Expected future volatility plays a central role in finance theory. Consequently, accurate estimation of this parameter is crucial to meaningful financial decision-making. Researchers generally on the past behavior of asset prices to estimate volatility, relating movements in volatility value with prior volatility and/or variables in the investors' information set. These procedures are by nature...

Forecasting volatility has received a great deal of research attention. Many articles have considered the relative performance of econometric model based and option implied volatility forecasts. While many studies have found that implied volatility is the preferred approach, a number of issues remain unresolved. One issue being the relative merit of combination forecasts. By utilising recent ec...

An estimation method is developed for extracting the latent stochastic volatility from VIX, a volatility index for the S&P 500 index return produced by the Chicago Board Options Exchange (CBOE) using the so-called model-free volatility construction. Our model specification encompasses all mean-reverting stochastic volatility option pricing models with a constant-elasticity of variance and those...

Volatility of exchange rate while changes from time to time, is expected to affect firm level operations as well as aggregate level outcomes i.e. macroeconomic performance. This paper, investigates the effects of exchange rate volatility on aggregate production in Iran using a Structural Vector Auto Regressive model with Exogenous Variables (SVARX). The model is estimated based on macroeconomic...

This paper introduces a no-arbitrage framework to assess how macroeconomic factors help explain the risk-premium agents require to bear the risk of fluctuations in stock market volatility. We develop a model in which return volatility and volatility risk-premia are stochastic and derive no-arbitrage conditions linking volatility to macroeconomic factors. We estimate the model using data related...

We construct a risk-neutral stochastic volatility model using no-arbitrage pricing principles. We then study the behavior of the implied volatility of options that are deep in and out of the money according to this model. The motivation of this study is to show the diierence in the asymptotic behavior of the distribution tails between the usual Black-Scholes log-normal distribution and the risk...

Nowadays many researchers use GARCH models to generate volatility forecasts. However, it is well known that volatility persistence, as indicated by the sum of the two parameters G1 and A1[1], in GARCH models is usually too high. Since volatility forecasts in GARCH models are based on these two parameters, this may lead to poor volatility forecasts. It has long been argued that this high persist...

We consider the pricing of volatility products and especially volatility and variance swaps. Under risk-neutral valuation we provide closed form formulae for volatility-average and variance swaps. Also we provide a general partial differential equation for derivatives that have an extra dependence on an average of the volatility. We give approximate solutions of this equation for volatility pro...

The literature on volatility modelling and option pricing is a large and diverse area due to its importance and applications. This paper provides a review of the most significant volatility models and option pricing methods, beginning with constant volatility models up to stochastic volatility. We also survey less commonly known models e.g. hybrid models. We explain various volatility types (e....