Artificial Neural Networks (ANNs) are very powerful tool in modern quantitative finance and have immerged as a powerful statistical modeling technology. This paper focuses on the problem of estimation of volatility of Indian Stock market. It begins with volatility calculation by Auto Regressive Conditional Heteroscedastic (ARCH), & Generalized Autoregressive Conditional Heteroscedasticity (GARC...

Optimal as well as recursive parameter estimation for semimartingales had been studied in Thavaneswaran and Thompson [1, 2]. Recently, there has been a growing interest in modeling volatility of the observed process by nonlinear stochastic processes (Taylor [3]). In this paper, we study the recursive estimates for various classes of discretely sampled continuous time stochastic volatility model...

This paper introduces quasi-maximum likelihood estimator for multivariate diffusions based on discrete observations. A numerical solution to the stochastic differential equation is obtained by higher order Wagner-Platen approximation and it is used to derive the first two conditional moments. Monte Carlo simulation shows that the proposed method has good finite sample property for both normal a...

the price fluctuations of chicken and its production inputs are one of the main challenges in broiler industry which affects the producer and consumer‘s welfare. this study investigates the price fluctuations of broiler and the price fluctuations of the two important inputs of broiler production -e.g. one day-old chick and soybean meal- in tehran province. to achieve the purpose, the non-linear...

Most procedures for modeling and forecasting financial asset return volatilities rely on restrictive and complicated parametric GARCH or stochastic volatility models. The method of realized volatility constructed from high-frequency intraday returns is an alternative choice for volatility measurement. In this paper we make an empirical analysis on Chinese stock index data by using the method of...

In this short note we show how to setup a one dimensional single asset model, e.g. equity model, which calibrates to a full (two dimensional) implied volatility surface. We show that the efficient calibration procedure used in LIBOR Markov functional models may be applied here too. In a addition to the calibration to a full volatility surface the model allows the calibration of the joint asset-...

25 years of volatility research has left the macroeconomic environment playing a minor role. This paper proposes modeling equity volatilities as a combination of macroeconomic effects and time series dynamics. High frequency return volatility is specified to be the product of a slow moving deterministic component, represented by an exponential spline, and a unit GARCH. This deterministic compon...

We analyze hedge fund performance using the stochastic discount factor (SDF) approach and imposing the arbitrage-free requirement to correctly value the derivatives and dynamic trading strategies used by hedge funds. Using SDFs of many asset-pricing models, we evaluate hedge fund portfolios based on style and characteristics. Without the arbitrage-free requirement, pricing errors are relatively...

In this paper we describe a legal framework for simple tax status detection of transaction parties as a part of a hybrid eCommerce information system. We also focus on one, in our view more interesting point encountered during the model setup which is the case of legal knowledge modeling in such a way that it is immune to fast-pace changing of jurisdiction. This kind of quality is especially im...

A stochastic volatility model consists of a pair of stochastic processes {Xt, Yt}, of which only Yt is observed, but where the conditional distribution of Yt|Xt = xt has a scale that depends on xt. The unobserved Xt is interpreted as a state variable that affects the processes that result in the observed Yt. The conditional heteroscedasticity (CH) approach to modeling volatility is based on the...