Glossary

A

absence of arbitrage, 23a.1.3
absolute deviation
    median, 35.4.1
abstract Bayes theorem, 23b.5.4
accounting signals, 46.5
accrued interest, 22.2.1
action, 36.1.1
activation function, 13.2.7
activity time, 1.8.2
actual cash-flow, 22.2.3
actual exchange rate, 22.2.3
actual value, 22.2.3
adaptive execution algorithm, 10.3.2
additive, 30.11.1
additive models, 13.2.6
additivity, 49.1
admissible, 37.2.2
algebraic Riccati equation, 48.1.2
allocation policy, 6.5
alpha, 7.3.1
alternative beta, 9b
alternative hypothesis, 40.1
ancestor, 13.8.4
angle, 49.1
angle-covariance, 35.3.1
angle-expectation, 35.3.1
anti-monotonic, 33.2
arbitrage pricing theory, 23b.4.1
area under curve (AUC), 13.4.2
arithmetic Brownian motion with drift, 43.1.2
Arrow-Debreu securities, 23b.1.3
Arrow-Pratt absolute risk aversion function, 7.4.1
artificial neural networks, 13.2.7
ask size, 1.8.1
assets, 6.7.1
at the money, 1.4.2
    foreign exchange market, 1.4.4
augmented view variables, 14.5.3
autocorrelation
    function, 45.1
autocovariance
    function, 45.1
autoencoders, -va
autoregressive conditional duration, 2.6.2
autoregressive moving average process
    autoregressive
        multivariate (VAR), 45.2.5
        univariate (AR), 45.2.1
    causal
        multivariate, 45.2.5
        univariate, 45.2.3
    integrated
        multivariate (VARIMA), 45.2.5
        univariate (ARIMA), 45.2.4
    invertible
        multivariate, 45.2.5
        univariate, 45.2.3
    moving average
        multivariate (VMA), 45.2.5
    moving average
        univariate (MA), 45.2.2
    multivariate (VARMA), 45.2.5
    univariate (ARMA), 45.2.3
autoregressive of order one, 44.1
autoregressive-fractionally integrated-moving average of order (p,d,q), 2.4
auxiliary measure, 30.9

B

Bachelier, 25.3.1
backward cash-flow-adjusted value, 22.1.5
backward/forward exponential weighted moving average, 3.11.7
bag of words, 13.8.3
bagging, 16.5.1
balance sheet, 6.7.1
balanced, 16.1
bandpass filter, 45.6
bandwidth, 3.1.2
    kernel density estimate, 3.2.3
bandwidth matrix, 3.1.2
bank account, 23a.4.3, 23a.4.3
bank deposit, 23a.4.3
    discrete rebalancing, 23a.4.3
base case, 14.6.1
base distribution, 14.1.1
base measure, 30.9
basic linear pricing equation, 23a.2
basis, 27.4.3
basis denominator, 27.4.3
basis instruments, 23b.1.1
Bayes classifier, sec-opt-point-class
Bayes error
    expected error, 39.1.2
    frequentist approach, 39.4.2
    frequentist prediction, 39.3.2
Bayes risk, 36.1.2
Bayes theorem, 37.2.1
Bayesian decision, 36.1.3
Bayesian networks, 13.8.6
benchmark, 27.5.1
best ask, 1.8.1
best bid, 1.8.1
best prediction, 49.1
    linear, 35.3.4
best prediction operator
    linear, 35.3.4
beta-adjusted excess return, 27.5.2
bets, 8b.5
Bhattacharyya coefficient, 16.5.4
bias, 35.6.4, 39.1.2
bid size, 1.8.1
bid-ask spread, 10.1
bilateral value adjustment, 6.2
binary classification, 13.4
binary matrix, sec-opt-point-class
binning, 1.8.3
binomial tree, 2.1.2
bins, 1.8.3, 48.4.2
bins width, 48.4.2
Black-Merton-Scholes model, 25.3.2
Boltzmann machine, 13.7.6
bond yield, 5.3.2
bootstrap aggregating, 16.5.1
boundedness, 14.1.7
breadth, 9b.5.2
breakdown point, 3.7.2
Bregman divergence, 31.1.4
Brownian motion, 2.1.1
Buhlmann pricing equation, 23b.3
Buhlmann principle, 24.2.4
butterfly, 1.4.4

C

calendar signal, 46.5
calibrate, 25
call option, 1.4.1
canonical angle, 35.3.1
canonical basis property, 30.8.2
canonical correlation matrix, 33.4
canonical parameters, 30.9
capital asset pricing model, 23b.2
capital gain, 22.1.6
carry signal, 46.1
CART, 13.2.5
cash-flow function, 5.1
cash-flow-adjusted value, 22.1.5
categorical distributions, 30.8.1
categories, 30.8.1
Cauchy-Schwartz inequality, 49.1
causal, 45.6
center of the bin, 48.4.2
central difference discrete first order partial derivative, 5.3
central difference discrete second order partial derivative, 5.3
central tendency
    affine equivariance, 35.4.1, 35.4.1
    variational problem, 35.4.1
certainty-equivalent, 7.4
certainty-equivalent principle, 24.2.2
chain rule, 13.8.6
characteristic function
    multivariate, 30.1
    univariate, 30.1
characteristic matrix, 9b.4
characteristic portfolio, 9b.2
characteristic portfolios, 9b.4
Chebyshev’s inequality, 35.2.7
chi-squared distribution, 30.4
child, 13.8.4
child orders, 10
Cholesky decomposition, 48.1.3
CIR, 43.2.2
claims, 1.6
classes, 30.8.1
classical-equivalent, 37.2.2
classification, 13.1.2
classification and regression trees, 13.2.5
clean price, 22.2.1, 22.2.1
clique, 13.8.4
clique factorization, 13.8.5
clustering, 13.1.2, 13.5.4
co-monotonic, 7.2
    co-monotonicity, 33.2
    copula invariance, 32.2.4
co-monotonic additivity, 7.2
coarseness level, 48.4.2
codes, 13.5
coherent satisfaction measure, 7.7.1
cointegrated, 41.1
cointegrated space, 2.8.1
cointegration signal, 46.3.3
cointegration vector, 2.8.1
    multivariate process, 41.1
collateral, 6.9.1
commonalities, 12.4.5
comparable instruments, 25
complete, 23b.1.1
completeness pricing formula, 23b.1.2
compound distribution, 30.3.3
compound Poisson process, 43.1.3
compounded rate of return
    (instantaneous) compounded rate of return, 27.4.6
    average compounded rate of return, 27.4.6
compounded return, 27.4.1
concavity, 7.2
condition number, 3.5.2
conditional, 37.2.1
conditional cdf, 30.3.1
conditional covariance, 30.3.2
conditional distribution of variable, 30.3.1
conditional excess distribution, 7.5.2
conditional expectation, 30.3.2
conditional independence, 13.8, 16.1
conditional pdf, 30.3.1
conditional principal component analysis, 12.6.15
conditional principal directions, 12.6.15
conditional principal variances, 12.6.15
conditional probability, 30.3.1
conditional statistical feature, 30.3.2
conditional value at risk, 7.6.2
conditioning-marginalization, 14.1.3
cone, 47.1.1
conic programming, 47.1.1
conjugate distribution, 37.2.4
consistency with weak dominance, 7.2
consistent with q-th order dominance, 7.2
constancy, 7.2
constant proportion portfolio insurance, 9c.4.1
constructed variables
    unsupervised learning, 13.1.1
continuous-state distributions, 2.1
convex programming, 47.1
convex set, 47.1.1
convexity, 5.3.2
    risky investment, 9c.6
    satisfaction/risk measures, 7.2
convolution, 45.6
copula, 32.2.2, 32.2.2
copula-marginal combination, 32.4.2
copula-marginal distributions, 32.4.2
copula-marginal separation, 32.4.1
copula-pdf, 32.2.3, 32.2.3
Cornish-Fisher approximation, 7.5.2
Cornish-Fisher expansion, 7.5.2
correlation, 33.3
correlation function, 33.3
correlation matrix, 33.3
cost of equity, 25.2.1
cost of trading, 9c.6
counterparty credit risk, 6.2
coupon, 1.11.2
coupon bond, 1.3.1
CoVaR, 6.6.1
covariance inner product
    multivariate case, 35.3.2
covariance matrix, 35.2.1
covariance principle, 23a.6
covariance stationary, 45.1
covariant, 31.1.1
covariates, 13.1.1
Cramer representation, 45.5
Cramer-Lundberg ruin model, 6.7.5
credit ratings, 1.5.3
credit structural model, 127
credit value adjustment , 6.2
cross the spread, 10.1
cross-autocovariance
    function, 45.1
cross-sectional, 9b, 9b
cross-sectional LFM, 12.5
cross-sectional sample median of default probabilities, 1.5.3
cumulant , 4.7.1
cumulative cash-flow, 22.1.3
cumulative distribution function
    multivariate, 30.1
    univariate, 30.1
cumulative link, 30.8.9
cumulative monetary amount, 1.8.1
cumulative number of migrations, 1.5.3
cumulative P&L, 1.9
cumulative signed volume, 1.8.1
cumulative trade sign, 1.8.1
cumulative volume, 1.8.1
currency carry trade, 5.2.2
curvature, 1.3.5
curve/surface signals, 46.5

D

data, 36.1.1
debt, 6.7.1
debt value adjustment, 6.2
decay, 1.3.5
decision, 36.1.1
decodes, unsup-const-var
degree of reversal, 9b.2.3
delta, 1.4.4
dependent variables, 12.1, 13.1.1
descendant, 13.8.4
Dirac delta, 30.8.2
directed graph, 13.8.4
dirty price, 22.2.1
discount, 1.11.2
discount factor, 22.2.3
discount function, 5.1
discounted cash-flow, 25.2.1
discounted cash-flow adjusted value, 24.3.1
discounted payoff, 24.2
discrete Fourier transform, 48.4.1
discrete-state distributions, 2.1
discrete-state random walk, 2.1.2
discretized cf, 48.4.2
discretized pdf, 48.4.2
discriminant model
    point, 13.1.6
    probabilistic, 13.1.6
discriminant next-step model, 312, 313
dispersion
    affine equivariance, 35.4.1
    variational problem, 35.4.1
distance, 49.1
    Lp space, 35.316
distance to default, 2.3.4
distance-covariance, equiv-hilbert-spaces
distance-expectation, equiv-hilbert-spaces
distorted cdf
    satisfaction indices/risk measures, 7.6.1
distorted ex-ante performance
    satisfaction indices/risk measures, 7.6.1
distorted pdf
    satisfaction indices/risk measures, 7.6.1
distortion expectation, 7.6.1
distortion function, 7.6.1
distortion principle, 24.2.3
distributional view, 14.1.3
diversification distribution, 8b.5
diversity, 16.5.4
dividend-adjusted value, 1.1
    stocks, 22.1.5
divisors, 30.11.2
dollar duration, 6.1.3
dollar-neutral constraint, 9b.2.2
dominant-residual LFM, 12.1.2
drawdown, 27.6
    maximum (absolute) drawdown, 27.6
    maximum percentage drawdown, 27.6
    percentage drawdown, 27.6
dscrete-time Fourier transform, 48.4.1
dual Legendre, 31.1.3
dually flat, 31.1.3
duration, 5.3.2
DV01, 6.1.3
dynamic allocation, 9b
dynamic conditional correlation, 3.9.3
dynamic graphical models, 45.3
dynamic models, 15
dynamic principal component, 15.6
dynamic regression model, 15.4

E

e-affine coordinates, 31.1.2
e-flat, 31.1.2
e-geodesic, 31.1.2
EBITDA, 6.7.2
economic capital, 7.10
economic net income, 6.7.2
edges, 13.8.4
effective convexity, 5.3.2
effective delta, 5.3.3
effective duration, 5.3.2
effective key rate convexities, 5.3.2
effective key rates durations, 5.3.2
effective m-square, 5.3.2
effective number of bets, 8b.5
effective number of scenarios, 3.1.4
effective rank, 14.1.6
effective vega, 5.3.3
efficient market hypothesis, 2.1
elastic net, 16.2.2
ellipsoid, 35.2.4
    location-dispersion ellipsoid, 35.2.4, 35.2.4
ellipsoid principal axes, 35.2.4
ellipsoid test for invariance, 42.1
elliptical distribution, 30.7.1
encoder, 13.5
energy, 37.1.2
ensemble approach, 36.1.4
enterprise value, 25.2.2
entropy, 37.1.2
equilibrium performance model
    Black-Litterman, 38.11
equilibrium returns
    Black-Litterman, 38.7
equity, 6.7.1
equity book value, 6.7.1
ergodic (in mean), 41.1
Erlang process, 43.1.3
error, 35.6.4, 39.1.2
    multivariate, 3590
    posterior error, 39.1.3
    univariate, 3572
error correction, 44.2.3
error matrix, 35.3.4
Esscher principle, 24.2.4
estimable, 7.2
estimate, 39.1.1
    model risk, estimation risk, 36.1.4
estimation, 13.1.4
estimation model, 3.4
    Bayesian estimation, 37.2.2
estimation risk
    model risk, estimation risk, 36.1.4
estimation set, 16.1
    point prediction, 39.3.1
    predictive distribution, 39.4.1
estimation uncertainty, 37.2.2
estimator, 39.1.1
European-style derivatives, 1.4
evidence lower bound, 37.1.2
ex-dividend date, 22.1.2
exotic beta, 9b
expectation, ??
expectation inner product, equiv-hilbert-spaces
expectation rule, 30.8.3
expected drawdown, 7.3.1
expected overperformance, 7.3.1
expected shortfall
    sensu stricto, 7.6.2
expected utility, 7.4
expectile, 7.7.2
expectile-VaR, 7.7.2
expiry, 1.4
explanatory variables, 13.1.1
exponential decay probabilities, 3.1.1
exponential family distribution, 30.9
exponential kernel, 3.1.2
exponential of the entropy, 3.1.4
exponential principle, 24.2.2
exponential tilting, 14.1.5
exponentially weighted moving average, 3.11.6
exponentially weighted moving correlation, 3.11.6
exponentially weighted moving covariance, 3.11.6
exponentially weighted moving quantile, 3.11.6
exponentially weighted moving standard deviation, 3.11.6
exposure, 6.1.3
    portfolio P&L, 9b.2
    risky investment, 9c.6
exposure at default, 1.5.2
extreme value theory, 7.5.2

F

face value, 1.3.1
factor analysis, 3.5.6
factor analysis matrix, 3.5.6
factor loadings, 12.1
factor premia, 9b.4.2
factor premium, 9b.1
factor-replicating portfolios
    arbitrage pricing theory, 23b.4.1
factors, 9b, 13.1.1
    linear factor model, 12.1
fair value, 22.1.2
false negative rate, 13.4.1
false positive rate, 13.4.1
fast Fourier transform, 48.4
feature engineering, point-vs-prob
features, 13.1.1
    feature engineering techniques, ??
Feller condition, 43.5.1
filtering, 13.1.4
financial instrument, 22.1.1
first in, 8b.1.1
Fisher consistent, 3.7.1
Fisher discriminant analysis (FDA), 13.4.4
Fisher information distance, 31.1.4
flexible probabilities, 3
    estimation, 30.8
forecast, 12.1
forecasting
    inference, 13.1.4
foreign exchange function, 5.1
foreign exchange rate, 1.2.1
forward, 1.2.2
forward cash-flow-adjusted value, 22.1.5
forward difference discrete first order partial derivative, 5.3
forward exchange rate, 1.2.1
forward rate, 1.11.2
forward swap, 1.3.1
forward variance swap rate, 1.4.6
fourier integral, 48.4.1
Fourier series, 48.4.1
fractional Brownian motion, 43.4
fractional integrated process, 2.4
Frechet-Hoeffding bounds, 33.1.1
frequency response function, 45.6
    gain, 45.6
    phase, 45.6
frequentist risk, 36.1.2
full-investment, 9b.2.2
fully constrained LFM, 12.6.16
fundamental accounting equation, 6.7.1
fundamental law of active management, 9b.5.2
fundamental LFM, 12.5
fundamental signals, 46.5
fundamental theorem of asset pricing, 23a.3
funding risk, 6.2
funding value adjustment, 6.2

G

gamma distribution, 30.4
Gaussian kernel, 3.1.2, 3.2.3
Gaussian Markov random fields, 13.8.5
generalized autoregressive conditional heteroscedastic, 2.6.1
generalized excess return, 27.5.2
generalized linear models (GLM), 13.7.5
generalized linear return, 27.4.3
generalized method of moments with flexible probabilities (GMMFP) estimate, 3.6.2
    minimization, 3.6.3
generalized Pareto distribution, 7.5.2
generalized weight, 27.4.4
generative model, 13.1.6
generator, 43.3.1
generic position, 25.1.3
geometric Brownian motion, 5.1.1
Gibbs distribution, 13.8.5
Gini coefficient, 13.4.2
glasso, 3.5.5
    Tikhonov, 16.2.3
Glivenko-Cantelli theorem, 3.2.1
Gordon growth model, 25.2.1
grade, 32.1
grades, 32.2.1
gradient, 49.5.1
grand mean, 3.5.1
graph, 13.8.4
graphical lasso, 3.5.5
Greeks, 5.3
gross exposure, 6.1.3
growth stocks, 46.2.1

H

half-life, 3.1.1
half-vectorization, 49.3.1
Hamiltonians, 30.9
hazard function, 1.6
Hellinger distance, 16.5.4
Hermitian inner product, 48.4.1
Hessian, 49.5.2
Heston model, 25.3.3
hidden variables, 13.1.1
    maximum likelihood, 37.1.2
    point prediction, 39.3.1
high breakdown estimators, 3.7.2
high breakdown point with flexible probabilities, 3.7.2
high minus low, 9b.1
high water mark, 27.6
historical cdf, 3.2.1
historical distribution, 3.2.1
historical pdf, 3.2.1
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, ??
Hotelling statistic, 40.1.4
Hurst coefficient, 43.4
hybrid Monte Carlo-historical, 4.5.2

I

ice-cream cone, 47.1.3
idiosyncratic, 12.1.3
ill-conditioned, 3.5.2
implementation shortfall, 27.3
implied returns
    Black-Litterman, 38.7
implied volatility, 1.4.3
implied volatility surface, 1.4.3
impulse response, 45.6
in the money, 1.4.2
in-sample error, 16.16
inception, 27.2.2
income, 22.1.6
income statement, 6.7.2
independent component analysis, 13.5.6
independent variables, 13.1.1
    linear factor model, 12.1
inefficiency, 35.6.4, 39.1.2
inference, 13.1.4
infinitely divisible, 30.11.2
inflator, 23a.3
influence function, 3.7.1
information, 2.10.1
    random time series, 3
information coefficient, 9b.5.2
information generator, 2.10.1
information ratio, 35.1.3
    conditional information ratio, 9b.5.2
    maximum (l2-mean unconditional) information ratio, 9b.5.2
    maximum conditional information ratio, 9b.5.2
information set, 2.10.1
    geometric interpretation, 13.2.1
    linear, 35.3.4
information/view, 14.6.1
inner product, 49.1
innovation
    weak, 35.3.4
innovation function
    weak, 35.3.4
inputs, 13.1.1
instantaneous exchange rate, 22.2.3
instantaneous forward curve, 1.11.2
instantaneous forward rate, 1.11.2
instantaneous spot rate, 1.11.2
integral Fourier transform, 48.4.1
integrated, 41.1
integration operator, 34.4
intensity models, 25.4.2
interaction, 13.2.4
interest rate, 1.3.3
internal rate of return, 27.4.6
interquantile range, ??
intuitive r-squared, 12.5.5
invariance rule, 30.8.3
invariant, 35.4
invariants, 2
inverse transform sampling, 32.1
inverse vectorization, 49.3.1
inverse-call, 1.3.4
inverse-Wishart, 30.6.7
investment factor, 27.4.6
    reinvested instrument, 22.1.4
iso-contour, 35.6.1
isolated, 8b.1.1

J

jackknife estimator, 3.7.1
Jacobian, 49.5.1
James-Stein estimator, 3.5.1
Jeffreys prior, 37.2.2
joint model, 36.1.2
joint scenario, 30.8
jump rule, 22.1.3

K

k-fold, ??
k-means clustering, 13.5.4
kappa ratio, 7.8.2
Karhunen-Loeve representation, ??
Kendall’s tau, 33.2.1
kernel, 3.2.3
kernel density estimate, 3.2.3
kernel stochastic discount factor, 23a.2.1
kernel trick, 49.2.4
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, 42.1
Krigin, ??
Kullback-Leibler divergence, 31.1.4

L

L2-space, geometry-of-random-variables
labels, 13.1.1
lag operator
    order p, 49.4
lag operator of order p, 49.4
Laplace approximation, ??
large capitalization stocks, 46.5
lasso, 16.2.2
lasso regression, 16.3.2
last in, 8b.1.2
last transaction price, 1.8.1
latent variables, 13.1.1
    maximum likelihood, 37.1.2
law invariant, 7.2
law of one price, 23a.1.1
law of total expectation, 30.3.2
Law of total variance
    expected value of the process variance (EVPV), 30.54
    variance of the hypothetical means (VHM), 30.54
law of total variance, 30.3.2
LDL decomposition, 48.1.3
learning, 13.1.4
leave-1-out, ??
leave-p-out, ??
leaves, 13.2.5
Legendre transformation, 31.1.3
length, 49.1
    Lp space, 35.5.1
length-covariance, 35.3.1
length-expectation, 35.3.1
level, 1.3.5
leverage, 6.1.4
leverage effect, 2.6.2
Levinson filtering, 15.14
Levy process, 43.1.1
Levy-Khintchine, 43.1.4
liabilities, 6.7.1
Libor, 1.11.2
likelihood, 3.4
    Bayesian estimation, 37.2.2
    estimators as random variables, 39.1.2
    maximum likelihood, 37.1.1
limit order book, 1.8.1
limit order placement, 10.3
linear classification, 13.170
linear discriminant analysis (LDA), ??
linear factor model, 12.1
linear observation equation, 45.3
linear programming, 47.1.5
linear regression, 13.7.2
linear return, 27.4.1
linear state space model, 45.3
linear time invariant filter, 45.6
linear transition equation, 45.3
linearity, 23a.1.2, 49.1
linearly constrained quadratic programming problem, 47.1.4
linearly separable, 13.172
link function, exponential-family-distributions
linkage matrix, 9b.5.2
liquidation, 27.2.2
liquidation valuation, 25.1.5
liquidity curve, 10.1
    "market buy" liquidity curve, 10.1
    "market sell" liquidity curve, 10.1
local Markov property, 13.8.6
location, 35.4.1
    affine equivariance, 35.4.1, 35.1.3
log-partition function
    exponential family distribution, 30.9
log-return, 27.4.6
log-sum-exp function, 30.9.2
logarithmic score, 13.1.3
logistic function, 30.403
logistic regression, 13.7.3
logit function, 30.9.2
lognormal distribution, 30.4
    shifted, 30.4
long holdings, 27.1
long memory, 2.4
long position, 25.1.1
longitudinal data, 16.1
Lorentz cone, 47.1.3
Lorenz curve, 30.13
loss, 35.6.4, 36.1.1
loss given default, 1.5.2
lower partial moment, 7.7.2
lower partial moment principle, 24.2.1
Lp-space, 35.5.1

M

m-affine coordinates, 31.1.2
m-flat, 31.1.2
m-geodesic, 31.1.2
m-square, 5.3.2
macro signals, 46.5
macroeconomic LFM, 12.2
Mahalanobis distance, 35.2.4
Marchenko-Pastur distribution, 3.5.3
marginal, 37.2.1
marginal cdf, 30.2
marginal characteristic function, 30.2
marginal contributions, 8b.2
marginal distribution, 30.2
marginal pdf, 30.2
marginal supply demand curve, 10.1
marked-to-market, 22.1.2
marked-to-model, 22.1.2
market beta, 9b.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, 25
market portfolio, 9b.1
market price of risk, 23a.5.1
Markov process, 41.1
    time homogenous, 41.1
Markov random fields, 13.8.5
MARS
    multivariate adaptive regression splines, 13.2.6
martingale, 41.1
matrix polynomial, 49.4
    autoregressive multivariate, 45.2.5
    autoregressive univariate, 45.2.1
    moving average multivariate, 45.2.5
    moving average univariate, 45.2.2
maturity, 1.3.1
maximum a posteriori, 37.2.2
maximum deviation, 35.4.1
maximum likelihood factorization, 13.8.1
maximum likelihood parameters, 37.1.1
maximum likelihood with flexible probabilities, 3.3.1
    normal assumption, 17.2.2
    Student t assumption, 17.2.3
maximum likelihood with flexible probabilities (MLFP) estimate, 3.3.1
maximum likelihood with flexible probabilities (MLFP) predictor, 39.3.4
mean absolute deviation, 35.4.1
mean reversion, 2.2
mean-lower partial moment, 7.7.2
mean-semideviation, 7.7.2
measure of concordance, 33.2
measure of dependence, 33.1
measurement equation, 15.9, 45.3
median, ??
Mercer kernel, 49.2.4
Mercer’s theorem, 49.2.4
method of moments (MM) estimate, 3.6.1
method of moments with flexible probabilities (MMFP) estimate, 3.6.1
metric geodesic, 31.1.2
Metropolis-Hastings algorithm, 48.2.1
microprice, 1.8.1
mid-quote, 1.8.1
midrange, 35.4.1
minimax decision, 36.1.2
minimum-torsion bets, 8b.5.2
minimum-torsion exposures, 8b.5.2
minimum-torsion transformation, 8b.5.2
misclassification error, 35.4.1
mixture distribution, 13.8.2
modal dispersion, ??
modal square-dispersion, ??
mode, ??
    n-dimensional random variable, ??
model
    Bayesian approach, 36.1.3
    frequentist approach, 36.1.2, 39.4.2
    frequentist prediction, 39.3.2
model risk, 36.1.4
model uncertainty, 36.1.4
moment generating function, 30.1
    multivariate, 30.1
momentum, 46.3.1
momentum signal, 46.3.1
money multiple, 27.4.6
money-equivalence, 7.2
moneyness, 1.4.2
monotonicity, 7.2, 14.1.7
mortgage backed securities, 22.2.1
multinomial logit function, 30.9.2
multiple, 22.1.4
multiple of invested capital (MOIC), 27.4.5
multivariate adaptive regression splines, 13.2.6
multivariate arithmetic Brownian motion, 9c.1.1
multivariate expectation, 35.2.1
multivariate Gaussian, 3.1.2
multivariate generalized autoregressive conditional heteroscedastic, 2.7.1
multivariate geometric Brownian motion, 9c.1.1
multivariate location
    variational, 35.5.7
multivariate Ornstein-Uhlenbeck, 44.4
multivariate random-walk, 2.7
multivariate square-dispersion
    variational, 35.5.7
multivariate uncertainty band, 35.6.1

N

naive Bayes classifiers, 13.8.3
naive Bayes models, 13.8.3
natural form, 30.9
natural parameters, 30.9
neighbors, 13.8.4
nested simulation, 5.5.2
net asset value, 25.1.4
net exposure, 6.1.3
neural network
    convolutional, 13.2.7
    deep, 13.2.7
neuron, 13.2.7
    convolution, 13.2.7
neutralization, 46.6.3
Neyman-Pearson lemma, 13.4.1
nodes, 13.8.4
non-parametric cross-sectional, 12.5.7
non-parametric principal component, 12.3.7
norm, 49.1
    Lp space, 35.5.1
norm symmetric, 32.3.2
normal copula, 32.3.1
normal distribution, 30.4
normal-inverse-Wishart (NIW) distribution, 3.4.2
normalized empirical histogram, 48.4.2
normalized heights, 48.4.2
normalized value characteristics, 9b.2
normalizing and variance stabilizing, 2.6.3
notional value, 1.3.1
nowcasting
    inference, 13.1.4
null hypothesis, 40.1
number of obligors, 1.5.3
numeraire, 23a.3
numeraire probability measure, 23a.3

O

observable
    decision theory, 36.1.1
    unsupervised learning, 13.1.1
observable features, 25
observable variables, 13.1.1, 37.1.1
observation equation, 15.9
offset cash, 27.4.4
omega ratio, 7.8.2
one-hot encoding, 30.8.1
operational loss, 1.7
operations, 1.7
opportunity cost, 36.1.1
optimal loadings, 35.3.4
optimal shifts, 35.3.4
option-based portfolio insurance, 9c.3
order placement, 10
order q dominance, 34.4
order routing, 10
order scheduling, 10
ordinal variables, 30.8.9
ordinary least squares, 12.2.7
ordinary least squares with flexible probabilities, 12.2.7
Ornstein-Uhlenbeck, 44.3
orthogonal, 49.1
orthogonal projection, 49.1
orthogonal to a linear space, 49.1
orthogonal-increment process, ??
out of the money, 1.4.2
out-of-sample error, 16.22
outputs, 13.1.1
outstanding order vector, 10.3
overnight index swap, 1.11.2

P

p-norm, 35.5.1
    standard Euclidean norm, 35.5.1
    taxicab norm, 35.5.1
p-value, 40.1.2
P&L, 22.1.6
    conditional ex-ante, 5.1
    pricing function, 5
P&L linearity, 27.1
P&L related exposures, 9b.2
pair-wise Markov property, 13.8.5
panel data, 16.1
paper P&L, 27.2.1
par, 1.11.2
par (swap) curve, 1.11.2
par (swap) rate, 1.11.2
par rate, 1.11.2
par swap rate, 1.3.1
parent, 13.8.4
parent order, 10
partial correlation, 33.4
partial views, 14.1.4
payment time, 22.1.3
payoff, 1.4
    call option, 1.4.1
    forward, 1.2.2
    variance swap, 1.4.6
Pearson parametrization
    Arrow-Pratt function, 7.4.1
perceptron, ??
performance "mean", 7.3.1
performance expectation, 7.3.1
performance mean-variance trade-off, 7.3.4
performance model
    Black-Litterman, 38.1
performance variance, 7.3.2
permanent impact, 10.1.3
permanent market impact model, 10.1.3
persistence, 9b.2.3
point statement, 13.1.3
point view, 14.1.2
point-in-time, 2.3.2
pointed, 47.1.1
Poisson process, 2.1.2, 43.1.3
polarization identity, 49.1
polynomial lag operator, 49.4
pool factor, 22.2.1
pooling
    convolution, 13.2.7
portfolio, 6.1.1
portfolio rebalancing P&L, 27.2.3
portfolio weights, 27.4.2
positive definiteness, 49.1
positive homogeneity of first degree, 7.2
positive homogenous of degree, 7.2
positive homogenous of first degree, 7.2
posterior, 39.1.3, 37.1.2, 39.1.3
    Bayesian statistics, 37.2.1
posterior distribution, 37.2.2
    Black-Litterman, 38.30
posterior error
    Bayesian approach, 39.4.3
    Bayesian estimation, 39.3.3
posterior predictive performance model
    Black-Litterman, 38.33
posterior risk, 36.1.3
potential function
    e-affine coordinates, 31.1.3
    m-affine coordinates, 31.1.3
precision matrix, 30.9.1
predicted variables, 13.1.1
prediction, 12.1
    inference, 13.1.4
    point prediction, 39.3.1
    predictive distribution, 39.4.1
prediction model, 37.2.3
predictive, 9b.5.2
predictive distribution, 6.4.1
    non-observable, 39.4.1
    posterior, 37.2.3
predictor
    point prediction, 39.3.1
    predictive distribution, 39.4.1
premium, 1.11.2
price manipulation, 10.5.2
pricing kernel, 23a.2
pricing operator, 23a.1.1
pricing signals, 46.2.2
principal axis factorization, 12.4.4
principal component analysis, 35.2.3
    sparse principal component analysis, 16.4
principal components, 35.2.3
principal directions, 35.2.3
principal factors, 35.2.3
principal variances, 35.2.3
principal-component, 12.3
prior distribution, 37.2.2
    Black-Litterman, 38.3
prior predictive distribution, 37.2.3
prior predictive performance model
    Black-Litterman, 38.11
probabilistic factor analysis, 13.8.1
probabilistic graphical model, 13.8.4
probabilistic prediction error, 39.4.2
probabilistic statement, 13.1.3
probabilities, 30.8
probability density function
    multivariate, 30.1
    univariate, 30.1
probability level, 30.1
probability mass function, 30.8.3
    multivariate, 30.1
    univariate, 30.8.3
probability of default, 1.5.2
probit model, 13.7.3
profit-and-loss, 22.1.6
projection stochastic discount factor, 23a.2.2
properties, 39.1.1
proportional hazards expectation, 7.6.2
proportional hazards principle, 24.2.3
proportional hazards transform, 7.6.2
proportional odds, 30.8.9
pseudo inverse, 49.3.1
pull-to-par, 1.11.2
pure endowment, 25.5.1
pure noise, 3.5.3
put-call parity, 23a.1.2
Pythagorean triangular identity, 49.1