This paper suggests stochastic volatility models incorporating both the leverage effect and information on the daily high/low prices of stocks. The leverage effect is measured using open-to-close returns and two distinct intraday data, ranges, defined by the differences between daily high and low log-prices, and extreme prices in order to detect asymmetric volatility behavior. The likelihood-based inferences of Markov Chain Monte Carlo (MCMC) are conducted to estimate parameters and volatility. The simulation study reveals that the proposed model is superior to a traditional stochastic volatility model using returns only but there is little difference between estimators using ranges or high/low prices. Performing an empirical analysis using the E-mini S&P 500 and the Nasdaq 100 Futures, we find strong evidence of the leverage effect even when information of high/low prices is incorporated.