Semi-positive definite (SPD) matrices in Layman’s terms…

A matrix is considered SPD if all its eigenvalues are non-negative. Intuitively, an SPD matrix represents a system where there is no "negative energy" or, in a geometric sense, where all directions lead to non-negative "squared lengths".
In terms of quadratic forms, an SPD matrix ensures that the quadratic form Q(x) = x^T A x is always non-negative, which means that any real vector x will not produce a negative value when applied to this form.

x : This is a column vector with real numbers. In the context of finance, it could represent a vector of asset holdings or weights in a portfolio.
A: This is a matrix that, in this context, is often symmetric and positive semi-definite. In finance, could represent the covariance matrix of asset returns.
x^T: This is the transpose of vector , turning it into a row vector.
Q(x): This scalar is the value of the quadratic form , and it represents a measure that is often related to energy, cost, or—in the context of finance—risk or variance. The transposition is crucial because it makes the multiplication conform to the rules of matrix algebra and results in the kind of product that we are interested in: a single number.

PSD matrices have several properties:

• They are symmetric: .
• The determinant (*) of a PSD matrix can be zero or positive; it cannot be negative.
• All principal minors (**) of the matrix have a non-negative determinant.

In portfolio theory, the variance-covariance matrix of asset returns is SPD. This ensures that the calculated variance of any portfolio (a quadratic form of the matrix) will be non-negative, which aligns with the concept that variance cannot be negative in reality.

SPD matrices are used to construct risk models where the matrix represents the covariance between different assets. The semi-positive definiteness guarantees that the estimated risks (variances) and correlations are consistent with a real-world scenario where portfolios can't have a negative variance. In the pricing of options, particularly in models like the Black-Scholes model, the volatility surface is often smoothed or interpolated to avoid arbitrage opportunities. SPD matrices help ensure that the resulting interpolated surface is arbitrage-free.

SPD matrices are integral in stochastic calculus, especially in multi-dimensional Itô processes, to ensure that the diffusion processes used to model various financial instruments behave correctly.

Thus, SPD matrices serve as a foundation to ensure the non-negativity of various quantities in finance and provide a stable framework for optimization problems, risk assessment, and derivatives pricing.

(*)

To learn more about the determinant: https://www.finance-tutoring.fr/2023/03/25/understanding-the-determinant-of-a-matrix-in-layman-s-terms/

(**)

A minor of a matrix is the determinant of some smaller square matrix, cut down from the larger matrix by removing one or more of its rows or columns.