Conference Agenda

Tensor data consisting of multivariate outcomes over the items and across the subjects with longitudinal and cross-sectional dependence are considered. A completely distribution-free and tweaking-parameter-free detection procedure for changepoints at different locations is designed, which does not require training data. A CUSUM-type test statistic is employed, and its asymptotic properties are derived for a large number of available individual profiles. The considered test is shown to be consistent. The aim is to propose eigenbootstrap superstructure that overcomes the computational curse of dimensionality without any loss of information, while it preserves all the dependencies within and between the panels. The validity of this new and fast resampling algorithm is proved in this general setting. The empirical properties of the detection technique are investigated through a simulation study. The fully data-driven test is applied to real-world data from EEG and psychometrics.

Risk reserving is a fundamental task in non-life insurance and is performed on a regular basis. It is typically carried out using parametric estimation and prediction methods applied to aggregated data structured in so-called run-off triangles. In this talk, we present nonparametric, functional-based reserving alternatives that rely on the completion of MNAR functional segments in the underlying run-off triangles.

In addition to the theoretical and methodological framework, we focus on algorithmic details implemented in the recent R package ProfileLadder. The package offers a flexible and computationally efficient tools for pointwise and distributional reserve prediction and includes relevant visualization and diagnostic tools implemented via standard S3 methods. These nonparametric approaches provide modern, transparent, and extensible alternatives to classical reserving methods used by researchers, actuarial scientists, or insurance practitioners.

We identify shocks in a structural MGARCH model of asset returns using news-based proxy instruments. Structural parameters, including an orthogonal matrix, are estimated via Riemannian optimization. We study daily returns on the S&P500, the 10-year Treasury yield, and the USD index. The proxies identify an equity valuation shock, capturing shifts in expected dividend growth and risk premia, and a bond valuation shock, reflecting fundamental shocks in safe-haven asset pricing. The dynamic impact matrix is asymmetric, and sign changes in the bond valuation shock loading drive switches between negative and positive stock–bond co-movement. A decomposition of the COVID-19 episode shows that bond valuation shocks partially offset equity market stress and explain the temporary yield surge in mid-March 2020.

We propose a new estimator of low-frequency skewness that exploits high-frequency data through a direct functional mapping consisting of layers of convolutional neural networks followed by layers of MLPs. We show that the relevant high-frequency features converge to a continuous limit and that the latent skewness admits a continuous functional representation. This allows us to establish the unbiasedness of our NN estimator using classical universal approximation results and Rademacher complexity arguments. Monte Carlo experiments under stochastic volatility models, with and without jumps, show that the estimator reduces finite-sample bias relative to existing realized-skewness estimators and remains accurate under model misspecification. Empirically, our estimator exhibits temporal stability and delivers superior cross-sectional pricing performance in skewness-sorted portfolios. Another application finds no evidence that ESG-oriented firms exhibit lower crash risk. Overall, the results demonstrate how learning-based functionals can improve the estimation of nonlinear distributional characteristics from high-frequency data.