Abstract
Volatility is an important variable in the financial market. We propose a model-free implied volatility method to measure the volatility and test the volatility risk premium. The model-free implied volatility does not depend on the option pricing model, and extracts information from all the option contracts. We provide empirical evidence from the S & P 500 index option that model-free implied volatility is more accurate to forecast the future volatility and the volatility risk premium does not exist.
Highlights
During the past twenty years, volatility forecasting and volatility risk premium are becoming more and more important in financial engineering
The theories of the model-free implied volatility and the time series model are used in the American S & P500 index option market
The squared volatility can be expressed as the integration of call option forward prices [8]: How to cite this paper: Cheng, J.F. (2015) Volatility Forecasting and Volatility Risk Premium
Summary
During the past twenty years, volatility forecasting and volatility risk premium are becoming more and more important in financial engineering. Model-free implied volatility originated from the variance swap theory. Since the jump is important in financial asset prices, Jiang and Tian [9] proved the conclusions from BrittenJones and Neuberger remain valid when the price jumps exist, ensuring the generalizability of this method. Jiang and Tian [10] demonstrated that DDKZ’s variance fair value and Britten-Jones and Neuberger’s yields squares are the same. They came to a conclusion that the model-free implied volatility has more information content than the Black-Scholes implied volatility. We compare model-free implied volatility method with GARCH model from the empirical aspect and test the volatility risk premium
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