Article history: Received October 1, 2011 Received in Revised form November, 14, 2011 Accepted 30 January 2012 Available online 20 February 2012 In this study, the backpropagation neural network (BPNN) is tested for the ability to forecast the daily volatility of two stock market indices from the Middle East and North Africa (MENA) region using volume; namely Morocco and Saudi Arabia. Volatilit...

Modeling and forecasting realized volatility is of paramount importance. Previous studies have examined the role of both the continuous and jump components of volatility in forecasting. This paper considers how to use index level jumps and cojumps across index constituents for forecasting index level volatility. In combination with the magnitude of past index jumps, the intensity of both index ...

Software development is considered to be a dynamic process where demands for changes seem to be inevitable. Modifications to software are prompted by all kinds of changes including changes to the requirements. This type of changes gives rise to an intrinsic volatility, which has several impacts on the software development lifecycle. This paper describes our findings of an extensive survey based...

This study develops and estimates a stochastic volatility model of commodity prices that nests many of the previous models in the literature. The model is an affine three-factor model with one state variable driving the volatility and is maximal among all such models that are also identifiable. The model leads to quasianalytical formulas for futures and options prices. It allows for time-varyin...

Motivated by marginals-mimicking results for Itô processes [15, 21] via SDEs and by their applications to volatility modeling in finance, we discuss the weak convergence of the law of a hypoelliptic diffusions conditioned to belong to a target affine subspace at final time, namely L(Zt|Yt = y) if X· = (Y·, Z·). To do so, we revisit Varadhan-type estimates in a small-noise regime, studying the d...

Volatility modeling and analysis are traditionally based on either historical price data or option data. Finance theory shows that option prices heavily depend on the underlying stocks’ prices, thus the two kinds of data are related. This paper explores the approach that combines both stock price data and option data to perform the statistical analysis of volatility. We investigate the Black-Sc...

Substantial progress has been made in developing more realistic option pricing models. Empirically, however, it is not known whether and by how much each generalization improves option pricing and hedging. We ll this gap by rst deriving an option model that allows volatility, interest rates and jumps to be stochastic. Using S&P 500 options, we examine several alternative models from three persp...

We propose a dynamic model for financial market volatility with an heterogeneous structure for three components: continuous volatiilty, leverage and jumps. We find that each of the three components plays a significant role in volatility forecasting and neglecting one of them is detrimental to the forecasting performance. Importantly, we find remarkable forecasting power for the negative past re...

This paper deals with modeling volatility of returns of Pliva stocks on Zagreb Stock Exchange for Value at Risk forecasting. Volatility reaction and volatility persistence are measured using asymmetric GARCH process. Croatian capital market characteristic is absence of intensive reaction on "good" information. But it is confirmed that Pliva stocks volatility on Croatian capital market are under...