TL;DR
Browse 112 quantitative trading terms with definitions, interview relevance, and links to concept deep-dives.
By Valenke Exam Prep Team·Last updated 2026-06-02
Quantitative Trading Glossary
112 terms for quant interview preparation
A
B
- Bayes' Theorem— A formula relating conditional probabilities: \( P(A|B) = \frac{P(B|A)P(A)}{P(B)} \). It provides a way to update prior ...
- Brownian Motion— A continuous-time stochastic process \( W_t \) with independent, normally distributed increments: \( W_t - W_s \sim N(0,...
- Binomial Coefficient— The number \( \binom{n}{k} = \frac{n!}{k!(n-k)!} \), representing the number of ways to choose k items from n. Generaliz...
- Backward Induction— A method for solving sequential games by analyzing from the last decision point backward to the first. At each node, the...
- Bayesian Inference— A statistical framework that updates beliefs via Bayes' theorem: \( p(\theta|data) \propto p(data|\theta) p(\theta) \). ...
- Bisection Method— A root-finding algorithm that repeatedly halves an interval \( [a,b] \) where \( f(a) \) and \( f(b) \) have opposite si...
- Black-Scholes Model— The foundational option pricing model. For a European call: \( C = S_0 N(d_1) - Ke^{-rT}N(d_2) \) where \( d_1 = \frac{\...
- Binomial Tree Model— A discrete-time option pricing model where the stock moves up by factor u or down by factor d each period. The risk-neut...
- Bid-Ask Spread— The difference between the best ask (lowest sell price) and best bid (highest buy price) in a market. It represents the ...
- Binomial Distribution— The distribution of the number of successes in n independent Bernoulli trials: \( P(X=k) = \binom{n}{k} p^k (1-p)^{n-k} ...
- Birthday Problem— The probability that in a group of n people, at least two share a birthday. With 23 people, the probability exceeds 50%....
C
- Conditional Probability— The probability of event A occurring given that event B has occurred, denoted \( P(A|B) = \frac{P(A \cap B)}{P(B)} \).
- Central Limit Theorem— States that the sum (or average) of a large number of independent, identically distributed random variables with finite ...
- Covariance— Measures the linear relationship between two random variables: \( \text{Cov}(X,Y) = E[(X-E[X])(Y-E[Y])] = E[XY] - E[X]E[...
- Characteristic Function— The Fourier transform of a probability distribution: \( \phi_X(t) = E[e^{itX}] \). Unlike the MGF, it always exists and ...
- Combinations— The number of unordered subsets of r items from n distinct items: \( \binom{n}{r} = \frac{n!}{r!(n-r)!} \), also called ...
- Catalan Number— The sequence \( C_n = \frac{1}{n+1}\binom{2n}{n} \) counting balanced parentheses, binary trees, non-crossing partitions...
- Covariance Matrix— A symmetric positive semi-definite matrix \( \Sigma \) where \( \Sigma_{ij} = \text{Cov}(X_i, X_j) \). It fully characte...
- Confidence Interval— An interval \( [\hat{\theta} - z_{\alpha/2}\cdot SE, \hat{\theta} + z_{\alpha/2}\cdot SE] \) that, over repeated samplin...
- Chi-Square Test— A test using the \( \chi^2 \) distribution. The goodness-of-fit test checks if data follows a hypothesized distribution:...
- Correlation— The normalized measure of linear dependence: \( \rho_{XY} = \frac{\text{Cov}(X,Y)}{\sigma_X \sigma_Y} \in [-1, 1] \). Co...
- Convexity— In fixed income, the second derivative of bond price with respect to yield: it measures how duration changes as rates ch...
- Copula— A function that couples marginal distributions to form a joint distribution. By Sklar's theorem, any joint CDF can be wr...
- Coupon Collector Problem— The expected number of draws (with replacement) needed to collect all n distinct coupons: \( E[T] = n \cdot H_n = n \sum...
- Cholesky Decomposition— For a positive definite matrix \( \Sigma \), the unique factorization \( \Sigma = LL^T \) where L is lower triangular wi...
D
- Derangement— A permutation where no element appears in its original position. The number of derangements of n elements is \( D_n = n!...
- Dominant Strategy— A strategy that yields a higher payoff than any alternative regardless of opponents' actions. Strictly dominant: always ...
- Delta (Greek)— The rate of change of option price with respect to underlying price: \( \Delta = \frac{\partial V}{\partial S} \). For a...
- Dynamic Programming— An optimization technique that breaks a complex problem into overlapping subproblems, solving each once and storing the ...
- Delta Hedging— Continuously adjusting a portfolio of the underlying asset to maintain a delta-neutral position, eliminating first-order...
E
- Expected Value— The probability-weighted average of all possible outcomes of a random variable: \( E[X] = \sum_x x \cdot P(X=x) \) for d...
- Eigenvalues— Scalars \( \lambda \) satisfying \( A\mathbf{v} = \lambda \mathbf{v} \) for some non-zero vector \( \mathbf{v} \) (the e...
- Eigenvectors— Non-zero vectors \( \mathbf{v} \) satisfying \( A\mathbf{v} = \lambda\mathbf{v} \). They define directions that are only...
- Exponential Distribution— A continuous distribution with CDF \( F(x) = 1 - e^{-\lambda x} \) for \( x \geq 0 \), describing inter-arrival times in...
F
- Finite Difference Method— A numerical technique for solving PDEs by discretizing the domain into a grid and approximating derivatives with differe...
- Fisher Information— A measure of the information a random variable carries about a parameter: \( I(\theta) = E\left[\left(\frac{\partial \lo...
- Fourier Transform— A transformation that decomposes a function into its frequency components: \( \hat{f}(\omega) = \int f(x) e^{-i\omega x}...
G
- Generating Function— A formal power series \( G(x) = \sum_{n=0}^{\infty} a_n x^n \) encoding a sequence \( \{a_n\} \). Operations on the powe...
- Gamma (Greek)— The rate of change of delta with respect to underlying price: \( \Gamma = \frac{\partial^2 V}{\partial S^2} = \frac{\par...
- Geometric Brownian Motion— The SDE \( dS = \mu S dt + \sigma S dW \) with solution \( S_t = S_0 \exp\left((\mu - \sigma^2/2)t + \sigma W_t\right) \...
- Gambler's Ruin— A gambler with initial wealth a plays fair (or biased) coin flips until reaching 0 (ruin) or target N. In the fair game,...
H
I
- Ito's Lemma— The chain rule for stochastic calculus. For a twice-differentiable function \( f \) of an Ito process \( X_t \): \( df =...
- Inclusion-Exclusion Principle— For the union of sets: \( |A_1 \cup \cdots \cup A_n| = \sum|A_i| - \sum|A_i \cap A_j| + \cdots + (-1)^{n+1}|A_1 \cap \cd...
- Implied Volatility— The volatility parameter \( \sigma_{imp} \) that, when input into the Black-Scholes formula, produces the observed marke...
- Information Asymmetry— A situation where one party in a transaction has more or better information than the other. In financial markets, some t...
- Indicator Random Variable— A random variable \( I_A \) that equals 1 if event A occurs and 0 otherwise. Its expectation is \( E[I_A] = P(A) \), and...
J
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- Law of Large Numbers— States that as the number of independent trials increases, the sample mean converges to the expected value. The weak for...
- Law of Total Probability— If \( \{B_1, B_2, \ldots\} \) is a partition of the sample space, then \( P(A) = \sum_i P(A|B_i)P(B_i) \). This decompos...
- Linear Regression— A model \( y = X\beta + \epsilon \) where \( \beta \) is estimated by minimizing squared residuals: \( \hat{\beta} = (X^...
- Liquidity— The ability to buy or sell an asset quickly without significant price impact. Measured by bid-ask spread, depth, and res...
- Linearity of Expectation— The property that \( E[aX + bY] = aE[X] + bE[Y] \) regardless of whether X and Y are dependent. Extends to any finite or...
- Log-Normal Distribution— A random variable X is log-normally distributed if \( \ln X \sim N(\mu, \sigma^2) \). Its mean is \( e^{\mu + \sigma^2/2...
- Least Squares— An optimization method that minimizes the sum of squared residuals: \( \min_\beta \sum_i (y_i - x_i^T \beta)^2 \). The s...
M
- Marginal Distribution— The probability distribution of a subset of random variables obtained by integrating (or summing) out the other variable...
- Moment Generating Function— For a random variable X, the MGF is \( M_X(t) = E[e^{tX}] \). The n-th moment equals \( M_X^{(n)}(0) \), the n-th deriva...
- Martingale— A stochastic process \( \{M_t\} \) where \( E[M_t | \mathcal{F}_s] = M_s \) for all \( s \leq t \). Intuitively, the bes...
- Markov Chain— A stochastic process where the future state depends only on the current state, not the history: \( P(X_{n+1}|X_n, X_{n-1...
- Minimax Theorem— In two-player zero-sum games, the maximum of the row player's minimum guaranteed payoff equals the minimum of the column...
- Mixed Strategy— A probability distribution over pure strategies. Player i plays strategy \( s_j \) with probability \( p_j \), where \( ...
- Matrix Decomposition— Factoring a matrix into a product of simpler matrices. Key decompositions: LU (triangular), QR (orthogonal-triangular), ...
- Maximum Likelihood Estimation— Estimates parameters by maximizing the likelihood function: \( \hat{\theta}_{MLE} = \arg\max_\theta \prod_i f(x_i|\theta...
- Monte Carlo Simulation— A computational method that uses random sampling to estimate mathematical quantities. Generate many random paths or scen...
- Market Making— A trading strategy that provides liquidity by continuously quoting bid and ask prices. Market makers profit from the bid...
- Mean Reversion— The tendency of a time series to return toward its long-run average. Formally, a process is mean-reverting if its drift ...
- Market Impact— The effect of a trade on the market price. Temporary impact reverses after the trade; permanent impact persists. Total m...
- Modes of Convergence— Different ways a sequence of random variables can approach a limit: almost surely (a.s.), in probability (\( \xrightarro...
N
- Nash Equilibrium— A strategy profile where no player can improve their payoff by unilaterally changing their strategy. Formally, for each ...
- Numerical Integration— Approximating definite integrals using discrete summations. Methods include the trapezoidal rule, Simpson's rule, and Ga...
- Newton's Method— An iterative root-finding algorithm: \( x_{n+1} = x_n - \frac{f(x_n)}{f'(x_n)} \). It converges quadratically near a sim...
- Normal Distribution— The bell curve distribution \( X \sim N(\mu, \sigma^2) \) with PDF \( f(x) = \frac{1}{\sigma\sqrt{2\pi}} e^{-(x-\mu)^2/(...
O
- Order Book— The list of all outstanding limit orders in a market, organized by price level. The bid side shows buy orders (descendin...
- Ornstein-Uhlenbeck Process— A mean-reverting SDE: \( dX_t = \theta(\mu - X_t)dt + \sigma dW_t \). The process is pulled toward \( \mu \) at rate \( ...
- Optimal Stopping— The problem of choosing the best time to take a particular action based on sequentially observed information. Formally, ...
P
- Prior Probability— The initial probability assigned to a hypothesis before observing data, representing prior beliefs or knowledge. In Baye...
- Posterior Probability— The updated probability of a hypothesis after observing data, computed via Bayes' theorem: \( P(H|D) = \frac{P(D|H)P(H)}...
- Poisson Process— A counting process \( N(t) \) where events occur independently at a constant rate \( \lambda \). The number of events in...
- Permutations— The number of ordered arrangements of r items from n distinct items: \( P(n,r) = \frac{n!}{(n-r)!} \). For all n items, ...
- Pigeonhole Principle— If n items are placed into m containers with \( n > m \), at least one container holds more than one item. The generaliz...
- Principal Component Analysis— A dimensionality reduction technique that finds orthogonal directions of maximum variance in the data. Mathematically, P...
- Positive Definite Matrix— A symmetric matrix A is positive definite if \( \mathbf{x}^T A \mathbf{x} > 0 \) for all non-zero \( \mathbf{x} \). Equi...
- P-Value— The probability of observing a test statistic at least as extreme as the one computed, assuming the null hypothesis is t...
- Put-Call Parity— The fundamental relationship between European call and put prices: \( C - P = S - Ke^{-rT} \). Equivalently, a call plus...
R
- Random Walk— A discrete-time process where each step is an independent random variable: \( S_n = S_0 + \sum_{i=1}^n X_i \). The simpl...
- Recurrence Relation— An equation that defines each term of a sequence as a function of preceding terms: \( a_n = f(a_{n-1}, a_{n-2}, \ldots) ...
- Rho (Greek)— The sensitivity of option price to the risk-free interest rate: \( \rho = \frac{\partial V}{\partial r} \). Positive for...
- Risk-Neutral Pricing— A pricing framework where derivatives are valued as discounted expected payoffs under a risk-neutral measure Q, where al...
- Risk-Free Rate— The theoretical rate of return on an investment with zero risk. In practice, approximated by short-term government secur...
S
- Statistical Independence— Events A and B are independent if \( P(A \cap B) = P(A)P(B) \). For random variables, X and Y are independent if their j...
- Stopping Time— A random variable \( \tau \) such that the event \( \{\tau \leq t\} \) depends only on information available at time t. ...
- Stochastic Differential Equation— An equation of the form \( dX_t = \mu(X_t, t)dt + \sigma(X_t, t)dW_t \), describing a process driven by both determinist...
- Singular Value Decomposition— Any matrix A can be decomposed as \( A = U\Sigma V^T \) where U and V are orthogonal and \( \Sigma \) is diagonal with n...
- Student's t-Test— A test for comparing means when the population variance is unknown. The test statistic \( t = \frac{\bar{x} - \mu_0}{s/\...
- Standard Error— The standard deviation of a sampling distribution, typically \( SE(\bar{x}) = \sigma/\sqrt{n} \). It measures the precis...
- Slippage— The difference between the expected execution price and the actual execution price. Caused by market movement during ord...
- Stochastic Volatility— Models where volatility is itself a random process, capturing the empirical observation that market volatility varies un...
- Sharpe Ratio— The risk-adjusted return: \( SR = \frac{E[R] - R_f}{\sigma_R} \), measuring excess return per unit of total risk. A Shar...
- Stationary Distribution— A probability distribution \( \pi \) satisfying \( \pi P = \pi \) for a Markov chain with transition matrix P. If the ch...
- Submartingale— A stochastic process where \( E[M_t | \mathcal{F}_s] \geq M_s \) for \( s \leq t \). It has an upward tendency on averag...
- Stochastic Dominance— A partial ordering on distributions. X first-order dominates Y if \( F_X(t) \leq F_Y(t) \) for all t (X is always at lea...
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V
- Variance— A measure of dispersion: \( \text{Var}(X) = E[(X - E[X])^2] = E[X^2] - (E[X])^2 \). The square root of variance is the s...
- Vega (Greek)— The sensitivity of option price to volatility: \( \mathcal{V} = \frac{\partial V}{\partial \sigma} \). Vega is highest f...
- Volatility Surface— A 3D surface plotting implied volatility as a function of strike price and time to expiration. It encodes the market's e...
- Value at Risk— The maximum loss at a given confidence level over a specified time horizon: \( P(L > VaR) = \alpha \). For example, 1-da...