IntroductionI. The “Checklist”II. Factor models and learningIII. ValuationIV. Performance analysisV. Quant toolboxGlossary
A
absence of arbitrage, 25a.1.3
abstract Bayes theorem, 25b.5.4
accounting signals, 50.5
accrued interest, 24.2.1
action, 37.1.1
activity time, 1.8.2
actual cash-flow, 24.2.3
actual exchange rate, 24.2.3
actual value, 24.2.3
adaptive execution algorithm, 10.3.2
additive, 32.8.1
additive model, 18.3
admissible, 38.2.2
affine equivariant, 47.1.1
Mahalanobis distance, 47.2.3
algebraic Riccati equation, 52.2.2
allocation policy, 6.4
alpha, 7.3.1
alternative beta, 9b
alternative hypothesis, 42.1
ancestor, 16.7.4
angle
multivariate case: covariance product, 48.2
random variable, 48.1
anti-monotonic, 35.2
arbitrage pricing theory, 25b.4.1
area under curve (AUC), 16.4.3
arithmetic Brownian motion with drift, 45.1.2
Arrow-Debreu securities, 25b.1.3
Arrow-Pratt absolute risk aversion function, 7.4.1
ask size, 1.8.1
assets, 6.6.1
at the money, 1.4.2
foreign exchange market, 1.4.4
augmented view variables, 39.5.3
autocorrelation, 49.2.1
autocovariance, 49.2.1
autoregressive conditional duration, 2.6.2
autoregressive integrated moving average of order (p,d,q), 2.2.5
autoregressive moving average of order (p,q), 2.2.4
autoregressive of order one, 46.1
autoregressive of order p, 2.2.2
autoregressive p-th degree polynomial, 2.2.2
autoregressive-fractionally integrated-moving average of order (p,d,q), 2.4
auxiliary measure, 32.6
B
Bachelier, 27.3.1
backward cash-flow-adjusted value, 24.1.5
backward/forward exponential weighted moving average, 3.11.7
bag of words, 16.7.3
bagging, 18.5.1
balance sheet, 6.6.1
balanced, 41.1
bandpass filter, 49.4
bandwidth, 3.1.2
kernel density estimate, 3.2.3
bandwidth matrix, 3.1.2
base case, 39.6.1
base distribution, 39.1.1
base measure, 32.6
basic linear pricing equation, 25a.2
basis, 29.4.3
basis denominator, 29.4.3
basis instruments, 25b.1.1
Bayes classifier, sec-opt-point-class
Bayes error
expected error, 41.2.2
frequentist approach, 41.5.2
frequentist prediction, 41.4.2
Bayes risk, 37.1.2
Bayes theorem, 38.2.1
Bayesian decision, 37.1.3
Bayesian networks, 16.7.6
benchmark, 29.5.1
best approximation, 48.5
best ask, 1.8.1
best bid, 1.8.1
beta-adjusted excess return, 29.5.2
bets, 8b.5
Bhattacharyya coefficient, 18.5.4
bias, 41.2.2
bid size, 1.8.1
bid-ask spread, 10.1
bilateral value adjustment, 6.8.2
binary classification, 16.4
binary matrix, sec-opt-point-class
binning, 1.8.3
binomial tree, 2.1.2
bins, 1.8.3, 52.5.1
bins width, 52.5.1
Black-Litterman posterior predictive, 40.3.2
Black-Litterman prior predictive, 40.1.2
Black-Merton-Scholes model, 27.3.2
Boltzmann machine, 18.1.6
bond yield, 5.3.2
bootstrap aggregating, 18.5.1
boundedness, 39.1.7
breadth, 9b.5.2
breakdown point, 3.7.2
Bregman divergence, 33.1.4
Brownian motion, 2.1.1
Buhlmann pricing equation, 25b.3
Buhlmann principle, 26.2.4
butterfly, 1.4.4
C
calendar signal, 50.5
calibrate, 27
call option, 1.4.1
canonical basis property, 32.5.2
canonical parameters, 32.6
capital asset pricing model, 25b.2
capital gain, 24.1.6
carry signal, 50.1
CART, 18.1.3
cash-flow function, 5.1
cash-flow function for a stock, 5.1.1
cash-flow function for defaultable instruments, 5.1.5
cash-flow function of a bond, 5.1.3
cash-flow function of a call option, 5.1.4
cash-flow-adjusted value, 24.1.5
categorical distributions, 32.5.1
categories, 32.5.1
Cauchy-Schwartz inequality, 48.1
causal, 49.4
cdf
multivariate, 32.1
center of the bin, 52.5.1
central difference discrete first order partial derivative, 5.3
central difference discrete second order partial derivative, 5.3
central distance, 48.3
random variable, 48.1
central length, 48.2
central tendency, 47.1
central tracking error, 48.3
random variable, 48.1
certainty-equivalent, 7.4
certainty-equivalent principle, 26.2.2
chain rule, 16.7.6
characteristic function
multivariate, 32.1
univariate, 32.1
characteristic matrix, 9b.4
characteristic portfolio, 9b.2
characteristic portfolios, 9b.4
child, 16.7.4
child orders, 10
Cholesky decomposition, 52.2.3
CIR, 45.2.2
claims, 1.6
classes, 32.5.1
classical-equivalent, 38.2.2
classification, 16.2.2
classification and regression trees, 18.1.3
clean price, 24.2.1, 24.2.1
clique, 16.7.4
clique factorization, 16.7.5
clustering, 16.2.2
co-monotonic, 7.2
co-monotonicity, 35.2
copula invariance, 34.2.4
co-monotonic additivity, 7.2
coarseness level, 52.5.1
codes, 16.6
coherent satisfaction measure, 7.7.1
cointegrated, 43.1
cointegrated space, 2.8.1
cointegration signal, 50.3.3
cointegration vector, 2.8.1
multivariate process, 43.1
collateral, 6.8.1
commonalities, 12.4.5
comparable instruments, 27
complete, 25b.1.1
completeness pricing formula, 25b.1.2
compound distribution, 32.3.3
compound Poisson process, 45.1.3
compounded rate of return
(instantaneous) compounded rate of return, 29.4.6
average compounded rate of return, 29.4.6
compounded return, 29.4.1
concavity, 7.2
condition number, 3.5.2
conditional, 38.2.1
conditional cdf, 32.3.1
conditional covariance, 32.3.2
conditional distribution of variable, 32.3.1
conditional excess distribution, 7.5.2
conditional expectation, 32.3.2
conditional independence, 41.1
conditional pdf, 32.3.1
conditional principal component analysis, 14.4
conditional principal directions, 14.4
conditional principal variances, 14.4
conditional probability, 32.3.1
conditional statistical feature, 32.3.2
conditional value at risk, 7.6.2
sensu lato, 7.6.2
conditionally uncorrelated, 16.1.3
conditioning-marginalization, 39.1.3
cone, 51.1.1
conic programming, 51.1.1
conjugate distribution, 38.2.4
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, 16.2.1
continuous-state distributions, 2.1
convex, 51.1.1
convex programming, 51.1
convexity, 5.3.2
risky investment, 9c.6
satisfaction/risk measures, 7.2
convolution, 49.4
copula, 34.2.2, 34.2.2
copula-marginal combination, 34.4.2
copula-marginal distributions, 34.4.2
copula-marginal separation, 34.4.1
copula-pdf, 34.2.3, 34.2.3
Cornish-Fisher approximation, 7.5.2
Cornish-Fisher expansion, 7.5.2
correlation matrix, 35.3
cost of equity, 27.2.1
cost of trading, 9c.6
counterparty credit risk, 6.8.2
coupon, 1.11.2
coupon bond, 1.3.1
CoVaR, 6.5.1
covariance inner product, 48.1
multivariate case, 48.2
covariance matrix, 47.3.1
covariance principle, 25a.6
covariant, 33.1.1
covariates, 16.2.1
Cramer representation, 49.3
Cramer-Lundberg ruin model, 6.6.5
credit ratings, 1.5.2
credit value adjustment , 6.8.2
cross the spread, 10.1
cross-autocovariance, 49.2.1
cross-sectional, 9b, 9b
cross-sectional LFM, 12.5
cross-sectional sample median of default probabilities, 1.5.2
cumulant , 4.6.1
cumulative cash-flow, 24.1.3
cumulative distribution function, 32.1
cumulative link, 32.5.9
cumulative monetary amount, 1.8.1
cumulative number of migrations, 1.5.2
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, 50.5
D
data, 37.1.1
debt, 6.6.1
debt value adjustment, 6.8.2
decay, 1.3.5
decision, 37.1.1
decodes, 16.6
degree of reversal, 9b.2.3
delta, 1.4.4
dependent variables, 12.1, 16.2.1
descendant, 16.7.4
Dirac delta, 32.5.2
directed graph, 16.7.4
dirty price, 24.2.1
discount, 1.11.2
discount factor, 24.2.3
discount function, 5.1
discounted cash-flow, 27.2.1
discounted cash-flow adjusted value, 26.3.1
discounted payoff, 26.2
discrete Fourier transform, 49.1
discrete-state distributions, 2.1
discrete-state random walk, 2.1.2
discretized cf, 52.5.1
discretized pdf, 52.5.1
discriminant model, 16.2.4
discriminant next-step model, 319, 320
dispersion, 47.1
distance, 48.5
distance to default, 1.5.4
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, 26.2.3
distribution, 32.1
distributional r-squared, 48.6.1
distributional view, 39.1.3
diversification distribution, 8b.5
diversity, 18.5.4
dividend-adjusted value, 1.1
stocks, 24.1.5
divisors, 32.8.2
dollar duration, 6.1.3
dollar-neutral constraint, 9b.2.2
dominant-residual LFM, 12.1.1
drawdown, 29.6
maximum (absolute) drawdown, 29.6
maximum percentage drawdown, 29.6
percentage drawdown, 29.6
dscrete-time Fourier transform, 49.1
dual Legendre, 33.1.3
dually flat, 33.1.3
duration, 5.3.2
DV01, 6.1.3
dynamic allocation, 9b
dynamic conditional correlation, 3.9.2
dynamic graphical models, 13.9
dynamic linear factor model, 13.7
dynamic principal component model, 13.8
dynamic regression model, 13.7
E
e-affine coordinates, 33.1.2
e-flat, 33.1.2
e-geodesic, 33.1.2
EBITDA, 6.6.2
economic capital, 7.10
economic net income, 6.6.2
edges, 16.7.4
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, 39.1.6
effective vega, 5.3.3
efficient market hypothesis, 2.1
eigenvalues, 2.9.6
elastic net, 18.4.1
ellipsoid, 47.2.2
location-dispersion ellipsoid, 47.2.2
ellipsoid principal axes, 47.2.2
ellipsoid test for invariance, 44.1
elliptical distribution, 32.4.1
encoder, 16.6
energy, 38.1.2
ensemble approach, 37.1.4
enterprise value, 27.2.2
entropy, 38.1.2
equilibrium returns, 40.1.1
equity, 6.6.1
equity book value, 6.6.1
ergodic (in mean), 43.1
Erlang process, 45.1.3
error, 41.2.2
posterior error, 41.2.3
error correction, 46.2.3
Esscher principle, 26.2.4
estimable, 7.2
estimate, 41.2.1
model risk, estimation risk, 37.1.4
estimation model, 3.4
Bayesian estimation, 38.2.2
estimation risk
model risk, estimation risk, 37.1.4
estimation set, 41.1
point prediction, 41.4.1
predictive distribution, 41.5.1
estimation uncertainty, 38.2.2
estimator, 41.2.1
European-style derivatives, 1.4
evidence lower bound, 38.1.2
ex-ante P&L, 5.1, 5.1
exotic beta, 9b
expectation, 47.1.2
expectation inner product, 48.3
expectation rule, 32.5.3
expected drawdown, 7.3.1
expected overperformance, 7.3.1
expected shortfall, 7.6.2
sensu stricto, 7.6.2
expected utility, 7.4
expectile, 7.7.2
expectile-VaR, 7.7.2
expiry, 1.4
explanatory variables, 16.2.1
exponential decay probabilities, 3.1.1
exponential family distribution, 32.6
exponential kernel, 3.1.2
exponential of the entropy, 3.1.4
exponential principle, 26.2.2
exponential tilting, 39.1.5
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, 1.5.1
extreme value theory, 7.5.2
F
face value, 1.3.1
factor analysis, 52.1
factor analysis matrix, 52.1
factor loadings, 12.1
factor premia, 9b.4.2
factor premium, 9b.1
factor-replicating portfolios
arbitrage pricing theory, 25b.4.1
factors, 9b, 16.2.1
linear factor model, 12.1
fair value, 24.1.2
false negative rate, 16.4.1
false positive rate, 16.4.1
fast Fourier transform, 52.5
features, 16.2.1
feature engineering techniques, 18.1.5
Feller condition, 45.5.1
financial instrument, 24.1.1
first in, 8b.1.1
Fisher consistent, 3.7.1
Fisher information distance, 33.1.4
flexible probabilities, 3
estimation, 32.5
forecast, 12.1
foreign exchange function, 5.1
foreign exchange rate, 1.2.1
forward, 1.2.2
forward cash-flow-adjusted value, 24.1.5
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, 49.1
Fourier series, 49.1
fractional Brownian motion, 45.4
fractional integrated process, 2.4
Frechet-Hoeffding bounds, 35.1.1
frequency response, 49.4
frequentist risk, 37.1.2
from a linear space, 48.5
full-investment, 9b.2.2
fully constrained LFM, 14.5
fundamental accounting equation, 6.6.1
fundamental law of active management, 9b.5.2
fundamental LFM, 12.5
fundamental signals, 50.5
fundamental theorem of asset pricing, 25a.3
funding risk, 6.8.2
funding value adjustment, 6.8.2
G
gain, 49.4
Gaussian kernel, 3.1.2, 3.2.3
Gaussian Markov random fields, 16.7.5
generalized autoregressive conditional heteroscedastic, 2.6.1
generalized distributional r-squared, 48.6.1
generalized excess return, 29.5.2
generalized linear models (GLM), 16.5.4
generalized linear return, 29.4.3
generalized method of moments with flexible probabilities (GMMFP) estimate, 3.6.2
minimization, 3.6.3
generalized Pareto distribution, 7.5.2
generalized population r-squared, 48.6.1
generalized weight, 29.4.4
generative model, 16.2.4
generator, 45.3.1
generic position, 27.1.3
geometric Brownian motion, 5.1.1
Gibbs distribution, 16.7.5
Gini coefficient, 16.4.3
glasso, 3.5.5
Tikhonov, 18.4.2
Glivenko-Cantelli theorem, 3.2.1
Gordon growth model, 27.2.1
grade, 34.1
grades, 34.2.1
gradient, 53.3.1
grand mean, 3.5.1
graph, 16.7.4
graphical lasso, 3.5.5
Greeks, 5.3
gross exposure, 6.1.3
growth stocks, 50.2.1
H
half-life, 3.1.1
half-vectorization, 53.2.1
Hamiltonians, 32.6
hazard function, 1.6
Hellinger distance, 18.5.4
Hermitian inner product, 49.1.1
Hessian, 53.3.2
Heston model, 27.3.3
hidden variable, 2.6.3
hidden variables, 16.2.1
maximum likelihood, 38.1.2
point prediction, 41.4.1
high breakdown estimators, 3.7.2
high breakdown point with flexible probabilities, 3.7.2
high minus low, 9b.1
high water mark, 29.6
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, 41.4.4
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, 41.4.4
historical with flexible probabilities (HFP) quantile, 3.7.2
historical with flexible probabilities (HFP) standard deviation vector, 3.2.2
hold-out, ??
Hotelling statistic, 42.1.4
Hurst coefficient, 45.4
hybrid Monte Carlo-historical, 4.4.2
I
ice-cream cone, 51.1.3
idiosyncratic, 12.1.2
ill-conditioned, 3.5.2
implementation shortfall, 29.3
implied returns, 40.1.1
implied volatility, 1.4.3
implied volatility surface, 1.4.3
impulse response, 49.4
in the money, 1.4.2
inception, 29.2.2
income, 24.1.6
income statement, 6.6.2
independent component analysis, 16.6.4
independent variables, 16.2.1
linear factor model, 12.1
inefficiency, 41.2.2
infinitely divisible, 32.8.2
inflator, 25a.3
influence function, 3.7.1
information, 2.11.1
random time series, 3
information coefficient, 9b.5.2
information generator, 2.11.1
information ratio, 47.1.1
conditional information ratio, 9b.5.2
maximum (l2-mean unconditional) information ratio, 9b.5.2
maximum conditional information ratio, 9b.5.2
information set, 2.11.1
geometric interpretation, 16.3.1
information/view, 39.6.1
inputs, 16.2.1
instantaneous exchange rate, 24.2.3
instantaneous forward curve, 1.11.2
instantaneous forward rate, 1.11.2
instantaneous spot rate, 1.11.2
integral Fourier transform, 49.1.1
integrated, 43.1
integration operator, 36.4
intensity models, 27.4.2
interaction, 18.1.1
interest rate, 1.3.3
internal rate of return, 29.4.6
interquantile range, 47.1.2
intuitive r-squared, 12.5.5
invariance rule, 32.5.3
invariant, 47.2.3
invariants, 2
inverse transform sampling, 34.1
inverse vectorization, 53.2.1
inverse-call, 1.3.4
inverse-Wishart, 32.9.3
investment factor, 29.4.6
reinvested instrument, 24.1.4
iso-contour, 47.2.1
isolated, 8b.1.1
J
jackknife estimator, 3.7.1
Jacobian, 53.3.1
James-Stein estimator, 3.5.1
Jeffreys prior, 38.2.2
joint model, 37.1.2
joint scenario, 32.5
jump rule, 24.1.3
K
k-fold, ??
k-means clustering, 16.6.3
across-cluster variance, 16.6.3
within-cluster variance, 16.6.3
kappa ratio, 7.8.2
Karhunen-Loeve representation, 49.3
Kendall’s tau, 35.2.1
kernel, 3.2.3
kernel density estimate, 3.2.3
kernel stochastic discount factor, 25a.2.1
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, 44.1
Kullback-Leibler divergence, 33.1.4
L
labels, 16.2.1
lag operator of order p, 2.2.2
large capitalization stocks, 50.5
lasso, 18.4.1
lasso regression, 14.2.2
last in, 8b.1.2
last transaction price, 1.8.1
latent mixture models, 16.7.2
latent variables, 16.2.1
maximum likelihood, 38.1.2
law invariant, 7.2
law of one price, 25a.1.1
law of total expectation, 32.3.2
law of total variance, 32.3.2
LDL decomposition, 52.2.3
leave-1-out, ??
leave-p-out, ??
leaves, 18.1.3
Legendre transformation, 33.1.3
length, 48.1
level, 1.3.5
leverage, 6.1.4
leverage effect, 2.6.2
Levy process, 45.1.1, 45.1.1
Levy-Khintchine, 45.1.4
liabilities, 6.6.1
Libor, 1.11.2
likelihood, 3.4
Bayesian estimation, 38.2.2
estimators as random variables, 41.2.2
maximum likelihood, 38.1.1
limit order book, 1.8.1
limit order placement, 10.3
linear discriminant analysis (LDA), ??
linear factor model, 12.1
linear information set, 16.3.2
linear observation equation, 13.9
linear programming, 51.1.5
linear regression, 16.5.1
linear return, 29.4.1
linear state space model, 13.9
linear time invariant filter, 49.4
linear transition equation, 13.9
linearity, 25a.1.2
link function, 32.6
linkage matrix, 9b.5.2
liquidation, 29.2.2
liquidation valuation, 27.1.5
liquidity curve, 10.1
"market buy" liquidity curve, 10.1
"market sell" liquidity curve, 10.1
local Markov property, 16.7.6
location, 47.1
log-leverage, 1.5.4
log-partition function
exponential family distribution, 32.6
log-return, 29.4.6
log-sum-exp function, 32.6.2
logarithmic score, 16.2.3
logistic function, scenario-prob-distrib-exponential
logistic regression, 16.5.2
logit function, 32.6.2
long holdings, 29.1
long memory, 2.4
long position, 27.1.1
longitudinal data, 41.1
Lorentz cone, 51.1.3
Lorenz curve, 32.13
loss, 37.1.1
loss given default, 1.5.1
lower partial moment, 7.7.2
lower partial moment principle, 26.2.1
lp-spaces, 47.1.3
M
m-affine coordinates, 33.1.2
m-flat, 33.1.2
m-geodesic, 33.1.2
m-square, 5.3.2
macro signals, 50.5
macroeconomic LFM, 12.2
Mahalanobis distance, 47.2.2
Marchenko-Pastur distribution, 3.5.3
marginal, 38.2.1
marginal cdf, 32.2
marginal characteristic function, 32.2
marginal contributions, 8b.2
marginal distribution, 32.2
marginal pdf, 32.2
marginal supply demand curve, 10.1
marked-to-market, 24.1.2
marked-to-model, 24.1.2
market beta, 9b.2
market capitalization, 6.6.1
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, 27
market portfolio, 9b.1
market price of risk, 25a.5.1
Markov process, 43.1
Markov random fields, 16.7.5
MARS
multivariate adaptive regression splines, 18.1.4
martingale, 43.1
matrix polynomial, 2.7.3
maturity, 1.3.1
maximum a posteriori, 38.2.2
maximum likelihood factorization, 16.7.1
maximum likelihood parameters, 38.1.1
maximum likelihood with flexible probabilities, 3.3.1
normal assumption, 19.2.2
Student t assumption, 19.2.3
maximum likelihood with flexible probabilities (MLFP) estimate, 3.3.1
maximum likelihood with flexible probabilities (MLFP) predictor, 41.4.4
mean reversion, 2.2
mean-lower partial moment, 7.7.2
mean-semideviation, 7.7.2
measure of concordance, 35.2
measure of dependence, 35.1
measurement equation, 13.9, 17.2
median, 47.1.2
Mercer’s theorem, 53.1.4
method of moments (MM) estimate, 3.6.1
method of moments with flexible probabilities (MMFP) estimate, 3.6.1
metric geodesic, 33.1.2
Metropolis-Hastings algorithm, 52.3.1
microprice, 1.8.1
mid-quote, 1.8.1
minimax decision, 37.1.2
minimum-torsion bets, 8b.5.2
minimum-torsion exposures, 8b.5.2
minimum-torsion transformation, 8b.5.2
mixture distribution, 16.7.2
modal dispersion, 47.1.2
modal square-dispersion, 47.2.4
mode, 47.1.2
n-dimensional random variable, 47.2.4
model
Bayesian approach, 37.1.3
frequentist approach, 37.1.2, 41.5.2
frequentist prediction, 41.4.2
model risk, 37.1.4
model uncertainty, 37.1.4
moment generating function, 32.1
multivariate, 32.1
momentum, 50.3.1
momentum signal, 50.3.1
money multiple, 29.4.6
money-equivalence, 7.2
moneyness, 1.4.2
monotonicity, 7.2, 39.1.7
mortgage backed securities, 24.2.1
moving average of order q, 2.2.3
moving average q-th degree polynomial, 2.2.3
multidimensional scaling, 48.7
multinomial logit function, 32.6.2
multiple, 24.1.4
multiple of invested capital (MOIC), 29.4.5
multivariate adaptive regression splines, 18.1.4
multivariate arithmetic Brownian motion, 9c.1.1
multivariate distributional r-squared, 48.6.1
multivariate expectation, 47.3.1
multivariate Gaussian, 3.1.2
multivariate generalized autoregressive conditional heteroscedastic, 2.7.1
multivariate geometric Brownian motion, 9c.1.1
multivariate Ornstein-Uhlenbeck, 46.4
multivariate random-walk, 2.7
multivariate symmetric regression, 12.2.6
multivariate uncertainty band, 47.2.1
N
naive Bayes classifiers, 16.7.3
naive Bayes models, 16.7.3
natural form, 32.6
natural parameters, 32.6
neighbors, 16.7.4
nested simulation, 5.5.2
net asset value, 27.1.4
net exposure, 6.1.3
net holdings, 29.1
neutralization, 50.6.3
Neyman-Pearson lemma, 16.4.1
nodes, 16.7.4
non-central angle, 48.3
non-central distance, 48.3
non-central length, 48.3
non-central tracking error, 48.3
non-parametric cross-sectional, 13.5.1
non-parametric principal component, 13.3
norm symmetric, 34.3.2
normal copula, 34.3.1
normal distribution, 32.4.5
normal-inverse-Wishart (NIW) distribution, 3.4.2
normalized empirical histogram, 52.5.1
normalized heights, 52.5.1
normalized value characteristics, 9b.2
normalizing and variance stabilizing, 2.6.3
notional value, 1.3.1
null hypothesis, 42.1
number of obligors, 1.5.2
numeraire, 25a.3
numeraire probability measure, 25a.3
O
observable
decision theory, 37.1.1
unsupervised learning, 16.2.1
observable features, 27
observable variables, 16.2.1, 38.1.1
observation equation, 17.2
offset cash, 29.4.4
omega ratio, 7.8.2
one-hot encoding, 18.1.2
operational loss, 1.7
operations, 1.7
opportunity cost, 37.1.1
option-based portfolio insurance, 9c.3
order placement, 10
order q dominance, 36.4
order routing, 10
order scheduling, 10
ordinal variables, 32.5.9
ordinary least squares, 13.2
ordinary least squares with flexible probabilities, 13.2
Ornstein-Uhlenbeck, 46.3
orthogonal, 48.5
orthogonal projection, 48.5
orthogonal to a linear space, 48.5
orthogonal-increment process, 49.3
out of the money, 1.4.2
outputs, 16.2.1
outstanding order vector, 10.3
overnight index swap, 1.11.2
P
p-distance, 48.41
p-norm, 47.1.3
p-value, 42.1.2
P&L, 24.1.6
P&L function, 5.1
P&L linearity, 29.1
P&L related exposures, 9b.2
pair-wise Markov property, 16.7.5
panel data, 41.1
paper P&L, 29.2.1
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, 16.7.4
parent order, 10
partial views, 39.1.4
payment time, 24.1.3
payoff, 1.4
call option, 1.4.1
forward, 1.2.2
variance swap, 1.4.6
pdf
multivariate, 32.1
Pearson parametrization
Arrow-Pratt function, 7.4.1
perceptron, 16.4.2
performance "mean", 7.3.1
performance expectation, 7.3.1
performance mean-variance trade-off, 7.3.4
performance variance, 7.3.2
periodogram, 49.2.2
permanent impact, 10.1.3
permanent market impact model, 10.1.3
persistence, 9b.2.3
phase, 49.4
point prediction, 16.2.3, 41.1
point view, 39.1.2
point-in-time, 2.3.4
pointed, 51.1.1
Poisson process, 2.1.2, 45.1.3
pool factor, 24.2.1
population r-squared, 48.6.1
portfolio, 6.1.1
portfolio rebalancing P&L, 29.2.3
portfolio weights, 29.4.2
positive homogeneity of first degree, 7.2
positive homogenous of degree, 7.2
positive homogenous of first degree, 7.2
posterior, 41.2.3, 38.1.2, 41.2.3
Bayesian statistics, 38.2.1
posterior distribution, 38.2.2
posterior error
Bayesian approach, 41.5.3
Bayesian estimation, 41.4.3
posterior risk, 37.1.3
potential function
e-affine coordinates, 33.1.3
m-affine coordinates, 33.1.3
precision matrix, 32.6.1
predicted variables, 16.2.1
prediction, 12.1
point prediction, 41.4.1
predictive distribution, 41.5.1
prediction model, 38.2.3
predictive, 9b.5.2
predictive distribution, 6.3.1
non-observable, 41.5.1
posterior, 38.2.3
predictor
point prediction, 41.4.1
predictive distribution, 41.5.1
premium, 1.11.2
price manipulation, 10.5.2
pricing kernel, 25a.2
pricing operator, 25a.1.1
pricing signals, 50.2.2
principal axis factorization, 12.4.4
principal component analysis
sparse principal component analysis, 14.3
principal components, 12.3.2
principal directions, 12.3.2
principal factors, 12.3.2
principal variances, 12.3.2
principal-component, 12.3
prior distribution, 38.2.2
prior predictive distribution, 38.2.3
probabilistic factor analysis, 16.7.1
probabilistic graphical model, 16.7.4
probabilistic prediction, 16.2.3, 41.1
probabilistic prediction error, 41.5.2
probabilities, 32.5
probability density function, 32.1
probability level, 32.1
probability mass function, 32.5.3
probability of default, 1.5.1
probit model, 16.5.2
profit-and-loss, 24.1.6
projection stochastic discount factor, 25a.2.2
properties, 41.2.1
proportional hazards expectation, 7.6.2
proportional hazards principle, 26.2.3
proportional hazards transform, 7.6.2
proportional odds, 32.5.9
pseudo inverse, 53.2.1
pull-to-par, 1.11.2
pure endowment, 27.5.1
pure noise, 3.5.3
put-call parity, 25a.1.2
Q
quadratic discriminant analysis (QDA), ??
quadratic programming, 51.1.4
quadratic variation, 3.11.4
quadratic-normal distribution, 32.9.2
quantile (VaR) satisfaction measure, 7.5.1
quantile function, 32.1
quantitative alpha, 9b
quantitative strategies, 9b
quasi-distance, 16.2.3
R
radial component, 32.4.2
Radon-Nikodym derivative, 25a.3
Radon-Nikodym process, 25b.5.4
rain distribution, 10.5.3
random field, 20.3
random forest, 18.5.1
random time series, 3
random walk, 2.1
multivariate random walk, 2
ranking, 50.6.3
ranking-distortion, 50.6.3
ranking-median, 50.6.3
ranking-terciles, 50.6.3
rating, 1.5.3
realized information, 38.2.2
realized information panel, 3
realized P&L, 24.1.6
portfolio, 29.2.1
realized time series, 3
realized variance, 1.4.6
receiver operating characteristic (ROC) curve, 16.4.3
receiver operating characteristic (ROC) function, 16.4.3
record date, 24.1.3
recovery rate, 1.5.1
regression, 16.2.2
analysis of variance, 16.3.3
cross-section, 12.5.5
least absolute deviation regression, 16.3.4
least squares regression, 16.3.1
linear least absolute distance regression, 16.3.5
linear least squares regression, 16.3.2
linear median regression, 16.3.5
linear quantile regression, 16.3.5
mean regression, 16.3.1, 16.3.2
median regression, 16.3.4
quantile regression, 16.3.4
regression LFM, 12.2
regret, 37.1.1
regularization, 39.6.6
reinforcement learning, 16.2.1
reinvested cumulative cash-flow, 24.1.4
reinvested instrument, 24.1.4
reinvestment function, 5.1
relative entropy, 33.1.4
relative marginal contributions, 8b.5.3
relative value, 9b
residuals, 12.1
responses, 16.2.1
return on collateral, 29.4.3
return on equity, 29.4.3
return on exposure, 29.4.3
return on value, 29.4.3
return related characteristics, 9b.2
reversal, 50.3.1
Riccati root, 52.2.2
ridge, 18.4.1
ridge regression, 14.2.3
Riemannian metric, 33.1.1
right-way risk, 6.8.2
risk aversion, 7.2
risk coverage ratio, 7.8.2
risk drivers, 1
risk drivers path, 4
risk market neutral, 9b.4.3
risk measure, 7
risk premia, 9b
arbitrage pricing theory, 25b.4.1
symmetry with arbitrage pricing theory, 12.5.6
risk premium, 7.2
risk reversal, 1.4.4
risk seeking, 7.2
risk-free interest rate, 1.3.1
risk-free numeraire, 25a.4.1
risk-neutral, 7.2
risk-neutral pricing, 25a.4.1
risk-neutral probability measure, 25a.4.1
risk-theoretical P&L, 5.1, 5.1
robust approach, 37.1.4
roll down, 5.2.3
rolling value, 1.3.2, 1.4.2
rolling zero-coupon, 1.3.2
rotation, 49.1.1
round-trip, 10.5.2
rule-based strategies, 9b
running maximum, path-analysis
S
sample correlation matrix, 3.2.2
sample covariance matrix, 3.2.2
sample mean, 3.2.2
sample r-squared, 48.6.1
sample space, 32.5
sample standard deviation vector, 3.2.2
satisfaction measure, 7.2, 7.6.1
scale-invariance, 7.2
scenario expansion, 6.5.1
scenario-probability distribution, 32.5
scenario-probability quantile, 32.5.6
scenarios, 32.5
Schweizer-Wolff measure, 35.1.1
score, 32.5.9
scoring, 1.5.3
scoring function
receiver operating characteristic, 16.4.3
scoring rule, 16.2.3
second order dominance, 36.3
second-order cone programming, 51.1.3
security market line, 25a.5.2
segmentation, 18.1.4
self-financing, 9b, 9b.2.2
self-similarity, 45.1.5
semidefinite cone, 51.1.2
semidefinite programming, 51.1.2
semideviation, 7.7.2
semideviation principle, 26.2.1
semivariance, 7.7.2
semivariance principle, 26.2.1
sensitivity curve, 3.7.1
separation systematic/residuals, 12.5.5
sequential attribution, 8b.1.3
settlement date, 24.1.2
settlement period, 24.1.3
Shapley attribution, 8b.1.4
Sharpe ratio, 7.8.1
generalized Sharpe ratio, 47.1.1
shift parameters, 12.1
short holdings, 29.1
short position, 27.1.2
short spot rate, 1.11.2
sign, 1.8.1
signal, 50
signal beta, 9b.2.1
signal characteristic, 9b.2.1
signal characteristic matrix, 9b.4.2
signal characteristic portfolio, 9b.2.1
signal characteristic portfolios, 9b.4.2
signal flexible factor portfolio, 9b.2.2
signal-induced factor, 9b.1, 9b.2.1
signal-induced factors, 9b.4.2
signal-to-noise, 47.1.1
size signal, 50.5
skew signal, 50.5
skill, 9b.5.2
Sklar’s theorem, 34.2.3
slippage, 24.1.2
slippage model, 10.1.3
slippage P&L, 29.2.1
slope, 1.3.5
small capitalization stocks, 50.5
small minus big, 9b.1
smart beta, 9b
smile, 1.4.3
smile signal, 50.5
smirk, 1.4.3
smooth kernel probabilities, 3.1.2
smooth quantile, 32.5.7
smoothing, 50.6.1
sofmax function, 32.6.2
solvency capital requirement, 7.10
solvency condition, 6.8.1
Sortino ratio, 7.8.2
space of portfolios/holdings, 48.8
Spearman’s rho, 35.2.2
spectral density, 49.2.1
spectral representation, 49.3
spectral theorem, 53.1.1
spectrum, 3.5.2
satisfaction indices/risk measures, 7.6.1
spot curve, 1.11.2
spot rate, 1.11.2
spot swap, 1.3.1
spread, 1.3.6
square-dispersion, 47.2
square-root, 45.2.2
square-root rule, 4.6
stable, 32.8.1
standard "beta", 12.4.2
standard Brownian motion, 45.1.2
standard deviation, 47.1.2
standard deviation principle, 26.2.1
standard error, 42.1.3
standard Wiener process, 45.1.2
standardized elliptical variable, 32.4.2
standardized holdings, 6.3.1
standardized invariants, 3.9.1
state, 37.1.1
state crisp probabilities, 3.1.4
state processes, 43.1
State space model
Probabilisitc linear, 13.9
state-space models, 17.2
static cross-sectional LFM, 13.5
static linear factor model, 13.1.1
static model, 17
static principal component LFM, 13.3
static regression LFM, 13.2
static systematic-idiosyncratic LFM, 13.4
stationary, 43.1
covariance stationary, 43.1
statistical angle, 48.8.1
statistical distance, 48.8.1
statistical inner product, 48.8.1
statistical length, 48.8.1
statistical LFM, 12.3
statistics, 42.1.1
steepness, 1.3.5
step transition matrix, 45.3.1
stochastic discount factor, 25a.2
stochastic dominance, 36.1
stochastic mean, 2.1.3
stochastic time, 45.1.5
stochastic volatility, 2.1.3
stochastic volatility inspired, 1.4.5
strategy, 1.9
strict stochastic dominance, 36.1
string, 20.3
strips, 1.3.1
structure, 16.2.2
sub-additivity, 7.2
sub-quantile function, 32.12
subjective probability, 16.4.1
subordinator, 45.1.5
sufficient statistics, 32.6
sum-of-parts, 27.1.6
liquidation valuation, 27.1.5
super-additivity, 7.2
supervised learning, 16.2.1
support vector machine, 16.4.2
surprise, 38.1.2
maximum likelihood parameters, 38.1.1
surprise score, 16.2.3
survival probability, 1.6
symmetric stable distributions, 32.8.1
systematic, 12.1.2
systematic-idiosyncratic LFM, 12.1.2
T
t copula, 34.3.1
tangent vector, 33.1
target variables, 16.2.1, 16.2.1, 39.1.1
target vector, 12.1
temporary impact, 10.1.3
temporary market impact, 10.1.3
tenor, 1.3.2
tercile, 50.6.3
test set, 41.1
through-the-cycle, 2.3.4
tick time, 1.8.2
tick time evolution, 1.8.3
tick-by-tick, 1.8.3
Tikhonov, 18.4.1
time crisp probabilities, 3.1.4
time horizon, 41.1
time to cash-flow, 5.3.2
time to cash-flow distribution, 5.3.2
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
time-inhomogeneous transition matrix, 2.3.2
times series, 41.2.1
timing P&L, 29.3
torsion, 8b.5
total net income, 6.6.2
total shares outstanding, 6.6.1
trace, 53.2.1
tracking error, 7.3.3
trading P&L, 29.2.2
trailing window, 3.11.6
training set, ??
transaction time, 24.1.2
transaction value, 24.1.2
transaction variables, 1.8.1
transition equation, 13.9
transition probabilities, 2.3.1
translation invariance, 7.2
transpose-square-root, 52.2
true negative rate, 16.4.1
true positive rate, 16.4.1
truncation, 13.6
turnover, 9b.2.3
U
unanimity, 39.1.7
unbalanced, 41.1
uncertainty band, 47.1
unconditional, 38.2.1
uncovered interest rate parity, 5.1.2
underwater chart, 29.6
undirected graph, 16.7.4
uniform component, 32.4.2
uniform distribution, 32.9.1
uniform probabilities, 3.1
unit, assumptions-prediction
unit-root, 46.2.3
unitary transformation, 49.1.1
univariate case, 47.2
univariate symmetric regression, 12.2.6
unrealized P&L, 24.1.6
portfolio, 29.2.1
unsupervised autoencoders, 16.6
unsupervised learning, 16.2.1
updated distribution, 39.1.2
updated state, 39.6.1
utility, 37.1.1
utility function, 7.4
V
validation set, ??
valuation function, 27
valuation multiple, 27.2.2
value at risk, 7.5.1
value function, 5.1
bond value function, 5.3.2
value function for a stock, 5.1.1
value function for defaultable instruments, 5.1.5
value function of a bond, 5.1.3
value function of a call option, 5.1.4
value function of a zero-coupon, 5.1.3
value signal, 50.2.1
value stocks, 50.2.1
variability, 47.1
variance, 47.3.1
variance minimization, 12.5.5
variance principle, 26.2.1
variance swap, 1.4.6
variational free energy, 38.1.2
varimax rotation, 12.4.7
vector autoregression integrated moving average, 2.7.4
vector autoregression moving average, 2.7.3
vector autoregressive of order one, 46.2
vectorization, 53.2.1
vertices, 16.7.4
view p-value, 39.2.2
view variables, 39.1.1
visualization basis, 48.7
visualization function, 48.7
volatility clustering, 2.6
volume, 1.8.1
volume time, 1.8.2
volume time evolution, 1.8.3
volume-weighted-average-price (VWAP) strategy, 10.1.3
W
Wald statistic, 42.1.4
Wang distortion principle, 26.2.3
Wang expectation, 7.6.2
Wang transform, 7.6.2
weak dominance, 36.2
weak signals, 9b.5.2
weight of evidence, 16.7.3
white noise
strong white noise, 43.1
weak white noise, 43.1
Wiener-Hopf equations, 13.7.1
Wiener-Kolmogorov filter, 13.7
Williamson n-transform, 34.3.2
Wishart distribution, 32.9.3
Wold theorem, 2.9.9
worst-case error, 41.2.2
frequentist approach, 41.5.2
frequentist prediction, 41.4.2
worst-case risk, 37.1.2
wrong-way risk, 5.1.5
credit value adjustment, 6.8.2
Y
yield, 1.11.2
yield curve, 1.3.3, 1.11.2
yield income, 5.2.3
yield to maturity, 1.3.3
Z
z-score, 47.1.1
multivariate absolute z-score, 47.1.1
z-statistic, 42.1.3
zero curve, 1.11.2
zero rate, 1.11.2
zero-coupon bond, 1.3.1
