Quora noscript

Glossary

Symbols

(functional) excess risk, 41
0-1 loss, 28.1.2

Wiener-Kolmogorov filter
    non-causal, 33.6.1

A

absence of arbitrage, 0b.1.3
absolute score, 18.9, 18.9
absolutely continuous component, 32.4.2
absolutely continuous spectral density, 32.4.2
abstract Bayes theorem, 18.14.3
accounting signals, 13.5
accrued interest, 0a.2.1
accuracy
    binary classification, 28.4
action, 23.1.2
    Bayes, 23.3.2
    minimax, 23.4.1
action space, 23.1.2
activation function, 41.4.1
activity time, 1.8.2
actual cash-flow, 0a.2.3
actual exchange rate, 0a.2.3
actual value, 0a.2.3
adapted basis, 14.1.3
adapted variable
    partition, 18.14.3
adaptive execution algorithm, 10.3.2
additive, 18.12.2
admissible, 37.8.1
algebraic multiplicity, 14.5.4
algebraic Riccati equation, 14.6.6
allocation policy, 6.5
almost everywhere
    function equality, 16.3.1
alpha, 7.3.1
    binary classification, 28.1
alternative beta, 9c
alternative hypothesis, 46.1
analysis of variance, 27.1.10
ancestor, 29.2.1
angle, 14.3.3
    canonical, 21.3.2
    covariance, 21.3.2
    expectation, 21.3.1
anti-monotonic
    function, 15.5.3
Arbitrage, 0b.17
arbitrage pricing theory, 0c.3.1
architecture, 41.4.1
area under curve (AUC), 28.1.4
arithmetic Brownian motion with drift, 36.1.3
Arrow-Debreu securities, 0c.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, 44.5.3
autocorrelation
    function, 32.2
    partial autocorrelation function, 32.2
autocovariance
    partial autocovariance function, 32.2
autocovariance function, 31.1.2
    random field, spatial-fields
autoencoder, 29.1
autoregressive conditional duration, 2.5.2
autoregressive fractionally integrated moving average, 2.4
autoregressive moving average process
    autoregressive
        multivariate (VAR), ??
        univariate (AR), ??
    autoregressive polynomial, 33.2.1
    causal, 33.2.2
    companion matrix, 33.2.5
    integrated
        multivariate (VARIMA), relationships-varma-vma-var
        univariate (ARIMA), ??
    invertible, 33.2.3
    LTI filter representation, 33.2.1
    moving average
        mutivariate (VMA), ??
        univariate (MA), ??
    moving average polynomial, 33.2.1
    multivariate (VARMA), 33.2.1
        error correction representation, 33.2.4
    univariate (ARMA), 33.2.1
    Yule-Walker equation
        multivariate, 33.2.2
        univariate, 33.2.2
autoregressive of order one
    univariate (AR), 33.1
    vector (VAR), 33.1
auxiliary measure, 18.10

B

Bachelier, 0e.3.1
backpropagation, 17.2.3, 41.4.1
backward cash-flow-adjusted value, 0a.1.5
backward/forward exponential weighted moving average, 37.9.3
bagging, 42.4.1
balance sheet, 6.7.1
balanced, 42.1
bandpass filter, 32.5.4
bandwidth, 37.1.2
    kernel density estimate, 37.3
bandwidth matrix, 37.1.2
base case, 44.6.1
base distribution, 44.1.1
base measure, 18.10
basis, 12.4.3, 14.1.2
    canonical, 14.1
    orthonormal, 14.3.4
basis denominator, 12.4.3
basis instruments, 0c.1.1
Bayes classifier, 28.2.3
Bayes theorem, 18.3.2
Bayesian networks, 29.2.6
benchmark, 12.5.1
Bernoulli distribution, 18.9
best ask, 1.8.1
best bid, 1.8.1
best prediction, 14.3.5
beta
    binary classification, 28.1
beta conditions
    projection coefficients, 14.3.4
    projection equation, 14.3.4
beta distortion index, 7.8.2
beta-adjusted excess return, 12.5.2
bets, 8b.4
between-cluster/group variance, 27.1.10
Bhattacharyya coefficient, 42.4.4
bias, 40.1.1
    binary classification, 28.1
bias reduction, 41
bid size, 1.8.1
bid-ask spread, 10.1
bilateral value adjustment, 6.2
binary classification, 28.1.5
binary classifier, 28.1.4
binary mixture distribution, 29.2.4
    binary Gaussian mixtures, 29.2.4
    binary mixture components, 29.2.4
    binary normal mixtures, 29.2.4
binary partition encoder, 18.9
binning, 1.8.3
binomial inverse theorem, 14.7.4
binomial tree, 2.1.2
bins, 1.8.3, 24.2
bins width, 24.2
Black-Merton-Scholes model, 0e.3.2
Bochner’s theorem, 16.6.1
bond yield, 5.3.2
bootstrap aggregating, 42.4.1
Borel sets, 18.14.1
boundedness, 44.1.7
breadth, ??, 41.4.1
breakdown point, 37.7.2
Brier scoring rule, 28.3.1
Brownian motion, 2.1.1
Buhlmann pricing equation, 0b.5
    expectation, 0b.5
Buhlmann principle, 0d.2.4
butterfly, 0c.1.2, 1.4.4

C

calendar signal, 13.5
calibrate, 0e
call option, 1.4.1
canonical correlation matrix, 22.3.1
canonical parameters, 18.10
capital asset pricing model, 0c.2
capital gain, 0a.1.6
cardinality, 16.1.2
cardinality constraint, 17.4
carry signal, 13.1
CART, 41.3.1
cash-flow function, 5.1
cash-flow-adjusted value, 0a.1.5
categorical distributions, 18.9
categories, 18.9
Cauchy distribution, 18.6.2
Cauchy-Schwarz inequality, 14.3.3
causal, 32.5.4
center of the bin, 24.2
central tendency, 21.5.1
    affine equivariant, 21.4.1, 21.4.2
certainty-equivalent, 7.5
certainty-equivalent principle, 0d.2.2
chain rule
    gradient, 15.1.2
    matrix-variate, 15.1.3
    multivariate, 15.1.2
    univariate, 15.1.1
change of variable formula
    multivariate, 15.4.3
    univariate, 15.4.1
Chapman-Kolmogorov equations, 31.2.4
characteristic equation, 14.5.1
characteristic function
    multivariate, 18.1.3
    univariate, 18.1.2
characteristic matrix, 9c.4
characteristic polynomial, 14.5.1
characteristic portfolio, 9c.2
characteristic portfolios, 9c.4
Chebyshev’s inequality, 21.2.7
chi-squared distribution, 18.5.1
child, 29.2.1
child orders, 10
Cholesky decomposition, 14.6.5
Cholesky root
    lower triangular, 14.6.5
    upper triangular, 14.6.5
CIR, 36.2.2
claims, 1.6
classes, 18.9
classical-equivalent, 37.8.1
classification
    discriminative classification, 28.3.5
    multinomial classification, 28.2
classification and regression trees, 41.3.1
clean price, 0a.2.1, 0a.2.1
clique, 29.2.1
    maximal clique, 29.2.1
clique factorization, 29.2.7
clustering, 29.1.2
co-monotonic
    function, 15.5.3
    variables, 19.2.4
co-monotonic additivity, 7.2
co-variogram
    random field, 31.5.1
coarseness level, 24.2
codes, 29.1
coherent satisfaction measure, 7.9.1
    coherent probability measure, 7.9.1
    worst case expectation representation, 7.9.1
cointegrated, 31.3.4
cointegrated space, 2.7.1
cointegration signal, 13.3.3
cointegration vector, 2.7.1
    multivariate process, 31.3.4
collateral, 6.8.1
commonalities, 25.4.5
comparable instruments, 0e
complete, 0c.1.1
complete class theorem, 23.3.7
complete metric space, 16.3.1
compound distribution, 18.3.4
compound Poisson process, 36.1.4
compounded rate of return
    (instantaneous) compounded rate of return, 12.4.6
    average compounded rate of return, 12.4.6
compounded return, 12.4.1
Comprehensive Capital Analysis and Review, 6.6
concave down function
    univariate, 15.6.1
concave function
    multivariate, 15.6.2
    univariate, 15.6.1
concave up function
    univariate, 15.6.1
concavity, 7.2
condition number, 38.6.2
conditional cdf, 18.3.1
conditional covariance, 18.3.3
conditional distribution of variable, 18.3.1
conditional excess distribution, 7.6.2
conditional expectation, 18.3.3
    Stochastic processes, 31.1.4
    with respect to the events set, 18.14.3
conditional independence, 26.1.4, 42.1
conditional pdf, 18.3.1
conditional principal component analysis, 25.6.16
conditional principal directions, 25.6.16
conditional principal variances, 25.6.16
conditional probability, 18.3.1
    with respect to the events set, 18.14.3
conditional statistical feature, 18.3.3
conditional value at risk, 7.8.2
conditioning-marginalization, 44.1.3
cone, 17.3.1
confusion matrix, 28.1
conic programming, 17.3.1
conjugate distribution, 37.8.3
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, 17.1
contingency table, 28.2.1
continuous-state distributions, 2.1
control variable, 26.1.3
convex function
    multivariate, 15.6.2
    univariate, 15.6.1
convex programming, 17.3
convex set, 17.3
convexity, 5.3.2, 5.3.2
    effective key-rates, 5.3.2
    risky investment, 9d.6
    satisfaction/risk measures, 7.2
convolution, 16.3.4
    cyclic, 16.3.4
    discrete, 16.3.4
    periodic, 16.3.4
coordinate descent, 17.4.7
copula, 19.2.2, 19.2.2
copula-marginal combination, 19.4.2
copula-marginal distributions, 19.4.2
copula-marginal separation, 19.4.1
copula-pdf, 19.2.3
Cornish-Fisher approximation, 7.6.2
Cornish-Fisher expansion, 7.6.2
correlation, 22.3
correlation function, 22.3
correlation matrix, 22.3
cost of equity, 0e.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, 21.2.1
    cross-covariance, 21.2.1
covariance principle, 0b.5
covariant, 20.1.1
covariates, 26.1
Cramer decomposition, 32.4.2
Cramer-Lundberg ruin model, 6.7.5
credit ratings, 1.5.3
credit structural model, 466
credit value adjustment , 6.2
critical point, 17.2.1
cross the spread, 10.1
cross-autocovariance
    function, 32.2, 32.2
cross-entropy, 26.5.1
cross-sectional, 9c, 9c
cross-sectional linear factor model, 25.5
cross-sectional sample median of default probabilities, 1.5.3
cross-spectral density, 32.4.2
cumulant , 4.7.1
cumulative cash-flow, 0a.1.3
cumulative distribution function
    multivariate, 18.1.3
    univariate, 18.1.2
cumulative link, 18.9.2
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, 13.5
cutoff classifier, 28.1.4

D

data, 37.4.1
data-generating process, 23.1.1
debt, 6.7.1
debt value adjustment, 6.2
decay, 1.3.5
decision
    deterministic, 23.3
decision function
    admissible, 23.3.7
    Bayes, 23.3.4
    minimax, ??
    optimal posterior, 23.3.5
    randomized, 23.3
decision problem
    statistical, ??
decision region
    binary classification, 28.2.3
decision regions
    multinomial classification, 28.2
decoder, 29.1
decreasing function, 15.5.1
    entrywise, 15.5.2
    matrix-valued, 15.5.2
    strictly, 15.5.1
degree of reversal, 9c.2.3
delta, 1.4.4
delta p, 28.4
delta rule, 41.4.1
dependent variables, 25.1, 26.1
depth, 41.4.1
derivative
    directional, 15.1.2
    first, 15.1.1
    higher order, 15.1.1
    partial, 15.1.2
    second, 15.1.1
    total, 15.1.2
    univariate, 15.1.1
descendant, 29.2.1
design of experiments, 26.1.3
determinant, 14.2.3
deviation
    expectile, 21.5.2
    maximum, 21.5.2
    mean absolute, 21.5.2
    median absolute, 21.5.2
    subquantile, 21.5.2
differentiable function
    multivariate, 15.1.2
    univariate, 15.1.1
dimension, 14.1.2
Dirac delta, 16.3.2
direct sum, 14.1.3
directed graph, 29.2.1
dirty price, 0a.2.1
discount, 1.10.2
discount factor, 0a.2.3
discount function, 5.1
discounted cash-flow, 0e.2.1
discounted cash-flow adjusted value, 0d.3.1
discounted payoff, 0d.2
discrete-state distributions, 2.1
discrete-state random walk, 2.1.2
discretized pdf, 24.2
discriminant variables, 18.9
discriminative model, 26.2.2
    generative embedding, 26.2.2
discriminative next-step model, 2.6.6
dispersion, 21.5.1
    affine equivariant, 21.4.1
    modal, 21.4.1
distance, 14.4.2
    absolute, 14.4.2
    covariance, 21.3.2
    Euclidean, 14.3.3
    expectation, 21.3.1
    identity of indiscernibles, 14.4.2
    Lp, 21.6.2
    Mahalanobis, 14.4.2
    p, 14.4.2
    subadditivity, 14.4.2
    symmetry, 14.4.2
    triangle inequality, 14.4.2
distance matrix, 28.2.1
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 function, 7.8.1
distortion principle, 0d.2.3
distortion satisfaction measure, 7.8.1
distribution function, 18.14.1
distributional view, 44.1.3
divergence, 14.4.3
    Bregman, 14.4.3
    difference, 14.4.3
    extended f, 14.4.3
    identity of indiscernibles, 14.4.3
    separable, 14.4.3
diversification distribution, 8b.4
diversity, 42.4.4
dividend-adjusted value, 1.1
    stocks, 0a.1.5
divisors, 18.12.3
dollar duration, 6.1.3
dollar-neutral constraint, 9c.2.2
domain
    circle, 16.1.1
    continuous, 16.1.1
    cyclic group, 16.1.1
    discrete, 16.1.1
    frequency, 16.4
    time, 16.4
    torus, 16.1.1
dominant-residual LFM, 25.1.2
dot product, 14.3
drawdown, 12.6
    maximum (absolute) drawdown, 12.6
    maximum percentage drawdown, 12.6
    percentage drawdown, 12.6
dual Legendre, 20.1.3
dually flat, 20.1.3
duration, 5.3.2
    effective key rates, 5.3.2
DV01, 6.1.3
dynamic allocation, 9c
dynamic conditional correlation, 3.1.3
dynamic linear factor model, 33.6
dynamic principal component, 33.6.2
dynamic regression linear factor model, 33.6.1

E

e-affine coordinates, 20.1.2
e-flat, 20.1.2
e-geodesic, 20.1.2
EBITDA, 6.7.2
economic capital, 7.12
economic net income, 6.7.2
edges, 29.2.1
effective delta, 5.3.3
effective duration
    effective, 5.3.2
effective number of bets, 8b.4
effective number of scenarios, 37.1.4
effective rank, 44.1.6
effective rho, 5.3.3
    key-rates, 5.3.3
effective volga, 5.3.3
efficient frontier, 9a.1
efficient market hypothesis, 2.1
eigenfunction, 16.5.2
eigenvalue, 14.5.1
    linear transformation, 14.5.1
    operator, 16.5.2
eigenvector, 14.5.1
    linear transformation, 14.5.1
elastic net, 17.4.7, 42.2.2
    constrained generalized elastic net, 17.4.7
elicitability, 23.3.2
    consistency, 23.3.2
    satisfaction measures, 7.2
ellipsoid, 21.2.4
    expectation-covariance, 21.2.4
    location-dispersion, 21.4.2
ellipsoid test for invariance, 35.1
elliptical distribution, 18.7.1
EM algorithm
    population, 45.2.6
encoder, 29.1
enterprise value, 0e.2.2
entropy
    generalized , 23.3.2
equal in distribution
    multivariate, 18.1.3
    univariate, 18.1.2
equally weighted portfolio, 9a.1.3
equilibrium performance model
    Black-Litterman, 43.9
equilibrium returns, 9a.1.3
equity, 6.7.1
equity book value, 6.7.1
equivalent optimization problem, 17.5.1
ergodic
    in autocovariance, 31.3.2
    in mean, 31.3.2
    strong, 31.3.2
Erlang process, 36.1.4
error
    multivariate, 21.5.3
    multivariate mean absolute, 21.5.3
    multivariate mean squared, 21.5.3
    univariate, 21.5.1
    univariate mean squared, 21.5.1
error prediction matrix, 25.2.6
Esscher principle, 0d.2.4
estimable, 7.2
estimate, 40.1
estimation, 26.3
estimation model, 38.5
    Bayesian estimation, 37.8.1
estimation set, 42.1
    point prediction, 40.4.1
    predictive distribution, 40.5.1
estimation uncertainty, 37.8.1
estimator, 40.1
European-style derivatives, 1.4
evidence
    maximum likelihood, 37.4.1
evidence lower bound, 45.2.5
ex-dividend date, 0a.1.2
exotic beta, 9c
expectation
    multivariate, 21.2.1
    univariate, 21.1.1
expectation function, 31.1.2
    random field, 31.5.1
expectation parameters, 18.10
expectation rule, 18.8.3
expectation-maximization, 37.5.2
    expectation step, 37.5.2
    maximization step step, 37.5.2
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), 27.1.10
expectile, 18.1.2
expectile-VaR, 7.9.2
expiry, 1.4
explanatory variables, 26.1
exponential decay probabilities, 37.1.1
exponential family distribution, 18.10
exponential kernel, 37.1.2
exponential of the entropy, 37.1.4
exponential operator, 15.3.1
exponential principle, 0d.2.2
exponential tilting, 44.1.5
exponentially weighted moving average, 37.2.4
exponentially weighted moving correlation, 37.2.4
exponentially weighted moving covariance, 37.2.4
exponentially weighted moving quantile, 37.2.4
exponentially weighted moving standard deviation, 37.2.4
exposure, 6.1.3
    portfolio P&L, 9c.2
    risky investment, 9d.6
exposure at default, 1.5.2
extrema
    local, 17.1
    relative, 17.1
extreme value theory, 7.6.2

F

f-divergence, 20.1.4
F-measure
    binary classification, 28.4
F1 score
    binary classification, 28.4
face value, 1.3.1
factor analysis, 38.6.6
    confirmatory, 25.4
    equamax, 25.4.4
    exploratory, 25.4
    orthomax, 25.4.4
    parsimax, 25.4.4
    quartimax, 25.4.4
    varimax , 25.4.4
factor analysis matrix, 38.6.6
factor loadings, 25.1
factor premia, 9c.4.2
factor premium, 9c.1
factor-analysis linear factor model, 25.4.1
factor-replicating portfolios
    arbitrage pricing theory, 0c.3.1
factors, 9c, 26.1
    linear factor model, 25.1
fair value, 0a.1.2
fallout, 28.1
false negative
    accuracy, 28.1
    probability, 28.1
    rate, 28.1
false positive
    accuracy, 28.1
    probability, 28.1
    rate, 28.1
feature engineering, 27.1
feature map
    canonical, 16.7.2
    Mercer, 16.7.2
Feller condition, 36.5.1
filter, 32.5
    anti-causal operator, 32.5
    causal operator, 32.5
    factor construction, 33.6.2
    invertible, 32.5
    linear time invariant , 32.5.4
filtering, 26.3
filtration, 31.6.1
    adapted process, 31.6.3
        (fully-recombining) binomial tree, 31.6.3t
        (fully-recombining) tree, 31.6.3t
    martingale, 31.6.4
    Radon-Nikodym process
        martingale, finite-discrete-time-stoch-process-sec
financial instrument, 0a.1.1
finite difference
    backward first order, 15.2.1, 15.2.2
    central first order, 15.2.1, 15.2.2
    central second order, 15.2.1, 15.2.2
    forward first order, 15.2.1, 15.2.2
finite-dimensional joint distributions, 31.1.1
first in, 8b.1.1
first order criterion, 17.2.1
first order differential, 15.1.2
    matrix-variate, 15.1.3
Fisher consistent, 37.7.1
Fisher discriminant analysis (FDA), 28.1.8
Fisher information distance, 20.1.4
Fisher’s linear discriminant, 28.1.8
flexible probabilities, 3
    estimation, 18.8
forecast, 25.1
    point, 26.2
    probabilistic, 26.2
forecasting
    inference, 26.3
foreign exchange function, 5.1
foreign exchange rate, 1.2.1
forward, 1.2.2
forward cash-flow-adjusted value, 0a.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 transform, 16.4
    convolution theorem, 32.193
    discrete (DFT), 16.4.5
    discrete time (DTFT), 16.4.3
    Fourier series, 16.4.4
    integral, 16.4.2
    inverse, 16.4
fractional Brownian motion, 36.4
Frechet derivative, 16.8.2
    second order, 16.8.3
Frechet-Hoeffding bounds, 22.1.1
full-investment, 9c.2.2
fully constrained LFM, 25.6.17
function space, 16.2.1
    addition, 16.2.1
    scalar multiplication, 16.2.1
functional, 16.8
functional derivative, 41.5
fundamental accounting equation, 6.7.1
fundamental law of active management, 9a.4.1
fundamental linear factor model, 25.5
fundamental signals, 13.5
fundamental theorem of asset pricing, 0b.2.2
    martingale pricing formula, 0c.4.3
fundamental theorem of calculus
    first, 15.4.2
    second, 15.4.2
funding risk, 6.2
funding value adjustment, 6.2

G

gamma distribution, 18.5.2
Gateaux derivative, 16.8.1
Gaussian kernel, 37.1.2, 37.3
generalized autoregressive conditional heteroscedastic, 2.5.1
generalized excess return, 12.5.2
generalized linear models (GLM), 27.3.6
generalized linear return, 12.4.3
generalized method of moments
    iterated GMM, 38.6.5
generalized method of moments with flexible probabilities (GMMFP) estimate, 37.6.3
    minimization, 37.6.4
generalized negative entropy, 26.5.1
generalized Pareto distribution, 7.6.2
generalized spectral density, 32.4.2
generalized weight, 12.4.4
generative model, 26.2.2
generative next-step model, 2.6.6
generator
    Markov chain, 36.3.1
    matrix, 14.2.3
generic position, 0e.1.3
geometric Brownian motion, 5.1.1
geometric multiplicity, 14.5.4
Gibbs distribution, 29.2.7
Gini coefficient, 28.1.4
Giny impurity, 28.3.3
glasso, 38.6.5
    Tikhonov, 42.2.3
Glivenko-Cantelli theorem, ??
Gordon growth model, 0e.2.1
grade, 19.1
grades, 19.2.1
gradient, 15.1.2
    matrix-variate, 15.1.3
    vector-valued function, 15.1.2
gradient descent, 17.2.3
    stochastic, 17.2.3
Gram matrix, 14.6.1
Gram-Schmidt process
    backward, 14.6.5
    forward, 14.6.5
Gramian, 14.6.1
grand mean, 38.6.1
Granger causal, 32.9
graph, 29.2.1
    directed acyclic, 29.2.1
graphical lasso, 38.6.5
Greeks, 5.3
gross exposure, 6.1.3
group
    general linear, 14.2.3
    orthogonal, 14.3.6
    special orthogonal, 14.3.6
    unitary, 14.3.6
growth stocks, 13.2.1

H

Hadamard product, 14.7.2
half-life, ??, 37.1.1
Hamiltonians, 18.10
harmonic process
    multivariate, ??
    univariate, 33.5
hat matrix, 25.2.6
hazard function, 1.6
Hellinger distance, 42.4.4
Herglotz theorem, 16.6.1
Hessian, 15.1.2
    matrix-variate, 15.1.3, 15.1.3
Heston model, 0e.3.3
hidden Markov model, 34.1
hidden variables, 26.1
    maximum likelihood, 37.5
    point prediction, 40.4.1
high breakdown estimators, 37.7.2
high breakdown point with flexible probabilities, 37.7.2
high minus low, 9c.1
high water mark, 12.6
Hilbert space, 16.3.1
historical cdf, ??
historical cross-sectional, 25.5.7
historical distribution, ??
historical pdf, ??
historical principal component, 25.3.7
historical repricing, 5.5.2
historical with flexible probabilities (HFP) autoencoder, 40.4.4
historical with flexible probabilities (HFP) cdf, 37.2
historical with flexible probabilities (HFP) correlation matrix, 38.1
historical with flexible probabilities (HFP) covariance matrix, 38.1
historical with flexible probabilities (HFP) distribution, ??
historical with flexible probabilities (HFP) estimate, 37.2
historical with flexible probabilities (HFP) mean, 38.1
historical with flexible probabilities (HFP) median, 37.7.2
historical with flexible probabilities (HFP) pdf, 37.2
historical with flexible probabilities (HFP) predictor, 40.4.4
historical with flexible probabilities (HFP) quantile, 37.7.2
historical with flexible probabilities (HFP) standard deviation vector, 38.1
hold-out, 40.4.5
Hotelling statistic, 46.1.4
Hurst coefficient, 36.4
hybrid Monte Carlo-historical, 4.5.2

I

ice-cream cone, 17.3.3
identity transformation, 14.2.3
idiosyncratic, 25.1.3
ill-conditioned, 38.6.2
IM algorithm, 45.2.5
image space, 14.2
implementation shortfall, 12.3
implied returns, 9a.1.3
implied volatility, 1.4.3
implied volatility surface, 1.4.3
improper integral, 15.4.1
impulse response, 32.5.4
in the money, 1.4.2
in-sample error, 42.16
inception, 12.2.2
income, 0a.1.6
income statement, 6.7.2
increasing function, 15.5.1
    entrywise, 15.5.2
    matrix-valued, 15.5.2
    strictly, 15.5.1
indefinite integral, 15.4.2
independence, 22.1
independent component analysis, 29.1.4
independent variables, 26.1
    linear factor model, 25.1
induced expectation, 7.10.1
inefficiency, 40.1.1
inference, 26.3
    marginalization, 26.3
    marginalization problem, 26.3
infinitely divisible, 18.12.3
inflator, 0b.2.2
influence function, 37.7.1
information, 31.1.4
    generator, 31.1.4
    linear set, 31.1.4
    random time series, 3
    set, 31.1.4
information coefficient, 9a.4.1
information measure, 26.5.1
information ratio, 7.11.1
    linearly predicted, 9a.4.1
information set, 27.1.10
    distributions, 45.2.1
    linearized, 25.2.6
information/view, 44.6.1
informedness, 28.4
inner product, 14.3
    covariance , 21.3.2
    expectation, 21.3.1
    Hermitian, 16.3.1
    Hermitian symmetry, 16.3.1
    L2, 16.3.1
    linearity, 14.3
    partial linearity, 16.3.1
    positive definiteness, 14.3, 16.3.1
    symmetry, 14.3
inner product space, basic-geom-sec
innovation
    weak, 25.2.6
Innovation process
    Mean-covariance, 31.4.1
        Error decomposition matrix, 31.4.1
    Probabilistic uncorrelated, 31.4.1
        Error decomposition matrix, linear-weak-decomp-secc
input variables, 23.3
inputs, 26.1
instantaneous exchange rate, 0a.2.3
instantaneous forward curve, 1.10.2
instantaneous forward rate, 1.10.2
instantaneous spot rate, 1.10.2
integral kernel, 16.2.3
    Mercer, 16.5.1
    positive definite, 16.5.1
    symmetric, 16.5.1
integral power spectrum
    matrix-valued, 16.6.2
integrated
    fractionally, 31.3.3
    order d, 31.3.3
    order zero, 31.3.1
integration by parts, 15.4.1
integration operator, 23.2.2
intensity models, 0e.4.2
interaction, 41.2.1
interest rate, 1.3.3
internal rate of return, 12.4.6
interquantile range, 21.4.1
intuitive r-squared, 25.5.5
invariance rule, 18.8.3
invariant, 21.4.2
Invariant process
    Mean-covariance, 31.4.1
    probabilistic, 31.4.1
    Probabilistic uncorrelated, 31.4.1
    Standardized mean-covariance, 31.4.1
    standardized probabilistic, 31.4.1
    Standardized probabilistic uncorrelated, 31.4.1
invariants, 2
inverse, 14.2.3
inverse transform sampling, 19.1
inverse-call, 1.3.4
inverse-Wishart distribution, 18.6.7
invertible, 14.2.3
investment factor, 12.4.6
    reinvested instrument, 0a.1.4
iso-contour, 21.6.1
isolated, 8b.1.1
iterated integral, 15.4.3

J

jackknife estimator, 37.7.1
Jacobian, 15.1.2
James-Stein estimator, 38.6.1
Jeffreys prior, 37.8.1
joint scenario, 18.8
jump component, 32.4.2
jump rule, 0a.1.3
jump spectral density, 32.4.2

K

k-fold, 40.4.5
k-means clustering, 29.1.2
kalman filter, 33.4.5
kalman gain matrix, 33.4.5
kappa ratio, 7.11.2
Karush-Kuhn-Tucker conditions, 17.2.2
Kendall’s tau, 22.2.1
kernel, 37.3
    of a linear transformation, 14.2
    Toeplitz, 16.3.4
kernel density estimate, 37.3
kernel stochastic discount factor, 0b.2.3
kernel trick, 16.7.2, 41.6
    linear kernel, 41.6
    polynomial kernel, 41.6
    radial basis functions, 41.6
kernel with flexible probabilities (KFP) generalized mean, 37.3
kernel with flexible probabilities (KFP) pdf, 37.3
key rates, 1.3.5
Kolmogorov-Smirnov test, 35.1
Kronecker delta, 16.3.2
Kronecker product, 14.7.2
Kullback-Leibler divergence, 20.1.4, 26.5.1

L

L2 space, 16.3.1, 21.3
Lévy process, 31.2.2
    univariate, 36.1.2
label encoding, 18.9
labels, 26.1
lag operator, 15.3.1, 32.5
Lagrange multiplier, 17.2.2
Lagrangian function, 17.2.2
Laplace approximation, 21.4.1
Laplacian, 15.1.2
large capitalization stocks, 13.5
lasso, 17.4.7, 42.2.2
lasso regression, 39.4.2
lasso shooting, 17.4.7
last in, 8b.1.2
last transaction price, 1.8.1
latent variables, 26.1
    maximum likelihood, 37.5
law invariant, 7.2
law of iterated expectations, 27.1.10
law of one price, 0b.1.1
law of total covariance, 27.1.10
law of total linear covariance, 25.6.13
law of total linear variance, 25.6.13
law of total variance, 27.1.10
LDL-Cholesky decomposition, 14.6.5
leaf, 41.3.1
learning, 26.3
least favorable prior, 23.3.6
least-squares residual, 27.1.2, 27.1.2
leave-1-out, 40.4.5
leave-p-out, 40.4.5
Lebesgue’s decompositon theorem, 16.1.3
Legendre transformation, 20.1.3
length, 14.4.1
    covariance, 21.3.2
    expectation , 21.3.1
    Lp, 21.6.2
level, 1.3.5
leverage, 6.1.4
leverage effect, 2.5.2
Levy process, 36.1.2
Levy-Khintchine, 36.1.5
liabilities, 6.7.1
Libor, 1.10.2
likelihood, 38.5
    estimators as random variables, 40.1.1
    maximum likelihood, 37.4.1
likelihood ratio, 28.1.3
limit order book, 1.8.1
limit order placement, 10.3
linear classification, 28.78
    bias, 28.1.5
linear combination, 14.1.2
linear dependence, 14.1.2
linear discriminant analysis (LDA), ??
linear factor model, 25.1
linear independence, 14.1.2
linear law of iterated projections, 25.6.13
linear loss matrix, 25.2.6
linear operator
    “flat”, 14.3
linear prediction
    point, 25.2.6
linear pricing equation, 0b.2.1
    intertemporal, 0c.4.2
linear programming, 17.3.6
linear projection, 25.2.6
linear return, 12.4.1
linear state space model, 33.4.1
    observation equation, 33.4.1
    transition equation, 33.4.1
linear state-space model
    covariance stationary, 33.4.2
    systematic-idiosyncratic, 33.4.3
linear time invariant filter
    frequency response function, 32.5.4
    transfer function, 32.5.4
linear transformation, 14.2
linearity, 0b.1.2
linearly constrained quadratic programming, 17.3.4
link function, 18.10
links, 29.2.1
liquidation, 12.2.2
liquidation valuation, 0e.1.5
liquidity curve, 10.1
    "market buy" liquidity curve, 10.1
    "market sell" liquidity curve, 10.1
local Markov property, 29.2.6
location, 21.5.1
    affine equivariant, 21.4.1, 21.4.2
    multivariate, 21.5.3, 21.5.3
log-partition function
    exponential family distribution, 18.10
log-return, 12.4.6
log-sum-exp function, 18.10.2
logistic function, 18.481
logistic regression
    binary, ??
    multinomial, ??
logit function, 18.10.2
logit parametrization
    binary logit parametrization, 18.9.2
    multinomial logit parametrization, 18.9.2
logit regression
    binary, ??
    multinomial, ??
lognormal distribution, 18.6.6
    shifted, 18.6.6
long holdings, 12.1
long memory, 2.4
long position, 0e.1.1
longitudinal data, 42.1
Lorentz cone, 17.3.3
Lorenz curve, 18.1.2
loss, 28.1.5
    0-1 loss, 28.1.5
    0-1 margin loss, 28.1.5
    exponential , 28.1.5
    hinge , 28.1.5
    logistic , 28.1.5
    margin loss, 28.1.5
    probabilistic worst-case, 23.4.1
    proper, 23.5.4
    square , 28.1.5
    strictly proper, 23.5.4
    tangent , 28.1.5
    worst-case, 23.4.1
loss function, 23.1.3
loss given default, 1.5.2
lower partial moment, 7.9.2
    root, 7.9.2
lower partial moment principle, 0d.2.1
Lp-space, 21.6.2

M

m-affine coordinates, 20.1.2
m-flat, 20.1.2
m-geodesic, 20.1.2
m-square, 5.3.2
machine learning
    experimental study, 26.1.3
    interventional study, 26.1.3
    observational study, 26.1.3
macro signals, 13.5
macroeconomic linear factor model, 25.2
Mahalanobis inner product, 14.3
Marchenko-Pastur distribution, 38.6.3
marginal cdf, 18.2
marginal characteristic function, 18.2
marginal contributions, 8b
    Euler decomposition, 8b.2
    Euler marginal contributions, 8b.2
marginal distribution, 18.2
marginal pdf, 18.2
marginal supply demand curve, 10.1
marked-to-market, 0a.1.2
marked-to-model, 0a.1.2
markedness, 28.4
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 impact square root law, 9a.3.3
market order placement, 10.3
market parameters, 0e
market portfolio, 9c.1
Markov chain
    Monte Carlo, 45.1
Markov network, 29.2.7
Markov process, 31.2.4
    mean-covariance, 31.2.4
    time homogenous, 31.2.4
Markov’s inequality, 21.2.7
MARS, 41.3.1
martingale, 31.2.3
matrix, 14.2.1
    addition, 14.2.2
    circulant, 16.3.4
    commutation, 14.7.4
    conformable, 14.2.2
    conjugate transpose, 14.3.1
    decomposition, 14.7.4
    hermitian, 14.3.1
    identity, 14.2.3
    inverse, 14.2.3
    invertible, 14.2.3
    low-rank-diagonal, 14.7.4
    multiplication, 14.2.2
    negative definite, 14.3.2
    negative semidefinite, 14.3.2
    non-singular, 14.2.3
    orthogonal, 14.3.6
    polynomial, 14.8
    positive definite, 14.3.2
    positive semidefinite, 14.3.2
    rank property, 14.7.4
    scalar multiplication, 14.2.2
    size, 14.2.1
    square, 14.2.1
    subtraction, 14.2.2
    symmetric, 14.3.1
    Toeplitz, 16.3.4
    transpose, 14.3.1
    transpose-square-root, 14.6.3
    unitary, 14.3.6
matrix exponential, 14.7.2
matrix-normal distribution, ??
matrix-valued kernel, 16.5.4
    Mercer, 16.5.4
    positive definite, 16.5.4
    symmetric, 16.5.4
    Toeplitz, 16.6.2
matrix-vector multiplication, 14.2.1
Matthews correlation, 28.4
maturity, 1.3.1
maximal Youden’s J statistic, 28.1.4
maximum
    global, 17.1
    local, 17.1
    relative, 17.1
Maximum a posteriori, 18.3.2
maximum classifier, 28.1.4
maximum expected return portfolio, 9a.1.1
maximum information ratio portfolio, 9a.1.1
maximum likelihood factorization, 29.2.3
maximum likelihood parameters, 37.4.1
maximum likelihood with flexible probabilities, 37.4.3
    normal assumption, 39.2.2
    Student t assumption, 39.2.3
maximum likelihood with flexible probabilities (MLFP) estimate, 37.4.4
maximum likelihood with flexible probabilities (MLFP) predictor, 40.4.4
maximum partition encoder, 18.9
maximum return signal-to-noise portfolio, 9a.1.1
maximum Sharpe ratio portfolio, 9a.1.1
mean maximum information ratio, 9a.4.1
mean reversion, 2.2
mean-covariance equivalence class, 25.1
mean-lower partial moment, 7.9.2
mean-semideviation, 7.9.2
measure, 16.1.2
    absolutely continuous, 16.1.3
    counting, 16.1.2
    finite, 16.1.2
    integral, 16.1.2
    Lebesgue, 16.1.2
    Radon-Nikodym derivative, 16.1.3
    Riemann-Stieltjes, 16.1.2
measure of concordance, 22.2
measure of dependence, 22.1
median
    multivariate, 21.5.3
    univariate, 21.4.1
Mercer’s theorem, 16.7.1
method of moments (MM) estimate, 37.6.1
method of moments with flexible probabilities (MMFP) estimate, 37.6.1
metric, 14.4.2
    discrete, 14.4.2
metric geodesic, 20.1.2
metric space, 14.4.2
Metropolis-Hastings algorithm, 45.1.1
microprice, 1.8.1
mid-quote, 1.8.1
midrange, sec-lp-error
minimum
    global, 17.1
    local, 17.1
    relative, 17.1
minimum relative entropy numeraire probability, 0b.2.3
minimum relative entropy stochastic discount factor, 0b.2.3
minimum tracking error portfolio, 9a.1.1
minimum variance portfolio, 9a.1.1
minimum-torsion bets, 8b.4.1
minimum-torsion exposures, 8b.4.1
minimum-torsion transformation, 8b.4.1
misclassification error, 21.5.2
Misclassification scoring rule, 28.3.1
miss rate, 28.1
mixture models, 29.2.4
    Gaussian mixtures, 29.2.4
    mixture of experts, 27.4.2
mode
    multivariate, 21.4.2
    univariate, 21.4.1
model
    frequentist approach, 40.5.2
    frequentist prediction, 40.4.2
model set
    distributions, 45.2.1
model uncertainty, 23.3.8
moment generating function, 18.1.2
    multivariate, 18.1.3
momentum, 13.3.1
momentum signal, 13.3.1
money multiple, 12.4.6
money-equivalence, 7.2
moneyness, 1.4.2
monotone map
    decreasing, 15.5.3
    increasing, 15.5.3, 15.5.3
    strictly increasing, 15.5.3
monotonic function, 15.5.1
    entrywise, 15.5.2
    matrix-valued, 15.5.2
    strictly, 15.5.1
monotonicity, 7.2, 44.1.7
mortgage backed securities, 0a.2.1
most powerful set, 28.1.3
multinomial logit function, 18.10.2
multinomial mixture distribution, 29.2.4
    multinomial Gaussian mixtures, 29.2.4
    multinomial mixture components, 29.2.4
    multinomial normal mixtures, 29.2.4
multinomial probit regression, ??
multiple, 0a.1.4
multiple of invested capital (MOIC), 12.4.5
multivariate adaptive regression splines, 41.3.1
multivariate arithmetic Brownian motion, 9d.1.1
multivariate Gaussian, 37.1.2
multivariate generalized autoregressive conditional heteroscedastic, 2.6.1
multivariate geometric Brownian motion, 9d.1.1
multivariate Ornstein-Uhlenbeck, 36.6
musical isomorphism, 14.3

N

naive Bayes models, 29.2.5
natural form, 18.10
natural parameters, 18.10
neighbors, 29.2.1
nested simulation, 5.5.2
net asset value, 0e.1.4
net exposure, 6.1.3
neural network
    artificial, 41.4.1
    convolutional, learning-deep
    deep artificial, 41.4.1
neuron, 41.4.1
    convolution, 41.4.1
neutralization, 13.6.3
Newton’s method, 17.2.4
Neyman-Pearson lemma, 28.1.3
nodes, 29.2.1
non-linear error prediction matrix, 25.2.6
non-linear partial covariance matrix, 27.1.10
norm, 14.4.1
    absolute homogeneity, 14.4.1
    counter, 14.4.1
    Frobenius, 14.4.1
    Lp, 21.6.2
    Mahalanobis, 14.3.3
    matrix p, 14.4.1
    maximum, 14.4.1
    p, 14.4.1
    positive definiteness, sec-norms
    standard Euclidean, 14.3.3
    subadditivity, 14.4.1
    taxicab, 14.4.1
norm symmetric, 19.3.2
normal copula, 19.3.1
normal distribution, 18.4
normal-inverse-Wishart (NIW) distribution, ??
normalized empirical histogram, 24.2
normalized heights, 24.2
normalized value characteristics, 9c.2
normalizing and variance stabilizing, 2.5.3
normed vector space, 14.4.1
notional value, 1.3.1
nowcasting
    inference, 26.1
null hypothesis, 46.1
null space, 14.2
nullity
    linear transformation, 14.2
number of obligors, 1.5.3
numeraire, 0b.2.2
    risk-free , 0b.3.1
    risk-neutral, 0b.3.1
numeraire probability measure, 0b.2.2

O

observable features, 0e
observable variables, 26.1
offset cash, 12.4.4
omega ratio, 7.11.2
one-hot encoding
    partitions, 18.14.3
one-versus-one (OvO) classifier, ??
one-versus-the-rest classifier, ??
operational loss, 1.7
operations, 1.7
operator, 16.2.2
    differentiation, 16.2.2
    integral, 16.2.2
    invertible, 16.2.2
    linear, 16.2.2
    unitary, 16.3.5
opportunity cost, 23.1.3
optimal discriminants, 28.2.3
optimal relative scoring, 28.1.3
optimal score, 28.1.3
optimal scoring, 28.1.3
optimization
    problem, 17.1
    unconstrained, 17.1
option-based portfolio insurance, 9d.3
order placement, 10
order routing, 10
order scheduling, 10
ordered logit, 18.9.2
ordered probit, 18.9.2
ordinal classifier, 28.1.4
ordinal partition encoder, 18.9
ordinary least squares, 25.2.7
ordinary least squares with flexible probabilities, 25.2.7
Ornstein-Uhlenbeck, 36.2.1
orthogonal, 14.3.3
    projection, 14.3.4
    projection equation, 14.3.4
    to a linear subspace, 14.3.4
    transformations, 14.3.6
orthogonal-increment process, 31.2.3
orthogonality
    covariance, 21.3.2
    expectation, 21.3.1
orthogonalization, 14.6.3
orthonormal set, 14.3.3
orthonormalization, 14.6.3
out of the money, 1.4.2
out-of-sample error, 42.22
outputs, 26.1
outstanding order vector, 10.3
overnight index swap, 1.10.2

P

p-value, 46.1.2
P&L, 0a.1.6
    conditional ex-ante, 5.1
    pricing function, 5
P&L linearity, 12.1
P&L related exposures, 9c.2
pair-wise Markov property, 29.2.7
panel data, 42.1
paper P&L, 12.2.1
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, 14.1.1
parallelotope, 14.2.3
parent, 29.2.1
parent order, 10
Parseval’s identity, 16.4
partial correlation matrix
    linear, 25.2.6
partial covariance matrix, 25.2.6
    cross-partial covariance matrix, 25.2.6
partial derivative
    second order, 15.1.2
    vector-valued, 15.59
partial standard deviation, 25.2.6
partial views, 44.1.4
partially orthogonal, 25.2.6
partition, 18.14.3
    adapted function, 18.14.3
partition encoder, 18.9
partition encoding, 18.9
partitioned matrix inversion, 14.7.4
payment time, 0a.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
    perceptron algorithm, ??
performance "mean", 7.3.1
performance expectation, 7.3.1
performance mean-variance trade-off, 7.3.4
performance model
    Black-Litterman, 43.1
performance variance, 7.3.2
permanent impact, 10.1.3
permanent market impact model, 10.1.3
persistence, 9c.2.3
Plancherel theorem, 16.4
point view, 44.1.2
point-in-time, 2.3.2
pointed, 17.3.1
Poisson process, 2.1.2, 36.1.4
polarization identity, 14.3.3
pool factor, 0a.2.1
pooling
    convolution, 41.4.1
portfolio, 6.1.1
portfolio rebalancing P&L, 12.2.3
portfolio weights, 12.4.2
positive definite, sec-mercer-kernel
    linear transformation, 2239
positive homogeneity of first degree, 7.2
positive homogenous of degree, 7.2
positive homogenous of first degree, 7.2
positive semidefinite
    linear transformation, 2239
posterior, 40.1.2, 40.1.2
    Bayesian statistics, 18.3.2
posterior distribution, 37.8.1
    Black-Litterman, 43.26
posterior predictive performance model
    Black-Litterman, 43.30
potential function
    e-affine coordinates, 20.1.3
    m-affine coordinates, 20.1.3
power market impact, 9a.3.3
power spectrum
    generalized, 16.6.1
    generalized, matrix-valued, 16.6.2
    integral, 16.6.1
    proper, 16.6.1
    proper, matrix-valued, 16.6.2
precision
    binary classification, 28.1
precision matrix, 18.10.1
predicted negative probability, 28.1
predicted positive probability, 28.1
predicted variables, 26.1
prediction, 25.1
    error prediction matrix, 31.1.4
    linear, 31.1.4
    observation vector, 32.3
    point, 26.2
    point prediction, 40.4.1
    prediction vector, 32.3
    predictive distribution, 40.5.1
    probabilistic, 26.2
    Wiener-Hopf equations, 32.3
prediction
    linear prediction random variable, 31.1.4
prediction model, 37.8.2
predictive, 9a.4.1
predictive distribution, 6.4.1
    non-observable, 40.5.1
    posterior, 37.8.2
predictor
    point, 26.2
    point prediction, 40.4.1
    predictive distribution, 40.5.1
    probabilistic, 26.2
premium, 1.10.2
prevalence of positive outcome, 28.1
price manipulation, 10.4.2
pricing kernel, 0b.2.1
pricing operator, 0b.1.1
pricing signals, 13.2.2
principal axes, 21.2.4
principal axis factorization, 25.4.2
principal component analysis, 21.2.5
    sparse principal component analysis, 42.3
principal components, 21.2.5
principal directions, 21.2.5
principal factors, 21.2.5
principal root, 14.6.6
principal variances, 21.2.5
principal-component linear factor model, 25.3
prior
    improper, 23.5.2
    proper, 23.5.2
prior distribution, 37.8.1
    Black-Litterman, 43.3
prior predictive distribution, 37.8.2
prior predictive performance model
    Black-Litterman, 43.9
probabilistic factor analysis, 29.2.3, 29.2.3
probabilistic graphical model, 29.2.1
probabilistic misclassification error, 28.3.1
probabilities, 18.8
    vector, 18.8
probability density function
    multivariate, 18.1.3
    univariate, 18.1.2
probability integral transform, 19.1
probability level, 18.1.2
probability mass function
    multivariate, 18.1.3
    univariate, 18.1.2
probability of default, 1.5.2
probability space, 18.1.1
    events, 18.1.1
    finite, 18.14.2
    outcomes, 18.1.1
    partition, 18.14.3
    sample space, 18.1.1
    sigma-algebra, 18.1.1
probit parametrization
    binary probit parametrization, 18.9.2
    multinomial probit parametrization, 18.9.2
probit regression
    binary, ??
process
    Gaussian, 31.1.3
product rule
    multivariate, 15.1.2
    univariate, 15.1.1
profit-and-loss, 0a.1.6
projection
    i-m-projection, 45.2.1
    I-projection, 45.2.1
    m-projection, 45.2.1
projection matrix, 25.2.6
Projection pursuit, 41.4.3
projection stochastic discount factor, 0b.2.3
properties, 40.1
proportional hazards expectation, 7.8.2
proportional hazards principle, 0d.2.3
proportional hazards transform, 7.8.2
proportional odds, 18.9.2
pseudo-inverse
    left, 14.7.3
    right, 14.7.3
pull-to-par, 1.10.2
pure endowment, 0e.5.1
put-call parity, 0b.1.2
Pythagorean triangular identity, 14.3.5

Q

quadrangle, 7.4
quadratic discriminant analysis (QDA), ??
quadratic form
    matrix, 14.3.2
quadratic market impact, 9a.3.3
quadratic programming, 17.3.4
quadratic variation, 3.2.3
quadratic-normal distribution, 18.5.3
quantile
    multivariate, 21.5.3, 31.4.3
    multivariate p, 21.5.3
quantile (VaR) satisfaction measure, 7.6.1, 7.6.1
quantile function, 18.1.2
quantitative alpha, 9c
quantitative strategies, 9c
quotient rule
    multivariate, 15.1.2
    univariate, 15.1.1

R

r-squared
    distributional , 25.1.1
    generalized distributional, 25.1.1
    generalized population, 25.1.1
    population, 25.1.1
    sample, 25.1.1
radial component, 18.7.3
Radon-Nikodym derivative, 18.14.1
    numeraire, 0b.2.2
        process, 0c.4.3
rain distribution, 10.4.3
random field, 31.5
    covariance stationary, 31.5.1
    free field, 31.5.3
        continuum, 31.5.3
        discrete, 31.5.3
    Gaussian, 31.5.5
    Gaussian Markov, 29.2.7
    isotropic, 31.5.1
    strongly stationary, 31.5.1
random forest, 42.4.1
random time series, 3
random variable, 18.14.1
random vector, 18.14.1
random-walk
    strong, random-walk-cheat-sheet
    weak, 31.2.2
rank
    full, 14.2.1
    linear transformation, 14.2
    matrix, 14.2.1
rank-nullity theorem, 14.2
ranking, 13.6.3
ranking-distortion, 13.6.3
ranking-median, 13.6.3
ranking-terciles, 13.6.3
real negative probability, 28.1
real positive probability, 28.1
realized information panel, 3
realized P&L, 0a.1.6
    portfolio, 12.2.1
realized time series, 3
realized variance, 1.4.6
recall, 28.1
receiver operating characteristic (ROC) curve, 28.1.4
receiver operating characteristic (ROC) function, 28.1.4
record date, 0a.1.3
recovery rate, 1.5.2
reflection, 14.3.6
regression
    cross-section, 25.5.5
    discriminative linear regression, 27.3.5
    discriminative regression, 27.3.4
    generalized, 27.1.2
    least absolute deviation regression, 27.2.2
    least squares regression, 27.1.2
    least squares, linear, 27.1.2
    linear least absolute distance regression, 27.2.4
    linear median regression, 27.2.4
    linear quantile regression, 27.2.4
    quantile regression, 27.2.2
regression factor scores, 25.4.4
regression linear factor model, 25.2
regret, 7.4, 23.1.3
    excess risk, 23.3.2
regularization, 44.6.6
reinforcement learning, 26.1.3
reinvested cumulative cash-flow, 0a.1.4
reinvested instrument, 0a.1.4
reinvestment function, 5.1
relative entropy, 20.1.4, 26.5.1
relative marginal contributions, ??
relative score, 18.9
relative scores, 18.9.2
relative value, 9c
reproducing kernel Hilbert space, 16.7.2
reproducing property, 16.7.2
residuals, 25.1
responses, 26.1
return on collateral, 12.4.3
return on equity, 12.4.3
return on exposure, 12.4.3
return on value, 12.4.3
return related characteristics, 9c.2
reversal, 13.3.1
reward, 23.1.4
Riccati root, 14.6.6
ridge, 17.3.4, 42.2.2
ridge regression, 39.4.3
Riemann integrable, 15.4.3
Riemann integrable function, 15.4.1
Riemann integral
    multivariate, 15.4.3
    univariate, 15.4.1
Riemann sum
    multivariate, 15.4.3
    univariate, 15.4.1
Riemannian metric, 20.1.1
Riesz representation, 16.3.3
Riesz representation theorem, 16.3.3
right-way risk, 6.2
risk
    average Bayes, 23.3.4
    Bayes risk, 40.1.1, 40.1.1, 40.1.1
    estimation, 23.3.8
    excess, 23.3.2
    frequentist, 23.3.4
    frequentist risk, 40.1.1
    model, 23.3.8
    posterior Bayes, 23.3.5
    posterior risk, 40.1.2, 40.1.2, 40.1.2
    prior Bayes, 23.3.2
    probabilistic prediction error, 40.5.2
    worst-case risk, 40.1.1, 40.1.1, 40.1.1
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, 0c.3.1
    symmetry with arbitrage pricing theory, 25.5.6
risk premium, 7.2
risk propensity, 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, 0b.3.1
risk-neutral probability, 0b.3.1
roll down, 5.2.3
rolling value, 1.3.2, 1.4.2
rolling zero-coupon, 1.3.2
rotation, 14.3.6
round-trip, 10.4.2
rule-based strategies, 9c
running maximum, 12.6

S

sample correlation matrix, 38.1
sample covariance matrix, 38.1
sample mean, 38.1
sample standard deviation vector, 38.1
satisfaction measure, 7.2, 7.8.1
    actuarial pricing, 7.10
    Buhlmann expectation, 7.10
    Esscher, 7.10
    numeraire, 7.10.1
scale-invariance, 7.2
scenario analysis
    inference, 26.3
scenario expansion, 6.6.1
scenario-probability distribution, 18.8
scenario-probability quantile, 18.8.6
scenarios
    panel, 18.8
Schweizer-Wolff measure, 22.1.1
score
    proper, 26.5.1
    strictly proper, 26.5.1
score function, 23.1.3
scoring rule, 26.5.1
    expected loss, 23.3.2
    Hyvarinen scoring rule, 26.5.1
    logarithmic scoring rule, 26.5.1
    scoring rule divergence, 26.5.1
sec-kernel-principal-components, 29.1.3
second order criterion, 17.2.1
second order differential, 15.1.2
    matrix-variate, 15.1.3
second-order cone programming, 17.3.3
security market line, 0b.4.2
segmentation, 41.3.1
selection problem, 17.4
selection set, 17.4
self-adjoint, 16.5.1
self-financing, 9c, 9c.2.2
self-similarity, 36.1.6
semidefinite cone, 17.3.2
semidefinite programming, 17.3.2
semideviation, 7.9.2
semideviation principle, 0d.2.1
semivariance, 7.9.2
semivariance principle, 0d.2.1
sensitivity, 28.1
sensitivity analysis
    inference, 26.3
sensitivity curve, 37.7.1
separable, 28.1.4
    linearly separable, 28.1.5
sequential attribution, 8b.1.3
settlement date, 0a.1.2
settlement period, 0a.1.3
Shannon entropy, 26.5.1
Shapley attribution, 8b.1.4
Sharpe ratio, 7.11.1
shift filter, 32.5
    polynomial, 32.5
shift operator, 15.3.1
shift parameters, 25.1
short holdings, 12.1, 12.1
short position, 0e.1.2
short spot rate, 1.10.2
sifting property, 16.3.2
sigmoid, 18.481
sign, 1.8.1
signal, 13
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 ratio, 7.11.1
simulated path, 4.4
singular value, 14.5.4
singular value decomposition, 14.5.4
size signal, 13.5
skew signal, 13.5
skill, ??
Sklar’s theorem, 19.2.3
slack variable, 17.5.1
slippage, 0a.1.2
slippage model, 10.1.3
slippage P&L, 12.2.1
slope, 1.3.5
small capitalization stocks, 13.5
small minus big, 9c.1
smart beta, 9c
smile, 1.4.3
smile signal, 13.5
smirk, 1.4.3
smooth kernel probabilities, 37.1.2
smooth quantile, 18.8.7
smoothing, 13.6.1
softmax function, 18.10.2
softplus function, 18.10.2
solvency capital requirement, 7.12
solvency condition, 6.8.1
Sorensen-Dice coefficient
    binary classification, 28.4
Sortino ratio, 7.11.2
span, 14.1.2
spanning set, 14.1.2
Spearman’s rho, 22.2.2
specificities, 25.4.5
specificity, 28.1
spectral density
    generalized, 16.6.1
    generalized, matrix-valued, 16.6.2
    proper, 16.6.1
    proper, matrix-valued, 16.6.2
spectral density function
    cepstrum, 32.6.4
spectral root, 14.6.4
spectral theorem, 14.5.2
spectrum
    matrix, 14.5.1
    satisfaction indices/risk measures, 7.8.1
splines, 41.3.1
spot curve, 1.10.2
spot rate, 1.10.2
spot swap, 1.3.1
spread, 1.3.6
square-dispersion, 21.5.3
    affine equivariant, 21.4.2
    modal, 21.4.2
square-root, 36.2.2
square-root rule, 4.7
stable distributions, 18.12.1
    symmetric, 18.12.1
standard "beta", ??
standard Brownian motion, 36.1.3
standard deviation, 21.1.1
standard deviation principle, 0d.2.1
standard error, 46.1.3
standard Wiener process, 36.1.3
standardized elliptical variable, 18.7.3
standardized holdings, 6.4.1
standardized invariants, 3.1.1
state crisp probabilities, 37.1.4
state of nature, 23.1.1
state space, 23.1.1
State space model
    Probabilisitc linear, 33.4
state-space model
    mean-covariance measurement equation, 31.2.5
    mean-covariance observation equation, 31.2.5
    mean-covariance process, 31.2.5
    mean-covariance transition equation, 31.2.5
    measurement equation, 31.2.5
    probabilistic observation equation, 31.2.5
    probabilistic process, 31.2.5
    probabilistic transition equation, 31.2.5
static linear factor model, 25.1.4
stationary
    covariance stationary, 31.3.1
    strongly stationary, 31.3.1
statistical linear factor model, 25.3
statistics, 46.1.1
steepness, 1.3.5
stochastic discount factor, 0b.2.1
    cumulative or process, 0c.4.2
    probability measure, 0c.4.2
stochastic dominance
    first order, 23.2.2
    order q, 23.2.3
    second order , 23.2.3
    strict strong, 23.2.1
    strong, 23.2.1
    weak, 23.2.2
stochastic mean, 2.1.3
Stochastic process
    decomposition
        Doob’s decomposition, 31.4.1
        mean-covariance, 31.4.1
        Probabilistic independent, 31.4.1
        probabilistic uncorrelated, 31.4.1
stochastic process, ??
    continuous time, ??
    discrete time, ??
    mean-covariance, 31.1.1
        de-trended, 32.2.3
        trend-covariance stationary, 32.2.3
    probabilistic, 31.1.1
stochastic time, 36.1.6
stochastic volatility, 2.1.3
stochastic volatility inspired, 1.4.5
strategic allocation, 9a.1.3
strategy, 1.9
stress-testing
    inference, 26.3
strictly concave function
    multivariate, 15.6.2
    univariate, 15.6.1
strictly convex function
    multivariate, 15.6.2
    univariate, 15.6.1
string, 31.5
strips, 1.3.1
structure, 26.1.1
Student t distribution, 18.6.1
sub-additivity, 7.2
sub-quantile function, 18.1.2
subordinator, 36.1.6
sufficient statistics, 18.10
sum-of-parts, 0e.1.6
    liquidation valuation, 0e.1.5
super-additivity, 7.2
Supervised learning,