Testing procedures for predictive regressions with lagged autoregressive variables imply a suboptimal inference in presence of small violations of ideal assumptions. We propose a novel testing framework resistant to such violations, which is consistent with nearly integrated regressors and...

We propose a nonparametric likelihood inference method for the integrated volatility under high frequency financial data. The nonparametric likelihood statistic, which contains the conventional statistics such as empirical likelihood and Pearson's chi-square as special cases, is not...

A (conservative) test is constructed to investigate the optimal lag structure for forecasting realized volatility dynamics. The testing procedure relies on the recent theoretical results that show the ability of the adaptive least absolute shrinkage and selection operator (adaptive lasso) to...

We propose a new modeling framework for the valuation of European options, in which dynamic short and long run volatility components drive the smile dynamics. The model state dynamics is driven by a matrix jump diffusion, provides efficient pricing formulas for plain vanilla options by means of...

We introduce a new class of flexible and tractable matrix affine jump-diffusions (AJD) to model multivariate sources of financial risk. We first provide a complete transform analysis of this model class, which opens a range of new potential applications to, e.g., multivariate option pricing with...

We study a new class of three-factor affine option pricing models with interdependent volatility dynamics and a stochastic skewness component unrelated to volatility shocks. These properties are useful in order (i) to model a term structure of implied volatility skews more consistent with the...