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.
This paper improves credit risk analysis by incorporating state-dependent recovery rates into a factor model.
New model captures state-dependent variability in partially observed systems.
Study controlled contagion with state-dependent killing, proving a comparison principle.
New algorithm speeds up MCMC for complex distributions.
We derive an arbitrage free relationship between recovery swap rates, digital default swap spreads and conventional CDS spreads, and argue that the fair forward recovery rate used in recovery swaps must contain a convexity premium over the expected recovery value.
In a dual risk model, the premiums are considered as the costs and the claims are regarded as the profits. The surplus can be interpreted as the wealth of a venture capital, whose profits depend on research and development. In most of the existing literature of dual risk models, the profits follow the compound Poisson …
Probabilistic proof of smooth boundaries in optimal stopping problems.
Study improves covariance estimation for SGD under Markovian data, matching best rates.
Paper reconciles minimax rates and optimal recovery rates for noisy observations.
We consider the effect of recovery rates on a pool of credit assets. We allow the recovery rate to depend on the defaults in a general way. Using the theory of large deviations, we study the structure of losses in a pool consisting of a continuum of types. We derive the corresponding rate function and show that it has …
We find that factors explaining bank loan recovery rates vary depending on the state of the economic cycle. Our modeling approach incorporates a two-state Markov switching mechanism as a proxy for the latent credit cycle, helping to explain differences in observed recovery rates over time. We are able to demonstrate ho…
This study shows ESG ratings reduce equity crash risk during market downturns.
In recent years research on credit risk modelling has mainly focused on default probabilities. Recovery rates are usually modelled independently, quite often they are even assumed constant. Then, however, the structural connection between recovery rates and default probabilities is lost and the tails of the loss distri…
While defaults are rare events, losses can be substantial even for credit portfolios with a large number of contracts. Therefore, not only a good evaluation of the probability of default is crucial, but also the severity of losses needs to be estimated. The recovery rate is often modeled independently with regard to th…
Proposes a new framework for optimizing utility with state-dependent benchmarks.
The paper solves a consumption-investment problem with state-dependent lower bounds.
Unique optimal strategy identified for state-dependent risk aversion.
Proposes a new method for determining LGD discount rates based on cost of capital.
A Hawkes process with state-dependent factor models order flows in limit order books.
We provide analytical pricing formula of corporate defaultable bond with both expected and unexpected default in the case with stochastic default intensity. In the case with constant short rate and exogenous default recovery using PDE method, we gave some pricing formula of the defaultable bond under the conditions tha…
Study of SGD with state-dependent noise, improving escape from local minima.
The study uses Random Matrix Theory to identify structural changes in stock markets during shocks.
In this paper we modify the model of Itkin, Shcherbakov and Veygman, (2019) (ISV2019), proposed for pricing Quanto Credit Default Swaps (CDS) and risky bonds, in several ways. First, it is known since the Lehman Brothers bankruptcy that the recovery rate could significantly vary right before or at default, therefore, i…
We study statistical aspects of state-dependent Hawkes processes, which are an extension of Hawkes processes where a self- and cross-exciting counting process and a state process are fully coupled, interacting with each other. The excitation kernel of the counting process depends on the state process that, reciprocally…
Hybrid QML model improves recovery rate prediction accuracy.
Researchers prove inner product recovery is impossible in latent space models.
The current research on credit risk is primarily focused on modeling default probabilities. Recovery rates are often treated as an afterthought; they are modeled independently, in many cases they are even assumed constant. This is despite of their pronounced effect on the tail of the loss distribution. Here, we take a …
Study models interest rates as CTMC, pricing and replicating derivatives.
We investigate the extension of the multilevel Monte Carlo path simulation method to jump-diffusion SDEs. We consider models with finite rate activity, using a jump-adapted discretisation in which the jump times are computed and added to the standard uniform dis- cretisation times. The key component in multilevel analy…
Theory integrates loss aversion into expected utility for monetary returns.
Gradient descent recovers low-rank matrices from corrupted measurements with double over-parameterization.
This paper develops the Jungle model in a credit portfolio framework. The Jungle model is able to model credit contagion, produce doubly-peaked probability distributions for the total default loss and endogenously generate quasi phase transitions, potentially leading to systemic credit events which happen unexpectedly …
Study efficient algorithms for nonconvex optimization with state-dependent Markov data.
Empirical study on UEEs reveals liquidity's role and universal recovery patterns.
In this paper we formulate a corporate bond (CB) pricing model for deriving the term structure of default probabilities (TSDP) and the recovery rate (RR) for each pair of industry factor and credit rating grade, and these derived TSDP and RR are regarded as what investors imply in forming CB prices in the market at eac…
Study robust recovery of low-rank matrices from corrupted measurements without rank prior.
The paper defines and characterizes conditional nonlinear expectations.
A new method enhances signal recovery with FDR control.
Two methods improve tensor recovery in Ising models, revealing gene interactions.
HSNLD solves robust Hankel recovery efficiently and robustly.
There is empirical evidence that recovery rates tend to go down just when the number of defaults goes up in economic downturns. This has to be taken into account in estimation of the capital against credit risk required by Basel II to cover losses during the adverse economic downturns; the so-called "downturn LGD" requ…
Investigates RI strategies for life insurers with LRD mortality rates.
We investigate the ergodic problem of growth-rate maximization under a class of risk constraints in the context of incomplete, Itô-process models of financial markets with random ergodic coefficients. Including {\em value-at-risk} (VaR), {\em tail-value-at-risk} (TVaR), and {\em limited expected loss} (LEL), these cons…
Proof of Gaussian ML estimator consistency in linear auto-regressive models.
New tensor recovery method uses Riemannian optimization on Segre manifold.
Paper addresses xVA models for market-implied skew and smile.
Distributed strategic learning has been getting attention in recent years. As systems become distributed finding Nash equilibria in a distributed fashion is becoming more important for various applications. In this paper, we develop a distributed strategic learning framework for seeking Nash equilibria under stochastic…
We prove quantitative convergence rates at which discrete Langevin-like processes converge to the invariant distribution of a related stochastic differential equation. We study the setup where the additive noise can be non-Gaussian and state-dependent and the potential function can be non-convex. We show that the key p…