Glossary

Symbols

(functional) excess risk, 33
(probabilistic) linear regression, 15.1.4
(probabilistic) regression, 15.1.3

A

absence of arbitrage, 20a.1.3
abstract Bayes theorem, 27.13.3
accounting signals, 43.5
accrued interest, 19.2.1
accuracy
    binary classification, 14.6
action, 34.1.1
activation function, 33.4.1
activity time, 1.8.2
actual cash-flow, 19.2.3
actual exchange rate, 19.2.3
actual value, 19.2.3
adapted basis, 47.1.3
adapted variable
    partition, 27.13.3
adaptive execution algorithm, 10.3.2
additive, 27.11.1
additive factor-residual, ??
admissible, 35.2.1
algebraic multiplicity, 47.3.4
algebraic Riccati equation, 47.7.6
allocation policy, 6.5
alpha, 7.3.1
    binary classification, 14.3
alternative beta, 9c
alternative hypothesis, 38.1
analysis of variance, 14.1.10
ancestor, 15.3.4
angle, 47.6.1
    canonical, 32.3.2
    covariance, 32.3.2
    expectation, 32.3.1
anti-monotonic
    function, 48.5.2
arbitrage pricing theory, 20b.3.1
architecture, 33.4.1
area under curve (AUC), 14.3.4
arithmetic Brownian motion with drift, 41.1.2
Arrow-Debreu securities, 20b.1.2
Arrow-Pratt absolute risk aversion function, 7.5.1
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, 42.1
autocovariance
    function, 42.1
autoencoders, 14.5
autoregressive conditional duration, 2.6.2
autoregressive moving average process
    autoregressive
        univariate (AR), ??, ??
    integrated
        multivariate (VARIMA), relationships-varma-vma-var
        univariate (ARIMA), ??
    moving average
        univariate (MA), ??, ??
    multivariate (VARMA), 42.3.2
    univariate (ARMA), 42.3.1
autoregressive of order one, 42.2
autoregressive-fractionally integrated-moving average of order (p,d,q), 2.4
auxiliary measure, 27.9

B

Bachelier, 22.3.1
backpropagation, 33.4.1, 45.1.1
backward cash-flow-adjusted value, 19.1.5
backward/forward exponential weighted moving average, 3.10.6
bag of words, 15.3.3
bagging, 37.4.1
balance sheet, 6.7.1
balanced, 37.1
bandpass filter, 42.5.3
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, 27.9
basis, 24.4.3, 47.1.2
    canonical, 47.1
    orthonormal, 47.6.3
basis denominator, 24.4.3
basis instruments, 20b.1.1
Bayes classifier, 14.4.2
Bayes error
    expected error, 36.1.2
    frequentist approach, 36.4.2
    frequentist prediction, 36.3.2
Bayes risk, 34.1.2
Bayes theorem, 27.3.2
Bayesian decision, 34.1.3
Bayesian networks, 15.3.6
benchmark, 24.5.1
best ask, 1.8.1
best bid, 1.8.1
best prediction, 47.6.4
beta
    binary classification, 14.3
beta conditions
    projection coefficients, 47.6.3
    projection equation, 47.6.3
beta distortion index, 7.8.2
beta-adjusted excess return, 24.5.2
bets, 8b.5
between-cluster/group variance, 14.1.10
Bhattacharyya coefficient, 37.4.4
bias, 36.1.2
    binary classification, 14.3
bias reduction, 33
bid size, 1.8.1
bid-ask spread, 10.1
bilateral value adjustment, 6.2
binary classification, 14.3.5
binary score, 14.4.5
binning, 1.8.3
binomial inverse theorem, 47.8.4
binomial tree, 2.1.2
bins, 1.8.3, 46.2.2
bins width, 46.2.2
Black-Merton-Scholes model, 22.3.2
bond yield, 5.3.2
bootstrap aggregating, 37.4.1
Borel sets, 27.13.1
boundedness, 16.1.7
breadth, 9c.5.2, 33.4.1
breakdown point, 3.7.2
Brier score, 15.2.1
Brownian motion, 2.1.1
Buhlmann pricing equation, 20a.5
    expectation, 20a.5
Buhlmann principle, 21.2.4
butterfly, 1.4.4, 20b.1.2

C

calendar signal, 43.5
calibrate, 22
call option, 1.4.1
canonical basis property, 27.8.2
canonical correlation matrix, 30.3.1
canonical parameters, 27.9
capital asset pricing model, 20b.2
capital gain, 19.1.6
carry signal, 43.1
CART, 33.3.1
cash-flow function, 5.1
cash-flow-adjusted value, 19.1.5
categorical distributions, 27.8.1
categories, 27.8.1
Cauchy distribution, 27.6.2
Cauchy-Schwartz inequality, 47.6.1
causal, 42.5.3
center of the bin, 46.2.2
central tendency, 32.5.1
    affine equivariant, 32.4.1, 32.4.2
certainty-equivalent, 7.5
certainty-equivalent principle, 21.2.2
chain rule, 15.3.6
    gradient, 48.1.2
    matrix-variate, 48.1.3
    multivariate, 48.1.2
    univariate, 48.1.1
change of variable formula
    multivariate, 48.4.3
    univariate, 48.4.1
characteristic equation, 47.3.1
characteristic function
    multivariate, 27.1.2
    univariate, 27.1.1
characteristic matrix, 9c.4
characteristic polynomial, 47.3.1
characteristic portfolio, 9c.2
characteristic portfolios, 9c.4
Chebyshev’s inequality, 32.2.7
chi-squared distribution, 27.5.2
child, 15.3.4
child orders, 10
Cholesky decomposition, 47.7.5
Cholesky root, 47.7.5
CIR, 41.2.2
claims, 1.6
classes, 27.8.1
classical-equivalent, 35.2.1
classification, 13.1.1
    multinomial classification, 14.4
classification and regression trees, 33.3.1
Classification trees, 15.2.8
    splitting criterion, 15.2.8
clean price, 19.2.1, 19.2.1
clique, 15.3.4
clique factorization, 15.3.5
clustering, 13.1.2, 14.5.3
co-monotonic
    function, 48.5.2
    variables, 29.2.4
co-monotonic additivity, 7.2
coarseness level, 46.2.2
codes, 14.5
coherent satisfaction measure, 7.9.1
cointegrated, 39.1
cointegrated space, 2.8.1
cointegration signal, 43.3.3
cointegration vector, 2.8.1
    multivariate process, 39.1
collateral, 6.8.1
commonalities, 12.4.5
comparable instruments, 22
complete, 20b.1.1
compound distribution, 27.3.4
compound Poisson process, 41.1.3
compounded rate of return
    (instantaneous) compounded rate of return, 24.4.6
    average compounded rate of return, 24.4.6
compounded return, 24.4.1
concave down function
    univariate, 48.6.1
concave function
    multivariate, 48.6.2
    univariate, 48.6.1
concave up function
    univariate, 48.6.1
concavity, 7.2
condition number, 3.5.2
conditional cdf, 27.3.1
conditional covariance, 27.3.3
conditional distribution of variable, 27.3.1
conditional excess distribution, 7.6.2
conditional expectation, 27.3.3
    with respect to the events set, 27.13.3
conditional independence, 15.3, 37.1
conditional pdf, 27.3.1
conditional principal component analysis, 12.6.16
conditional principal directions, 12.6.16
conditional principal variances, 12.6.16
conditional probability, 27.3.1
    with respect to the events set, 27.13.3
conditional statistical feature, 27.3.3
conditional value at risk, 7.8.2
conditioning-marginalization, 16.1.3
cone, 45.1.3
confusion matrix, 14.3
conic programming, 45.1.3
conjugate distribution, 35.2.3
consistency with weak dominance, 7.2
consistent, 7.2
consistent with q-th order dominance, 7.2
constancy, 7.2
constant proportion portfolio insurance, 9d.4.1
constraint set, 45.1
constructed variables
    unsupervised learning, 13.1
contingency table, 14.3
continuous-state distributions, 2.1
convex function
    multivariate, 48.6.2
    univariate, 48.6.1
convex programming, 45.1.2
convex set, 45.1.3
convexity, 5.3.2
    risky investment, 9d.6
    satisfaction/risk measures, 7.2
coordinate descent, 45.2.7
copula, 29.2.2, 29.2.2
copula-marginal combination, 29.4.2
copula-marginal distributions, 29.4.2
copula-marginal separation, 29.4.1
copula-pdf, 29.2.3, 29.2.3
Cornish-Fisher approximation, 7.6.2
Cornish-Fisher expansion, 7.6.2
correlation, 30.3
correlation function, 30.3
correlation matrix, 30.3
cost of equity, 22.2.1
cost of trading, 9d.6
counterparty credit risk, 6.2
coupon, 1.10.2
coupon bond, 1.3.1
CoVaR, 6.6.1
covariance matrix, 32.2.1
covariance principle, 20a.5
covariance stationary, 42.1
covariant, 28.1.1
covariates, 13.1
Cramer representation, 42.6
Cramer-Lundberg ruin model, 6.7.5
credit ratings, 1.5.3
credit structural model, 128
credit value adjustment , 6.2
critical point, 45.1
cross the spread, 10.1
cross-autocovariance
    function, 42.1
cross-entropy, 15.2.1
cross-sectional, 9c, 9c
cross-sectional linear factor model, 12.5
cross-sectional sample median of default probabilities, 1.5.3
cumulant , 4.7.1
cumulative cash-flow, 19.1.3
cumulative distribution function
    multivariate, 27.1.2
    univariate, 27.1.1
cumulative link, 15.4.3
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, 43.5
cutoff classifier, 14.3.4

D

data, 34.1.1, 35.1
debt, 6.7.1
debt value adjustment, 6.2
decay, 1.3.5
decision, 34.1.1
decision regions, 14.4
decoder, 14.5
decreasing function, 48.5.1
    strictly, 48.5.1
degree of reversal, 9c.2.3
delta, 1.4.4
delta p, 14.6
delta rule, 33.4.1
dependent variables, 12.1, 13.1
depth, 33.4.1
derivative
    directional, 48.1.2
    first, 48.1.1
    higher order, 48.1.1
    partial, 48.1.2
    second, 48.1.1
    total, 48.1.2
    univariate, 48.1.1
descendant, 15.3.4
determinant, 47.2.3
deviation
    expectile, 32.5.2
    maximum, 32.5.2
    mean absolute, 32.5.2
    median absolute, 32.5.2
    subquantile, 32.5.2
differentiable function
    multivariate, 48.1.2
    univariate, 48.1.1
dimension, 47.1.2
Dirac delta, 27.8.2
direct sum, 47.1.3
directed graph, 15.3.4
dirty price, 19.2.1
discount, 1.10.2
discount factor, 19.2.3
discount function, 5.1
discounted cash-flow, 22.2.1
discounted cash-flow adjusted value, 21.3.1
discounted payoff, 21.2
discrete Fourier transform, 46.2.1
discrete-state distributions, 2.1
discrete-state random walk, 2.1.2
discretized cf, 46.2.2
discretized pdf, 46.2.2
discriminant model
    probabilistic, 13.2.3
discriminant next-step model, 2.7.6
discriminant variables, 14.4.4
dispersion, 32.5.1
    affine equivariant, 32.4.1
    modal, 32.4.1
distance, 47.5.2
    absolute, 47.5.2
    covariance, 32.3.2
    Euclidean, 47.5.2
    expectation, 32.3.1
    identity of indiscernables, 47.5.2
    Lp, 32.6.2
    Mahalanobis, 47.5.2
    p, 47.5.2
    subadditivity, 47.5.2
    symmetry, 47.5.2
    triangle inequality, 47.5.2
distance to default, 2.3.4
distorted cdf
    satisfaction indices/risk measures, 7.8.1
distorted pdf
    satisfaction indices/risk measures, 7.8.1
distortion expectation, 7.8.1
distortion function, 7.8.1
distortion principle, 21.2.3
distribution function, 27.13.1
distributional view, 16.1.3
divergence, 47.5.1
    Bregaman, 47.5.1
    p-th absolute percentage, 47.5.1
    p-th relative, 47.5.1
diversification distribution, 8b.5
diversity, 37.4.4
dividend-adjusted value, 1.1
    stocks, 19.1.5
divisors, 27.11.2
dollar duration, 6.1.3
dollar-neutral constraint, 9c.2.2
dominant-residual LFM, 12.1.2
dot product, 47.6
drawdown, 24.6
    maximum (absolute) drawdown, 24.6
    maximum percentage drawdown, 24.6
    percentage drawdown, 24.6
dscrete-time Fourier transform, 46.2.1
dual Legendre, 28.1.3
dually flat, 28.1.3
duration, 5.3.2
DV01, 6.1.3
dynamic allocation, 9c
dynamic conditional correlation, 3.9.3
dynamic graphical models, 42.4
dynamic linear factor model, 17.2
dynamic models, 17
dynamic principal component, 17.6
dynamic regression model, 17.4

E

e-affine coordinates, 28.1.2
e-flat, 28.1.2
e-geodesic, 28.1.2
EBITDA, 6.7.2
economic capital, 7.12
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, 47.3.1
    linear transformation, 47.3.1
eigenvector, 47.3.1
    linear transformation, 47.3.1
elastic net, 37.2.2, 45.2.7
    constrained generalized elastic net, 45.2.7
elicitability, 7.2
ellipsoid, 32.2.4
    expectation-covariance, 32.2.4
    location-dispersion, 32.4.2
ellipsoid test for invariance, 40.1
elliptical distribution, 27.7.1
EM algorithm
    population, 35.4.6
encoder, 14.5
ensemble approach, 34.1.4
enterprise value, 22.2.2
equilibrium performance model
    Black-Litterman, 44.11
equilibrium returns
    Black-Litterman, 44.7
equity, 6.7.1
equity book value, 6.7.1
equivalent optimization problem, 45.3.1
ergodic (in mean), 39.1
Erlang process, 41.1.3
error, 36.1.2
    multivariate, 32.5.3
    multivariate mean squared, 32.5.3
    posterior error, 36.1.3
    univariate, 32.5.1
    univariate mean squared, 32.5.1
error correction, 42.2.7
Esscher principle, 21.2.4
estimable, 7.2
estimate, 36.1.1
    model risk, estimation risk, 34.1.4
estimation, 13.3
estimation model, 3.4
    Bayesian estimation, 35.2.1
estimation risk
    model risk, estimation risk, 34.1.4
estimation set, 37.1
    point prediction, 36.3.1
    predictive distribution, 36.4.1
estimation uncertainty, 35.2.1
estimator, 36.1.1
European-style derivatives, 1.4
evidence, 15.3
    maximum likelihood, 35.1
evidence lower bound, 35.4.5
ex-dividend date, 19.1.2
exotic beta, 9c
expectation
    multivariate, 32.2.1
    univariate, 32.1.1
expectation rule, 27.8.3
expectation-maximization, 35.1.3
    expectation step, 35.1.3
    maximization step step, 35.1.3
expected drawdown, 7.3.1
expected overperformance, 7.3.1
expected shortfall, 7.8.2
expected utility, 7.5
expected value of the process variance (EVPV), 14.1.10
expectile, 27.1.1
expectile-VaR, 7.9.2
expiry, 1.4
explanatory variables, 13.1
exponential decay probabilities, 3.1.1
exponential family distribution, 27.9
exponential kernel, 3.1.2
exponential of the entropy, 3.1.4
exponential principle, 21.2.2
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, 45.1
    relative, 45.1
extreme value theory, 7.6.2

F

f-divergence, 28.1.4
F-measure
    binary classification, 14.6
F1 score
    binary classification, 14.6
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-analysis linear factor model, 12.4.1
factor-replicating portfolios
    arbitrage pricing theory, 20b.3.1
factors, 9c, 13.1
    linear factor model, 12.1
fair value, 19.1.2
fallout, 14.3
false negative
    accuracy, 14.3
    probability, 14.3
    rate, 14.3
false positive
    accuracy, 14.3
    probability, 14.3
    rate, 14.3
fast Fourier transform, 46.2
feature engineering, 14.1
features, 13.1
Feller condition, 41.5.1
filter, 42.5
    backward shift
        polynomial, 47.9.2
    linear time invariant , 42.5.3
filtering, 13.3
filtration, 39.4
    adapted process, 39.4
        (fully-recombining) binomial tree, 39.4t
        (fully-recombining) tree, 39.4t
        martingale, 39.4
    Radon-Nikodym process
        martingale, finite-discrete-time-stoch-process-sec
financial instrument, 19.1.1
finite difference
    backward first order, 48.2.1, 48.2.2
    central first order, 48.2.1, 48.2.2
    central second order, 48.2.1, 48.2.2
    forward first order, 48.2.1, 48.2.2
first in, 8b.1.1
first order criterion, 45.1
first order differential, 48.1.2
    matrix-variate, 48.1.3
Fisher consistent, 3.7.1
Fisher discriminant analysis (FDA), 14.3.8
Fisher information distance, 28.1.4
flexible probabilities, 3
    estimation, 27.8
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, 19.1.5
forward exchange rate, 1.2.1
forward rate, 1.10.2
forward swap, 1.3.1
forward variance swap rate, 1.4.6
fourier integral, 46.2.1
Fourier series, 46.2.1
fractional Brownian motion, 41.4
fractional integrated process, 2.4
Frechet-Hoeffding bounds, 30.1.1
frequency response function, 42.5.3
frequentist risk, 34.1.2
full-investment, 9c.2.2
fully constrained LFM, 12.6.17
functional derivative, 33.5
fundamental accounting equation, 6.7.1
fundamental law of active management, 9c.5.2
fundamental linear factor model, 12.5
fundamental signals, 43.5
fundamental theorem of asset pricing, 20a.2.2
    martingale pricing formula, 20b.4.3
fundamental theorem of calculus
    first, 48.4.2
    second, 48.4.2
funding risk, 6.2
funding value adjustment, 6.2

G

gamma distribution, 27.5.2
Gaussian kernel, 3.1.2, 3.2.3
Gaussian process, 27.4.2
generalized autoregressive conditional heteroscedastic, 2.6.1
generalized excess return, 24.5.2
generalized linear models (GLM), 15.1.6
generalized linear return, 24.4.3
generalized method of moments with flexible probabilities (GMMFP) estimate, 3.6.2
    minimization, 3.6.3
generalized Pareto distribution, 7.6.2
generalized weight, 24.4.4
generative model, 13.2.3
generative next-step model, 2.7.6
generator, 41.3.1
generic position, 22.1.3
geometric Brownian motion, 5.1.1
geometric multiplicity, 47.3.4
Gibbs distribution, 15.3.5
Gini coefficient, 14.3.4
Giny impurity, 15.2.1
glasso, 3.5.5
    Tikhonov, 37.2.3
Glivenko-Cantelli theorem, 3.2.1
global minimum variance portfolio, ??
Gordon growth model, 22.2.1
grade, 29.1
grades, 29.2.1
gradient, 48.1.2
    matrix-variate, 48.1.3
    vector-valued function, 48.1.2
gradient descent, 45.1.1
    stochastic, 45.1.1
Gram matrix, 47.7.1
Gram-Schmidt process
    backward, 47.7.5
    forward, 47.7.5
Gramian, 47.7.1
grand mean, 3.5.1
graph, 15.3.4
graphical lasso, 3.5.5
Greeks, 5.3
gross exposure, 6.1.3
group
    general linear, 47.2.3
    orthogonal, 47.6.2
    special orthogonal, 47.6.2
    unitary, 47.6.2
growth stocks, 43.2.1

H

Hadamard product, 47.8.2
half-life, 3.1.1
Hamiltonians, 27.9
hat matrix, 12.2.6
hazard function, 1.6
Hellinger distance, 37.4.4
Hermitian inner product, 46.2.1
Hessian, 48.1.2
    matrix-variate, 48.1.3, 48.1.3
Heston model, 22.3.3
hidden variables, 13.1
    maximum likelihood, 35.1
    point prediction, 36.3.1
high breakdown estimators, 3.7.2
high breakdown point with flexible probabilities, 3.7.2
high minus low, 9c.1
high water mark, 24.6
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, 36.3.4
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, 36.3.4
historical with flexible probabilities (HFP) quantile, 3.7.2
historical with flexible probabilities (HFP) standard deviation vector, 3.2.2
hold-out, 36.3.5
Hotelling statistic, 38.1.4
Hurst coefficient, 41.4
hybrid Monte Carlo-historical, 4.5.2

I

ice-cream cone, 45.1.5
identity transformation, 47.2.3
idiosyncratic, 12.1.3
ill-conditioned, 3.5.2
IM algorithm, 35.4.5
image space, 47.2
implementation shortfall, 24.3
implied returns
    Black-Litterman, 44.7
implied volatility, 1.4.3
implied volatility surface, 1.4.3
improper integral, 48.4.1
impulse response, 42.5.3
in the money, 1.4.2
in-sample error, 37.16
inception, 24.2.2
income, 19.1.6
income statement, 6.7.2
increasing function, 48.5.1
    strictly, monotonicity-univariate
indefinite integral, 48.4.2
independence, 30.1
independent component analysis, 14.5.5
independent variables, 13.1
    linear factor model, 12.1
inefficiency, 36.1.2
inference, 13.3
infinitely divisible, 27.11.2
inflator, 20a.2.2
influence function, 3.7.1
information, 2.9.1
    random time series, 3
information coefficient, 9c.5.2
information generator, 2.9.1
information measure, 13.2
information ratio, 32.1.1
    conditional information ratio, 9c.5.2
    maximum (l2-mean unconditional) information ratio, 9c.5.2
    maximum conditional information ratio, 9c.5.2
information set, 2.9.1, 14.1.10
    distributions, 35.4.1
    linearized, 12.2.6
information/view, 16.6.1
informedness, 14.6
inner product, 47.6
    covariance , 32.3.2
    expectation, 32.3.1
    linearity, 47.6
    positive definiteness, 47.6
    symmetry, 47.6
inner product space, 47.6
innovation
    weak, 12.2.6
inputs, 13.1
instantaneous exchange rate, 19.2.3
instantaneous forward curve, 1.10.2
instantaneous forward rate, 1.10.2
instantaneous spot rate, 1.10.2
integral Fourier transform, 46.2.1
integrated, 39.1
integration by parts, 48.4.1
integration operator, 31.4
intensity models, 22.4.2
interaction, 33.2.1
interest rate, 1.3.3
internal rate of return, 24.4.6
interquantile range, 32.4.1
intuitive r-squared, 12.5.5
invariance rule, 27.8.3
invariant, 32.4.2
invariants, 2
inverse, 47.2.3
inverse transform sampling, 29.1
inverse-call, 1.3.4
inverse-Wishart distribution, 27.6.7
invertible, 47.2.3
investment factor, 24.4.6
    reinvested instrument, 19.1.4
iso-contour, 32.6.1
isolated, 8b.1.1
iterated integral, 48.4.3

J

jackknife estimator, 3.7.1
Jacobian, 48.1.2
James-Stein estimator, 3.5.1
Jeffreys prior, 35.2.1
joint model, 34.1.2
joint scenario, 27.8
jump rule, 19.1.3

K

k-fold, 36.3.5
k-means clustering, 14.5.3
kappa ratio, 7.11.2
Karhunen-Loeve representation, 42.6
Karush-Kuhn-Tucker conditions, 45.1.1
Kendall’s tau, 30.2.1
kernel, 3.2.3
kernel density estimate, 3.2.3
kernel stochastic discount factor, 20a.2.3
kernel trick, 33.6, 47.4
    linear kernel, 33.6
    polynomial kernel, 33.6
    radial basis functions, 33.6
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, 40.1
Kronecker product, 47.8.2
Kullback-Leibler divergence, 28.1.4

L

L2-space, 32.3
labels, 13.1
lag operator, 42.5
    order p, ??
Lagrange multiplier, 45.1.1
Lagrangian function, 45.1.1
Laplace approximation, 32.4.1
large capitalization stocks, 43.5
lasso, 37.2.2, 45.2.7
lasso regression, C.7.3.2
lasso shooting, 45.2.7
last in, 8b.1.2
last transaction price, 1.8.1
latent variables, 13.1
    maximum likelihood, 35.1
law invariant, 7.2
law of iterated expectations, 14.1.10
law of one price, 20a.1.1
law of total covariance, 14.1.10
law of total linear covariance, 12.6.13
law of total linear variance, 12.6.13
law of total variance, 14.1.10
LDL-Cholesky decomposition, 47.7.5
leaf, 33.3.1
learning, 13.3
least-squares residual, 14.1.2, 14.1.2
leave-1-out, 36.3.5
leave-p-out, 36.3.5
Legendre transformation, 28.1.3
length, 47.5.3
    covariance, 32.3.2
    expectation , 32.3.1
    Lp, 32.6.2
level, 1.3.5
leverage, 6.1.4
leverage effect, 2.6.2
Levy process, 41.1.1
Levy-Khintchine, 41.1.4
liabilities, 6.7.1
Libor, 1.10.2
likelihood, 3.4
    estimators as random variables, 36.1.2
    maximum likelihood, 35.1
likelihood ratio, 14.3.3
limit order book, 1.8.1
limit order placement, 10.3
linear classification, 14.198
    bias, 14.3.5
linear combination, 47.1.2
linear dependence, 47.1.2
linear discriminant analysis (LDA), ??
linear factor model, 12.1
linear independence, 47.1.2
linear law of iterated projections, 12.6.13
linear loss matrix, 12.2.6
linear observation equation, 42.4
linear prediction
    point, 12.2.6
linear pricing equation, 20a.2.1
    intertemporal, 20b.4.2
linear programming, 45.1.8
linear projection, 12.2.6
linear return, 24.4.1
linear state space model, 42.4
linear transformation, 47.2
linear transition equation, 42.4
linearity, 20a.1.2
linearly constrained quadratic programming, 45.1.6
link function, 27.9
liquidation, 24.2.2
liquidation valuation, 22.1.5
liquidity curve, 10.1
    "market buy" liquidity curve, 10.1
    "market sell" liquidity curve, 10.1
local Markov property, 15.3.6
location, 32.5.1
    affine equivariant, 32.4.1, 32.4.2
    multivariate, 32.5.3, 32.5.3
log-partition function
    exponential family distribution, 27.9
log-return, 24.4.6
log-sum-exp function, 27.9.2
logarithmic score, 13.2
    entropy, 13.2
    Kullback-Leibler divergence, 13.2
    relative entropy, 13.2
logistic function, 27.402
logistic regression, ??
logit function, 27.9.2
lognormal distribution, 27.6.6
    shifted, 27.6.6
long holdings, 24.1
long memory, 2.4
long position, 22.1.1
longitudinal data, 37.1
Lorentz cone, 45.1.5
Lorenz curve, 27.1.1
loss, 14.3.5, 34.1.1
    0-1 loss, 14.3.5
    0-1 margin loss, 14.3.5
    exponential , 14.3.5
    hinge , 14.3.5
    logistic , 14.3.5
    margin loss, 14.3.5
    square , 14.3.5
    tangent , 14.3.5
loss given default, 1.5.2
lower partial moment, 7.9.2
    root, 7.9.2
lower partial moment principle, 21.2.1
Lp-space, 32.6.2

M

m-affine coordinates, 28.1.2
m-flat, 28.1.2
m-geodesic, 28.1.2
m-square, 5.3.2
macro signals, 43.5
macroeconomic linear factor model, 12.2
Mahalanobis inner product, 47.6
Marchenko-Pastur distribution, 3.5.3
marginal cdf, 27.2
marginal characteristic function, 27.2
marginal contributions, 8b.2
marginal distribution, 27.2
marginal pdf, 27.2
marginal supply demand curve, 10.1
marked-to-market, 19.1.2
marked-to-model, 19.1.2
markedness, 14.6
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, 22
market portfolio, 9c.1
market price of risk, 20a.4.1
Markov chain
    Monte Carlo, 35.3
Markov process, 39.1
    time homogenous, 39.1
MARS, 33.3.1
martingale, 39.1
matrix, 47.2.1
    addition, 47.2.2
    commutation, 47.8.4
    conformable, 47.2.2
    decomposition, 47.8.4
    identity, 47.2.3
    inverse, 47.2.3
    invertible, 47.2.3
    low-rank-diagonal, 47.8.4
    multiplication, 47.2.2
    negative definite, 47.2.4
    negative semidefinite, 47.2.4
    non-singular, 47.2.3
    orthogonal, 47.6.2
    polynomial, 47.9
    positive definite, 47.2.4
    positive semidefinite, 47.2.4
    rank property, 47.8.4
    scalar multiplication, 47.2.2
    size, 47.2.1
    square, 47.2.1
    subtraction, 47.2.2
    symmetric, 47.2.4
    transpose, 47.2.4
    transpose-square-root, 47.7.3
    unitary, 47.6.2
matrix exponential, 47.8.2
matrix-normal distribution, 27.4.2
matrix-vector multiplication, 47.2.1
Matthews correlation, 14.6
maturity, 1.3.1
max classifier, 14.3.4
maximal Youden’s J statistic, 14.3.4
maximum
    global, 45.1
    local, 45.1
    relative, 45.1
Maximum a posteriori, 27.3.2
maximum likelihood factorization, 15.3.1
maximum likelihood parameters, ??
maximum likelihood with flexible probabilities, 3.3.1
    normal assumption, C.7.2.2
    Student t assumption, C.7.2.3
maximum likelihood with flexible probabilities (MLFP) estimate, 3.3.1
maximum likelihood with flexible probabilities (MLFP) predictor, 36.3.4
maximum Sharpe ratio portfolio, ??
mean reversion, 2.2
mean-lower partial moment, 7.9.2
mean-semideviation, 7.9.2
measure of concordance, 30.2
measure of dependence, 30.1
measurement equation, 17.9, 42.4
median
    multivariate, 32.5.3
    univariate, 32.4.1
Mercer kernel, 47.4
Mercer’s theorem, 47.4
method of moments (MM) estimate, 3.6.1
method of moments with flexible probabilities (MMFP) estimate, 3.6.1
metric, 47.5.2
metric geodesic, 28.1.2
metric space, 47.5.2
Metropolis-Hastings algorithm, 35.3.1
microprice, 1.8.1
mid-quote, 1.8.1
midrange, 32.5.2
minimax decision, 34.1.2
    distributionally robust stochastic optimization, 34.1.2
minimum
    global, 45.1
    local, 45.1
    relative, 45.1
minimum relative entropy numeraire probability, 20a.2.3
minimum relative entropy stochastic discount factor, 20a.2.3
minimum-torsion bets, 8b.5.1
minimum-torsion exposures, 8b.5.1
minimum-torsion transformation, 8b.5.1
misclassification error, 32.5.2
miss rate, 14.3
mixture components, 15.3.2
mixture distribution, 15.3.2, 15.3.2
mixture models, 15.3.2
    mixture of experts, 15.4.2
    normal mixtures, 15.3.2
mode
    multivariate, 32.4.2
    univariate, 32.4.1
model
    Bayesian approach, 34.1.3
    frequentist approach, 34.1.2, 36.4.2
    frequentist prediction, 36.3.2
model risk, 34.1.4
model set
    distributions, 35.4.1
model uncertainty, 34.1.4
moment generating function, 27.1.1
    multivariate, 27.1.2
momentum, 43.3.1
momentum signal, 43.3.1
money multiple, 24.4.6
money-equivalence, 7.2
moneyness, 1.4.2
monotone map
    decreasing, 48.5.2
    increasing, 48.5.2, 48.5.2
    strictly increasing, 48.5.2
monotonic function, 48.5.1
    strictly, 48.5.1
monotonicity, 7.2, 16.1.7
mortgage backed securities, 19.2.1
most powerful set, 14.3.3
Multi-layer perceptron classifiers, 15.2.7
multinomial logistic regression, ??
multinomial logit function, 27.9.2
multinomial probit regression, ??
multiple, 19.1.4
multiple of invested capital (MOIC), 24.4.5
multivariate adaptive regression splines, 33.3.1
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, 41.6
multivariate random-walk, 2.7

N

naive Bayes classifiers, 15.3.3
naive Bayes models, 15.3.3
natural form, 27.9
natural parameters, 27.9
neighbors, 15.3.4
nested simulation, 5.5.2
net asset value, 22.1.4
net exposure, 6.1.3
neural network
    artificial, 33.4.1
    convolutional, learning-deep
    deep artificial, 33.4.1
neuron, 33.4.1
    convolution, 33.4.1
neutralization, 43.6.3
Newton boosting, 45.1.1
Neyman-Pearson lemma, 14.3.3
nodes, 15.3.4
non-linear partial covariance matrix, 14.1.10
norm, 47.5.3
    absolute homogeneity, 47.5.3
    counter, 47.5.3
    Frobenius, 47.5.3
    Lp, 32.6.2
    Mahalanobis, 47.7.1
    matrix p, 47.5.3
    maximum, 47.5.3
    p, 47.5.3
    positive definiteness, 47.5.3
    standard Euclidean, 47.5.3
    subadditivity, 47.5.3
    taxicab, 47.5.3
norm symmetric, 29.3.2
normal copula, 29.3.1
normal distribution, 27.4
normal-inverse-Wishart (NIW) distribution, 3.4.2
normalized empirical histogram, 46.2.2
normalized heights, 46.2.2
normalized value characteristics, 9c.2
normalizing and variance stabilizing, 2.6.3
normed vector space, 47.5.3
notional value, 1.3.1
nowcasting
    inference, 13.3
null hypothesis, 38.1
number of obligors, 1.5.3
numeraire, 20a.2.2
    risk-free , 20a.3.1
    risk-neutral, 20a.3.1
numeraire probability measure, 20a.2.2

O

observable
    decision theory, 34.1.1
    unsupervised learning, 13.1
observable features, 22
observable variables, 13.1, 35.1
observation equation, 17.9
offset cash, 24.4.4
omega ratio, 7.11.2
one-hot encoding, 27.8.1
    partitions, 27.13.3
one-versus-one (OvO) classifier, 14.4.5
one-versus-one score
    one-versus-one score, 14.4.6
one-versus-the-rest classifier, 14.4.5
one-versus-the-rest score
    one-versus-the-rest score, 14.4.6
operational loss, 1.7
operations, 1.7
opportunity cost, 34.1.1
optimal discriminants, 14.4.2
optimal relative scoring, 14.3.3
optimal score, 14.3.3
optimal scoring, 14.3.3
optimization
    unconstrained, 45.1
option-based portfolio insurance, 9d.3
order placement, 10
order q dominance, 31.4
order routing, 10
order scheduling, 10
ordered classifier, 14.3.4
ordinary least squares, 12.2.7
ordinary least squares with flexible probabilities, 12.2.7
Ornstein-Uhlenbeck, 41.2.1
orthogonal, 47.6.1
    processes, 42.1
    projection, 47.6.3
    projection equation, 47.6.3
    to a linear subspace, 47.6.3
    transformations, 47.6.2
orthogonal-increment process, 39.1.10
orthogonalization, 47.7.3
orthonormal set, 47.6.1
orthonormalization, 47.7.3
out of the money, 1.4.2
out-of-sample error, 37.22
outputs, 13.1
outstanding order vector, 10.3
overnight index swap, 1.10.2

P

p-value, 38.1.2
P&L, 19.1.6
    conditional ex-ante, 5.1
    pricing function, 5
P&L linearity, 24.1
P&L related exposures, 9c.2
pair-wise Markov property, 15.3.5
panel data, 37.1
paper P&L, 24.2.1
par, 1.10.2
par (swap) curve, 1.10.2
par (swap) rate, 1.10.2
par rate, 1.10.2
par swap rate, 1.3.1
parallelogram rule, 47.1.1
parallelotope, 47.2.3
parent, 15.3.4
parent order, 10
partial correlation, 30.3.1
partial correlation matrix
    linear, 12.2.6
partial covariance matrix
    linear, 12.2.6
partial derivative
    second order, 48.1.2
partial views, 16.1.4
partially orthogonal, 12.2.6
partition, 27.13.3
    adapted function, 27.13.3
partitioned matrix inversion, 47.8.4
payment time, 19.1.3
payoff, 1.4
    call option, 1.4.1
    forward, 1.2.2
    variance swap, 1.4.6
Pearson parametrization
    Arrow-Pratt function, 7.5.1
perceptron, ??
performance "mean", 7.3.1
performance expectation, 7.3.1
performance mean-variance trade-off, 7.3.4
performance model
    Black-Litterman, 44.1
performance variance, 7.3.2
permanent impact, 10.1.3
permanent market impact model, 10.1.3
persistence, 9c.2.3
point view, 16.1.2
point-in-time, 2.3.2
pointed, 45.1.3
Poisson process, 2.1.2, 41.1.3
polarization identity, 47.6.1
pool factor, 19.2.1
pooling
    convolution, 33.4.1
portfolio, 6.1.1
portfolio rebalancing P&L, 24.2.3
portfolio weights, 24.4.2
positive homogeneity of first degree, 7.2
positive homogenous of degree, 7.2
positive homogenous of first degree, 7.2
posterior, 36.1.3, 36.1.3
    Bayesian statistics, 27.3.2
posterior distribution, 35.2.1
    Black-Litterman, 44.30
posterior error
    Bayesian approach, 36.4.3
    Bayesian estimation, 36.3.3
posterior predictive performance model
    Black-Litterman, 44.33
posterior risk, 34.1.3
potential function
    e-affine coordinates, 28.1.3
    m-affine coordinates, 28.1.3
precision
    binary classification, 14.3
precision matrix, 27.9.1
predicted negative probability, 14.3
predicted positive probability, 14.3
predicted variables, 13.1
prediction, 12.1
    point, 13.2
    point prediction, 36.3.1
    predictive distribution, 36.4.1
    probabilistic, 13.2
prediction model, 35.2.2
predictive, 9c.5.2
predictive distribution, 6.4.1
    non-observable, 36.4.1
    posterior, 35.2.2
predictor
    point prediction, 36.3.1
    predictive distribution, 36.4.1
premium, 1.10.2
prevalence of positive outcome, 14.3
price manipulation, 10.4.2
pricing kernel, 20a.2.1
pricing operator, 20a.1.1
pricing signals, 43.2.2
principal axes, 32.2.4
principal axis factorization, 12.4.2
principal component analysis, 32.2.3
    sparse principal component analysis, 37.3
principal components, 32.2.3
principal directions, 32.2.3
principal factors, 32.2.3
principal variances, 32.2.3
principal-component linear factor model, 12.3
prior distribution, 35.2.1
    Black-Litterman, 44.3
prior predictive distribution, 35.2.2
prior predictive performance model
    Black-Litterman, 44.11
probabilistic classification, 15.2.4
probabilistic factor analysis, 15.3.1, 15.3.1
probabilistic graphical model, 15.3.4
probabilistic misclassification error, 15.2.1
probabilistic prediction error, 36.4.2
probabilities, 27.8
probability density function
    multivariate, 27.1.2
    univariate, 27.1.1
probability integral transform, 29.1
probability level, 27.1.1
probability mass function, 27.8.3
    multivariate, 27.1.2
    univariate, 27.1.1
probability of default, 1.5.2
probability space, 27.13.1
    events, 27.13.1
    finite, 27.13.2
    outcomes, 27.13.1
    partition, 27.13.3
    sample space, 27.13.1
    sigma-algebra, 27.13.1
probit model, ??
product rule
    multivariate, 48.1.2
    univariate, 48.1.1
profit-and-loss, 19.1.6
projection
    i-m-projection, 35.4.1
    I-projection, 35.4.1
    m-projection, 35.4.1
projection matrix, 12.2.6
Projection pursuit, 33.4.3
projection stochastic discount factor, 20a.2.3
proper scoring rules, 13.2
properties, 36.1.1
proportional hazards expectation, 7.8.2
proportional hazards principle, 21.2.3
proportional hazards transform, 7.8.2
proportional odds, 15.4.3
pseudo-inverse
    left, 47.8.3
    right, 47.8.3
pull-to-par, 1.10.2
pure endowment, 22.5.1
pure noise, 3.5.3
put-call parity, 20a.1.2
Pythagorean triangular identity, 47.6.4

Q

quadrangle, 7.4
quadratic discriminant analysis (QDA), ??
quadratic form
    matrix, 47.2.4
quadratic programming, 45.1.6
quadratic variation, 3.10.4
quadratic-normal distribution, 27.5.1
quantile
    multivariate, 32.5.3
    multivariate p, 32.5.3
quantile (VaR) satisfaction measure, 7.6.1, 7.6.1
quantile function, 27.1.1
quantitative alpha, 9c
quantitative strategies, 9c
quotient rule
    multivariate, 48.1.2
    univariate, 48.1.1

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, 27.7.3
Radon-Nikodym derivative, 27.13.1
    numeraire, 20a.2.2
        process, 20b.4.3
rain distribution, 10.4.3
random field, 39.1.9
    Gaussian, 27.4.2
    Gaussian Markov, 15.3.5
    Markov, 15.3.5
random forest, 37.4.1
random time series, 3
random variable, 27.13.1
random vector, 27.13.1
random walk, 2.1
    multivariate random walk, 2
rank
    full, 47.2.1
    linear transformation, 47.2
    matrix, 47.2.1
ranking, 43.6.3
ranking-distortion, 43.6.3
ranking-median, 43.6.3
ranking-terciles, 43.6.3
real negative probability, 14.3
real positive probability, 14.3
realized information panel, 3
realized P&L, 19.1.6
    portfolio, 24.2.1
realized time series, 3
realized variance, 1.4.6
recall, 14.3
receiver operating characteristic (ROC) curve, 14.3.4
receiver operating characteristic (ROC) function, 14.3.4
record date, 19.1.3
recovery rate, 1.5.2
reflection, 47.2.3
regression, 13.1.1
    cross-section, 12.5.5
    generalized, 14.1.2
    least absolute deviation regression, 14.2.2
    least squares regression, 14.1.2
    least squares, linear, 14.1.2
    linear least absolute distance regression, 14.2.4
    linear median regression, 14.2.4
    linear quantile regression, 14.2.4
    quantile regression, 14.2.2
regression linear factor model, 12.2
regret, 7.4, 34.1.1
regularization, 16.6.6
reinforcement modeling, 13.1
reinvested cumulative cash-flow, 19.1.4
reinvested instrument, 19.1.4
reinvestment function, 5.1
relative entropy, 28.1.4
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, 24.4.3
return on equity, 24.4.3
return on exposure, 24.4.3
return on value, 24.4.3
return related characteristics, 9c.2
reversal, 43.3.1
Riccati root, 47.7.6
ridge, 37.2.2, 45.1.6
ridge regression, C.7.3.3
Riemann integrable, 48.4.3
Riemann integrable function, 48.4.1
Riemann integral
    multivariate, 48.4.3
    univariate, 48.4.1
Riemann sum
    multivariate, 48.4.3
    univariate, 48.4.1
Riemannian metric, 28.1.1
right-way risk, 6.2
risk aversion, 7.2
risk coverage ratio, 7.11.2
risk drivers, 1
risk drivers process, 4
risk market neutral, 9c.4.3
risk measure, 7
risk premia, 9c
    arbitrage pricing theory, 20b.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, 20a.3.1
risk-neutral probability, 20a.3.1
robust approach, 34.1.4
roll down, 5.2.3
rolling value, 1.3.2, 1.4.2
rolling zero-coupon, 1.3.2
rotation, 46.2.1, 47.6.2
round-trip, 10.4.2
rule-based strategies, 9c
running maximum, 24.6

S

sample correlation matrix, 3.2.2
sample covariance matrix, 3.2.2
sample mean, 3.2.2
sample space, 27.8
sample standard deviation vector, 3.2.2
satisfaction measure, 7.2, 7.8.1, 7.10.1
    actuarial pricing, 7.10
    Buhlmann expectation, 7.10
    Esscher, 7.10
    numeraire, 7.10.1
scale-invariance, 7.2
scenario expansion, 6.6.1
scenario-probability distribution, 27.8
scenario-probability quantile, 27.8.6
scenarios, 27.8
    panel, 27.8
Schweizer-Wolff measure, 30.1.1
scoring rule, 13.2
sec-kernel-principal-components, 14.5.4
second order criterion, 45.1
second order differential, 48.1.2
    matrix-variate, 48.1.3
second order dominance, 31.3
second-order cone programming, 45.1.5
security market line, 20a.4.2
segmentation, 33.3.1
selection problem, 45.2
selection set, 45.2
self-financing, 9c, 9c.2.2
self-similarity, 41.1.5
semidefinite cone, 45.1.4
semidefinite programming, 45.1.4
semideviation, 7.9.2
semideviation principle, 21.2.1
semivariance, 7.9.2
semivariance principle, 21.2.1
sensitivity, 14.3
sensitivity curve, 3.7.1
separable, 14.3.4
    linearly separable, 14.3.5
sequential attribution, 8b.1.3
settlement date, 19.1.2
settlement period, 19.1.3
Shapley attribution, 8b.1.4
Sharpe ratio, 7.11.1
    generalized, 32.1.1
shift filter, 42.5
shift parameters, 12.1
short holdings, 24.1, 24.1
short position, 22.1.2
short spot rate, 1.10.2
sigmoid, 27.402
sign, 1.8.1
signal, 43
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, 32.1.1
simulated path, 4.4
singular value, 47.3.4
singular value decomposition, 47.3.4
size signal, 43.5
skew signal, 43.5
skill, 9c.5.2
Sklar’s theorem, 29.2.3
slack variable, 45.3.1
slippage, 19.1.2
slippage model, 10.1.3
slippage P&L, 24.2.1
slope, 1.3.5
small capitalization stocks, 43.5
small minus big, 9c.1
smart beta, 9c
smile, 1.4.3
smile signal, 43.5
smirk, 1.4.3
smooth kernel probabilities, 3.1.2
smooth quantile, 27.8.7
smoothing, 43.6.1
    inference, 13.3
softmax function, 27.9.2
softplus function, 27.9.2
solvency capital requirement, 7.12
solvency condition, 6.8.1
Sorensen-Dice coefficient
    binary classification, 14.6
Sortino ratio, 7.11.2
span, 47.1.2
spanning set, 47.1.2
Spearman’s rho, 30.2.2
specificities, 12.4.5
specificity, 14.3
spectral density function, 42.1
spectral representation, 42.6
spectral root, 47.7.4
spectral theorem, 47.3.2
spectrum, 3.5.2
    satisfaction indices/risk measures, 7.8.1
splines, 33.3.1
spot curve, 1.10.2
spot rate, 1.10.2
spot swap, 1.3.1
spread, 1.3.6
square-dispersion, 32.5.3
    affine equivariant, 32.4.2
    modal, 32.4.2
square-root, 41.2.2
square-root rule, 4.7
stable distributions, 27.11.1
    symmetric, 27.11.1
standard "beta", ??
standard Brownian motion, 41.1.2
standard deviation, 32.1.1
standard deviation principle, 21.2.1
standard error, 38.1.3
standard Wiener process, 41.1.2
standardized elliptical variable, 27.7.3
standardized holdings, 6.4.1
standardized invariants, 3.9.1
state, 34.1.1
state crisp probabilities, 3.1.4
state process, 39.1
State space model
    Probabilisitc linear, 42.4
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, 39.1
statistical linear factor model, 12.3
statistics, 38.1.1
steepness, 1.3.5
stochastic discount factor, 20a.2.1
    cumulative or process, 20b.4.2
    probability measure, 20b.4.2
stochastic dominance, 31.1
stochastic mean, 2.1.3
stochastic time, 41.1.5
stochastic volatility, 2.1.3
stochastic volatility inspired, 1.4.5
strategy, 1.9
strict stochastic dominance, 31.1
strictly concave function
    multivariate, 48.6.2
    univariate, 48.6.1
strictly convex function
    multivariate, 48.6.2
    univariate, 48.6.1
string, 39.1.9
strips, 1.3.1
strong white noise, 39.1
structure, 13.1.1
Student t distribution, 27.6.1
sub-additivity, 7.2
sub-quantile function, 27.1.1
subordinator, 41.1.5
sufficient statistics, 27.9
sum-of-parts, 22.1.6
    liquidation valuation, 22.1.5
super-additivity, 7.2
Supervised modeling, 13.1
support vector machine, 14.3.7
surprise
    maximum likelihood parameters, 35.1
survival probability, 1.6
Sylvester’s determinant theorem, 47.8.4
systematic, 12.1.3
systematic-idiosyncratic LFM, 12.1.3

T

t copula, 29.3.1
tangent vector, 28.1
target parameters, 15.1.3, 15.2.4
target variables, 13.1, 13.1, 16.1.1
target vector, 12.1
Taylor expansion
    univariate, 48.3.1
Taylor polynomial
    multivariate, 48.3.2
    univariate, 48.3.1
Taylor series
    univariate, 48.3.1
temporary impact, 10.1.3
temporary market impact, 10.1.3
tenor, 1.3.2
tercile, 43.6.3
test set, 37.1
through-the-cycle, 2.3.2
tick time, 1.8.2
tick time evolution, 1.8.3
tick-by-tick, 1.8.3
Tikhonov, 37.2.2
Tikhonov regularization, 45.1.6
time crisp probabilities, 3.1.4
time dependent transition matrix, 2.3.2
time horizon, 37.1
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, 36.1.1
timing P&L, 24.3
torsion, 8b.5
total net income, 6.7.2
total shares outstanding, 6.7.1
trace, 47.8.2
tracking error, 7.3.3
trading P&L, 24.2.2
trailing window, 3.2.4
training set, 36.3.5
transaction time, 19.1.2
transaction value, 19.1.2
transaction variables, 1.8.1
transition equation, 42.4
transition matrix
    homogenous, arbitrary step, 41.3.1
    inhomogenous, arbitrary step, 41.3.1
transition probabilities, 2.3.1, 2.3.1
translation invariance, 7.2
true negative
    accuracy, 14.3
    probability, 14.3
    rate, 14.3
true positive
    accuracy, 14.3
    probability, 14.3
    rate, 14.3
truncation, 12.1.4
turnover, 9c.2.3
two-fund separation theorem, ??
type I error, 14.3
type II error, 14.3

U

unanimity, 16.1.7
unbalanced, 37.1
uncertainty band
    multivariate, 32.6.1
    univariate, 32.1.2
uncovered interest rate parity, 5.1.2
underwater chart, 24.6
undirected graph, 15.3.4
uniform component, 27.7.3
uniform distribution, 27.6.3
uniform probabilities, 3.1
unit, assumptions-prediction
unit simplex, 15.4.3
unit-root, 42.2.7
unitary transformation, 46.2.1
univariate score, 14.4.5
universal approximation theorem, 33.4.1
unrealized P&L, 19.1.6
    portfolio, 24.2.1
unsupervised modeling, 13.1
updated distribution, 16.1.2
updated state, 16.6.1
utility function, 7.5

V

validation set, 36.3.5
valuation function, 22
valuation multiple, 22.2.2
value at risk, 7.6.1
value function, 5.1
value signal, 43.2.1
value stocks, 43.2.1
variability, 32.5.1
    affine equivariant, 32.4.1, 32.4.2
variance, 32.1.1
variance minimization, 12.5.5
variance of the hypothetical means (VHM), 14.1.10
variance principle, 21.2.1
variance swap, 1.4.6
variation, 33.5
variation ratio, 32.5.2
variational free energy, 35.4.5
varimax rotation, 12.4.4
vector, 47.1
    coordinates, 47.1
    subspace, 47.1.2
vector autoregressive of order one, 42.2.6
vector operations
    addition, 47.1.1
    scalar multiplication, 47.1.1
vector space, 47.1
vectorization, 47.8.1
    half-, 47.8.1
    inverse, 47.8.1
vertices, 15.3.4
view p-value, 16.2.2
view variables, 16.1.1
visualization function, 32.3.6
volatility clustering, 2.6
volatility function, 30.3
volume, 1.8.1
volume time, 1.8.2
volume time evolution, 1.8.3
volume-weighted-average-price (VWAP) strategy, 10.1.3
Voronoi diagram, 33.3.1

W

Wald statistic, 38.1.4
Wang distortion principle, 21.2.3
Wang expectation, 7.8.2
Wang transform, 7.8.2
weak dominance, 31.2
weak signals, 9c.5.2
weight of evidence, 15.3.3
white noise
    weak
        multivariate, 42.2.1
        univariate, 42.2.1
Wiener-Hopf equations, 17.4.1
Wiener-Kolmogorov filter, 17.4
Williamson n-transform, 29.3.2
Wishart distribution, 27.6.7
within-cluster/group variance, 14.1.10
Wold theorem, 42.7
    linearly deterministic component, 42.7
    linearly regular component, 42.7
worst-case error, 36.1.2
    frequentist approach, 36.4.2
    frequentist prediction, 36.3.2
worst-case risk, 34.1.2
wrong-way risk, 5.1.5
    credit value adjustment, 6.2

Y

yield, 1.10.2
yield curve, 1.3.3, 1.10.2
yield income, 5.2.3
yield to maturity, 1.3.3
Youden’s J statistic, 14.6
Yule-Walker equations, 17.14

Z

z-score, 32.1.1
    multivariate absolute, 32.2.1
z-statistic, 38.1.3
zero curve, 1.10.2
zero rate, 1.10.2
zero-coupon bond, 1.3.1