A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Study shows OAT decomposition generates unexplained profit and loss, while SU decompositions depend on risk factor order.
New risk decompositions clarify domain adaptation issues.
This paper finds a new method for decomposing insurer profits and losses.
New method decomposes profits and losses continuously, avoiding discrete reporting issues.
Researchers propose a new SSL risk decomposition method to evaluate and improve self-supervised learning models.
The study examines how different interpolation methods affect the decomposition of life insurance surplus.
Paper breaks down risk contribution into inherent and correlation risk components.
We analyze bias-variance of margin losses.
A new method decomposes subjective risk into epistemic and aleatoric uncertainties.
We present an algorithm for the decomposition of periodic financial return data into orthogonal factors of expected return and "systemic", "productive", and "nonproductive" risk. Generally, when the number of funds does not exceed the number of periods, the expected return of a portfolio is an affine function of its pr…
Novel framework for risk-sensitive reinforcement learning using martingale decomposition.
The paper analyzes risk spillovers between AI ETFs, AI tokens, and green markets.
A new method to break down insurance costs into risk and uncertainty.
We consider the problem of decomposing monetary risk in the presence of a fully traded market in {\it some} risks. We show that a mark-to-market approach to pricing leads to such a decomposition if the risk measure is time-consistent in the sense of Delbaen.
The focus of this paper is the efficient computation of counterparty credit risk exposure on portfolio level. Here, the large number of risk factors rules out traditional PDE-based techniques and allows only a relatively small number of paths for nested Monte Carlo simulations, resulting in large variances of estimator…
Paper introduces a new method for efficient portfolio risk quantification.
Paper decomposes risk into aleatoric and epistemic uncertainties and generates predictive uncertainty measures.
We consider families of strongly consistent multivariate conditional risk measures. We show that under strong consistency these families admit a decomposition into a conditional aggregation function and a univariate conditional risk measure as introduced Hoffmann et al. (2016). Further, in analogy to the univariate cas…
Sensitivity analysis for individualized effects in OTRs with binary risk factors.
The paper analyzes risk assessment for cash flows in continuous time using the notion of convex risk measures for processes. By combining a decomposition result for optional measures, and a dual representation of a convex risk measure for bounded \cd processes, we show that this framework provides a systematic approach…
New approach avoids restrictive assumptions for optimal portfolio in default risk scenarios.
Paper introduces Modular Jets for diagnosing model decompositions in pipelines.
Paper proposes a new framework to improve stability-based bounds in deep learning.
Bayesian neural networks (BNNs) with latent variables are probabilistic models which can automatically identify complex stochastic patterns in the data. We describe and study in these models a decomposition of predictive uncertainty into its epistemic and aleatoric components. First, we show how such a decomposition ar…
Novel framework for systemic risk analysis in financial markets.
New framework shows much of equity market risk may come from asset returns themselves.
Paper introduces a new principle for fair redistribution of insurance surplus.
From SA-CCR to RSA-CCR: making SA-CCR self-consistent and appropriately risk-sensitive by cashflow decomposition in a 3-Factor Gaussian Market Model
Bayesian neural networks with latent variables are scalable and flexible probabilistic models: They account for uncertainty in the estimation of the network weights and, by making use of latent variables, can capture complex noise patterns in the data. We show how to extract and decompose uncertainty into epistemic and…
In this paper we study a risk-minimizing hedging problem for a semimartingale incomplete financial market where d+1 assets are traded continuously and whose price is expressed in units of the numéraire portfolio. According to the so-called benchmark approach, we investigate the (benchmarked) risk-minimizing strategy in…
We decompose the squared price-of-risk premium into three components: intervention-stable premium, confounding wedge, and information loss.
In the present work we address the problem of evaluating the historical performance of a trading strategy or a certain portfolio of assets. Common indicators such as the Sharpe ratio and the risk adjusted return have significant drawbacks. In particular, they are global indices, that is they do not preserve any 'local'…
Corrects GCV for inconsistent risk estimation in finite ensembles of penalized estimators.
The study reveals unspanned risks in equity option risk premiums, explaining negative premiums for certain options.
BSG learns dynamic network spillovers and uncertainty quantification.
Study calculates tail risk for various mixture distributions.
We study dynamic hedging of counterparty risk for a portfolio of credit derivatives. Our empirically driven credit model consists of interacting default intensities which ramp up and then decay after the occurrence of credit events. Using the Galtchouk-Kunita-Watanabe decomposition of the counterparty risk price paymen…
Paper studies pricing and hedging of nonreplicable insurance contracts using benchmark-neutral approach.
Any optimization algorithm based on the risk parity approach requires the formulation of portfolio total risk in terms of marginal contributions. In this paper we use the independence of the underlying factors in the market to derive the centered moments required in the risk decomposition process when the modified vers…
We construct a binomial model for a guaranteed minimum withdrawal benefit (GMWB) rider to a variable annuity (VA) under optimal policyholder behaviour. The binomial model results in explicitly formulated perfect hedging strategies funded using only periodic fee income. We consider the separate perspectives of the insur…
This paper proposes a simple approach to derive efficient error bounds for learning multiple components with sparsity-inducing regularization. We show that for such regularization schemes, known decompositions of the Rademacher complexity over the components can be used in a more efficient manner to result in tighter b…
Study examines how risk tolerance impacts long-term investment returns.
In the context of a locally risk-minimizing approach, the problem of hedging defaultable claims and their Follmer-Schweizer decompositions are discussed in a structural model. This is done when the underlying process is a finite variation Levy process and the claims pay a predetermined payout at maturity, contingent on…
This paper begins with a study on the dual representations of risk and regret measures and their impact on modeling multistage decision making under uncertainty. A relationship between risk envelopes and regret envelopes is established by using the Lagrangian duality theory. Such a relationship opens a door to a decomp…
Implied volatilities form a well-known structure of smile or surface which accommodates the Bachelier model and observed market prices of interest rate options. For the swaptions that we study, three parameters are taken into account for indexing the implied volatilities and form a "volatility cube": strike (or moneyne…
Adversarial training leads to large generalization gap, decomposed into bias and variance.
New methods for scoring function decomposition improve forecast evaluation.
This paper proposes RiskRank as a joint measure of cyclical and cross-sectional systemic risk. RiskRank is a general-purpose aggregation operator that concurrently accounts for risk levels for individual entities and their interconnectedness. The measure relies on the decomposition of systemic risk into sub-components …