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Found 7 papers, 0 papers with code
Spatial and Spatiotemporal Volatility Models: A Review
In this paper, we aim to provide a comprehensive review of the recent literature on spatial and spatiotemporal volatility models.
Statistical inference for the EU portfolio in high dimensions
In this paper, using the shrinkage-based approach for portfolio weights and modern results from random matrix theory we construct an effective procedure for testing the efficiency of the expected utility (EU) portfolio and discuss the asymptotic behavior of the proposed test statistic under the high-dimensional asymptotic regime, namely when the number of assets $p$ increases at the same rate as the sample size $n$ such that their ratio $p/n$ approaches a positive constant $c\in(0, 1)$ as $n\to\infty$.
Evaluating Active Learning Heuristics for Sequential Diagnosis
As initial system observations usually do not suffice to deterministically pin down just one explanation of the system's misbehavior, additional system measurements can help to differentiate between possible explanations.
Bayesian mean-variance analysis: Optimal portfolio selection under parameter uncertainty
The parameters of the posterior predictive distributions are functions of the observed data values and, consequently, the solution of the optimization problem is expressed in terms of data only and does not depend on unknown quantities.
Statistical Finance Portfolio Management
A Generally Applicable, Highly Scalable Measurement Computation and Optimization Approach to Sequential Model-Based Diagnosis
To this end, Sequential Diagnosis approaches ask an oracle for additional system measurements.
Inexpensive Cost-Optimized Measurement Proposal for Sequential Model-Based Diagnosis
In this work we present strategies for (optimal) measurement selection in model-based sequential diagnosis.
Scalable Computation of Optimized Queries for Sequential Diagnosis
During query computation, existing sequential diagnosis methods often require the generation of many unnecessary query candidates and strongly rely on expensive logical reasoners.
