Quora noscript

Glossary

Symbols

(functional) excess risk, 16
0-1 loss, 13.1.2

Wiener-Kolmogorov filter
    non-causal, 31.6.1

A

absence of arbitrage, 41a.1.3
absolute score, 39.6, 39.6
absolutely continuous component, 31.1.2
absolutely continuous spectral density, 31.1.2
abstract Bayes theorem, 38.3.5
acceptance region, 22.1.1
accounting signals, 50c.1.4
accrued interest, 55.1.3
accuracy
    binary classification, 13.4
action, 6b.1.2
    Bayes, 6b.3.1
    minimax, 6b.4.1
action space, 6b.1.2
activation function, 16.4.1
activity time, 42.8.1
actual cash-flow, 54.8.3
actual exchange rate, 54.8.3
actual value, 54.8.3
adapted basis, 33.1.3
adaptive execution algorithm, 51.3.2
additive, 39.9.2
admissible, 17.8.1
affine concordance
    negative, 5a.3
    positive, 5a.3
affine transformation, 2a.3
algebraic multiplicity, 33.9.1
algebraic Riccati equation, 33.6.6
allocation policy, 47.5
almost everywhere
    function equality, 36.3.1
alpha, 48.3.1
    binary classification, 13.1
alternative beta, 50c
alternative hypothesis, 22.3
analysis of variance, 3b.7.4
ancestor, 15.1
angle, 33.3.3
    canonical, 38.2.4
anti-monotonic
    function, 34.4.3
approximation-estimation trade-off, ??
Arbitrage, 41a.16
arbitrage pricing theory, 41b.3.1
architecture, 16.4.1
area under curve (AUC), 13.1.4
arithmetic Brownian motion with drift, 32.1.2
Arrow-Debreu securities, 41b.1.2
Arrow-Pratt absolute risk aversion function, 48.5.1
ask size, 53.4.1
assets, 47.7.1
at the money, 55.2.1
    foreign exchange market, 42.4.3
augmented view variables, 25.5.3
autocorrelation
    function, 27.4.1
    partial autocorrelation function, 27.4.1
autocovariance
    partial autocovariance function, 27.4.1
autocovariance function, 27.1
    random field, 27.4.2
autoencoder, 14.1
autoregressive conditional duration, 43.5.3
autoregressive fractionally integrated moving average, 43.3
autoregressive moving average process
    autoregressive
        multivariate (VAR), 29.3.1
        univariate (AR), 29.3.1
    autoregressive polynomial, 29.3.1
    companion matrix, 29.3.1
    integrated
        multivariate (VARIMA), relationships-varma-vma-var
        univariate (ARIMA), 29.4
    invertible, 29.3.3
    LTI filter representation, 29.3.1
    moving average
        mutivariate (VMA), 29.3.1
        univariate (MA), 29.3.1
    moving average polynomial, 29.3.1
    multivariate (VARMA), 29.3.1
    univariate (ARMA), 29.3.1
    Yule-Walker equation
        univariate, sec-covstat-arma
autoregressive of order one
    Gaussian, 29.2.5
    univariate (AR), 29.2.1
    vector (VAR), 29.2.1
auxiliary measure, 39.7

B

Bachelier, 41d.2.1
backpropagation, 16.4.1, 35.2.3
backward cash-flow-adjusted value, 53.3.4
backward/forward exponential weighted moving average, 17.12.3
bagging, 17.11.1
balance sheet, 47.7.1
balanced, 17.9
bandpass filter, 31.2.4
bandwidth, 17.1.2
    kernel density estimate, 17.3
bandwidth matrix, 17.1.2
base case, 25.6.1
base distribution, 25.1.1
base measure, 39.7
basis, 33.1.2, 54.5.3
    canonical, 33.1
    orthonormal, 33.3.4
basis denominator, 54.5.3
basis instruments, 41b.1.1
Bayes classifier, 13.2.3
Bayes risk
    of a decision, 6b.3.4
    of an action, 6b.3.1
Bayes theorem, 3b.3.1
Bayesian networks, 15.6
benchmark, 54.6.1
Bernoulli distribution, 39.6
best approximation
    linear, 3a.2.1
best ask, 53.4.1
best bid, 53.4.1
best prediction, 33.3.5
beta
    binary classification, 13.1
beta conditions
    projection coefficients, 33.3.4
    projection equation, 33.3.4
beta distortion index, 48.8.2
beta-adjusted excess return, 54.6.2
bets, 49b.4
between-cluster/group variance, 3b.7.4
Bhattacharyya coefficient, 17.11.4
bias, 20.1.1
    binary classification, 13.1
bias-variance trade-off, 21.3.2
bid size, 53.4.1
bid-ask spread, 51.1
bilateral value adjustment, 47.2
binary classification, 13.1.5
binary classifier, 13.1.4
binary mixture distribution, 15.4
    binary Gaussian mixtures, 15.4
    binary mixture components, 15.4
    binary normal mixtures, 15.4
binary partition encoder, 39.6
binning, 42.8.2
binomial inverse theorem, 33.7.4
binomial tree, 43.1.2
bins, 39.5.1, 42.8.2
Black-Merton-Scholes model, 41d.2.2
block-wise orthogonality
    partial, 3a.5
Bochner’s theorem, 36.6.1
bond yield, 46.3.2
bootstrap aggregating, 17.11.1
Borel sets, 38.1.2
boundedness, 25.1.7
breadth, 16.4.1, 50a.4.4
breakdown point, 17.7.2
Brier scoring rule, 13.3.1
Buhlmann pricing equation, 41a.5
    expectation, 41a.5
Buhlmann principle, 41c.2.4
butterfly, 41b.1.2, 42.4.3

C

calendar signal, 50c.1.4
calibrate, 41d
call option
    European, 55.2.1
        expiry time, 55.2.1
        strike, 55.2.1
calm drivers, 47.6.3
canonical correlation matrix, 5a.4
canonical parameters, 39.7
capital asset pricing model, 41b.2
capital gain, 54.1.1
cardinality, 36.1.2
cardinality constraint, 35.5, 37.2
carry signal, 50c.1.1
CART, 16.3.1
cash-flow function, 46.1
cash-flow-adjusted value, 53.3.4
categorical distributions, 39.6
categories, 39.6
Cauchy distribution, 39.4.3
Cauchy transform, 2b.1.5
Cauchy-Schwarz inequality, 33.3.3
causal, 31.2.4
central tendency, 7.2.1
    affine equivariant, 7.1.1, 7.1.4
certainty-equivalent, 48.5
certainty-equivalent principle, 41c.2.2
chain rule
    gradient, 34.1.2
    matrix-variate, 34.1.3
    multivariate, 34.1.2
    univariate, 34.1.1
change of variable formula
    multivariate, 34.3.4
    univariate, 34.3.2
Chapman-Kolmogorov equations, 30.2
characteristic equation, 33.5.1
characteristic function, 2b.1.4
characteristic matrix, 50c.5.1
characteristic polynomial, 33.5.1
characteristic portfolio, 50c.3.1
characteristic portfolios, 50c.5.1
Chebyshev’s inequality, 2a.4.2
chi-squared distribution, 39.3.1
    generalized, 39.3.3
child, 15.1
child orders, 51
Cholesky decomposition, 33.6.5
    upper, 33.6.5
Cholesky root
    lower triangular, 33.6.5
    upper triangular, 33.6.5
CIR, 32.2.2
claims, 42.6
classes, 39.6
classical-equivalent, 17.8.1
classification
    discriminative classification, 13.3.5
    multinomial classification, 13.2
classification and regression trees, 16.3.1
clean price, 55.1.3, 55.1.3
clique, 15.1
    maximal clique, 15.1
clique factorization, 15.7
clustering, 14.1.2
co-monotonic
    function, 34.4.3
    pair of vectors, 5b.3.3
co-monotonic additivity, 48.2
co-variogram
    random field, 38.1.4
coalition, 37.2
codes, 14.1
coherent satisfaction measure, 48.9.1
    coherent probability measure, 48.9.1
    worst case expectation representation, 48.9.1
cointegrated space, 43.7.1
cointegration signal, 50c.1.3
cointegration vector, 43.7.1
collateral, 47.8.3
combination, 37.2
combinatorial heuristic, 37.2.6
    2-step forward, 37.2.4
    naive selection, 37.2.2
    stepwise backward elimination, 37.2.4
    stepwise bidirectional elimination, 37.2.4
    stepwise forward selection, 37.2.3
combinatorial programming, 37.2
commonalities, 8.4.5
comparable instruments, 41d
complete, 41b.1.1
complete class theorem, 6b.3.6
complete metric space, 36.3.1
compound distribution, 3b.3.1
compound Poisson process, 32.1.3
compounded rate of return
    (instantaneous) compounded rate of return, 54.5.6
    average compounded rate of return, 54.5.6
compounded return, 54.5.1
Comprehensive Capital Analysis and Review, 47.6
concave down function
    univariate, 34.5.1
concave function
    multivariate, 34.5.2
    univariate, 34.5.1
concave up function
    univariate, 34.5.1
concavity, 48.2
condition number, 18.6.2
conditional cdf, 3b.2.1
conditional covariance, 3b.7.3
conditional excess distribution, 48.6.2
conditional expectation
    Stochastic processes, 28.4.1
    with respect to partition, 38.3.3
conditional independence, 3b.5, 17.9
conditional mean, 3b.7.2
conditional mean estimator
    square bias, 21.3.2
    variance, 21.3.2
conditional median, 3b.7.5
conditional mode, 3b.7.1
conditional pdf, 3b.2.1
conditional pmf, 3b.2.1
conditional principal component analysis, 8.6.16
conditional principal directions, 8.6.16
conditional principal variances, 8.6.16
conditional probability, 38.1.8
    with respect to partition, 38.3.2
conditional quantile, 3b.7.5
conditional value at risk, 48.8.2
conditioning-marginalization, 25.1.3
cone, 35.3.6
confidence, 22.1.1
confusion matrix, 13.1
conic programming, 35.3.6
conjugate distribution, 17.8.3
consistency with weak dominance, 48.2
consistent with q-th order dominance, 48.2
constancy, 48.2
constant proportion portfolio insurance, 50d.4.1
constraint set, 35.1.1
    selection, 35.5
contingency table, 13.2.1
continuous-state distributions, 43.1
control variable, 11.1.3
convex function
    multivariate, 34.5.2
    univariate, 34.5.1
convex programming, 35.3
    epigraph form, 35.6.2
convex set, 34.5.2
convexity, 46.3.2, 46.3.2
    effective key-rates, 46.3.2
    risky investment, 50d.6
    satisfaction/risk measures, 48.2
convolution, 36.4.3
    cyclic, 36.4.7
    discrete, 36.4.5
    integral, 36.4.4
    periodic, 36.4.6
convolution theorem, 36.4.3
coordinate descent, 35.5.4
Copula
    independence copula, 5b.3.1
copula, 5b.3
    Archimedean, 5b.5
    elliptical, 5b.4
    pdf, 5b.2
copula-marginal combination, 5b.6.2
copula-marginal distributions, 5b.6.2
copula-marginal separation, 5b.6.1
Cornish-Fisher approximation, 48.6.2
Cornish-Fisher expansion, 48.6.2
correlation, 5a.2
correlation function, 5a.2
correlation matrix, 5a.2
cost of equity, 41d.1.1
cost of trading, 50d.6
counterparty credit risk, 47.2
coupling function, 3b.4.2
    linear, non-standardized, 3a.4.1
coupon bond, 55.1.3
CoVaR, 47.6.1
covariance
    abstract, 38.2.1
covariance inner product
    multivariate, 38.2.6
    univariate, 38.2.6
covariance matrix, 2a.1.2
    cross-covariance, 2a.1.2
    partial, 3a.2.1
covariance principle, 41a.5
covariant, 4.1.1
covariates, 11.1
Cramer decomposition, 31.1.2
Cramer-Lundberg ruin model, 47.7.5
credit ratings, 42.5.2
credit structural model, 42.5.1
credit value adjustment , 47.2
critical point, 35.2.1
critical region, 22.1.1
cross the spread, 51.1
cross-autocorrelation
    function, 27.4.1
cross-autocovariance
    function, 27.1, 27.2
cross-entropy, 11.4.1
cross-sectional, 50c, 50c
cross-sectional linear factor model, 8.5
cross-sectional sample median of default probabilities, 42.5.2
cross-spectral density, 31.1.2
cumulant , 45.7.1
cumulative cash-flow, 53.3
cumulative distribution function, 2b.1.1
cumulative link, 39.6.2
cumulative monetary amount, 53.4.1
cumulative number of migrations, 42.5.2
cumulative P&L, 42.9
cumulative signed volume, 53.4.1
cumulative trade sign, 53.4.1
cumulative volume, 53.4.1
currency carry trade, 46.2.2
curvature, 42.3.4
curve, 33.4.4
curve/surface signals, 50c.1.4
cutoff classifier, 13.1.4

D

data, 17.4.1, 21.3
data-generating process, 6b.1.1
debt, 47.7.1
debt value adjustment, 47.2
decay, 42.3.4
decision
    deterministic, 6b.3
decision function
    admissible, 6b.3.6
    Bayes, 6b.3.4
    minimax, 6b.4.1
    optimal posterior, 6b.3.3
    randomized, 6b.3
decision problem
    statistical, 6b.1
decision region
    binary classification, 13.2.3
decision regions
    multinomial classification, 13.2
decoder, 14.1
decreasing function, 34.4.1
    entrywise, 34.4.2
    matrix-valued, 34.4.2
    strictly, 34.4.1
degree of reversal, 50c.3.4
delta, 42.4.3
delta p, 13.4
delta rule, 16.4.1
dependent variables, 8.1, 11.1
depth, 16.4.1
derivative, 55.2
    Asian-style, 55.2A
    directional, 34.1.2
    European-style, 55.2
        expiry, 55.2
    first, 34.1.1
    higher order, 34.1.1
    partial, 34.1.2
    second, 34.1.1
    total, 34.1.2
    univariate, 34.1.1
descendant, 15.1
design of experiments, 11.1.3
determinant, 33.2.3
deviation
    expectile, 7.2.3
    maximum, 7.2.4
    mean absolute, 2b.4.3
    median absolute, 2b.4.3
    subquantile, 7.2.3
differentiable function
    multivariate, 34.1.2
    univariate, 34.1.1
Differential entropy, 11.4.1
dimension, 33.1.2
Dirac delta, 36.3.2
direct sum, 33.1.3
directed graph, 15.1
dirty price, 55.1.3
discount, 55.1.3
discount factor, 54.8.3
discount function, 46.1
discounted cash-flow, 41d.1.1
discounted cash-flow adjusted value, 41c.3.1
discounted payoff, 41c.2
discrete derivative
    central first order, 37.1.1
    central second order, 37.1.1, 37.1.2
discrete distribution, 2b.1.3
discrete Laplacian, 37.1.2
discrete-state distributions, 43.1
discrete-state random walk, 43.1.2
discriminant variables, 39.6
discriminative model, 11.2.2
    generative embedding, 11.2.2
discriminative next-step model, 43.6.6
dispersion, 7.2.1
    affine equivariant, 7.1.1
distance, 33.4.2
    absolute, 33.4.2
    Euclidean, 33.3.3
    identity of indiscernibles, 33.4.2
    Lp, 36.3.5
    Mahalanobis, 33.4.2
    p, 33.4.2
    subadditivity, 33.4.2
    symmetry, 33.4.2
    triangle inequality, 33.4.2
distance matrix, 13.2.1
distance to default, 43.4.3
distorted cdf
    satisfaction indices/risk measures, 48.8.1
distorted pdf
    satisfaction indices/risk measures, 48.8.1
distortion function, 48.8.1
distortion principle, 41c.2.3
distortion satisfaction measure, 48.8.1
distribution
    (absolutely) continuous, 2b.1.2
    mixed, 2b.1.1
distributional view, 25.1.3
divergence, 33.4.3
    Bregman, 33.4.3
    difference, 33.4.3
    extended f, 33.4.3
    identity of indiscernibles, 33.4.3
    separable, 33.4.3
diversification distribution, 49b.4
diversity, 17.11.4
dividend-adjusted value, 42.1
    stocks, 53.3.4
divisors, 39.9.3
dollar duration, 53.1.4
dollar-neutral constraint, 50c.3.3
domain
    circle, 36.1.1
    continuous, 36.1.1
    cyclic group, 36.1.1
    discrete, 36.1.1
    frequency, 36.4
    space, 36.4
    time, 36.4
    torus, 36.1.1
dominant-residual LFM, 8.1.2
dot product, 33.3
drawdown, 54.7
    maximum (absolute) drawdown, 54.7
    maximum percentage drawdown, 54.7
    percentage drawdown, 54.7
dual Legendre, 4.1.3
dually flat, 4.1.3
duration, 46.3.2
    effective key rates, 46.3.2
DV01, 53.1.4
dynamic allocation, 50c
dynamic conditional correlation, 44.1.3
dynamic linear factor model, 31.6
dynamic principal component, 31.6.2
dynamic regression linear factor model, 31.6.1

E

e-affine coordinates, 4.1.2
e-flat, 4.1.2
e-geodesic, 4.1.2
EBITDA, 47.7.2
economic capital, 48.12
economic net income, 47.7.2
edges, 15.1
effective delta, 46.3.3
effective duration
    effective, 46.3.2
effective number of bets, 49b.4
effective number of scenarios, 17.1.4
effective rank, 25.1.6
effective rho, 46.3.3
    key-rates, 46.3.3
effective volga, 46.3.3
efficient frontier, 50a.1
efficient market hypothesis, 43.1
eigenfunction
    kernel, 36.2.4
    operator, 36.2.4
eigenvalue
    linear transformation, 33.5.1
    matrix, 33.5.1
    operator, 36.2.4
eigenvector
    linear transformation, 33.5.1
    matrix, 33.5.1
elastic net, 17.10.2, 35.4.3
    constrained generalized elastic net, 35.5.4
elicitability
    satisfaction measures, 48.2
elicitable, 2b.4.1
ellipsoid, 2a.2.2
    location-dispersion, 7.1.4
    mean-covariance, 2a.2.2
ellipsoid test for invariance, 23.1
elliptical distribution, 39.4.1
EM algorithm
    population, 26.2.6
empirical histogram, 39.5.1
    normalized, 39.5.1
encoder, 14.1
enterprise value, 41d.1.2
entropy
    generalized , 6b.3.1
equally weighted portfolio, 50a.1.3
equilibrium performance model
    Black-Litterman, 24.15
equilibrium returns, 50a.1.3
equity, 47.7.1
equity book value, 47.7.1
equivalent optimization problem, 35.6
ergodic
    in autocovariance, 28.2.2
    in mean, 28.2.2
    strong, 28.2.2
Erlang process, 32.1.3
error
    mean-squared, var-sol-loc-disp
    multivariate mean absolute, 7.2.7
    univariate, 7.2.1
error prediction matrix
    linear, 3a.2.1
Esscher principle, 41c.2.4
estimable, 48.2
estimate, 20.1, 21.3
    empirical risk, 21.3.5
    generalization error, 21.3.2
    generalization risk, 21.3.2
estimation, 11.3
estimation model, 18.5
    Bayesian estimation, 17.8.1
estimation set, 17.9
    point prediction, 20.3.1
    predictive distribution, 20.4.1
estimation uncertainty, 17.8.1
estimator, 20.1, 6b.5.5
    approximation error, ??
    approximation risk, ??
    empirical risk minimization, 21.3.5
    estimation error, ??
    estimation risk, ??
    excess error, 21.3.2
    excess risk, 21.3.2
    expected error, 21.3.2
    expected risk, 21.3.2
    irreducible error, 21.3.2
    irreducible risk, 21.3.2
    noise, 21.3.2
    population risk, 21.3.2
    prediction error, 21.3.2
    structural risk minimization, 21.3.5
evidence
    maximum likelihood, 17.4.1
evidence lower bound, 26.2.5
ex-dividend date, 53.3.1
exotic beta, 50c
expectation
    abstract, 38.1.5
expectation angle, 38.2.2
expectation distance, 38.2.2
expectation function, 27.1
    random field, 38.1.4
expectation inner product
    multivariate, 38.2.4
    univariate, 38.2.1
expectation length, 38.2.2
expectation parameters, 39.7
expectation rule, 39.5.3
expectation-maximization, 17.5.2
    expectation step, 17.5.2
    maximization step step, 17.5.2
expected drawdown, 48.3.1
expected overperformance, 48.3.1
expected shortfall, 48.8.2
expected utility, 48.5
expected value of the process variance (EVPV), 3b.7.3
expectile, 2b.4.3
expectile-VaR, 48.9.2
explanatory variables, 11.1
exponential decay probabilities, 17.1.1
exponential family distribution, 39.7
exponential kernel, 17.1.2
exponential of the entropy, 17.1.4
exponential operator, 34.2.1
exponential principle, 41c.2.2
exponential tilting, 25.1.5
exponentially weighted moving average, 17.2.4
exponentially weighted moving correlation, 17.2.4
exponentially weighted moving covariance, 17.2.4
exponentially weighted moving quantile, 17.2.4
exponentially weighted moving standard deviation, 17.2.4
exposure, 53.1.4
    portfolio P&L, 50c.3.1
    risky investment, 50d.6
exposure at default, 42.5.1
extrema
    local, 35.1.2
    relative, 35.1.2
extreme value theory, 48.6.2

F

f-divergence, 4.1.4
F-measure
    binary classification, 13.4
F1 score
    binary classification, 13.4
face value, 55.1.1
factor analysis, 18.6.6
    confirmatory, 8.4
    equamax, 8.4.4
    exploratory, 8.4
    orthomax, 8.4.4
    parsimax, 8.4.4
    quartimax, 8.4.4
    varimax , 8.4.4
factor analysis matrix, 18.6.6
factor loadings, 8.1
factor premia, 50c.5.2
factor premium, 50c.2.1
factor-analysis linear factor model, 8.4.1
factor-replicating portfolios
    arbitrage pricing theory, 41b.3.1
factors, 11.1, 50c
    linear factor model, 8.1
fair value, 53.1
fallout, 13.1
false negative
    accuracy, 13.1
    probability, 13.1
    rate, 13.1
false positive
    accuracy, 13.1
    probability, 13.1
    rate, 13.1
feature engineering, 12.1
feature map
    canonical, 36.7.2
    Mercer, 36.7.2
Feller condition, 32.5.1
filter, 31.2
    anti-causal operator, 31.2
    causal operator, 31.2
    factor construction, 31.6.2
    invertible, 31.2
    linear time invariant , 31.2.4
filtering, 11.3
filtration, 38.4.1
    adapted process, 38.4.3
        (fully-recombining) binomial tree, 38.4.3
        (fully-recombining) tree, 38.4.3
    martingale, 38.4.4
    Radon-Nikodym process
        martingale, 38.4.4
    set
        natural, 28.4.1
financial instrument, 53.1
finite difference
    backward first order, 37.1.1
    central first order, 37.1.1, 37.1.2
    central second order, 37.1.1, 37.1.2
    forward first order, 37.1.1
finite-dimensional joint distributions, 28.1
first in, 49b.1.1
first order criterion, 35.2.1
first order differential, 34.1.2
    matrix-variate, 34.1.3
Fisher consistent, 17.7.1
Fisher discriminant analysis (FDA), 13.1.8
Fisher information distance, 4.1.4
Fisher’s linear discriminant, 13.1.8
flexible probabilities, 44
    estimation, 39.5
forecast, 8.1
    mean-covariance, 11.2
    point, 11.2
    probabilistic, 11.2
forecasting
    inference, 11.3
foreign exchange function, 46.1
foreign exchange rate, 55.4
forward, 55.4.2
forward cash-flow-adjusted value, 53.3.4
forward exchange rate, 55.4
forward rate, 55.1.1
forward swap, 55.1.4
forward variance swap rate, 42.4.5
Fourier transform, 36.4.3
    discrete (DFT), 36.4.7
    discrete time (DTFT), 36.4.5
    Fourier series, 36.4.6
    integral, 36.4.4
    inverse, 36.4.3
fractional Brownian motion, 32.4
Frechet derivative, 36.8.2
    second order, 36.8.3
Frechet-Hoeffding bounds, 5c.1.1
full-investment, 50c.3.3
fully constrained LFM, 8.6.17
function space, 36.2.1
    addition, 36.2.1
    scalar multiplication, 36.2.1
functional, 36.8
functional derivative, 16.5
fundamental accounting equation, 47.7.1
fundamental law of active management, 50a.4.4
fundamental linear factor model, 8.5
fundamental signals, 50c.1.4
fundamental theorem of asset pricing, 41a.2.2
    martingale pricing formula, 41b.4.3
fundamental theorem of calculus
    first, 34.3.3
    second, 34.3.3
funding risk, 47.2
funding value adjustment, 47.2

G

gamma distribution, 39.3.2
Gateaux derivative, 36.8.1
Gaussian kernel, 17.1.2, 17.3
generalized autoregressive conditional heteroscedastic, 43.5.1
generalized excess return, 54.6.2
generalized linear models (GLM), 12.3.6
generalized linear return, 54.5.3
generalized method of moments
    iterated GMM, 18.6.5
generalized method of moments with flexible probabilities (GMMFP) estimate, 17.6.3
    minimization, 17.6.4
generalized negative entropy, 11.4.1
generalized Pareto distribution, 48.6.2
generalized spectral density, 31.1.2
generalized weight, 54.5.4
generative model, 11.2.2
generative next-step model, 43.6.6
generator
    Markov chain, 32.3.1
    matrix, 33.2.3
generic position, 53.2.3
geodesic
    metric, 33.4.4
geodesic space, 33.4.4
geometric Brownian motion, 46.1.1
geometric multiplicity, 33.9.1
Gibbs distribution, 15.7
Gini coefficient, 13.1.4
Giny impurity, 13.3.3
glasso, 18.6.5
    Tikhonov, 17.10.3
Glivenko-Cantelli theorem, 17.2.1
Gordon growth model, 41d.1.1
grade, 5b.1
    mean-covariance, 5a.1
gradient, 34.1.2
    matrix-variate, 34.1.3
    vector-valued function, 34.1.2
gradient descent, 35.2.3
    stochastic, 35.2.3
Gram matrix, 33.6.1
Gram-Schmidt process
    backward, 33.6.5
    forward, 33.6.5
Gramian, 33.6.1
grand mean, 18.6.1
Granger causal, 27.4.3
graph, 15.1
    directed acyclic, 15.1
graphical lasso, 18.6.5
Greeks, 46.3
gross exposure, 47.1.4
group
    general linear, 33.2.3
    orthogonal, 33.3.6
    special orthogonal, 33.3.6
    unitary, 33.3.6
growth stocks, 50c.1.2

H

Hadamard product, 33.7.2
half-life, 17.1.1, 29.2.2
Hamiltonians, 39.7
harmonic process
    multivariate, 29.6.1
    univariate, 29.6.1
hat matrix, 3a.2.2
hazard function, 2b.1.5
Hellinger distance, 17.11.4
Herglotz theorem, 36.6.1
Hessian, 34.1.2
    matrix-variate, 34.1.3, 34.1.3
Heston model, 41d.2.3
hidden Markov model, 29.5.5
hidden variables, 11.1
    maximum likelihood, 17.5
    point prediction, 20.3.1
high breakdown estimators, 17.7.2
high breakdown point with flexible probabilities, 17.7.2
high minus low, 50c.2.2
high water mark, 54.7
Hilbert space, 36.3.1
historical cdf, 17.2.1
historical cross-sectional, 8.5.7
historical distribution, 17.2.1
historical pdf, 17.2.1
historical principal component, 8.3.7
historical repricing, 46.5.2
historical with flexible probabilities (HFP) autoencoder, 20.3.4
historical with flexible probabilities (HFP) cdf, 17.2.2
historical with flexible probabilities (HFP) correlation matrix, 18.1
historical with flexible probabilities (HFP) covariance matrix, 18.1
historical with flexible probabilities (HFP) distribution, 17.2.2
historical with flexible probabilities (HFP) estimate, 17.2.3
historical with flexible probabilities (HFP) mean, 18.1
historical with flexible probabilities (HFP) median, 17.7.2
historical with flexible probabilities (HFP) pdf, 17.2.2
historical with flexible probabilities (HFP) predictor, 20.3.4
historical with flexible probabilities (HFP) quantile, 17.7.2
historical with flexible probabilities (HFP) standard deviation vector, 18.1
hold-out, 20.3.5
Hotelling statistic, 22.3.2
Hurst coefficient, 32.4
hybrid Monte Carlo-historical, 45.5.2

I

ice-cream cone, 35.3.4
identity transformation, 33.2.3
idiosyncratic, 8.1.3
ill-conditioned, 18.6.2, 35.4
ill-posed, 35.4
IM algorithm, 26.2.5
image space, 33.2
implementation shortfall, 54.4
implied returns, 50a.1.3
implied volatility, 42.4.2
implied volatility surface, 42.4.2
improper integral, 34.3.2
impulse response, 31.2.4
in the money, 55.2.1
in-sample error, 17.230
inception, 54.3.2
income, 54.1.1
income statement, 47.7.2
increasing function, 34.4.1
    entrywise, 34.4.2
    matrix-valued, 34.4.2
    strictly, 34.4.1
indefinite integral, 34.3.3
independence
    random variables, 3b.1
independent component analysis, 3b.6
independent variables, 11.1
    linear factor model, 8.1
induced expectation, 48.10.1
inefficiency, 20.1.1
inference, 11.3
    marginalization, 11.3
    marginalization problem, 11.3
infinitely divisible, 39.9.3
inflator, 41a.2.2
influence function, 17.7.1
information, 27.4.4
    generator, 27.4.4
    linearized, 27.3.2
    random time series, 44
    set, 28.4.1
    set, of a random variable, 2b.3.1
information coefficient, 50a.4.1
information measure, 11.4.1
information ratio, 48.11.1
    linearly predicted, 50a.4.1
information set
    distributions, 26.2.1
    linearized, 2a.3.3
information/view, 25.6.1
informedness, 13.4
inner product, 33.3
    Dirichlet, 38.1.4
    Hermitian, 36.3.1
    Hermitian symmetry, 36.3.1
    L2, 36.3.1
    linearity, 33.3
    partial linearity, 36.3.1
    positive definiteness, 33.3, 36.3.1
    symmetry, 33.3
inner product space, basic-geom-sec
innovation
    linear, non-standardized, 3a.4.1
    probabilistic, 3b.4.2
innovation function, 3b.4.3
    linear, non-standardized, 3a.4.2
Innovation process
    Mean-covariance, 27.4.4
        Error decomposition matrix, 27.4.4
    Probabilistic uncorrelated, 28.4.2
        Error decomposition matrix, 28.4.2
input variables, 6b.3
inputs, 11.1
instantaneous exchange rate, 54.8.3
instantaneous forward curve, 55.1.1
instantaneous forward rate, 55.1.1
instantaneous spot rate, 55.1.1
integral kernel, 36.2.3
    Mercer, 36.5.2
    positive definite, 36.5.2
    symmetric, 36.5.2
integral power spectrum
    matrix-valued, 36.6.3
integrated
    fractionally, 27.2.3
    order d, 27.2.3
    order zero, 27.2.3
integration by parts, 34.3.2
integration operator, 6b.2.2
intensity, 32.1.3
intensity models, 41d.3.2
interaction, 16.2.1
interest rate, 55.1.1
internal rate of return, 54.5.6
interquantile range, 7.1.3
intuitive r-squared, 8.5.5
invariance rule, 39.5.3
invariant, 7.1.4
Invariant process
    Mean-covariance, 27.4.4
    probabilistic, 28.4.2
    Probabilistic uncorrelated, 28.4.2
    Standardized mean-covariance, 27.4.4
    standardized probabilistic, 28.4.2
    Standardized probabilistic uncorrelated, 28.4.2
invariants, 43
inverse, 33.2.3
inverse transform sampling, 5b.1
    mean-covariance, 5a.1
inverse-call, 42.3.3
inverse-Wishart distribution, 39.3.5
invertible, 33.2.3
investment factor, 54.5.6
    reinvested instrument, 53.3.3
iso-contour, 2b.2
isolated, 49b.1.1
iterated integral, 34.3.4

J

jackknife estimator, 17.7.1
Jacobian, 34.1.2
James-Stein estimator, 18.6.1
Jeffreys prior, 17.8.1
joint scenario, 39.5
jump component, 31.1.2
jump rule, 53.3
jump spectral density, 31.1.2

K

k-fold, 20.3.5
k-means clustering, 14.1.2
kalman filter, 29.5.6
kalman gain matrix, 29.5.6
kappa ratio, 48.11.2
Karush-Kuhn-Tucker conditions, 35.2.2
Kendall’s tau, 5c.2.1
kernel, 17.3
    of a linear transformation, 33.2
kernel density estimate, 17.3
kernel principal component analysis, 14.1.3
kernel stochastic discount factor, 41a.2.3
kernel trick, 16.6, 36.7.2
    linear kernel, 16.6
    polynomial kernel, 16.6
    radial basis functions, 16.6
kernel with flexible probabilities (KFP) generalized mean, 17.3
kernel with flexible probabilities (KFP) pdf, 17.3
key rates, 42.3.4
Kolmogorov-Smirnov test, 23.1
Kronecker delta, 36.3.2
Kronecker product, 33.7.2
Kullback-Leibler divergence, 4.1.4, 11.4.1

L

L2 space, 36.3.1, 38.2.1
label encoding, 34.3.1
labels, 11.1
lag operator, 31.2, 34.2.1
Lagrange multiplier, 35.2.2
Lagrangian function, 35.2.2
Laplace approximation, 7.1.4
Laplacian, 34.1.2
large capitalization stocks, 50c.1.4
lasso, 17.10.2, 35.5.4
lasso regression, 19.4.2
lasso shooting, 35.5.4
last in, 49b.1.2
last transaction price, 53.4.1
latent variables, 11.1
    maximum likelihood, 17.5
law invariant, 48.2
law of iterated expectations, 3b.7.2
law of one price, 41a.1.1
law of the unconscious statistician, 2b.3.4
law of total covariance, 3b.7.3
law of total linear covariance, 3a.2.1
law of total linear variance, 3a.2.1
law of total variance, 3b.7.3
LDL-Cholesky decomposition, 33.6.5
leaf, 16.3.1
learning, 11.3
least favorable prior, 6b.3.5
least-squares residual, 12.1.2, 12.1.2
leave-1-out, 20.3.5
leave-p-out, 20.3.5
Lebesgue’s decompositon theorem, 36.1.3
left singular vector, 33.5.4
Legendre transformation, 4.1.3
length, 33.4.1, 33.4.4
    Lp, 36.3.5
level, 42.3.4
leverage, 53.1.5
Levy process, 32.1.1
Levy-Khintchine, 32.1.5
liabilities, 47.7.1
Libor, 55.1.4
likelihood, 18.5, 21.3
    estimators as random variables, 20.1.1
    maximum likelihood, 17.4.1
likelihood ratio, 13.1.3
limit order book, 53.4.1
limit order placement, 51.3
linear classification, 13.81
    bias, 13.1.5
linear combination, 33.1.2
linear dependence, 33.1.2
linear discriminant analysis (LDA), 13.3.6
linear factor model, 8.1
linear independence, 33.1.2
linear law of iterated projections, 3a.2.1
linear operator
    “flat”, 33.3
linear pricing equation, 41a.2.1
    intertemporal, 41b.4.2
linear programming, 35.3.2
linear regression, 3a.2.1
linear return, 54.5.1
linear space, 33.1.1
linear state space model, 29.5.1
    observation equation, 29.5.1
    transition equation, 29.5.1
linear state-space model
    covariance stationary, 29.5.2
    systematic-idiosyncratic, 29.5.4
linear time invariant filter
    frequency response function, 31.2.4
    transfer function, 31.2.4
linear transformation, 33.2
    symmetric, 33.3.1
linearity, 53.2
linearly constrained quadratic programming, 35.3.3
link function, 39.7
links, 15.1
liquidation, 54.3.2
liquidation valuation, 47.1.2
liquidity curve, 51.1
    "market buy" liquidity curve, 51.1
    "market sell" liquidity curve, 51.1
local Markov property, 15.6
location, 7.2.1
    affine equivariant, 7.1.1, 7.1.4
    minimum volmue ellipsoid, multivariate, 7.2.7
log-partition function
    exponential family distribution, 39.7
log-return, 54.5.6
log-sum-exp function, 39.7.2
logistic function, 39.439
logistic regression
    binary, 13.3.6
    multinomial, 13.3.6
logit function, 39.7.2
logit parametrization
    binary logit parametrization, 39.6.2
    multinomial logit parametrization, 39.6.2
logit regression
    binary, 13.3.6
    multinomial, 13.3.6
lognormal distribution, 39.2.1
    shifted, 39.2.4
long holdings, 54.2
long memory, 43.3
long position, 53.2.1
longitudinal data, 17.9
Lorentz cone, 35.3.4
Lorenz curve, 2b.1.5
loss, 13.1.5
    0-1 loss, 13.1.5
    0-1 margin loss, 13.1.5
    consistent, 2b.4.1
    Dawid-Sebastiani, 2a.4
    exponential , 13.1.5
    hinge , 13.1.5
    logistic , 13.1.5
    margin loss, 13.1.5
    probabilistic worst-case, 6b.4.1
    proper, 6b.5.4
    square , 13.1.5
    strictly proper, 6b.5.4
    tangent , 13.1.5
    worst-case, 6b.4.1
loss function, 6b.1.3
loss given default, 42.5.1
lower partial moment, 48.9.2
    root, 48.9.2
lower partial moment principle, 41c.2.1
Lp space, 36.3.5

M

m-affine coordinates, 4.1.2
m-flat, 4.1.2
m-geodesic, 4.1.2
m-square, 46.3.2
machine learning
    experimental study, 11.1.3
    interventional study, 11.1.3
    observational study, 11.1.3
macro signals, 50c.1.4
macroeconomic linear factor model, 8.2
Mahalanobis inner product, 33.3.2
Marchenko-Pastur distribution, 18.6.3
marginal contributions, 49b
    Euler decomposition, 49b.2
    Euler marginal contributions, 49b.2
marginal distribution, 2b.3.2
marginal supply demand curve, 51.1
marginalization
    mean-covariance, 2a.3.4
    probabilistic, 2b.3.2
marked-to-market, 53.1
marked-to-model, 53.1
markedness, 13.4
market beta, 50c.3
market capitalization, 47.7.1
market impact, 51.1.2
market impact decay kernel, 51.1.2
market impact function, 51.1.2
market impact model, 51.1.2
market impact P&L, 51.2.2
market impact square root law, 50a.3.3
market order placement, 51.3
market parameters, 41d
market portfolio, 50c.2.1
Markov chain
    Monte Carlo, 26.1
Markov network, 15.7
Markov process, 30.2
    mean-covariance, 30.2
    time homogenous, 30.2
    transition density function, 30.2
Markov property, 30.2
Markov’s inequality, 2a.4.2
MARS, 16.3.1
martingale, 28.3
matrix, 33.2.1
    addition, 33.2.2
    anti-symmetric, 33.5.3
    circulant, 36.4.2
    commutation, 33.7.4
    conformable, 33.2.2
    conjugate transpose, 33.3.1
    decomposition, 33.7.4
    Hermitian, 33.3.1
    identity, 33.2.3
    inverse, 33.2.3
    invertible, 33.2.3
    low-rank-diagonal, 33.7.4
    multiplication, 33.2.2
    negative definite, 33.3.2
    negative semidefinite, 33.3.2
    non-singular, 33.2.3
    orthogonal, 33.3.6
    polynomial, 33.8
    positive definite, 33.3.2
    positive semidefinite, 33.3.2
    rank property, 33.7.4
    scalar multiplication, 33.2.2
    size, 33.2.1
    square, 33.2.1
    subtraction, 33.2.2
    symmetric, 33.3.1
    Toeplitz, 36.4.1
    transpose, 33.3.1
    transpose-square-root, 33.6.3
    unitary, 33.3.6
matrix exponential, 33.7.2
matrix-normal distribution, 39.1.6
matrix-valued kernel, 36.5.3
    Mercer, 36.5.3
    positive definite, 36.5.3
    symmetric, 36.5.3
matrix-vector multiplication, 33.2.1
Matthews correlation, 13.4
maturity, 55.1.1
maximal Youden’s J statistic, 13.1.4
maximum
    global, 35.1.1
    local, 35.1.2
    relative, 35.1.2
maximum a posteriori, 3b.7.1
maximum classifier, 13.1.4
maximum expected return portfolio, 50a.1.1
maximum information ratio portfolio, 50a.1.1
maximum likelihood factorization, 15.3.2
maximum likelihood parameters, 17.4.1
maximum likelihood with flexible probabilities, 17.4.3
    normal assumption, 19.2.2
    Student t assumption, 19.2.3
maximum likelihood with flexible probabilities (MLFP) estimate, 17.4.4
maximum likelihood with flexible probabilities (MLFP) predictor, 20.3.4
maximum partition encoder, 39.6
maximum return signal-to-noise portfolio, 50a.1.1
maximum Sharpe ratio portfolio, 50a.1.1
mean estimator
    square bias, 21.3.2
    variance, 21.3.2
mean maximum information ratio, 50a.4.1
mean reversion, 43.2
mean vector, 2a.1.1
mean-covariance equivalence class, 2a.1.3
mean-lower partial moment, 48.9.2
mean-semideviation, 48.9.2
measure, 36.1.2
    absolutely continuous, 36.1.3
    counting, 36.1.2
    finite, 36.1.2
    integral, 36.1.2
    Lebesgue, 36.1.2
    Radon-Nikodym derivative, 36.1.3
    Riemann-Stieltjes, 36.1.2
measure of concordance, 5c.2
measure of dependence, 5c.1
median, 2b.4.3
    multivariate, 7.2.7
Mercer’s theorem, 36.7.1
method of moments (MM) estimate, 17.6.1
method of moments with flexible probabilities (MMFP) estimate, 17.6.1
metric, 33.4.2
    discrete, 33.4.2
metric geodesic, 4.1.2
metric space, 33.4.2
Metropolis-Hastings algorithm, 26.1.1
microprice, 53.4.1
mid-quote, 53.4.1
midrange, 7.2.4
minimum
    global, 35.1.1
    local, 35.1.2
    relative, 35.1.2
minimum relative entropy numeraire probability, 41a.2.3
minimum relative entropy principle, 25.1.4
minimum relative entropy stochastic discount factor, 41a.2.3
minimum tracking error portfolio, 50a.1.1
minimum variance portfolio, 50a.1.1
minimum-torsion bets, 49b.4.1
minimum-torsion exposures, 49b.4.1
minimum-torsion transformation, 49b.4.1
misclassification error, 7.2.4
Misclassification scoring rule, 13.3.1
miss rate, 13.1
mixture models, 15.4
    Gaussian mixtures, 15.4
    mixture of experts, 12.4.2
modal dispersion, 7.1.2
modal square-dispersion, 7.1.4
mode, 2b.4.2
model
    frequentist approach, 20.4.2
    frequentist prediction, 20.3.2
model parametrization
    features of interest, 21.3
    hypothesis, 21.3
    nuisance parameters, 21.3
model selection
    Bayesian model comparison, 21.2
    estimation model, 21.3
    hypothesis set, 21.3
    maximum a posteriori, 21.2
    model uncertainty, 21.3.5
model set
    distributions, 26.2.1
moment generating function, 2b.1.4
momentum, 50c.1.3
momentum signal, 50c.1.3
money multiple, 54.5.6
money-equivalence, 48.2
moneyness, 55.2.1
monotone map
    decreasing, 34.4.3
    increasing, 34.4.3, 34.4.3
    strictly increasing, 34.4.3
monotonic function, 34.4.1
    entrywise, 34.4.2
    matrix-valued, 34.4.2
    strictly, 34.4.1
monotonicity, 25.1.7, 48.2
mortgage backed securities, 55.1.5
most powerful set, 13.1.3
multinomial logit function, 39.7.2
multinomial mixture distribution, 15.4
    multinomial Gaussian mixtures, 15.4
    multinomial mixture components, 15.4
    multinomial normal mixtures, 15.4
multinomial probit regression, 13.3.8
multiple, 53.3.3
multiple of invested capital (MOIC), 54.5.5
multivariate adaptive regression splines, 16.3.1
multivariate arithmetic Brownian motion, 50d.1.1
multivariate Gaussian, 17.1.2
multivariate generalized autoregressive conditional heteroscedastic, 43.6.5
multivariate geometric Brownian motion, 50d.1.1
multivariate Ornstein-Uhlenbeck, 32.6
musical isomorphism, 33.3
Mutual information, 5c.1.2

N

naive Bayes models, 15.5
natural form, 39.7
natural parameters, 39.7
neighbors, 15.1
nested simulation, 46.5.2
nested subset methods, 21.3.6
net asset value, 53.2.4
net exposure, 47.1.4
neural network
    artificial, 16.4.1
    convolutional, learning-deep
    deep artificial, 16.4.1
neuron, 16.4.1
    convolution, 16.4.1
neutralization, 50c.1.5
Newton’s method, 35.2.4
Neyman-Pearson lemma, 13.1.3
nodes, 15.1
non-linear error prediction matrix, 3b.7.3
non-linear partial covariance matrix, 3b.7.3
norm, 33.4.1
    absolute homogeneity, 33.4.1
    counter, 33.4.1
    Frobenius, 33.4.1
    Lp, 36.3.5
    Mahalanobis, 33.3.3
    matrix p, 33.4.1
    maximum, 33.4.1
    p, 33.4.1
    positive definiteness, sec-norms
    standard Euclidean, 33.3.3
    subadditivity, 33.4.1
    taxicab, 33.4.1
norm symmetric, 5b.5
normal copula, 5b.4
normal distribution, 39.1
normal-inverse-Wishart (NIW) distribution, 18.5.2
normalized value characteristics, 50c.3
normalizing and variance stabilizing, 43.5.2
normed vector space, 33.4.1
notional value, 55.1.1
nowcasting
    inference, 11.1
null hypothesis, 22.3
null space, 33.2
nullity
    linear transformation, 33.2
number of obligors, 42.5.2
numeraire, 41a.2.2
    risk-free , 41a.3.1
    risk-neutral, 41a.3.1
numeraire probability measure, 41a.2.2

O

objective function, 35.1.1
observable features, 41d
observable variables, 11.1
offset cash, 54.5.4
omega ratio, 48.11.2
one-hot encoding
    partitions, 34.3.1
one-versus-one (OvO) classifier, 13.2.6
one-versus-the-rest classifier, 13.2.6
operational loss, 42.7
operations, 42.7
operator, 36.2.2
    differentiation, 36.2.2
    integral, 36.2.2
    invertible, 36.2.2
    linear, 36.2.2
    unitary, 36.3.4
opportunity cost, 6b.1.3
optimal discriminants, 13.2.3
optimal relative scoring, 13.1.3
optimal score, 13.1.3
optimal scoring, 13.1.3
optimization
    problem, 35.1.1
    unconstrained, 35.1.1
option-based portfolio insurance, 50d.3
order placement, 51
order routing, 51
order scheduling, 51
ordered logit, 39.6.2
ordered probit, 39.6.2
ordinal classifier, 13.1.4
ordinal partition encoder, 39.6
ordinary least squares, 8.2.6
ordinary least squares with flexible probabilities, 8.2.6
Ornstein-Uhlenbeck, 32.2.1
orthogonal, 33.3.3
    projection, 33.3.4
    projection equation, 33.3.4
    to a linear subspace, 33.3.4
    transformations, 33.3.6
orthogonal-increment process, 28.3
orthogonality
    block-wise L2 central , 3a.1
    L2 non-central, 3a.1
orthogonality in expectation, 38.2.2
orthogonalization, 33.6.3
orthonormal set, 33.3.3
orthonormalization, 33.6.3
out of the money, 55.2.1
out-of-sample error, 17.236
outputs, 11.1
outstanding order vector, 51.3

P

p-quantile
    functional, multivariate, 7.2.7
p-value, 22.1.2
P&L, 54.1.1
    conditional ex-ante, 46.1
    pricing function, 46
P&L linearity, 54.2
P&L related exposures, 50c.3.1
pair-wise Markov property, 15.7
panel data, 17.9
panic copula, 47.6.3
panic drivers, 47.6.3
paper P&L, 54.3.1
par, 55.1.3
par (swap) curve, 55.1.4
par (swap) rate, 55.1.4
par rate, 55.1.4
parallelogram rule, 33.1.1
parallelotope, 33.2.3
parent, 15.1
parent order, 51
Parseval’s identity, 36.4.3
partial correlation matrix
    linear, 3a.2.1
partial covariance matrix, 3a.5
partial derivative
    second order, 34.1.2
    vector-valued, 34.59
partial standard deviation, 3a.2.1
partial views, 25.1.4
partition, 34.3.1
    encoding, 38.3.3
partition encoder, 39.6
partition encoding, 39.6
partitioned matrix inversion, 33.7.4
path, 33.4.4
payment time, 53.3
payoff
    forward, 55.4.2
    variance swap, 42.4.5
Pearson parametrization
    Arrow-Pratt function, 48.5.1
perceptron
    perceptron algorithm, 13.1.6
performance "mean", 48.3.1
performance expectation, 48.3.1
performance mean-variance trade-off, 48.3.4
performance model
    Black-Litterman, 24.8
performance variance, 48.3.2
permanent impact, 51.1.2
permanent market impact model, 51.1.2
persistence, 50c.3.4
Plancherel theorem, 36.4.3
point view, 25.1.2
point-in-time, 43.4.1
pointed, 35.3.6
Poisson process, 32.1.3, 43.1.2
polarization identity, 33.3.3
pool factor, 55.1.5
pooling
    convolution, 16.4.1
portfolio, 47.1
portfolio rebalancing P&L, 54.3.3
portfolio weights, 54.5.2
positive definite, 36.5.2
    linear transformation, 3611
positive homogeneity of first degree, 48.2
positive homogenous of degree, 48.2
positive semidefinite
    linear transformation, 3612
posterior, 20.1.2, 20.1.2
    Bayesian statistics, 3b.3.1
posterior distribution, 17.8.1
    Black-Litterman, 24.32
posterior predictive performance model
    Black-Litterman, 24.36
potential function
    e-affine coordinates, 4.1.3
    m-affine coordinates, 4.1.3
power, 22.1.1
power market impact, 50a.3.3
power spectrum
    generalized, 36.6.1
    generalized, matrix-valued, 36.6.3
    integral, 36.6.1
    proper, 36.6.1
    proper, matrix-valued, 36.6.3
precision
    binary classification, 13.1
precision matrix, 39.7.1
predicted negative probability, 13.1
predicted positive probability, 13.1
predicted variables, 11.1
prediction, 8.1
    forecasting
        multi-period, 38.1.3
    linear mean, 3a.2.1
    linear mean-covariance, 3a.2.1
    mean-covariance, 11.2
    missing data recovery
        multi-period, 38.1.3
    nowcasting
        multi-period, 38.1.3
    point, 11.2
    point prediction, 20.3.1
    predictive distribution, 20.4.1
    probabilistic, 11.2
    smoothing
        multi-period, 38.1.3
    stress-testing
        multi-period, 38.1.3
    what-if analysis
        multi-period, 38.1.3
prediction model, 17.8.2
predictive, 50a.4.1
predictive distribution, 47.4.1
    non-observable, 20.4.1
    posterior, 17.8.2
predictor
    point, 11.2
    point prediction, 20.3.1
    predictive distribution, 20.4.1
    probabilistic, 11.2
premium, 55.1.3
prevalence of positive outcome, 13.1
price manipulation, 51.5.2<