Peter Gruber

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We develop a new framework for multivariate intertemporal portfolio choice that allows us to derive optimal portfolio implications for economies in which the degree of correlation across industries, countries, or asset classes is stochastic. Optimal portfolios include distinct hedging components against both stochastic volatility and correlation risk. We(More)
In this work, we present a method to extract high-amplitude artefacts from single channel electroencephalogram (EEG) signals. The method is called local singular spectrum analysis (local SSA). It is based on a principal component analysis (PCA) applied to clusters of the multidimensional signals obtained after embedding the signals in their time-delayed(More)
We describe the status of the effort to realize a first neutrino factory and the progress made in understanding the problems associated with the collection and cooling of muons towards that end. We summarize the physics that can be done with neutrino factories as well as with intense cold beams of muons. The physics potential of muon colliders is reviewed,(More)
An important tool in high-dimensional, explorative data mining is given by clustering methods. They aim at identifying samples or regions of similar characteristics, and often code them by a single codebook vector or centroid. One of the most commonly used partitional clustering techniques is the k-means algorithm, which in its batch form partitions the(More)
We transfer the ICA model to the case where the underlying field is not the set of reals but an arbitrary finite field. We give conditions for separability of the model, pointing out existing parallels to the real case. Three algorithms capable of solving the task are suggested and we demonstrate their viability through simulations and a possible(More)
Independent Component Analysis is usually performed over the fields of reals or complex numbers and the only other field where some insight has been gained so far is GF(2), the finite field with two elements. We extend this to arbitrary finite fields, proving separability of the model if the sources are non-uniform and non-degenerate and present algorithms(More)
In this paper we present denoising algorithms for enhancing noisy signals based on Local ICA (LICA), Delayed AMUSE (dAMUSE) and Kernel PCA (KPCA). The algorithm LICA relies on applying ICA locally to clusters of signals embedded in a high dimensional feature space of delayed coordinates. The components resembling the signals can be detected by various(More)
In independent component analysis (ICA) the common task is to achieve either spatial or temporal independence by linearly mapping into a feature space. If the data possesses both spatial and temporal structures such as a sequence of images or 3d-scans taken at fixed time intervals, we can require the transformed data to be as independent as possible in both(More)
This paper studies the term structure implications of a simple structural model in which the representative agent displays ambiguity aversion, modeled by Multiple Priors Recursive Utility. Bond excess returns reflect a premium for ambiguity, which is observationally distinct from the risk premium of affine yield curve models. The ambiguity premium can be(More)