This study compares the efficacy of Black–Scholes implied volatility (BSIV) with model-free implied volatility (MFIV) in providing volatility forecasts for 13 North American, European, and Asian stock market indexes: S&P 500 (United States), S&P/ASX 200 (Australia), S&P/TSX 60 (Canada), AEX...

The hedging of weather risks has become extremely relevant in recent years, promoting the diffusion of weather derivative contracts. The pricing of such contracts require the development of appropriate models for the prediction of the underlying weather variables. Within this framework, we...

A Self-Exciting Threshold AutoRegressive (SETAR) model is applied to the Italian stock market volatility, to obtain volatility forecasts and Value-at-Risk (VaR) estimates. There is almost nothing dealing with Italian markets in the literature of Threshold models, which have never been used for...

Un conjunto de modelos GARCH multivariados son estimados y su validez empírica comparada a partir del cálculo de la medida VaR, para los retornos diarios de la tasa de cambio nominal del peso colombiano con respecto al dólar americano, euro, libra esterlina y yen japonés en el periodo...

This paper considers the performance of different long-memory dynamic models when forecasting volatility in the stock market using implied volatility as an exogenous variable in the information set. Observed volatility is separated into its continuous and jump components in a framework that...

This article explores nonlinearities in the response of speculators’ trading activity to price changes in live cattle, corn, and lean hog futures markets. Analyzing weekly data from March 4, 1997 to December 27, 2005, we reject linearity in all of these markets. Using smooth transition...

In this paper, we apply a collection of parametric (Normal, Normal GARCH, Student GARCH, RiskMetrics and high-frequency duration models) and non-parametric (empirical quantile, extreme distributions models) Value-at-Risk (VaR) techniques to intraday data for three stocks traded on the NewY ork...

We investigate intradaily seasonal patterns on the distribution of high frequency financial returns. Using quantile regression we show the expansions and shrinks of the probability law through the day for three years of 15 minutes sampled stock returns. Returns are more dispersed and less...