We derive a nonparametric higher-order asymptotic expansion for small-time changes of conditional characteristic functions of Itô semimartingale increments. The asymptotics setup is of joint type: both the length of the time interval of the increment of the underlying process and the time gap...

This paper is concerned with significance testing of the effects of exogenous determinants upon the one-sided deviation term of a semiparametric stochastic frontier model. Two nonparametric significance tests for all or a subset of the determinants of inefficiency are proposed. The proposed...

The random preference (RP) model provides an integral framework for modeling within-individual heterogeneity in choice behavior, by attributing this heterogeneity to preference parameters in the underlying theory of risk attitudes instead of an additive error term that is external to the theory....

According to the log-linear return approximation, the ability of a predictor to predict future stock returns may arise from its ability to predict either the cash flows or the discount rates, or both. This paper introduces novel nonparametric approaches for estimating and testing the time...

The increasing availability of retail scanner data makes it possible to study consumer demand at more disaggregated levels than ever before. This paper derives the asymptotic properties for a tractable iterated three stage least squares (I3SLS) estimator and applies it to estimate a quadratic...

The article presents the results of an econometric estimation of probabilistic default models on a sample of medium-sized manufacturing companies in Russia for the period from 2012 to 2020. Characteristics of the macroeconomic environment were included in the models. According to the results,...

This study employs machine learning models to forecast and comprehend the implied volatility of China ETF50. We develop a hybrid model named LSTM-ML, leveraging historical implied volatility, moneyness, and time-to-maturity as input features. The LSTM component captures dynamic hidden...

The European banking regulation does neither specify a methodology nor a level of confidence for the purpose of quantifying the margin of conservatism for the general estimation error (MoC C) of risk parameter estimates. In order to fill this gap, Casellina et al. (2023) determine the MoC C of...

This study proposes a novel multivariate neural network model, coined as Realized Covariance Matrix NBEATSx (RCM-NBEATSx), for forecasting realized covariance matrices (RCMs) of financial securities. RCM-NBEATSx ensures that the predicted RCMs are positive semidefinite through the employment of...