Glossary
Symbols
(functional) excess risk, 36
(probabilistic) linear regression, 15.1.4
(probabilistic) regression, 15.1.3
A
absence of arbitrage, 20a.1.3
absolute score, 31.9
abstract Bayes theorem, 31.14.3
accounting signals, 46.5
accrued interest, 19.2.1
accuracy
binary classification, 14.6
action, 37.2
Bayesian, 37.2.2
minimax, 37.2.3
activation function, 36.4.1
activity time, 1.8.2
actual cash-flow, 19.2.3
actual exchange rate, 19.2.3
actual value, 19.2.3
adapted basis, 27.1.3
adapted variable
partition, 31.14.3
adaptive execution algorithm, 10.3.2
additive, 31.12.1
admissible, 38.2.1
algebraic multiplicity, 27.5.4
algebraic Riccati equation, 27.6.6
allocation policy, 6.5
alpha, 7.3.1
binary classification, 14.3
alternative beta, 9c
alternative hypothesis, 41.1
analysis of variance, 14.1.10
ancestor, 15.3.4
angle, 27.3.1
canonical, 35.3.2
covariance, 35.3.2
expectation, 35.3.1
anti-monotonic
function, 28.5.3
arbitrage pricing theory, 20b.3.1
architecture, 36.4.1
area under curve (AUC), 14.3.4
arithmetic Brownian motion with drift, 44.1.3
Arrow-Debreu securities, 20b.1.2
Arrow-Pratt absolute risk aversion function, 7.5.1
ask size, 1.8.1
assets, 6.7.1
at the money, 1.4.2
foreign exchange market, 1.4.4
augmented view variables, 16.5.3
autocorrelation
function, 45.1
autocovariance
function, 45.1
autoencoders, 14.5
autoregressive conditional duration, 2.6.2
autoregressive moving average process
autoregressive
univariate (AR), ??, ??
integrated
multivariate (VARIMA), relationships-varma-vma-var
univariate (ARIMA), ??
moving average
univariate (MA), ??, ??
multivariate (VARMA), 45.3.2
univariate (ARMA), 45.3.1
autoregressive of order one, 45.2
autoregressive-fractionally integrated-moving average of order (p,d,q), 2.4
auxiliary measure, 31.10
B
Bachelier, 22.3.1
backpropagation, 30.1.1, 36.4.1
backward cash-flow-adjusted value, 19.1.5
backward/forward exponential weighted moving average, 3.10.6
bag of words, 15.3.3
bagging, 40.4.1
balance sheet, 6.7.1
balanced, 40.1
bandpass filter, 45.5.3
bandwidth, 3.1.2
kernel density estimate, 3.2.3
bandwidth matrix, 3.1.2
base case, 16.6.1
base distribution, 16.1.1
base measure, 31.10
basis, 24.4.3, 27.1.2
canonical, 27.1
orthonormal, 27.3.2
basis denominator, 24.4.3
basis instruments, 20b.1.1
Bayes classifier, 14.4.3
Bayes error
expected error, 39.1.2
frequentist approach, 39.4.2
frequentist prediction, 39.3.2
Bayes risk, 37.3.1
Bayes theorem, 31.3.2
Bayesian networks, 15.3.6
benchmark, 24.5.1
best ask, 1.8.1
best bid, 1.8.1
best prediction, 27.3.3
beta
binary classification, 14.3
beta conditions
projection coefficients, 27.3.2
projection equation, 27.3.2
beta distortion index, 7.8.2
beta-adjusted excess return, 24.5.2
bets, 8b.4
between-cluster/group variance, 14.1.10
Bhattacharyya coefficient, 40.4.4
bias, 39.1.2
binary classification, 14.3
bias reduction, 36
bid size, 1.8.1
bid-ask spread, 10.1
bilateral value adjustment, 6.2
binary classification, 14.3.5
binary classifier, 14.3.4
binary partition encoder, 31.9
binning, 1.8.3
binomial inverse theorem, 27.7.4
binomial tree, 2.1.2
bins, 1.8.3, 48.2
bins width, 48.2
Black-Merton-Scholes model, 22.3.2
Bochner’s theorem, 29.6.1
bond yield, 5.3.2
bootstrap aggregating, 40.4.1
Borel sets, 31.14.1
boundedness, 16.1.7
breadth, 9c.5.2, 36.4.1
breakdown point, 3.7.2
Brier score, 15.2.1
Brownian motion, 2.1.1
Buhlmann pricing equation, 20a.5
expectation, 20a.5
Buhlmann principle, 21.2.4
butterfly, 1.4.4, 20b.1.2
C
calendar signal, 46.5
calibrate, 22
call option, 1.4.1
canonical correlation matrix, 34.3.1
canonical parameters, 31.10
capital asset pricing model, 20b.2
capital gain, 19.1.6
cardinality, 29.1.2
cardinality constraint, 30.2
carry signal, 46.1
CART, 36.3.1
cash-flow function, 5.1
cash-flow-adjusted value, 19.1.5
categorical distributions, 31.9
categories, 31.9
Cauchy distribution, 31.6.2
Cauchy-Schwarz inequality, 27.3.1
causal, 45.5.3
center of the bin, 48.2
central tendency, 35.5.1
affine equivariant, 35.4.1, 35.4.2
certainty-equivalent, 7.5
certainty-equivalent principle, 21.2.2
chain rule, 15.3.6
gradient, 28.1.2
matrix-variate, 28.1.3
multivariate, 28.1.2
univariate, 28.1.1
change of variable formula
multivariate, 28.4.3
univariate, 28.4.1
characteristic equation, 27.5.1
characteristic function
multivariate, 31.1.3
univariate, 31.1.2
characteristic matrix, 9c.4
characteristic polynomial, 27.5.1
characteristic portfolio, 9c.2
characteristic portfolios, 9c.4
Chebyshev’s inequality, 35.2.7
chi-squared distribution, 31.5.2
child, 15.3.4
child orders, 10
Cholesky decomposition, 27.6.5
Cholesky root, 27.6.5
CIR, 44.2.2
claims, 1.6
classes, 31.9
classical-equivalent, 38.2.1
classification
multinomial classification, 14.4
classification and regression trees, 36.3.1
Classification trees
splitting criterion, 15.2.9
classification trees, 15.2.9
clean price, 19.2.1, 19.2.1
clique, 15.3.4
clique factorization, 15.3.5
clustering, 14.5.3
co-monotonic
function, 28.5.3
variables, 33.2.4
co-monotonic additivity, 7.2
coarseness level, 48.2
codes, 14.5
coherent satisfaction measure, 7.9.1
coherent probability measure, 7.9.1
worst case expectation representation, 7.9.1
cointegrated, 42.1
cointegrated space, 2.8.1
cointegration signal, 46.3.3
cointegration vector, 2.8.1
multivariate process, 42.1
collateral, 6.8.1
commonalities, 12.4.5
comparable instruments, 22
complete, 20b.1.1
complete metric space, 29.3.1
compound distribution, 31.3.4
compound Poisson process, 44.1.4
compounded rate of return
(instantaneous) compounded rate of return, 24.4.6
average compounded rate of return, 24.4.6
compounded return, 24.4.1
concave down function
univariate, 28.6.1
concave function
multivariate, 28.6.2
univariate, 28.6.1
concave up function
univariate, 28.6.1
concavity, 7.2
condition number, 3.5.2
conditional cdf, 31.3.1
conditional covariance, 31.3.3
conditional distribution of variable, 31.3.1
conditional excess distribution, 7.6.2
conditional expectation, 31.3.3
with respect to the events set, 31.14.3
conditional independence, 15.3, 40.1
conditional pdf, 31.3.1
conditional principal component analysis, 12.6.16
conditional principal directions, 12.6.16
conditional principal variances, 12.6.16
conditional probability, 31.3.1
with respect to the events set, 31.14.3
conditional statistical feature, 31.3.3
conditional value at risk, 7.8.2
conditioning-marginalization, 16.1.3
cone, 30.1.3
confusion matrix, 14.3
conic programming, 30.1.3
conjugate distribution, 38.2.3
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, 30.1
constructed variables
unsupervised learning, 13.1
contingency table, 14.4.1
continuous-state distributions, 2.1
convex function
multivariate, 28.6.2
univariate, 28.6.1
convex programming, 30.1.2
convex set, 30.1.3
convexity, 5.3.2
risky investment, 9d.6
satisfaction/risk measures, 7.2
convolution, 29.3.3
cyclic, 29.3.3
discrete, 29.3.3
periodic, 29.3.3
coordinate descent, 30.2.7
copula, 33.2.2, 33.2.2
copula-marginal combination, 33.4.2
copula-marginal distributions, 33.4.2
copula-marginal separation, 33.4.1
copula-pdf, 33.2.3
Cornish-Fisher approximation, 7.6.2
Cornish-Fisher expansion, 7.6.2
correlation, 34.3
correlation function, 34.3
correlation matrix, 34.3
cost of equity, 22.2.1
cost of trading, 9d.6
counterparty credit risk, 6.2
coupon, 1.10.2
coupon bond, 1.3.1
CoVaR, 6.6.1
covariance matrix, 35.2.1
covariance principle, 20a.5
covariant, 32.1.1
covariates, 13.1
Cramer-Lundberg ruin model, 6.7.5
credit ratings, 1.5.3
credit structural model, 135
credit value adjustment , 6.2
critical point, 30.1
cross the spread, 10.1
cross-autocovariance
function, 45.1
cross-sectional, 9c, 9c
cross-sectional linear factor model, 12.5
cross-sectional sample median of default probabilities, 1.5.3
cumulant , 4.7.1
cumulative cash-flow, 19.1.3
cumulative distribution function
multivariate, 31.1.3
univariate, 31.1.2
cumulative link, 31.9.2
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, 46.5
cutoff classifier, 14.3.4
D
data, 38.1
debt, 6.7.1
debt value adjustment, 6.2
decay, 1.3.5
decision function
admissible, 37.3.3
deterministic, 37.3
randomized , 37.3
decision problem
data-driven, 37.3
no-data, 37.2
decision regions, 14.4
decoder, 14.5
decreasing function, 28.5.1
entrywise, ??
matrix-valued, ??
strictly, 28.5.1
degree of reversal, 9c.2.3
delta, 1.4.4
delta p, 14.6
delta rule, 36.4.1
dependent variables, 12.1, 13.1
depth, 36.4.1
derivative
directional, 28.1.2
first, 28.1.1
higher order, 28.1.1
partial, 28.1.2
second, 28.1.1
total, 28.1.2
univariate, 28.1.1
descendant, 15.3.4
determinant, 27.2.3
deviation
expectile, 35.5.2
maximum, 35.5.2
mean absolute, 35.5.2
median absolute, 35.5.2
subquantile, 35.5.2
differentiable function
multivariate, 28.1.2
univariate, 28.1.1
dimension, 27.1.2
Dirac delta, 29.3.2
direct sum, 27.1.3
directed graph, 15.3.4
dirty price, 19.2.1
discount, 1.10.2
discount factor, 19.2.3
discount function, 5.1
discounted cash-flow, 22.2.1
discounted cash-flow adjusted value, 21.3.1
discounted payoff, 21.2
discrete-state distributions, 2.1
discrete-state random walk, 2.1.2
discretized pdf, 48.2
discriminant model
probabilistic, 13.2.3
discriminant next-step model, 2.7.6
discriminant variables, 31.9
dispersion, 35.5.1
affine equivariant, 35.4.1
modal, 35.4.1
distance, 27.4.2
absolute, 27.4.2
covariance, 35.3.2
Euclidean, 27.3.1
expectation, 35.3.1
identity of indiscernibles, 27.4.2
Lp, 35.6.2
Mahalanobis, 27.4.2
p, 27.4.2
subadditivity, 27.4.2
symmetry, 27.4.2
triangle inequality, 27.4.2
distance matrix, 14.4.1
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, 21.2.3
distortion satisfaction measure, 7.8.1
distribution function, 31.14.1
distributional view, 16.1.3
divergence, 27.4.3
Bregman, 27.4.3
difference, 27.4.3
extended f, 27.4.3
identity of indiscernibles, 27.4.3
separable, 27.4.3
diversification distribution, 8b.4
diversity, 40.4.4
dividend-adjusted value, 1.1
stocks, 19.1.5
divisors, 31.12.2
dollar duration, 6.1.3
dollar-neutral constraint, 9c.2.2
domain
circle, 29.1.1
continuous, 29.1.1
cyclic group, 29.1.1
discrete, 29.1.1
frequency, 29.4
time, 29.4
torus, 29.1.1
dominant-residual LFM, 12.1.2
dot product, 27.3
drawdown, 24.6
maximum (absolute) drawdown, 24.6
maximum percentage drawdown, 24.6
percentage drawdown, 24.6
dual Legendre, 32.1.3
dually flat, 32.1.3
duration, 5.3.2
DV01, 6.1.3
dynamic allocation, 9c
dynamic conditional correlation, 3.9.3
dynamic graphical models, 45.4
dynamic linear factor model, 17.2
dynamic models, 17
dynamic principal component, 17.6
dynamic regression model, 17.4
E
e-affine coordinates, 32.1.2
e-flat, 32.1.2
e-geodesic, 32.1.2
EBITDA, 6.7.2
economic capital, 7.12
economic net income, 6.7.2
edges, 15.3.4
effective convexity, 5.3.2
effective delta, 5.3.3
effective duration, 5.3.2
effective key rates durations, 5.3.2
effective number of bets, 8b.4
effective number of scenarios, 3.1.4
effective rank, 16.1.6
effective rho, 5.3.3
key-rates, 5.3.3
effective volga, 5.3.3
efficient market hypothesis, 2.1
eigenfunction, 29.5.2
eigenvalue, 27.5.1
linear transformation, 27.5.1
eigenvector, 27.5.1
linear transformation, 27.5.1
elastic net, 30.2.7, 40.2.2
constrained generalized elastic net, 30.2.7
ellipsoid, 35.2.4
expectation-covariance, 35.2.4
location-dispersion, 35.4.2
ellipsoid test for invariance, 43.1
elliptical distribution, 31.7.1
EM algorithm
population, 38.4.6
encoder, 14.5
enterprise value, 22.2.2
equilibrium performance model
Black-Litterman, 47.11
equilibrium returns
Black-Litterman, 47.7
equity, 6.7.1
equity book value, 6.7.1
equivalent optimization problem, 30.3.1
ergodic (in mean), 42.1
Erlang process, 44.1.4
error, 39.1.2
multivariate, 35.5.3
multivariate mean absolute, 35.5.3
multivariate mean squared, 35.5.3
posterior error, 39.1.3
univariate, 35.5.1
univariate mean squared, 35.5.1
error correction, 45.2.7
Esscher principle, 21.2.4
estimable, 7.2
estimate, 39.1.1
estimation, 13.3
estimation model, 3.4
Bayesian estimation, 38.2.1
estimation risk, 37.3.4
estimation set, 40.1
point prediction, 39.3.1
predictive distribution, 39.4.1
estimation uncertainty, 38.2.1
estimator, 39.1.1
European-style derivatives, 1.4
evidence, 15.3
maximum likelihood, 38.1
evidence lower bound, 38.4.5
ex-dividend date, 19.1.2
exotic beta, 9c
expectation
multivariate, 35.2.1
univariate, 35.1.1
expectation rule, 31.8.3
expectation-maximization, 38.1.3
expectation step, 38.1.3
maximization step step, 38.1.3
expected drawdown, 7.3.1
expected overperformance, 7.3.1
expected shortfall, 7.8.2
expected utility, 7.5
expected value of the process variance (EVPV), 14.1.10
expectile, 31.1.2
expectile-VaR, 7.9.2
expiry, 1.4
explanatory variables, 13.1
exponential decay probabilities, 3.1.1
exponential family distribution, 31.10
exponential kernel, 3.1.2
exponential of the entropy, 3.1.4
exponential principle, 21.2.2
exponential tilting, 16.1.5
exponentially weighted moving average, 3.2.4
exponentially weighted moving correlation, 3.2.4
exponentially weighted moving covariance, 3.2.4
exponentially weighted moving quantile, 3.2.4
exponentially weighted moving standard deviation, 3.2.4
exposure, 6.1.3
portfolio P&L, 9c.2
risky investment, 9d.6
exposure at default, 1.5.2
extrema
local, 30.1
relative, 30.1
extreme value theory, 7.6.2
F
f-divergence, 32.1.4
F-measure
binary classification, 14.6
F1 score
binary classification, 14.6
face value, 1.3.1
factor analysis, 3.5.6
factor analysis matrix, 3.5.6
factor loadings, 12.1
factor premia, 9c.4.2
factor premium, 9c.1
factor-analysis linear factor model, 12.4.1
factor-replicating portfolios
arbitrage pricing theory, 20b.3.1
factors, 9c, 13.1
linear factor model, 12.1
fair value, 19.1.2
fallout, 14.3
false negative
accuracy, 14.3
probability, 14.3
rate, 14.3
false positive
accuracy, 14.3
probability, 14.3
rate, 14.3
feature engineering, 14.1
feature map
canonical, 29.7.2
Mercer, 29.7.2
features, 13.1
Feller condition, 44.5.1
filter, 45.5
backward shift
polynomial, 27.8.2
linear time invariant , 45.5.3
filtering, 13.3
filtration, 42.4
adapted process, 42.4
(fully-recombining) binomial tree, 42.4t
(fully-recombining) tree, 42.4t
martingale, 42.4
Radon-Nikodym process
martingale, finite-discrete-time-stoch-process-sec
financial instrument, 19.1.1
finite difference
backward first order, 28.2.1, 28.2.2
central first order, 28.2.1, 28.2.2
central second order, 28.2.1, 28.2.2
forward first order, 28.2.1, 28.2.2
first in, 8b.1.1
first order criterion, 30.1
first order differential, 28.1.2
matrix-variate, 28.1.3
Fisher consistent, 3.7.1
Fisher discriminant analysis (FDA), 14.3.8
Fisher information distance, 32.1.4
Fisher’s linear discriminant, 14.3.8
flexible probabilities, 3
estimation, 31.8
forecast, 12.1
forecasting
inference, 13.3
foreign exchange function, 5.1
foreign exchange rate, 1.2.1
forward, 1.2.2
forward cash-flow-adjusted value, 19.1.5
forward exchange rate, 1.2.1
forward rate, 1.10.2
forward swap, 1.3.1
forward variance swap rate, 1.4.6
Fourier transform, 29.4
convolution theorem, 45.145
discrete (DFT), 29.4.5
discrete time (DTFT), 29.4.3
Fourier series, 29.4.4
integral, 29.4.2
inverse, 29.4
fractional Brownian motion, 44.4
fractional integrated process, 2.4
Frechet derivative, 29.8.2
second order, 29.8.3
Frechet-Hoeffding bounds, 34.1.1
frequency response function, 45.5.3
frequentist risk, 37.3.1
full-investment, 9c.2.2
fully constrained LFM, 12.6.17
function space, 29.2.1
addition, 29.2.1
scalar multiplication, 29.2.1
functional, 29.8
functional derivative, 36.5
fundamental accounting equation, 6.7.1
fundamental law of active management, 9c.5.2
fundamental linear factor model, 12.5
fundamental signals, 46.5
fundamental theorem of asset pricing, 20a.2.2
martingale pricing formula, 20b.4.3
fundamental theorem of calculus
first, 28.4.2
second, 28.4.2
funding risk, 6.2
funding value adjustment, 6.2
G
gamma distribution, 31.5.2
Gateaux derivative, 29.8.1
Gaussian kernel, 3.1.2, 3.2.3
Gaussian process, 31.4.2
generalized autoregressive conditional heteroscedastic, 2.6.1
generalized excess return, 24.5.2
generalized linear models (GLM), 15.1.6
generalized linear return, 24.4.3
generalized method of moments with flexible probabilities (GMMFP) estimate, 3.6.2
minimization, 3.6.3
generalized Pareto distribution, 7.6.2
generalized weight, 24.4.4
generative model, 13.2.3
generative next-step model, 2.7.6
generator
Markov chain, 44.3.1
matrix, 27.2.3
generic position, 22.1.3
geometric Brownian motion, 5.1.1
geometric multiplicity, 27.5.4
Gibbs distribution, 15.3.5
Gini coefficient, 14.3.4
Giny impurity, 15.2.3
glasso, 3.5.5
Tikhonov, 40.2.3
Glivenko-Cantelli theorem, 3.2.1
global minimum variance portfolio, ??
Gordon growth model, 22.2.1
grade, 33.1
grades, 33.2.1
gradient, 28.1.2
matrix-variate, 28.1.3
vector-valued function, 28.1.2
gradient descent, 30.1.1
stochastic, 30.1.1
Gram matrix, 27.6.1
Gram-Schmidt process
backward, 27.6.5
forward, 27.6.5
Gramian, 27.6.1
grand mean, 3.5.1
graph, 15.3.4
graphical lasso, 3.5.5
Greeks, 5.3
gross exposure, 6.1.3
group
general linear, 27.2.3
orthogonal, 27.3.4
special orthogonal, 27.3.4
unitary, 27.3.4
growth stocks, 46.2.1
H
Hadamard product, 27.7.2
half-life, 3.1.1
Hamiltonians, 31.10
hat matrix, 12.2.6
hazard function, 1.6
Hellinger distance, 40.4.4
Herglotz theorem, 29.6.1
Hessian, 28.1.2
matrix-variate, 28.1.3, 28.1.3
Heston model, 22.3.3
hidden Markov model, 17.9
hidden state, 37.2
hidden variables, 13.1
maximum likelihood, 38.1
point prediction, 39.3.1
high breakdown estimators, 3.7.2
high breakdown point with flexible probabilities, 3.7.2
high minus low, 9c.1
high water mark, 24.6
Hilbert space, 29.3.1
historical cdf, 3.2.1
historical cross-sectional, 12.5.7
historical distribution, 3.2.1
historical pdf, 3.2.1
historical principal component, 12.3.7
historical repricing, 5.5.2
historical with flexible probabilities (HFP) autoencoder, 39.3.4
historical with flexible probabilities (HFP) cdf, 3.2.1
historical with flexible probabilities (HFP) correlation matrix, 3.2.2
historical with flexible probabilities (HFP) covariance matrix, 3.2.2
historical with flexible probabilities (HFP) distribution, 3.2.1
historical with flexible probabilities (HFP) estimate, 3.2.1
historical with flexible probabilities (HFP) mean, 3.2.2
historical with flexible probabilities (HFP) median, 3.7.2
historical with flexible probabilities (HFP) pdf, 3.2.1
historical with flexible probabilities (HFP) predictor, 39.3.4
historical with flexible probabilities (HFP) quantile, 3.7.2
historical with flexible probabilities (HFP) standard deviation vector, 3.2.2
hold-out, 39.3.5
Hotelling statistic, 41.1.4
Hurst coefficient, 44.4
hybrid Monte Carlo-historical, 4.5.2
I
ice-cream cone, 30.1.5
identity transformation, 27.2.3
idiosyncratic, 12.1.3
ill-conditioned, 3.5.2
IM algorithm, 38.4.5
image space, 27.2
implementation shortfall, 24.3
implied returns
Black-Litterman, 47.7
implied volatility, 1.4.3
implied volatility surface, 1.4.3
improper integral, 28.4.1
impulse response, 45.5.3
in the money, 1.4.2
in-sample error, 40.16
inception, 24.2.2
income, 19.1.6
income statement, 6.7.2
increasing function, 28.5.1
entrywise, ??
matrix-valued, ??
strictly, monotonicity-univariate
indefinite integral, 28.4.2
independence, 34.1
independent component analysis, 14.5.5
independent variables, 13.1
linear factor model, 12.1
induced expectation, 7.10.1
inefficiency, 39.1.2
inference, 13.3
infinitely divisible, 31.12.2
inflator, 20a.2.2
influence function, 3.7.1
information, 2.9.1
random time series, 3
information coefficient, 9c.5.2
information generator, 2.9.1
information measure, 13.2
information ratio, 35.1.1
conditional information ratio, 9c.5.2
maximum (l2-mean unconditional) information ratio, 9c.5.2
maximum conditional information ratio, 9c.5.2
information set, 2.9.1, 14.1.10
distributions, 38.4.1
linearized, 12.2.6
information/view, 16.6.1
informedness, 14.6
inner product, 27.3
covariance , 35.3.2
expectation, 35.3.1
Hermitian, 29.3.1
Hermitian symmetry, 29.3.1
L2, 29.3.1
linearity, 27.3
partial linearity, 29.3.1
positive definiteness, 27.3, 29.3.1
symmetry, 27.3
inner product space, 27.3
innovation
weak, 12.2.6
inputs, 13.1
instantaneous exchange rate, 19.2.3
instantaneous forward curve, 1.10.2
instantaneous forward rate, 1.10.2
instantaneous spot rate, 1.10.2
integral kernel, 29.2.3
Mercer, 29.5.1
positive definite, 29.5.1
symmetric, 29.5.1
integral power spectrum
matrix-valued, 29.6.2
integrated, 42.1
integration by parts, 28.4.1
integration operator, ??
intensity models, 22.4.2
interaction, 36.2.1
interest rate, 1.3.3
internal rate of return, 24.4.6
interquantile range, 35.4.1
intuitive r-squared, 12.5.5
invariance rule, 31.8.3
invariant, 35.4.2
invariants, 2
inverse, 27.2.3
inverse transform sampling, 33.1
inverse-call, 1.3.4
inverse-Wishart distribution, 31.6.7
invertible, 27.2.3
investment factor, 24.4.6
reinvested instrument, 19.1.4
iso-contour, 35.6.1
isolated, 8b.1.1
iterated integral, 28.4.3
J
jackknife estimator, 3.7.1
Jacobian, 28.1.2
James-Stein estimator, 3.5.1
Jeffreys prior, 38.2.1
joint scenario, 31.8
jump rule, 19.1.3
K
k-fold, 39.3.5
k-means clustering, 14.5.3
kappa ratio, 7.11.2
Karush-Kuhn-Tucker conditions, 30.1.1
Kendall’s tau, 34.2.1
kernel, 3.2.3
Toeplitz, 29.3.3
kernel density estimate, 3.2.3
kernel stochastic discount factor, 20a.2.3
kernel trick, 29.7.2, 36.6
linear kernel, 36.6
polynomial kernel, 36.6
radial basis functions, 36.6
kernel with flexible probabilities (KFP) generalized mean, 3.2.3
kernel with flexible probabilities (KFP) pdf, 3.2.3
key rates, 1.3.5
Kolmogorov-Smirnov test, 43.1
Kronecker delta, 29.3.2
Kronecker product, 27.7.2
Kullback-Leibler divergence, 32.1.4
L
L2 space, 29.3.1, 35.3
label encoding, 31.9
labels, 13.1
lag operator, 45.5
order p, ??
Lagrange multiplier, 30.1.1
Lagrangian function, 30.1.1
Laplace approximation, 35.4.1
Laplacian, 28.1.2
large capitalization stocks, 46.5
lasso, 30.2.7, 40.2.2
lasso regression, C.7.3.2
lasso shooting, 30.2.7
last in, 8b.1.2
last transaction price, 1.8.1
latent variables, 13.1
maximum likelihood, 38.1
law invariant, 7.2
law of iterated expectations, 14.1.10
law of one price, 20a.1.1
law of total covariance, 14.1.10
law of total linear covariance, 12.6.13
law of total linear variance, 12.6.13
law of total variance, 14.1.10
LDL-Cholesky decomposition, 27.6.5
leaf, 36.3.1
learning, 13.3
least-squares residual, 14.1.2, 14.1.2
leave-1-out, 39.3.5
leave-p-out, 39.3.5
Lebesgue’s decompositon theorem, 29.1.3
Legendre transformation, 32.1.3
length, 27.4.1
covariance, 35.3.2
expectation , 35.3.1
Lp, 35.6.2
level, 1.3.5
leverage, 6.1.4
leverage effect, 2.6.2
Levy process, 44.1.2
Levy-Khintchine, 44.1.5
liabilities, 6.7.1
Libor, 1.10.2
likelihood, 3.4
estimators as random variables, 39.1.2
maximum likelihood, 38.1
likelihood ratio, 14.3.3
limit order book, 1.8.1
limit order placement, 10.3
linear classification, 14.195
bias, 14.3.5
linear combination, 27.1.2
linear dependence, 27.1.2
linear discriminant analysis (LDA), ??
linear factor model, 12.1
linear independence, 27.1.2
linear law of iterated projections, 12.6.13
linear loss matrix, 12.2.6
linear observation equation, 45.4
linear prediction
point, 12.2.6
linear pricing equation, 20a.2.1
intertemporal, 20b.4.2
linear programming, 30.1.8
linear projection, 12.2.6
linear return, 24.4.1
linear state space model, 45.4
linear transformation, 27.2
linear transition equation, 45.4
linearity, 20a.1.2
linearly constrained quadratic programming, 30.1.6
link function, 31.10
liquidation, 24.2.2
liquidation valuation, 22.1.5
liquidity curve, 10.1
"market buy" liquidity curve, 10.1
"market sell" liquidity curve, 10.1
local Markov property, 15.3.6
location, 35.5.1
affine equivariant, 35.4.1, 35.4.2
multivariate, 35.5.3, 35.5.3
log-partition function
exponential family distribution, 31.10
log-return, 24.4.6
log-sum-exp function, 31.10.2
logarithmic score, 13.2
cross-entropy, 13.2
entropy, 13.2
expected log-loss, 13.2
Kullback-Leibler divergence, 13.2
relative entropy, 13.2
logistic function, 31.455
logistic regression
binary, ??
multinomial, ??
logit function, 31.10.2
logit regression
binary, ??
multinomial, ??
lognormal distribution, 31.6.6
shifted, 31.6.6
long holdings, 24.1
long memory, 2.4
long position, 22.1.1
longitudinal data, 40.1
Lorentz cone, 30.1.5
Lorenz curve, 31.1.2
loss, 14.3.5
0-1 loss, 14.3.5
0-1 margin loss, 14.3.5
exponential , 14.3.5
hinge , 14.3.5
logistic , 14.3.5
margin loss, 14.3.5
square , 14.3.5
tangent , 14.3.5
loss function, 37.2
Bayesian expected, 37.2.2
minimax, 37.2.3
loss given default, 1.5.2
lower partial moment, 7.9.2
root, 7.9.2
lower partial moment principle, 21.2.1
Lp-space, 35.6.2
M
m-affine coordinates, 32.1.2
m-flat, 32.1.2
m-geodesic, 32.1.2
m-square, 5.3.2
macro signals, 46.5
macroeconomic linear factor model, 12.2
Mahalanobis inner product, 27.3
Marchenko-Pastur distribution, 3.5.3
marginal cdf, 31.2
marginal characteristic function, 31.2
marginal contributions, 8b
Euler decomposition, 8b.2
Euler marginal contributions, 8b.2
marginal distribution, 31.2
marginal pdf, 31.2
marginal supply demand curve, 10.1
marked-to-market, 19.1.2
marked-to-model, 19.1.2
markedness, 14.6
market beta, 9c.2
market capitalization, 6.7.1
market impact, 10.1.3
market impact decay kernel, 10.1.3
market impact function, 10.1.3
market impact model, 10.1.3
market impact P&L, 10.2.2
market order placement, 10.3
market parameters, 22
market portfolio, 9c.1
market price of risk, 20a.4.1
Markov chain
Monte Carlo, 38.3
Markov process, 42.1
time homogenous, 42.1
MARS, 36.3.1
martingale, 42.1
matrix, 27.2.1
addition, 27.2.2
circulant, 29.3.3
commutation, 27.7.4
conformable, 27.2.2
decomposition, 27.7.4
identity, 27.2.3
inverse, 27.2.3
invertible, 27.2.3
low-rank-diagonal, 27.7.4
multiplication, 27.2.2
negative definite, 27.2.4
negative semidefinite, 27.2.4
non-singular, 27.2.3
orthogonal, 27.3.4
polynomial, 27.8
positive definite, 27.2.4
positive semidefinite, 27.2.4
rank property, 27.7.4
scalar multiplication, 27.2.2
size, 27.2.1
square, 27.2.1
subtraction, 27.2.2
symmetric, 27.2.4
Toeplitz, 29.3.3
transpose, 27.2.4
transpose-square-root, 27.6.3
unitary, 27.3.4
matrix exponential, 27.7.2
matrix-normal distribution, 31.4.2
matrix-valued kernel, 29.5.4
Mercer, 29.5.4
positive definite, 29.5.4
symmetric, 29.5.4
Toeplitz, 29.6.2
matrix-vector multiplication, 27.2.1
Matthews correlation, 14.6
maturity, 1.3.1
maximal Youden’s J statistic, 14.3.4
maximum
global, 30.1
local, 30.1
relative, 30.1
Maximum a posteriori, 31.3.2
maximum classifier, 14.3.4
maximum likelihood factorization, 15.3.1
maximum likelihood parameters, ??
maximum likelihood with flexible probabilities, 3.3.1
normal assumption, C.7.2.2
Student t assumption, C.7.2.3
maximum likelihood with flexible probabilities (MLFP) estimate, 3.3.1
maximum likelihood with flexible probabilities (MLFP) predictor, 39.3.4
maximum partition encoder, 31.9
maximum Sharpe ratio portfolio, ??
mean reversion, 2.2
mean-lower partial moment, 7.9.2
mean-semideviation, 7.9.2
measure, 29.1.2
absolutely continuous, 29.1.3
counting, 29.1.2
finite, 29.1.2
integral, 29.1.2
Lebesgue, 29.1.2
Radon-Nikodym derivative, 29.1.3
measure of concordance, 34.2
measure of dependence, 34.1
measurement equation, 17.9, 45.4
median
multivariate, 35.5.3
univariate, 35.4.1
Mercer’s theorem, 29.7.1
method of moments (MM) estimate, 3.6.1
method of moments with flexible probabilities (MMFP) estimate, 3.6.1
metric, 27.4.2
metric geodesic, 32.1.2
metric space, 27.4.2
Metropolis-Hastings algorithm, 38.3.1
microprice, 1.8.1
mid-quote, 1.8.1
midrange, sec-lp-error
minimum
global, 30.1
local, 30.1
relative, 30.1
minimum relative entropy numeraire probability, 20a.2.3
minimum relative entropy stochastic discount factor, 20a.2.3
minimum-torsion bets, 8b.4.1
minimum-torsion exposures, 8b.4.1
minimum-torsion transformation, 8b.4.1
misclassification error, 35.5.2
miss rate, 14.3
mixture components, 15.3.2
mixture distribution, 15.3.2, 15.3.2
mixture models, 15.3.2
mixture of experts, 15.4.2
normal mixtures, 15.3.2
mode
multivariate, 35.4.2
univariate, 35.4.1
model
frequentist approach, 39.4.2
frequentist prediction, 39.3.2
model set
distributions, 38.4.1
moment generating function, 31.1.2
multivariate, 31.1.3
momentum, 46.3.1
momentum signal, 46.3.1
money multiple, 24.4.6
money-equivalence, 7.2
moneyness, 1.4.2
monotone map
decreasing, 28.5.3
increasing, 28.5.3, 28.5.3
strictly increasing, 28.5.3
monotonic function, 28.5.1
entrywise, 28.5.2
matrix-valued, 28.5.2
strictly, 28.5.1
monotonicity, 7.2, 16.1.7
mortgage backed securities, 19.2.1
most powerful set, 14.3.3
multi-layer perceptron, 15.2.8
multinomial logit function, 31.10.2
multinomial logit parametrization, 31.9.2
multinomial probit parametrization, 31.9.2
multinomial probit regression, ??
multiple, 19.1.4
multiple of invested capital (MOIC), 24.4.5
multivariate adaptive regression splines, 36.3.1
multivariate arithmetic Brownian motion, 9d.1.1
multivariate Gaussian, 3.1.2
multivariate generalized autoregressive conditional heteroscedastic, 2.7.1
multivariate geometric Brownian motion, 9d.1.1
multivariate Ornstein-Uhlenbeck, 44.6
multivariate random-walk, 2.7
N
naive Bayes classifiers, 15.3.3
naive Bayes models, 15.3.3
natural form, 31.10
natural parameters, 31.10
neighbors, 15.3.4
nested simulation, 5.5.2
net asset value, 22.1.4
net exposure, 6.1.3
neural network
artificial, 36.4.1
convolutional, learning-deep
deep artificial, 36.4.1
neuron, 36.4.1
convolution, 36.4.1
neutralization, 46.6.3
Newton boosting, 30.1.1
Neyman-Pearson lemma, 14.3.3
nodes, 15.3.4
non-linear partial covariance matrix, 14.1.10
norm, 27.4.1
absolute homogeneity, 27.4.1
counter, 27.4.1
Frobenius, 27.4.1
Lp, 35.6.2
Mahalanobis, 27.3.1
matrix p, 27.4.1
maximum, 27.4.1
p, 27.4.1
positive definiteness, 27.4.1
standard Euclidean, 27.3.1
subadditivity, 27.4.1
taxicab, 27.4.1
norm symmetric, 33.3.2
normal copula, 33.3.1
normal distribution, 31.4
normal-inverse-Wishart (NIW) distribution, 3.4.2
normalized empirical histogram, 48.2
normalized heights, 48.2
normalized value characteristics, 9c.2
normalizing and variance stabilizing, 2.6.3
normed vector space, 27.4.1
notional value, 1.3.1
nowcasting
inference, 13.3
null hypothesis, 41.1
number of obligors, 1.5.3
numeraire, 20a.2.2
risk-free , 20a.3.1
risk-neutral, 20a.3.1
numeraire probability measure, 20a.2.2
O
observable
unsupervised learning, 13.1
observable features, 22
observable variables, 13.1, 37.3, 38.1
observation equation, 17.9
offset cash, 24.4.4
omega ratio, 7.11.2
one-hot encoding
partitions, 31.14.3
one-versus-one (OvO) classifier, ??
one-versus-the-rest classifier, ??
operational loss, 1.7
operations, 1.7
operator, 29.2.2
linear, 29.2.2
unitary, 29.3.4
opportunity cost function, 37.2
optimal discriminants, 14.4.3
optimal relative scoring, 14.3.3
optimal score, 14.3.3
optimal scoring, 14.3.3
optimization
unconstrained, 30.1
option-based portfolio insurance, 9d.3
order placement, 10
order q dominance, ??
order routing, 10
order scheduling, 10
ordered logit, 31.9.2
ordered probit, 31.9.2
ordinal classifier, 14.3.4
ordinal partition encoder, 31.9
ordinary least squares, 12.2.7
ordinary least squares with flexible probabilities, 12.2.7
Ornstein-Uhlenbeck, 44.2.1
orthogonal, 27.3.1
processes, 45.1
projection, 27.3.2
projection equation, 27.3.2
to a linear subspace, 27.3.2
transformations, 27.3.4
orthogonal-increment process, 42.1.10
orthogonalization, 27.6.3
orthonormal set, 27.3.1
orthonormalization, 27.6.3
out of the money, 1.4.2
out-of-sample error, 40.22
outputs, 13.1
outstanding order vector, 10.3
overnight index swap, 1.10.2
P
p-value, 41.1.2
P&L, 19.1.6
conditional ex-ante, 5.1
pricing function, 5
P&L linearity, 24.1
P&L related exposures, 9c.2
pair-wise Markov property, 15.3.5
panel data, 40.1
paper P&L, 24.2.1
par, 1.10.2
par (swap) curve, 1.10.2
par (swap) rate, 1.10.2
par rate, 1.10.2
par swap rate, 1.3.1
parallelogram rule, 27.1.1
parallelotope, 27.2.3
parent, 15.3.4
parent order, 10
Parseval’s identity, 29.4
partial correlation, 34.3.1
partial correlation matrix
linear, 12.2.6
partial covariance matrix
linear, 12.2.6
partial derivative
second order, 28.1.2
partial views, 16.1.4
partially orthogonal, 12.2.6
partition, 31.14.3
adapted function, 31.14.3
partition encoder, 31.9
partition encoding, 31.9
partitioned matrix inversion, 27.7.4
payment time, 19.1.3
payoff, 1.4
call option, 1.4.1
forward, 1.2.2
variance swap, 1.4.6
Pearson parametrization
Arrow-Pratt function, 7.5.1
perceptron, ??
performance "mean", 7.3.1
performance expectation, 7.3.1
performance mean-variance trade-off, 7.3.4
performance model
Black-Litterman, 47.1
performance variance, 7.3.2
permanent impact, 10.1.3
permanent market impact model, 10.1.3
persistence, 9c.2.3
Plancherel theorem, 29.4
point view, 16.1.2
point-in-time, 2.3.2
pointed, 30.1.3
Poisson process, 2.1.2, 44.1.4
polarization identity, 27.3.1
pool factor, 19.2.1
pooling
convolution, 36.4.1
portfolio, 6.1.1
portfolio rebalancing P&L, 24.2.3
portfolio weights, 24.4.2
positive homogeneity of first degree, 7.2
positive homogenous of degree, 7.2
positive homogenous of first degree, 7.2
posterior, 39.1.3, 39.1.3
Bayesian statistics, 31.3.2
posterior distribution, 38.2.1
Black-Litterman, 47.30
posterior error
Bayesian approach, 39.4.3
Bayesian estimation, 39.3.3
posterior predictive performance model
Black-Litterman, 47.33
posterior risk, 37.3.2
potential function
e-affine coordinates, 32.1.3
m-affine coordinates, 32.1.3
power spectrum
generalized, 29.6.1
generalized, matrix-valued, 29.6.2
integral, 29.6.1
proper, 29.6.1
proper, matrix-valued, 29.6.2
precision
binary classification, 14.3
precision matrix, 31.10.1
predicted negative probability, 14.3
predicted positive probability, 14.3
predicted variables, 13.1
prediction, 12.1
point, 13.2
point prediction, 39.3.1
predictive distribution, 39.4.1
probabilistic, 13.2
prediction model, 38.2.2
predictive, 9c.5.2
predictive distribution, 6.4.1
non-observable, 39.4.1
posterior, 38.2.2
predictor
point prediction, 39.3.1
predictive distribution, 39.4.1
premium, 1.10.2
prevalence of positive outcome, 14.3
price manipulation, 10.4.2
pricing kernel, 20a.2.1
pricing operator, 20a.1.1
pricing signals, 46.2.2
principal axes, 35.2.4
principal axis factorization, 12.4.2
principal component analysis, 35.2.3
sparse principal component analysis, 40.3
principal components, 35.2.3
principal directions, 35.2.3
principal factors, 35.2.3
principal variances, 35.2.3
principal-component linear factor model, 12.3
prior distribution, 38.2.1
Black-Litterman, 47.3
prior predictive distribution, 38.2.2
prior predictive performance model
Black-Litterman, 47.11
probabilistic classification, 15.2.5
probabilistic factor analysis, 15.3.1, 15.3.1
probabilistic graphical model, 15.3.4
probabilistic misclassification error, 15.2.1
probabilistic prediction error, 39.4.2
probabilities, 31.8
vector, 31.8
probability density function
multivariate, 31.1.3
univariate, 31.1.2
probability integral transform, 33.1
probability level, 31.1.2
probability mass function
multivariate, 31.1.3
univariate, 31.1.2
probability of default, 1.5.2
probability space, 31.1.1
events, 31.1.1
finite, 31.14.2
outcomes, 31.1.1
partition, 31.14.3
sample space, 31.1.1
sigma-algebra, 31.1.1
probit regression
binary, ??
product rule
multivariate, 28.1.2
univariate, 28.1.1
profit-and-loss, 19.1.6
projection
i-m-projection, 38.4.1
I-projection, 38.4.1
m-projection, 38.4.1
projection matrix, 12.2.6
Projection pursuit, 36.4.3
projection stochastic discount factor, 20a.2.3
proper scoring rules, 13.2
properties, 39.1.1
proportional hazards expectation, 7.8.2
proportional hazards principle, 21.2.3
proportional hazards transform, 7.8.2
proportional odds, 31.9.2
pseudo-inverse
left, 27.7.3
right, 27.7.3
pull-to-par, 1.10.2
pure endowment, 22.5.1
pure noise, 3.5.3
put-call parity, 20a.1.2
Pythagorean triangular identity, 27.3.3
Q
quadrangle, 7.4
quadratic discriminant analysis (QDA), ??
quadratic form
matrix, 27.2.4
quadratic programming, 30.1.6
quadratic variation, 3.10.4
quadratic-normal distribution, 31.5.1
quantile
multivariate, 35.5.3
multivariate p, 35.5.3
quantile (VaR) satisfaction measure, 7.6.1, 7.6.1
quantile function, 31.1.2
quantitative alpha, 9c
quantitative strategies, 9c
quotient rule
multivariate, 28.1.2
univariate, 28.1.1
R
r-squared
distributional , 12.1.1
generalized distributional, 12.1.1
generalized population, 12.1.1
population, 12.1.1
sample, 12.1.1
radial component, 31.7.3
Radon-Nikodym derivative, 31.14.1
numeraire, 20a.2.2
process, 20b.4.3
rain distribution, 10.4.3
random field, 42.1.9
Gaussian, 31.4.2
Gaussian Markov, 15.3.5
Markov, 15.3.5
random forest, 40.4.1
random time series, 3
random variable, 31.14.1
random vector, 31.14.1
random walk, 2.1
multivariate random walk, 2
rank
full, 27.2.1
linear transformation, 27.2
matrix, 27.2.1
ranking, 46.6.3
ranking-distortion, 46.6.3
ranking-median, 46.6.3
ranking-terciles, 46.6.3
real negative probability, 14.3
real positive probability, 14.3
realized information panel, 3
realized P&L, 19.1.6
portfolio, 24.2.1
realized time series, 3
realized variance, 1.4.6
recall, 14.3
receiver operating characteristic (ROC) curve, 14.3.4
receiver operating characteristic (ROC) function, 14.3.4
record date, 19.1.3
recovery rate, 1.5.2
reflection, 27.2.3
regression
cross-section, 12.5.5
generalized, 14.1.2
least absolute deviation regression, 14.2.2
least squares regression, 14.1.2
least squares, linear, 14.1.2
linear least absolute distance regression, 14.2.4
linear median regression, 14.2.4
linear quantile regression, 14.2.4
quantile regression, 14.2.2
regression linear factor model, 12.2
regret, 7.4
regret function, 37.2
regularization, 16.6.6
reinforcement modeling, 13.1
reinvested cumulative cash-flow, 19.1.4
reinvested instrument, 19.1.4
reinvestment function, 5.1
relative entropy, 32.1.4
relative marginal contributions, ??
relative score, 31.9
relative scores, 31.9.2
relative value, 9c
reproducing kernel Hilbert space, 29.7.2
reproducing property, 29.7.2
residuals, 12.1
responses, 13.1
Restricted P&L function
bonds, 5.3.3
European call options, 5.3.3
return on collateral, 24.4.3
return on equity, 24.4.3
return on exposure, 24.4.3
return on value, 24.4.3
return related characteristics, 9c.2
reversal, 46.3.1
Riccati root, 27.6.6
ridge, 30.1.6, 40.2.2
ridge regression, C.7.3.3
Riemann integrable, 28.4.3
Riemann integrable function, 28.4.1
Riemann integral
multivariate, 28.4.3
univariate, 28.4.1
Riemann sum
multivariate, 28.4.3
univariate, 28.4.1
Riemannian metric, 32.1.1
right-way risk, 6.2
risk aversion, 7.2
risk coverage ratio, 7.11.2
risk drivers, 1
risk drivers process, 4
risk market neutral, 9c.4.3
risk measure, 7
risk premia, 9c
arbitrage pricing theory, 20b.3.1
symmetry with arbitrage pricing theory, 12.5.6
risk premium, 7.2
risk 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, 20a.3.1
risk-neutral probability, 20a.3.1
roll down, 5.2.3
rolling value, 1.3.2, 1.4.2
rolling zero-coupon, 1.3.2
rotation, 27.3.4
round-trip, 10.4.2
rule-based strategies, 9c
running maximum, 24.6
S
sample correlation matrix, 3.2.2
sample covariance matrix, 3.2.2
sample mean, 3.2.2
sample standard deviation vector, 3.2.2
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 expansion, 6.6.1
scenario-probability distribution, 31.8
scenario-probability quantile, 31.8.6
scenarios
panel, 31.8
Schweizer-Wolff measure, 34.1.1
scoring function, 27.4.3
scoring rule, 13.2
sec-kernel-principal-components, 14.5.4
second order criterion, 30.1
second order differential, 28.1.2
matrix-variate, 28.1.3
second order dominance, 37.1.3
second-order cone programming, 30.1.5
security market line, 20a.4.2
segmentation, 36.3.1
selection problem, 30.2
selection set, 30.2
self-financing, 9c, 9c.2.2
self-similarity, 44.1.6
semidefinite cone, 30.1.4
semidefinite programming, 30.1.4
semideviation, 7.9.2
semideviation principle, 21.2.1
semivariance, 7.9.2
semivariance principle, 21.2.1
sensitivity, 14.3
sensitivity curve, 3.7.1
separable, 14.3.4
linearly separable, 14.3.5
sequential attribution, 8b.1.3
settlement date, 19.1.2
settlement period, 19.1.3
Shapley attribution, 8b.1.4
Sharpe ratio, 7.11.1
generalized, 35.1.1
shift filter, 45.5
shift parameters, 12.1
short holdings, 24.1, 24.1
short position, 22.1.2
short spot rate, 1.10.2
sifting property, 29.3.2
sigmoid, 31.455
sign, 1.8.1
signal, 46
signal beta, 9c.2.1
signal characteristic, 9c.2.1
signal characteristic matrix, 9c.4.2
signal characteristic portfolio, 9c.2.1
signal characteristic portfolios, 9c.4.2
signal flexible factor portfolio, 9c.2.2
signal-induced factor, 9c.1, 9c.2.1
signal-induced factors, 9c.4.2
signal-to-noise, 35.1.1
simulated path, 4.4
singular value, 27.5.4
singular value decomposition, 27.5.4
size signal, 46.5
skew signal, 46.5
skill, 9c.5.2
Sklar’s theorem, 33.2.3
slack variable, 30.3.1
slippage, 19.1.2
slippage model, 10.1.3
slippage P&L, 24.2.1
slope, 1.3.5
small capitalization stocks, 46.5
small minus big, 9c.1
smart beta, 9c
smile, 1.4.3
smile signal, 46.5
smirk, 1.4.3
smooth kernel probabilities, 3.1.2
smooth quantile, 31.8.7
smoothing, 46.6.1
inference, 13.3
softmax function, 31.10.2
softplus function, 31.10.2
solvency capital requirement, 7.12
solvency condition, 6.8.1
Sorensen-Dice coefficient
binary classification, 14.6
Sortino ratio, 7.11.2
span, 27.1.2
spanning set, 27.1.2
Spearman’s rho, 34.2.2
specificities, 12.4.5
specificity, 14.3
spectral density
generalized, 29.6.1
generalized, matrix-valued, 29.6.2
proper, 29.6.1
proper, matrix-valued, 29.6.2
spectral root, 27.6.4
spectral theorem, 27.5.2
spectrum, 3.5.2
satisfaction indices/risk measures, 7.8.1
splines, 36.3.1
spot curve, 1.10.2
spot rate, 1.10.2
spot swap, 1.3.1
spread, 1.3.6
square-dispersion, 35.5.3
affine equivariant, 35.4.2
modal, 35.4.2
square-root, 44.2.2
square-root rule, 4.7
stable distributions, 31.12.1
symmetric, 31.12.1
standard "beta", ??
standard Brownian motion, 44.1.3
standard deviation, 35.1.1
standard deviation principle, 21.2.1
standard error, 41.1.3
standard Wiener process, 44.1.3
standardized elliptical variable, 31.7.3
standardized holdings, 6.4.1
standardized invariants, 3.9.1
state crisp probabilities, 3.1.4
state process, 42.1
State space model
Probabilisitc linear, 45.4
state-space models, 17.9
static linear factor model, 12.1.4
static model, 17
mean-variance static model, 17
probabilistic static model, 17
stationary
covariance stationary, 45.1
strongly stationary, 42.1
statistical linear factor model, 12.3
statistics, 41.1.1
steepness, 1.3.5
stochastic discount factor, 20a.2.1
cumulative or process, 20b.4.2
probability measure, 20b.4.2
stochastic dominance, 37.1.1
stochastic mean, 2.1.3
stochastic time, 44.1.6
stochastic volatility, 2.1.3
stochastic volatility inspired, 1.4.5
strategy, 1.9
strict stochastic dominance, 37.1.1
strictly concave function
multivariate, 28.6.2
univariate, 28.6.1
strictly convex function
multivariate, 28.6.2
univariate, 28.6.1
string, 42.1.9
strips, 1.3.1
strong white noise, 42.1
structure, 13.1.1
Student t distribution, 31.6.1
sub-additivity, 7.2
sub-quantile function, 31.1.2
subordinator, 44.1.6
sufficient statistics, 31.10
sum-of-parts, 22.1.6
liquidation valuation, 22.1.5
super-additivity, 7.2
Supervised modeling, 13.1
supervised models
classification, 13.1.1
regression, 13.1.1
support vector machine, 14.3.7
surprise
maximum likelihood parameters, 38.1
survival probability, 1.6
Sylvester’s determinant theorem, 27.7.4
systematic, 12.1.3
systematic-idiosyncratic LFM, 12.1.3
T
t copula, 33.3.1
tangent vector, 32.1
target parameters, 15.1.3, 15.2.5
target variables, 13.1, 13.1, 16.1.1
target vector, 12.1
Taylor expansion
univariate, 28.3.1
Taylor polynomial
multivariate, 28.3.2
univariate, 28.3.1
Taylor series
univariate, 28.3.1
temporary impact, 10.1.3
temporary market impact, 10.1.3
tenor, 1.3.2
tercile, 46.6.3
test set, 40.1
through-the-cycle, 2.3.2
tick time, 1.8.2
tick time evolution, 1.8.3
tick-by-tick, 1.8.3
Tikhonov, 40.2.2
Tikhonov regularization, 30.1.6
time crisp probabilities, 3.1.4
time dependent transition matrix, 2.3.2
time horizon, 40.1
time to maturity, 1.3.2
time-changed risk driver, 1.8.3
time-homogeneous Markov chain, 2.3.1
time-homogeneous transition matrix, 2.3.1
time-inhomogeneous Markov chain, 2.3.2
times series, 39.1.1
timing P&L, 24.3
Toeplitz section
kernel, 29.3.3
matrix, 29.3.3
torsion, 8b.4
total net income, 6.7.2
total shares outstanding, 6.7.1
trace, 27.7.2
tracking error, 7.3.3
trading P&L, 24.2.2
trailing window, 3.2.4
training set, 39.3.5
transaction time, 19.1.2
transaction value, 19.1.2
transaction variables, 1.8.1
transition equation, 45.4
transition matrix
homogenous, arbitrary step, 44.3.1
inhomogenous, arbitrary step, 44.3.1
transition probabilities, 2.3.1, 2.3.1
translation invariance, 7.2
true negative
accuracy, 14.3
probability, 14.3
rate, 14.3
true positive
accuracy, 14.3
probability, 14.3
rate, 14.3
truncation, 12.1.4
turnover, 9c.2.3
two-fund separation theorem, ??
type I error, 14.3
type II error, 14.3
U
unanimity, 16.1.7
unbalanced, 40.1
uncertainty band
multivariate, 35.6.1
univariate, 35.1.2
uncovered interest rate parity, 5.1.2
underwater chart, 24.6
undirected graph, 15.3.4
uniform component, 31.7.3
uniform distribution, 31.6.3
uniform probabilities, 3.1
unit, assumptions-prediction
unit-root, 45.2.7
universal approximation theorem, 36.4.1
unrealized P&L, 19.1.6
portfolio, 24.2.1
unsupervised
clustering, 13.1.2
unsupervised modeling, 13.1
updated distribution, 16.1.2
updated state, 16.6.1
utility function, 7.5, 37.2
V
validation set, 39.3.5
valuation function, 22
valuation multiple, 22.2.2
value at risk, 7.6.1
value function, 5.1
value signal, 46.2.1
value stocks, 46.2.1
variability, 35.5.1
affine equivariant, 35.4.1, 35.4.2
variance, 35.1.1
variance minimization, 12.5.5
variance of the hypothetical means (VHM), 14.1.10
variance principle, 21.2.1
variance swap, 1.4.6
variation, 36.5
variation ratio, sec-lp-error
variational free energy, 38.4.5
varimax rotation, 12.4.4
vector, 27.1
coordinates, 27.1
subspace, 27.1.2
vector autoregressive of order one, 45.2.6
vector operations
addition, 27.1.1
scalar multiplication, 27.1.1
vector space, 27.1
vectorization, 27.7.1
half-, 27.7.1
inverse, 27.7.1
vertices, 15.3.4
view p-value, 16.2.2
view variables, 16.1.1
visualization function, 35.3.6
volatility clustering, 2.6
volatility function, 34.3
volume, 1.8.1
volume time, 1.8.2
volume time evolution, 1.8.3
volume-weighted-average-price (VWAP) strategy, 10.1.3
Voronoi diagram, 36.3.1
W
Wald statistic, 41.1.4
Wang distortion principle, 21.2.3
Wang expectation, 7.8.2
Wang transform, 7.8.2
weak dominance, 37.1.2
weak signals, 9c.5.2
weight of evidence, 15.3.3
white noise
weak
multivariate, 45.2.1
univariate, 45.2.1
Wiener-Hopf equations, 17.4.1
Wiener-Kolmogorov filter, 17.4
Williamson transform, 33.3.2
Wishart distribution, 31.6.7
within-cluster/group variance, 14.1.10
Wold theorem, 45.7
linearly deterministic component, 45.7
linearly regular component, 45.7
worst-case error, 39.1.2
frequentist approach, 39.4.2
frequentist prediction, 39.3.2
wrong-way risk, 5.1.5
credit value adjustment, 6.2
Y
yield, 1.10.2
yield curve, 1.3.3, 1.10.2
yield income, 5.2.3
yield to maturity, 1.3.3
Youden’s J statistic, 14.6
Yule-Walker equations, 17.14
Z
z-score, 35.1.1
multivariate absolute, 35.2.1
z-statistic, 41.1.3
zero curve, 1.10.2
zero rate, 1.10.2
zero-coupon bond, 1.3.1