Volatility is a key parameter used in many financial applications, from derivatives valuation to asset management and risk management. Volatility measures the size of the errors made in modeling returns and other financial variables. It was discovered that, for vast classes of models, the average size of volatility is not constant but changes with time and is predictable. Autoregressive conditi...

This paper revisits event-study methodology based on regression estimation of abnormal returns. The paper reviews the traditional event study and gives a more detailed discussion of the regression based approach with quantitative event variables. The paper discusses also briefly the the dummy variable regression which is a special case of the quantitative case. Use of GARCH to predict event per...

Engle’s footsteps range widely. His major contributions include early work on band-spectral regression, development and unification of the theory of model specification tests (particularly Lagrange multiplier tests), clarification of the meaning of econometric exogeneity and its relationship to causality, and his later stunningly influential work on common trend modeling (cointegration) and vol...

The conventional wisdom in macroeconomic modeling is to attribute business cycle fluctuations to innovations in the level of the fundamentals. Though volatility shocks could be important too, their propagating mechanism is still not well understood partly because modeling the latent volatilities can be quite demanding. This paper suggests a simply methodology that can separate the level factors...

Concerning price processes, the fact that the volatility is not constant has been observed for a long time. So we deal with models as dXt = μtdt + σtdWt where σ is a stochastic process. Recent works on volatility modeling suggest that we should incorporate jumps in the volatility process. Empirical observations suggest that simultaneous jumps on the price and the volatility [8, 9] exist. The hy...

This paper focuses on modeling foreign exchange return behavior that would result in more accurate currency options pricing. These alternative approaches namely, implied volatility model (IVM), realized volatility model (RVM) and GARCH (1,1) volatility model (GVM) are used in this study. The results, in general suggest that RVM outperforms both IVM and GVM in pricing currency options. In-sample...

Neural networks are the artificial intelligence techniques for modeling complex target functions. Now-a-days it has made remarkable contributions to advancement of various field of finance such as time series prediction, volatility estimation etc. The present work examines the volatilities in the Indian stock market (BSE-SENSEX & NSE-NIFTY) by comparing the volatilities, using Parkinson method,...

We compare forecasts of the realized volatility of the exchange rate returns of the Euro against the U.S. Dollar and the Japanese Yen obtained both directly and through decomposition. Decomposing the realized volatility into its continuous sample path and jump components, and modeling and forecasting them separately instead of directly forecasting the realized volatility, is shown to lead to im...

Multivariate stochastic volatility models with skew distributions are proposed. Exploiting Cholesky stochastic volatility modeling, univariate stochastic volatility processes with leverage effect and generalized hyperbolic skew t-distributions are embedded to multivariate analysis with time-varying correlations. Bayesian prior works allow this approach to provide parsimonious skew structure and...