Accurate volatility predictions are crucial for the successful implementation of risk management. The use of high frequency data approximately renders volatility from a latent to an observable quantity, and opens new directions to forecast future volatilities. Our goals in this paper are: (i) to select an accurate forecasting procedure for predicting volatilities based on high frequency data fr...

This paper tackles the design of an optimal early warning system (EWS) for sovereign default from two distinct angles: the choice of the econometric methodology and the evaluation of the EWS itself. It compares K-means clustering of macrodata, a logit regression for macrodata, a logit regression for credit ratings, and the combined forecasts from all three methods. The optimal choice of forecas...

In many stated preference settings stakeholders will be uncertain as to their exact willingness-topay for a proposed environmental amenity. To accommodate this possibility analysts have designed elicitation formats with multiple bids and response options that allow for the expression of uncertainty. We argue that the information content flowing from such elicitation has not yet been fully and e...

General methodology is developed here to deal with the association between a a binary variable and network connections with or without confounding covariates. Also the case when the network is observed at several time periods is treated. As an application we consider the diffusion of organic farming in the province of North Karelia in Finland. It turns out that organic farms are more clustered ...

Evaluation of forecast optimality in economics and finance has almost exclusively been conducted under the assumption of mean squared error loss. Under this loss function optimal forecasts should be unbiased and forecast errors serially uncorrelated at the single period horizon with increasing variance as the forecast horizon grows. Using analytical results we show that standard properties of o...

In this paper we consider the estimation of Markov models where the transition density is unknown. The approach we propose is the empirical characteristic function (ECF) estimation procedure with an approximate optimal weight function. The approximate optimal weight function is obtained through an Edgeworth/Gram-Charlier expansion of the logarithmic transition density of the Markov process. Bas...

This paper analyses the application of a switching volatility model to forecast the Ž . distribution of returns and to estimate the Value-at-Risk VaR of both single assets and portfolios. We calculate the VaR value for 10 Italian stocks and a number of portfolios based on these stocks. The calculated VaR values are also compared with the variance–coŽ . variance approach used by JP Morgan in Ris...

Beginning in 1998, U.S. commercial banks may determine their regulatory capital requirements for financial market risk exposure using value-at-risk (VaR) models. Currently, regulators have available three hypothesis-testing methods for evaluating the accuracy of VaR models: the binomial, interval forecast and distribution forecast methods. Given the low power often exhibited by their correspond...

We consider a set of linear regression models that differ in their choice of regressors, and derive a method for inference that controls for the set of models under investigation. The method is based around an estimate of the distribution for a class of statistics, which can depend on two or more models. An example is the largest R2 over a set of regression models. The distribution will typical...

Fisher’s pivot functions (PFs) continue to dominate statistical inference and bootstrap literature, despite Efron and Hinkley and Royall’s attempts to inject robustness. Vinod uses Godambe’s pivot functions (GPFs) based on Godambe—Durbin estimating functions (EFs) to develop numerically computed GPF roots. Such GPF roots can fill a long-standing need in the bootstrap literature for robust pivot...