Time-varying volatility is important in financial markets. Stochastic volatility models are used instead of deterministic ones. Markov Chain Monte Carlo methods help estimate volatilities and model parameters better. Sequential importance sampling is a powerful tool for high-dimensional problems like stochastic volatility. These methods are applied to financial models like stock prices and interest rates. Option pricing formulas with stochastic volatility are also tackled using this method. Gaussian approximations in each iteration of the method reduce computational intensity.