Question

Volatility forecasting - How to forecast variances for many assets - VCV matrix, shrinkage estimates, smoothed returns to estimate, ARCH models

A) Historical Volatility
B) Implied Volatility
C) GARCH Models
D) Black-Scholes Model

Answer 1

Volatility forecasting involves various methods such as historical volatility, implied volatility, GARCH models, and the Black-Scholes model. The correct option is D.

Step-by-step explanation:

Volatility forecasting is a crucial aspect of investment analysis. There are various methods to forecast variances for many assets, including:

1. Historical Volatility: This method involves calculating the standard deviation of past asset returns. It is based on the assumption that past volatility can be a reasonable predictor of future volatility.
2. Implied Volatility: Implied volatility is derived from options prices and reflects the market's expectation of future volatility. It can be used to estimate future variances.
3. GARCH Models: Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models are econometric models that account for time-varying volatility. They incorporate past volatility and bring in additional information to forecast future variances.
4. Black-Scholes Model: The Black-Scholes model is a mathematical formula used to price options. While it doesn't directly forecast variances, it can be used to estimate implied volatility, which can indirectly provide insights into future variances.