Asset prices are typically modeled with the geometric Brownian motion (GBM). Correlation between the assets is exogenously modeled and then ad-hoc assigned to the asset prices. This is conceptually and mathematically unsatisfying. We create a new, simple approach, which simultaneously models stochastic volatility and stochastic correlation. This approach replicates the realworld volatility – co...

We consider a k-GARMA generalization of the long-memory stochastic volatility (LMSV) model, discuss the properties of the model and propose a wavelet-based Whittle estimator for its parameters. Its consistency is shown. Monte Carlo experiments show favorable properties of the proposed method with respect to the Whittle estimator and a wavelet-based approximate maximum likelihood estimator. An a...

modeling and forecasting the volatility of tehran exchange dividend price index (tedpix) the present research, analyses the forecasting performance of a variety of conditional and non-conditional models of tedpix volatility at the daily frequencies performance criterion namely the root mean square error (rmse). under rmse, results show ma250 and cgarch models had better performance between non ...

in this paper various arch models and relevant news impact curves including a partially nonparametric (pnp) one are compared and estimated with daily iran stock return data. diagnostic tests imply the asymmetry of the volatility response to news. the egarch model, which passes all the tests and appears relatively matching with the asymmetry in the data, seems to be the most adequate characteriz...

Volatility permeates modern financial theories and decision making processes. As such, accurate measures and good forecasts of future volatility are critical for the implementation and evaluation of asset and derivative pricing theories as well as trading and hedging strategies. In response to this, a voluminous literature has emerged for modeling the temporal dependencies in financial market v...

The volatility clustering frequently observed in financial/economic time series is often ascribed to GARCH and/or stochastic volatility models. This paper demonstrates the usefulness of reconceptualizing the usual definition of conditional heteroscedasticity as the (h = 1) special case of h-step-ahead conditional heteroscedasticity, where the conditional volatility in period t depends on observ...

Regime switching models have proven to be well-suited for capturing the time series behavior of many financial variables. In particular, they have become a popular framework for pricing equity-linked insurance products. The success of these models demonstrates that realistic modeling of financial time series must allow for random changes in volatility. In the context of valuation of contingent ...

We extend recently introduced latent threshold dynamic models to include dependencies among dynamic latent factors underlying multivariate volatility. With an ability to induce time-varying sparsity into factor loadings, these models now also allow time-varying correlations among factors; this may be exploited to improve volatility forecasts. We couple multi-period, out-of-sample forecasting wi...

Requirements of a project are found to change in various ways during the course of the same. The effects of varying magnitude of requirement volatility on different project parameters like effort, schedule, etc have been well investigated. However, there has been no analysis on how different ‘patterns’ of requirement volatility influence selection of project management approaches and the result...