High frequency volatility estimation and option pricing

In recent years, the volatility literature has benefited from the availability of high-frequency financial data. As a result, the approach to volatility measurement and modelling has changed significantly. A new approach termed Realized Volatility that utilises the information in high-frequency returns has been proposed. Although high frequency data has been demonstrated to improve our ability to understand and forecast financial volatility, its usefulness in the pricing of financial derivatives has not been fully investigated. In this study, we assessed the performance of high-frequency volatility estimators in option pricing. To this end, we priced European call and put options on Bank of America, Coca-Cola and Microsoft stocks using high frequency volatility estimates (forecasts) and compared the predicted theoretical price (model value of the options) to their market prices (ask prices). The findings of the study indicate that although no single volatility estimator is preferred all the time, the realized kernel estimator is superior to the other competing volatility estimators in pricing call and put options on Bank of America, Coca-Cola and Microsoft stocks.