Final answer:
b) Doesn't matter
The ordering of variables in covariance doesn't matter, as cov(X, Y) equals cov(Y, X). While covariance measures how much two variables vary together, it doesn't imply causality. It is important in risk management and portfolio diversification in finance.
Step-by-step explanation:
When expressing covariance between two securities, the ordering of variables doesn't matter. Covariance is a measure used to determine how much two random variables vary together. It is symmetric in nature, meaning that the covariance between variable X and variable Y is the same as the covariance between variable Y and X, denoted as cov(X, Y) = cov(Y, X). However, it's important to note that covariance alone does not imply causality, meaning one variable does not necessarily cause the other to change.
Understanding the concept of covariance is central to portfolio theory and risk management in finance. Investors use these statistical measurements to make informed decisions about the diversification of their portfolios. It's also crucial to understand that positive covariance indicates that the securities tend to move in the same direction, while negative covariance suggests they move inversely. Yet, this does not distinguish between the influence of different variables such as outside economic factors, which might affect both securities and thereby influence their covariance.