This book focuses on developing Methodologies for Estimating Stochastic Volatility (SV) parameters from the Stock-Price Time-Series under a Classical framework. The text contains three chapters and is structured as follows:
In the first chapter, we shall introduce and discuss the concept of various parametric SV models. This chapter represents a brief survey of the existing literature on the subject of nondeterministic volatility.
We start with the concept of log-normal distribution and historic volatility. We then will introduce the Black-Scholes  framework. We shall also mention alternative interpretations as suggested by Cox and Rubinstein . We shall state how these models are unable to explain the negative-skewness and the leptokurticity commonly observed in the stock markets. Also, the famous implied-volatility smile would not exist under these assumptions.
At this point we consider the notion of level-dependent volatility as advanced by researchers such as Cox and Ross [69, 70] as well as Bensoussan, Crouhy, and Galai . Either an artificial expression of the instantaneous variance will be used, as is the case for Constant Elasticity Variance (CEV) models, or an implicit expression will be deduced from a Firm model similar to Merton's , for instance.
We also will bring up the subject of Poisson Jumps  in the distributions providing a negative-skewness and larger kurtosis. These jump-diffusion models offer a link ...