LossVol: Loss Function for volatility forecasts

In MCS: Model Confidence Set Procedure

Description

Calculate the losses associated with volatility (standard deviation) forecasts

Usage

LossVol(realized, evaluated, which = "SE1")

Arguments

realized	a vector with some realized volatility measure
evaluated	a vector or a matrix of volatility forecasts
which	The loss function to use. possible choices are: 'SE1','SE2','QLIKE','R2LOG','AE1','AE2', for further information see Bernardi and Catania (2014) or Hansen and Lunde (2005).

Value

A matrix with the forecast losses

Author(s)

Leopoldo Catania & Mauro Bernardi

References

Koenker, R., & Bassett, G. (1978). Regression quantiles. Econometrica, 46(1), 33-50.

Gonzalez-Rivera G, Lee TH, Mishra S (2004). Forecasting volatility: A reality check based on option pricing, utility function, value-at-risk, and predictive likelihood." International Journal of Forecasting, 20(4), 629-645. ISSN 0169-2070. URL http://www.sciencedirect.com/science/article/pii/S0169207003001420.

Hansen PR, Lunde A (2005). A forecast comparison of volatility models: does anything beat a GARCH(1,1)?" Journal of Applied Econometrics, 20(7), 873-889. ISSN 1099-1255. URL http://dx.doi.org/10.1002/jae.800.

Bernardi M. and Catania L. (2014) The Model Confidence Set package for R. URL http://arxiv.org/abs/1410.8504

MCS documentation built on May 2, 2019, 7:55 a.m.