arXiv research

New framework identifies hidden risks and optionality in American options.

Study tail risk aggregation under dependence uncertainty.

Multivariate regular variation plays a role assessing tail risk in diverse applications such as finance, telecommunications, insurance and environmental science. The classical theory, being based on an asymptotic model, sometimes leads to inaccurate and useless estimates of probabilities of joint tail regions. This pro…

The study compares clustering risk in Hidden Markov and i.i.d. models, showing the Bayes classifier is nearly optimal.

Hidden regular variation is a sub-model of multivariate regular variation and facilitates accurate estimation of joint tail probabilities. We generalize the model of hidden regular variation to what we call hidden domain of attraction. We exhibit examples that illustrate the need for a more general model and discuss de…

We consider the maximum likelihood (Viterbi) alignment of a hidden Markov model (HMM). In an HMM, the underlying Markov chain is usually hidden and the Viterbi alignment is often used as the estimate of it. This approach will be referred to as the Viterbi segmentation. The goodness of the Viterbi segmentation can be me…

Model shows how banks' hidden-to-maturity accounting can mask run risk and lead to financial instability.

Model predicts operational risk using HMMs with economic covariates.

New method estimates hidden binary mixture model centers efficiently.

Model predicts risk-adjusted returns across various financial markets.

Continuous Hidden Markov Models for Equity Returns

Hidden regular variation defines a subfamily of distributions satisfying multivariate regular variation on $\mathbb{E} = [0, \infty]^d \backslash \{(0,0, ..., 0) \}$ and models another regular variation on the sub-cone $\mathbb{E}^{(2)} = \mathbb{E} \backslash \cup_{i=1}^d \mathbb{L}_i$, where $\mathbb{L}_i$ is the $i…

Characterizing a patient's progression through stages of sepsis is critical for enabling risk stratification and adaptive, personalized treatment. However, commonly used sepsis diagnostic criteria fail to account for significant underlying heterogeneity, both between patients as well as over time in a single patient. W…

The paper offers simple, near-optimal algorithms for multi-group learning.

The paper examines utility maximization in markets with hidden Gaussian drift, finding restrictions on model parameters.

Study incentive efficiency in monopoly insurance markets with hidden information.

Estimates crypto risk premia using hidden factors and finds significant integration with traditional markets.

We consider the smoothing probabilities of hidden Markov model (HMM). We show that under fairly general conditions for HMM, the exponential forgetting still holds, and the smoothing probabilities can be well approximated with the ones of double sided HMM. This makes it possible to use ergodic theorems. As an applicatio…

We study finite sample expressivity, i.e., memorization power of ReLU networks. Recent results require $N$ hidden nodes to memorize/interpolate arbitrary $N$ data points. In contrast, by exploiting depth, we show that 3-layer ReLU networks with $Ω(\sqrt{N})$ hidden nodes can perfectly memorize most datasets with $N$ po…

The paper improves asset allocation using a skew-normal distribution in the Black-Litterman model.

B-Learner provides bounds on CATE under hidden confounding risks.

Motivated by the unceasing interest in hidden Markov models (HMMs), this paper re-examines hidden path inference in these models, using primarily a risk-based framework. While the most common maximum a posteriori (MAP), or Viterbi, path estimator and the minimum error, or Posterior Decoder (PD), have long been around, …

New benchmarks focus on LLM risk in finance, not just accuracy.

We solve a high-dimensional model where nonlinear autoencoders detect hidden structure missed by PCA.

The study uses Bayesian Hidden Markov Models to predict cryptocurrency returns.

These notes review six lectures given by Prof. Andrea Montanari on the topic of statistical estimation for linear models. The first two lectures cover the principles of signal recovery from linear measurements in terms of minimax risk. Subsequent lectures demonstrate the application of these principles to several pract…

Paper connects two portfolio methods, HRP and Minimum Variance, revealing their underlying similarity.

We study the problem of learning one-hidden-layer neural networks with Rectified Linear Unit (ReLU) activation function, where the inputs are sampled from standard Gaussian distribution and the outputs are generated from a noisy teacher network. We analyze the performance of gradient descent for training such kind of n…

We study model recovery for data classification, where the training labels are generated from a one-hidden-layer neural network with sigmoid activations, also known as a single-layer feedforward network, and the goal is to recover the weights of the neural network. We consider two network models, the fully-connected ne…

The paper sets information-theoretic lower bounds for neural networks' parameter recovery and excess risk.

After the release of the final accounting standards for impairment in July 2014 by the IASB, banks will face the next significant methodological challenge after Basel 2. In this paper, first methodological thoughts are presented, and ways how to approach underlying questions are proposed. It starts with a detailed disc…

LLM trading agents show risk feedback can improve alignment without fine-tuning.

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 applicable to multi-predictor…

GraphShield uses dynamic graph learning to detect and visualize financial risks.

Uncertainty in economics still poses some fundamental problems illustrated, e.g., by the Allais and Ellsberg paradoxes. To overcome these difficulties, economists have introduced an interesting distinction between 'risk' and 'ambiguity' depending on the existence of a (classical Kolmogorovian) probabilistic structure m…

This paper tackles hidden technical debts in fair ML systems for Fintech.

Develops a model for analyzing cryptocurrency returns focusing on extreme values.

Traditional voxel-level multiple testing procedures in neuroimaging, mostly $p$-value based, often ignore the spatial correlations among neighboring voxels and thus suffer from substantial loss of power. We extend the local-significance-index based procedure originally developed for the hidden Markov chain models, whic…

Financial markets are not random, but hard to predict due to hidden causes and strategic use.

Dual model combines HMM and neural networks for energy trading during volatile periods.

In this paper we consider a reduced-form intensity-based credit risk model with a hidden Markov state process. A filtering method is proposed for extracting the underlying state given the observation processes. The method may be applied to a wide range of problems. Based on this model, we derive the joint distribution …

Study Transformer layers under cross-entropy training using mean field control.

More and more AI services are provided through APIs on cloud where predictive models are hidden behind APIs. To build trust with users and reduce potential application risk, it is important to interpret how such predictive models hidden behind APIs make their decisions. The biggest challenge of interpreting such predic…

Bayesian framework forecasts financial tail risks using realized volatility and nonlinear thresholds.

A statistical decision problem is hidden in the core of option pricing. A simple form for the price C of a European call option is obtained via the minimum Bayes risk, R_B, of a 2-parameter estimation problem, thus justifying calling C Bayes (B-)price. The result provides new insight in option pricing, among others obt…

Study uses MLP models to predict large-cap US stocks, finding 2-3 hidden layers more flexible.

DWTS uses observational data to improve clinical trial efficiency.

This paper introduces a novel approach to measuring privacy risks in deep computer vision models based on intermediate outputs.