.1
Marchenko-Pastur distribution, 3.5.3
marginal, 38.2.1
marginal cdf, 31.2
marginal characteristic function, 31.2
marginal contributions, 8b.2
marginal distribution, 31.2
marginal pdf, 31.2
marginal supply demand curve, 10.1
marked-to-market, 23.1.2
marked-to-model, 23.1.2
market beta, 9c.2
market capitalization, 6.7.1
market impact, 10.1.3
market impact decay kernel, 10.1.3
market impact function, 10.1.3
market impact model, 10.1.3
market impact P&L, 10.2.2
market order placement, 10.3
market parameters, 26
market portfolio, 9c.1
market price of risk, 24a.4.1
Markov process, 42.1
    time homogenous, 42.1
MARS, 14.2.3
martingale, 42.1
matrix, 50.2.1
    addition, 50.2.2
    commutation, 50.5.4
    conformable, 50.2.2
    decomposition, 50.5.4
    identity, 50.2.3
    inverse, 50.2.3
    invertible, 50.2.3
    low-rank-diagonal, 50.5.4
    multiplication, 50.2.2
    non-singular, 50.2.3
    polynomial, 50.9
    positive definite, 50.3
    positive semidefinite, 50.3
    rank property, 50.5.4
    scalar multiplication, 50.2.2
    size, 50.2.1
    square, 50.2.1
    subtraction, 50.2.2
    symmetric, 50.3
    transpose, 50.3
    transpose-square-root, 50.8
matrix polynomial
    autoregressive multivariate, 45.3.6
    autoregressive univariate, 45.3.1
    moving average multivariate, 45.3.6
    moving average univariate, 45.3.2
matrix-normal distribution, 31.4.2
matrix-vector multiplication, 50.2.1
maturity, 1.3.1
maximum
    global, 48.1
    local, 48.1
    relative, 48.1
maximum a posteriori, 38.2.2
maximum likelihood factorization, 15.3.1
maximum likelihood parameters, 38.1.1
maximum likelihood with flexible probabilities, 3.3.1
    normal assumption, 18.2.2
    Student t assumption, 18.2.3
maximum likelihood with flexible probabilities (MLFP) estimate, 3.3.1
maximum likelihood with flexible probabilities (MLFP) predictor, 39.3.4
maximum Sharpe ratio portfolio, ??
mean reversion, 2.2
mean-lower partial moment, 7.7.2
mean-semideviation, 7.7.2
measure of concordance, 34.2
measure of dependence, 34.1
measurement equation, 17.9, 45.4
median
    univariate, 36.4.1
Mercer kernel, 50.7
Mercer’s theorem, 50.7
method of moments (MM) estimate, 3.6.1
method of moments with flexible probabilities (MMFP) estimate, 3.6.1
metric geodesic, 32.1.2
Metropolis-Hastings algorithm, 49.1.1
microprice, 1.8.1
mid-quote, 1.8.1
midrange, 36.5.3
minimax decision, 37.1.2
minimum
    global, 48.1
    local, 48.1
    relative, 48.1
minimum relative entropy numeraire probability, 24a.2.3
minimum relative entropy stochastic discount factor, 24a.2.3
minimum-torsion bets, 8b.5.1
minimum-torsion exposures, 8b.5.1
minimum-torsion transformation, 8b.5.1
misclassification error, 36.5.3
mixture components, 15.3.2
mixture distribution, 15.3.2, 15.3.2
mixture of experts, 15.1.2
mode
    multivariate, 36.4.2
    univariate, 36.4.1
model
    Bayesian approach, 37.1.3
    frequentist approach, 37.1.2, 39.4.2
    frequentist prediction, 39.3.2
model risk, 37.1.4
model uncertainty, 37.1.4
moment generating function, 31.1
    multivariate, 31.1
momentum, 46.3.1
momentum signal, 46.3.1
money multiple, 28.4.6
money-equivalence, 7.2
moneyness, 1.4.2
monotone map, 51.5.2
    strictly, 51.5.2
monotonic function, 51.5.1
    strictly, 51.5.1
monotonicity, 7.2, 16.1.7
mortgage backed securities, 23.2.1
multinomial logistic regression, 15.2.1
multinomial logit function, 31.9.2
multiple, 23.1.4
multiple of invested capital (MOIC), 28.4.5
multivariate adaptive regression splines, 14.2.3
multivariate arithmetic Brownian motion, 9d.1.1
multivariate Gaussian, 3.1.2
multivariate generalized autoregressive conditional heteroscedastic, 2.7.1
multivariate geometric Brownian motion, 9d.1.1
multivariate Ornstein-Uhlenbeck, 44.6
multivariate random-walk, 2.7

N

naive Bayes classifiers, 15.3.3
naive Bayes models, 15.3.3
natural form, 31.9
natural parameters, 31.9
neighbors, 15.3.4
nested simulation, 5.5.2
net asset value, 26.1.4
net exposure, 6.1.3
network, 14.2.7
neural network
    artificial, 14.2.5
    convolutional, 14.2.5
    deep artificial, 14.2.5
neuron, 14.2.5
    convolution, 14.2.5
neutralization, 46.6.3
Neyman-Pearson lemma, 14.4.3
nodes, 15.3.4
norm, 50.4.1
    counter, 36.6.2
    Frobenius, 36.6.2
    Lp, 36.6.2
    p, 36.6.2
    standard Euclidean, 50.4.1
    taxicab, 36.6.2
norm symmetric, 33.3.2
normal copula, 33.3.1
normal distribution, 31.4
normal-inverse-Wishart (NIW) distribution, 3.4.2
normalized empirical histogram, 49.3.2
normalized heights, 49.3.2
normalized value characteristics, 9c.2
normalizing and variance stabilizing, 2.6.3
notional value, 1.3.1
nowcasting
    inference, 13.3
null hypothesis, 41.1
number of obligors, 1.5.3
numeraire, 24a.2.2
    risk-free , 24a.3.1
    risk-neutral, 24a.3.1
numeraire probability measure, 24a.2.2

O

observable
    decision theory, 37.1.1
    unsupervised learning, 13.1
observable features, 26
observable variables, 13.1, 38.1.1
observation equation, 17.9
offset cash, 28.4.4
omega ratio, 7.8.2
one-hot encoding, 31.8.1
operational loss, 1.7
operations, 1.7
opportunity cost, 37.1.1
optimization
    unconstrained, 48.1
option-based portfolio insurance, 9d.3
order placement, 10
order q dominance, 35.4
order routing, 10
order scheduling, 10
ordinal variables, 31.13.1
ordinary least squares, 12.2.9
ordinary least squares with flexible probabilities, 12.2.9
Ornstein-Uhlenbeck, 44.2.1
orthogonal, 50.4.2
    projection, 50.4.2
    to a linear subspace, 50.4.2
orthogonal-increment process, 42.1.10
out of the money, 1.4.2
out-of-sample error, 40.22
outputs, 13.1
outstanding order vector, 10.3
overnight index swap, 1.11.2

P

p-value, 41.1.2
P&L, 23.1.6
    conditional ex-ante, 5.1
    pricing function, 5
P&L linearity, 28.1
P&L related exposures, 9c.2
pair-wise Markov property, 15.3.5
panel data, 40.1
paper P&L, 28.2.1
par, 1.11.2
par (swap) curve, 1.11.2
par (swap) rate, 1.11.2
par rate, 1.11.2
par swap rate, 1.3.1
parallelogram rule, 50.1.1
parent, 15.3.4
parent order, 10
partial correlation, 34.3.1
partial derivative
    second order, 51.1.2
partial views, 16.1.4
partitioned matrix inversion, 50.5.4
payment time, 23.1.3
payoff, 1.4
    call option, 1.4.1
    forward, 1.2.2
    variance swap, 1.4.6
Pearson parametrization
    Arrow-Pratt function, 7.4.1
perceptron, 14.4.7
performance "mean", 7.3.1
performance expectation, 7.3.1
performance mean-variance trade-off, 7.3.4
performance model
    Black-Litterman, 47.1
performance variance, 7.3.2
permanent impact, 10.1.3
permanent market impact model, 10.1.3
persistence, 9c.2.3
phi-divergence, 15.1.3
point statement, 13.2
point view, 16.1.2
point-in-time, 2.3.2
pointed, 48.1.3
Poisson process, 2.1.2, 44.1.3
polarization identity, 50.4.5
pool factor, 23.2.1
pooling
    convolution, 14.2.5
portfolio, 6.1.1
portfolio rebalancing P&L, 28.2.3
portfolio weights, 28.4.2
positive homogeneity of first degree, 7.2
positive homogenous of degree, 7.2
positive homogenous of first degree, 7.2
posterior, 39.1.3, 38.1.2, 39.1.3
    Bayesian statistics, 38.2.1
posterior distribution, 38.2.2
    Black-Litterman, 47.30
posterior error
    Bayesian approach, 39.4.3
    Bayesian estimation, 39.3.3
posterior predictive performance model
    Black-Litterman, 47.33
posterior risk, 37.1.3
potential function
    e-affine coordinates, 32.1.3
    m-affine coordinates, 32.1.3
precision matrix, 31.9.1
predicted variables, 13.1
prediction, 12.1
    inference, 13.3
    point prediction, 39.3.1
    predictive distribution, 39.4.1
prediction model, 38.2.3
predictive, 9c.5.2
predictive distribution, 6.4.1
    non-observable, 39.4.1
    posterior, 38.2.3
predictor
    point prediction, 39.3.1
    predictive distribution, 39.4.1
premium, 1.11.2
price manipulation, 10.5.2
pricing kernel, 24a.2.1
pricing operator, 24a.1.2
pricing signals, 46.2.2
principal axes, 36.2.4
principal axis factorization, 12.4.4
principal component analysis, 36.2.3
    sparse principal component analysis, 40.3
principal component root, 50.8.1
principal components, 36.2.3
principal directions, 36.2.3
principal factors, 36.2.3
principal variances, 36.2.3
principal-component, 12.3
prior distribution, 38.2.2
    Black-Litterman, 47.3
prior predictive distribution, 38.2.3
prior predictive performance model
    Black-Litterman, 47.11
probabilistic factor analysis, 15.3.1, 15.3.1
probabilistic graphical model, 15.3.4
probabilistic misclassification error, 15.2.3
probabilistic prediction error, 39.4.2
probabilistic statement, 13.2
probabilities, 31.8
probability density function
    multivariate, 31.1
    univariate, 31.1
probability level, 31.1
probability mass function, 31.8.3
    multivariate, 31.1
    univariate, 31.8.3
probability of default, 1.5.2
probit model, 15.2.2
product rule
    multivariate, 51.1.2
    univariate, 51.1.1
profit-and-loss, 23.1.6
projection matrix, 12.2.8
projection pursuit, 14.2.6
projection stochastic discount factor, 24a.2.3
properties, 39.1.1
proportional hazards expectation, 7.6.2
proportional hazards principle, 25.2.3
proportional hazards transform, 7.6.2
proportional odds, 31.13.1
pseudo-inverse
    left, 50.5.3
    right, 50.5.3
pull-to-par, 1.11.2
pure endowment, 26.5.1
pure noise, 3.5.3
put-call parity, 24a.1.1
Pythagorean triangular identity, 50.4.4

Q

quadrangle, 36.6.3
quadratic discriminant analysis (QDA), 15.2.2
quadratic form, 50.3
quadratic programming, 48.1.6
quadratic variation, 3.11.4
quadratic-normal distribution, 31.5.1
quantile (VaR) satisfaction measure, 7.5.1
quantile function, 31.1
quantitative alpha, 9c
quantitative strategies, 9c
quotient rule
    multivariate, 51.1.2
    univariate, 51.1.1

R

r-squared
    distributional , 12.1.1
    generalized distributional, 12.1.1
    generalized population, 12.1.1
    population, 12.1.1
    sample, 12.1.1
radial component, 31.7.3
Radon-Nikodym derivative, 24a.2.2
Radon-Nikodym process, 24b.4.4
rain distribution, 10.5.3
random field, 42.1.9
    Gaussian, 31.4.2
    Gaussian Markov, 15.3.5
    Markov, 15.3.5
random forest, 40.4.1
random time series, 3
random walk, 2.1
    multivariate random walk, 2
rank, 50.2.3
    full, 50.2.3
ranking, 46.6.3
ranking-distortion, 46.6.3
ranking-median, 46.6.3
ranking-terciles, 46.6.3
realized information, 38.2.2
realized information panel, 3
realized P&L, 23.1.6
    portfolio, 28.2.1
realized path, 4.4
realized time series, 3
realized variance, 1.4.6
receiver operating characteristic (ROC) curve, 14.4.4
receiver operating characteristic (ROC) function, 14.4.4
record date, 23.1.3
recovery rate, 1.5.2
regression, 13.1.1
    cross-section, 12.5.5
    generalized, 14.1.1
    least absolute deviation regression, 14.3.2
    least squares regression, 14.1.1
    least squares, linear, 14.1.1
    linear least absolute distance regression, 14.3.4
    linear median regression, 14.3.4
    linear quantile regression, 14.3.4
    quantile regression, 14.3.2
regression LFM, 12.2
regret, 36.6.3, 37.1.1
regularization, 16.6.6
reinforcement modeling, 13.1
reinvested cumulative cash-flow, 23.1.4
reinvested instrument, 23.1.4
reinvestment function, 5.1
relative entropy, 32.1.4
relative marginal contributions, 8b.2.2
relative value, 9c
residuals, 12.1
responses, 13.1
Restricted P&L function
    bonds, 5.3.3
    European call options, 5.3.3
return on collateral, 28.4.3
return on equity, 28.4.3
return on exposure, 28.4.3
return on value, 28.4.3
return related characteristics, 9c.2
reversal, 46.3.1
Riccati root, 50.8.2
ridge, 40.2.2, 48.1.6
ridge regression, 18.3.3
Riemann integrable, 51.4.3
Riemann integrable function, 51.4.1
Riemann integral
    multivariate, 51.4.3
    univariate, 51.4.1
Riemann sum
    multivariate, 51.4.3
    univariate, 51.4.1
Riemannian metric, 32.1.1
right-way risk, 6.2
risk aversion, 7.2
risk coverage ratio, 7.8.2
risk drivers, 1
risk drivers process, 4
risk market neutral, 9c.4.3
risk measure, 7
risk premia, 9c
    arbitrage pricing theory, 24b.3.1
    symmetry with arbitrage pricing theory, 12.5.6
risk premium, 7.2
risk reversal, 1.4.4
risk seeking, 7.2
risk-free interest rate, 1.3.1
risk-neutral, 7.2
risk-neutral pricing, 24a.3.1
risk-neutral probability, 24a.3.1
robust approach, 37.1.4
roll down, 5.2.3
rolling value, 1.3.2, 1.4.2
rolling zero-coupon, 1.3.2
rotation, 49.3.1
round-trip, 10.5.2
rule-based strategies, 9c
running maximum, 28.6

S

sample correlation matrix, 3.2.2
sample covariance matrix, 3.2.2
sample mean, 3.2.2
sample space, 31.8
sample standard deviation vector, 3.2.2
satisfaction measure, 7.2, 7.6.1
scale-invariance, 7.2
scenario expansion, 6.6.1
scenario-probability distribution, 31.8
scenario-probability quantile, 31.8.6
scenarios, 31.8
    panel, 31.8
Schweizer-Wolff measure, 34.1.1
score
    absolute score, 31.13.1
    relative score, 31.13.1
scoring function
    receiver operating characteristic, 14.4.4
sec-kernel-principal-components, 14.5.7
second order criterion, 48.1
second order differential, 51.1.2
    matrix-variate, 51.1.3
second order dominance, 35.3
second-order cone programming, 48.1.5
security market line, 24a.4.2
segmentation, 14.2.3
selection problem, 48.2
selection set, 48.2
self-financing, 9c, 9c.2.2
self-similarity, 44.1.5
semidefinite cone, 48.1.4
semidefinite programming, 48.1.4
semideviation principle, 25.2.1
semivariance, 36.5.4
semivariance principle, 25.2.1
sensitivity curve, 3.7.1
sequential attribution, 8b.1.3
settlement date, 23.1.2
settlement period, 23.1.3
Shapley attribution, 8b.1.4
Sharpe ratio, 7.8.1
    generalized, 36.1.1
shift parameters, 12.1
short holdings, 28.1, 28.1
short position, 26.1.2
short spot rate, 1.11.2
sigmoid, 31.388
sign, 1.8.1
signal, 46
signal beta, 9c.2.1
signal characteristic, 9c.2.1
signal characteristic matrix, 9c.4.2
signal characteristic portfolio, 9c.2.1
signal characteristic portfolios, 9c.4.2
signal flexible factor portfolio, 9c.2.2
signal-induced factor, 9c.1, 9c.2.1
signal-induced factors, 9c.4.2
signal-to-noise, 36.1.1
size, 36.6.3
size signal, 46.5
skew signal, 46.5
skill, 9c.5.2
Sklar’s theorem, 33.2.3
slack variable, 48.3.1
slippage, 23.1.2
slippage model, 10.1.3
slippage P&L, 28.2.1
slope, 1.3.5
small capitalization stocks, 46.5
small minus big, 9c.1
smart beta, 9c
smile, 1.4.3
smile signal, 46.5
smirk, 1.4.3
smooth kernel probabilities, 3.1.2
smooth quantile, 31.8.7
smoothing, 46.6.1
    inference, 13.3
softmax function, 31.9.2
softplus function, 31.9.2
solvency capital requirement, 7.10
solvency condition, 6.9.1
Sortino ratio, 7.8.2
span, 50.1.2
spanning set, 50.1.2
Spearman’s rho, 34.2.2
spectral density, 45.1
    function, 45.1
spectral representation, 45.6
spectral theorem, 50.6.2
spectrum, 3.5.2
    satisfaction indices/risk measures, 7.6.1
splines, 14.2.3
spot curve, 1.11.2
spot rate, 1.11.2
spot swap, 1.3.1
spread, 1.3.6
square-dispersion, 36.5.7
    affine equivariant, 36.4.2
    modal, 36.4.2
square-root, 44.2.2
square-root rule, 4.7
stable distributions, 31.11.1
    symmetric, 31.11.1
standard "beta", 12.4.2
standard Brownian motion, 44.1.2
standard deviation, 36.1.1
standard deviation principle, 25.2.1
standard error, 41.1.3
standard Wiener process, 44.1.2
standardized elliptical variable, 31.7.3
standardized holdings, 6.4.1
standardized invariants, 3.9.1
state, 37.1.1
state crisp probabilities, 3.1.4
state process, 42.1
State space model
    Probabilisitc linear, 45.4
state-space models, 17.9
static linear factor model, 12.1.4
static model, 17
    mean-variance static model, 17
    probabilistic static model, 17
stationary, 42.1
statistical LFM, 12.3
statistics, 41.1.1
steepness, 1.3.5
stochastic discount factor, 24a.2.1
stochastic dominance, 35.1
stochastic mean, 2.1.3
stochastic time, 44.1.5
stochastic volatility, 2.1.3
stochastic volatility inspired, 1.4.5
strategy, 1.9
strict stochastic dominance, 35.1
strictly concave function
    multivariate, 51.6.2
    univariate, 51.6.1
strictly convex function
    multivariate, 51.6.2
    univariate, 51.6.1
string, 42.1.9
strips, 1.3.1
strong linearly separable, 14.208
strong white noise, 42.1
structure, 13.1.1
Student t distribution, 31.6.1
sub-additivity, 7.2
sub-quantile function, 31.12
subordinator, 44.1.5
sufficient statistics, 31.9
sum-of-parts, 26.1.6
    liquidation valuation, 26.1.5
super-additivity, 7.2
Supervised modeling, 13.1
support vector machine, 14.4.8
surprise, 38.1.2
    maximum likelihood parameters, 38.1.1
survival probability, 1.6
Sylvester’s determinant theorem, 50.5.4
systematic, 12.1.3
systematic-idiosyncratic LFM, 12.1.3

T

t copula, 33.3.1
tangent vector, 32.1
target variables, 13.1, 13.1, 16.1.1
target vector, 12.1
Taylor expansion
    univariate, 51.3.1
Taylor polynomial
    multivariate, 51.3.2
    univariate, 51.3.1
Taylor series
    univariate, 51.3.1
temporary impact, 10.1.3
temporary market impact, 10.1.3
tenor, 1.3.2
tercile, 46.6.3
test set, 40.1
through-the-cycle, 2.3.2
tick time, 1.8.2
tick time evolution, 1.8.3
tick-by-tick, 1.8.3
Tikhonov, 40.2.2
Tikhonov regularization, 48.1.6
time crisp probabilities, 3.1.4
time dependent transition matrix, 2.3.2
time horizon, 40.1
time to maturity, 1.3.2
time-changed risk driver, 1.8.3
time-homogeneous Markov chain, 2.3.1
time-homogeneous transition matrix, 2.3.1
time-inhomogeneous Markov chain, 2.3.2
times series, 39.1.1
timing P&L, 28.3
torsion, 8b.5
total net income, 6.7.2
total shares outstanding, 6.7.1
trace, 50.5.2
tracking error, 7.3.3
trading P&L, 28.2.2
trailing window, 3.2.4
training set, 39.3.5
transaction time, 23.1.2
transaction value, 23.1.2
transaction variables, 1.8.1
transition equation, 45.4
transition matrix
    homogenous, arbitrary step, 44.3.1
    inhomogenous, arbitrary step, 44.3.1
transition probabilities, 2.3.1, 2.3.1
translation invariance, 7.2
true negative rate, 14.4.3
true positive rate, 14.4.3
truncation, 12.1.4
turnover, 9c.2.3
two-fund separation theorem, ??

U

unanimity, 16.1.7
unbalanced, 40.1
uncertainty band
    multivariate, 36.6.1
    univariate, 36.1.2
unconditional, 38.2.1
uncovered interest rate parity, 5.1.2
underwater chart, 28.6
undirected graph, 15.3.4
uniform component, 31.7.3
uniform distribution, 31.6.3
uniform probabilities, 3.1
unit, assumptions-prediction
unit simplex, 31.13.1
unit-root, 45.2.9
unitary transformation, 49.3.1
universal approximation theorem, 14.2.5
unrealized P&L, 23.1.6
    portfolio, 28.2.1
unsupervised modeling, 13.1
updated distribution, 16.1.2
updated state, 16.6.1
utility function, 7.4

V

validation set, 39.3.5
valuation function, 26
valuation multiple, 26.2.2
value at risk, 7.5.1
value function, 5.1
value signal, 46.2.1
value stocks, 46.2.1
variability, 36.5.1
    affine equivariant, 36.4.1, 36.4.2
variance, 36.1.1
variance minimization, 12.5.5
variance of the hypothetical means (VHM), 14.1.5
variance principle, 25.2.1
variance swap, 1.4.6
variation ratio, 36.5.3
variational free energy, 38.1.2
varimax rotation, 12.4.7
vector, 50.1
    addition, 50.1.1
    coordinates, 50.1
    scalar multiplication, 50.1.1
    subspace, 50.1.2
    subtraction, 50.1.1
vector autoregressive of order one, 45.2.6
vector space, 50.1
vectorization, 50.5.1
    half-, 50.5.1
    inverse, 50.5.1
vertices, 15.3.4
view p-value, 16.2.2
view variables, 16.1.1
visualization function, 36.3.6
volatility clustering, 2.6
volatility function, 34.3
volume, 1.8.1
volume time, 1.8.2
volume time evolution, 1.8.3
volume-weighted-average-price (VWAP) strategy, 10.1.3

W

Wald statistic, 41.1.4
Wang distortion principle, 25.2.3
Wang expectation, 7.6.2
Wang transform, 7.6.2
weak dominance, 35.2
weak signals, 9c.5.2
weight of evidence, 15.3.3
white noise
    weak , 45.2.1
Wiener-Hopf equations, 17.4.1
Wiener-Kolmogorov filter, 17.4
Williamson n-transform, 33.3.2
Wishart distribution, 31.6.7
within-cluster/group variance, 14.1.5
Wold theorem, 45.5
    linearly deterministic component, 45.5
    linearly regular component, 45.5
worst-case error, 39.1.2
    frequentist approach, 39.4.2
    frequentist prediction, 39.3.2
worst-case risk, 37.1.2
wrong-way risk, 5.1.5
    credit value adjustment, 6.2

Y

yield, 1.11.2
yield curve, 1.3.3, 1.11.2
yield income, 5.2.3
yield to maturity, 1.3.3
Yule-Walker equations, 17.14

Z

z-score, 36.1.1
    multivariate absolute, 36.2.1
z-statistic, 41.1.3
zero curve, 1.11.2
zero rate, 1.11.2
zero-coupon bond, 1.3.1