Development of high-frequency volatility estimators in pricing and trading stock options

Economic & mathematical methods and models

Asset return volatility plays a key role in derivative pricing and hedging, risk management and portfolio allocation decisions. This study examined the economic benefit of high-frequency volatility estimators (measures realized) in option pricing and trading. We evaluated the forecasting ability of high-frequency volatility estimators based on the profits that option dealers would derive from trading on the basis of alternative high-frequency volatility forecasts. To this end, we traded European call and put options on Bank of America, Coca-Cola and Microsoft stocks for a period of 24 trading days using high-frequency volatility-based option trading strategies. The study results show that the realized kernel estimators for Bank of America stock options were the only volatility estimators that earned a positive profit from trading (a profit of $20.42 per option over a period of 24 trading days). For Coca-Cola stock options, the best volatility estimator turned out to be the two-time scale covariance estimator. It earned a total profit of $26.88 per option during the same period. For Microsoft stock options, the preferred volatility estimator was the Range-based realized variance estimator. It outperformed all the other competing estimators with a total profit of $54.07 per option which was significantly greater than the profits of the other estimators. It was concluded that high-frequency volatility forecasts by the realized kernel, two-time scale realized variance and realized range-based variance estimators yield accurate volatility forecasts and are very useful in pricing and trading Bank of America, Coca-Cola and Microsoft stock options, respectively.