heteroskedastic SVAR-GARCH model and propose a bootstrap-based inference procedure on structural impulse responses. We compare the … finite-sample properties of our bootstrap method with those of two competing bootstrap methods via extensive Monte Carlo … simulations. We also present a three-step estimation procedure of the parameters of the SVAR-GARCH model that promises numerical …

-Gaussian dependency structures with a small number of parameters. In this paper we develop a novel adaptive estimation technique of the … ; Archimedean copula ; adaptive estimation …

This paper develops a Monte-Carlo backtesting procedure for risk premia strategies and employs it to study Time … results are robust to using different time-series models, time periods, asset classes, and risk measures. …

momentum strategy. The estimation of this modeling and strategy approach can be done using an extended and modified version of … and risk features than very simple and very complex models. Combinations of two strategies help, in particular, to reduce … risk features like volatility and largest loss, which indicates that complete densities provide useful information for risk. …

This paper employs weighted least squares to examine the risk-return relation by applying high-frequency data from four … returns and expected risk. However, by using quantile regressions, we find that the risk-return relation moves from negative … to positive as the returns’ quantile increases. A positive risk-return relation is valid only in the upper quantiles. The …

This article provides an introduction to methods and challenges underlying application of the bootstrap in econometric … modelling of economic and financial time series. Validity, or asymptotic validity, of the bootstrap is discussed as this is a … key element in deciding whether the bootstrap is applicable in empirical contexts. That is, as detailed here, bootstrap …

methods. Finally, we apply the wild bootstrap approach to study the forecast ability of variance risk premia to predict future …We introduce a wild multiplicative bootstrap for M and GMM estimators in nonlinear models when autocorrelation … structures of moment functions are unknown. The implementation of the bootstrap algorithm does not require any parametric …