Spreads and Combinations
Until this point in the curriculum, the strategies we discussed involved combinations... Read More
The simplest and most commonly used method for estimating constant variances and covariances is to use variance or covariance–computed from historical return data. These elements are then assembled into a VCV matrix. The following table shows a simple example of a VCV matrix for a group of stocks:
$$ \begin{array}{c|c|c|c} & \textbf{Stock A} & \textbf{Stock B} & \textbf{Stocks C} \\ \hline \text{Stock A} & 1.000 & 1.073 & 1.431 \\ \hline \text{Stock B} & 1.073 & 1.000 & 0.886 \\ \hline \text{Stock C} & 1.431 & 0.886 & 1.000 \end{array} $$
In addition to using variance-covariance matrices, other tools that can be added to an analyst’s processes include:
$$ \left(\frac { (1 + \lambda) }{ (1 – \lambda) } \right) = \text{true volatility} $$
Where \(\lambda\) is the true volatility.
Question
From which of the following does data used to forecast volatility most commonly come?
- Observations of past volatility.
- News and event forecasting.
- Bayes theorem approach.
Solution
The correct answer is A.
Forecasting volatility in financial markets is essential for investment, option pricing, and financial market regulation. The most common approach to forecasting volatility is to use observations of past volatility, such as historical average, exponentially weighted moving average (EWMA), and GARCH2. These models use past observations of volatility to forecast future volatility.
B and C are incorrect. While news and event forecasting, or the Bayes theorem approach, may also be used, they are not as commonly used as observations of past volatility.
Asset Allocation: Learning Module 2: Capital Market Expectations – Part 2 Forecasting Asset Class Returns; Los 2(g) Discuss methods of forecasting volatility