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Results for “ED” · papers 18 · wiki 36
Academic Papers · 18arXiv q-fin live 8 · desk corpus 1537
arXiv · arXiv · 2026

Corporate Bond Yield Curve Modeling: A Rating-Based Regime-Switching Generalized CIR Approach

Persistent shifts in term-structure dynamics undermine the stability of single-regime models in long samples. We develop an arbitrage-free regime-switching generalized CIR (RS-GCIR) model that jointly prices the Chinese government bond (CGB) curve and corporate bond curves. To capture the systematic transmission from interest-rate conditions to credit spreads, we structure the model into two blocks and price corporat…

Maochun Xu, Yunqi Liang, Yi Hong
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arXiv · arXiv · 2024

Credit Spreads' Term Structure: Stochastic Modeling with CIR++ Intensity

This paper introduces a novel stochastic model for credit spreads. The stochastic approach leverages the diffusion of default intensities via a CIR++ model and is formulated within a risk-neutral probability space. Our research primarily addresses two gaps in the literature. The first is the lack of credit spread models founded on a stochastic basis that enables continuous modeling, as many existing models rely on fa…

Mohamed Ben Alaya, Ahmed Kebaier, Djibril Sarr
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OpenAlex · American Economic Review · 2012 · cites 2283

Credit Spreads and Business Cycle Fluctuations

Using micro-level data, we construct a credit spread index with considerable predictive power for future economic activity. We decompose the credit spread into a component that captures firm-specific information on expected defaults and a residual component–– the excess bond premium. Shocks to the excess bond premium that are orthogonal to the current state of the economy lead to declines in economic activity and ass…

Simon Gilchrist, Egon Zakrajšek
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OpenAlex · The Journal of Finance · 2001 · cites 2190

The Determinants of Credit Spread Changes

ABSTRACT Using dealer's quotes and transactions prices on straight industrial bonds, we investigate the determinants of credit spread changes. Variables that should in theory determine credit spread changes have rather limited explanatory power. Further, the residuals from this regression are highly cross‐correlated, and principal components analysis implies they are mostly driven by a single common factor. Although …

Pierre Collin-Dufresn, Robert S. Goldstein, J. Spencer Martin
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OpenAlex · The Journal of Finance · 2001 · cites 824

Do Credit Spreads Reflect Stationary Leverage Ratios?

ABSTRACT Most structural models of default preclude the firm from altering its capital structure. In practice, firms adjust outstanding debt levels in response to changes in firm value, thus generating mean‐reverting leverage ratios. We propose a structural model of default with stochastic interest rates that captures this mean reversion. Our model generates credit spreads that are larger for low‐leverage firms, and …

Pierre Collin‐Dufresne, Robert S. Goldstein
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OpenAlex · The Journal of Finance · 1996 · cites 2072

Optimal Capital Structure, Endogenous Bankruptcy, and the Term Structure of Credit Spreads

ABSTRACT This article examines the optimal capital structure of a firm that can choose both the amount and maturity of its debt. Bankruptcy is determined endogenously rather than by the imposition of a positive net worth condition or by a cash flow constraint. The results extend Leland's (1994a) closed‐form results to a much richer class of possible debt structures and permit study of the optimal maturity of debt as …

Hayne E. Leland, Klaus Bjerre Toft
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OpenAlex · Review of Financial Studies · 2022 · cites 55

Commonality in Credit Spread Changes: Dealer Inventory and Intermediary Distress

Abstract Two intermediary-based factors—a corporate bond dealer inventory measure and a broad intermediary distress measure—explain more than 40$\%$ of the puzzling common variation in credit spread changes beyond canonical structural factors. A simple intermediary-based model with partial market segmentation accounts for intermediary factors’ explanatory power and delivers three further implications with empirical s…

Zhiguo He, Paymon Khorrami, Zhaogang Song
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arXiv · arXiv · 2026

Filtering Credit Risk with Stochastic Discontinuities

We develop a structural credit-risk model under incomplete information in which investors observe firm value only indirectly through noisy market signals and scheduled corporate disclosures. While disclosure dates are known in advance, their informational content is random, leading to stochastic discontinuities in the observation process. We derive the Kushner-Stratonovich equation for structural credit-risk models w…

Félix B. Tambe-Ndonfack
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arXiv · arXiv · 2026

Three-Currency HJM for Brazilian Credit Markets

This paper develops a three-currency Heath-Jarrow-Morton framework in which corporate credit is treated as a separate economy, connected to the nominal and real economies through synthetic inflation and credit exchange rates. The framework produces a testable identity. Under joint no-arbitrage, the credit spread of an issuer expressed over the inflation-rateindexed risk-free curve equals the same issuer's credit spre…

Raphael Coelho
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arXiv · arXiv · 2024

Stress index strategy enhanced with financial news sentiment analysis for the equity markets

This paper introduces a new risk-on risk-off strategy for the stock market, which combines a financial stress indicator with a sentiment analysis done by ChatGPT reading and interpreting Bloomberg daily market summaries. Forecasts of market stress derived from volatility and credit spreads are enhanced when combined with the financial news sentiment derived from GPT-4. As a result, the strategy shows improved perform…

Baptiste Lefort, Eric Benhamou, Jean-Jacques Ohana, David Saltiel, Beatrice Guez
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OpenAlex · RePEc: Research Papers in Economics · 2016 · cites 152

Covered interest parity lost: understanding the cross-currency basis

Covered interest parity verges on a physical law in international finance. And yet it has been systematically violated since the Great Financial Crisis. Especially puzzling have been the violations since 2014, even once banks had strengthened their balance sheets and regained easy access to funding. We offer a framework to think about these violations, stressing the combination of hedging demand and tighter limits to…

Claudio Borio, Robert N. McCauley, Patrick McGuire, Vladyslav Sushko
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OpenAlex · The Journal of Finance · 2014 · cites 837

A Pyrrhic Victory? Bank Bailouts and Sovereign Credit Risk

ABSTRACT We model a loop between sovereign and bank credit risk. A distressed financial sector induces government bailouts, whose cost increases sovereign credit risk. Increased sovereign credit risk in turn weakens the financial sector by eroding the value of its government guarantees and bond holdings. Using credit default swap (CDS) rates on European sovereigns and banks, we show that bailouts triggered the rise o…

Viral V. Acharya, Itamar Drechsler, Philipp Schnabl
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OpenAlex · National Bureau of Economic Research · 2007 · cites 233

How Sovereign is Sovereign Credit Risk?

We study the nature of sovereign credit risk using an extensive sample of CDS spreads for 26 developed and emerging-market countries.Sovereign credit spreads are surprisingly highly correlated, with just three principal components accounting for more than 50 percent of their variation.Sovereign credit spreads are generally more related to the U.S. stock and high-yield bond markets, global risk premia, and capital flo…

Francis A. Longstaff, Jun Pan, Lasse Heje Pedersen, Kenneth J. Singleton
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OpenAlex · Journal of Financial and Quantitative Analysis · 2016 · cites 124

Real Economic Shocks and Sovereign Credit Risk

Abstract We provide new empirical evidence that U.S. expected growth and consumption volatility are closely related to the strong comovement in sovereign spreads. We rationalize these findings in an equilibrium model with recursive utility for credit default swap (CDS) spreads. The framework links a reduced-form default process with country-specific sensitivity to expected growth and macroeconomic uncertainty. Exploi…

Patrick Augustin, Roméo Tédongap
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arXiv · arXiv · 2026

Derivative-Informed Operator Learning for Finance: On-the-Fly Greeks, Surfaces, Hedging, and Control

Financial decision systems require fast surrogate models for pricing, calibration, hedging, XVA, stress testing, and portfolio optimization. Standard neural surrogates reproduce prices or risk quantities, but downstream tasks depend as much on derivatives: deltas, vegas, curve and credit-spread sensitivities, exposure and objective gradients. We formulate a derivative-informed operator-learning framework in which the…

Miquel Noguer I Alonso
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arXiv · arXiv · 2026

SAiFE-gym: Model-based Environments for Automated Market Making with Concentrated Liquidity

We present SAiFE_gym, a Python module that provides a collection of simulation environments for studying trading problems in Constant Product Markets (CPMs) with Concentrated Liquidity (CL). These markets give Liquidity Providers (LPs) granular control over how their capital is allocated and enable them to adjust their range of liquidity provision dynamically based on market conditions, which in turn, dictates how th…

Georgios Chionas, Charalampos Kleitsikas, Stefanos Leonardos, Leandro Sánchez-Betancourt, Carmine Ventre
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arXiv · arXiv · 2026

Mitigating Adverse Selection in Concentrated Liquidity AMMs with Dynamic Fees: An Agent-Based Model Approach

Automated Market Makers based on concentrated liquidity, such as Uniswap v3, significantly improve capital efficiency but expose Liquidity Providers (LPs) to adverse selection costs, formalized as Loss-Versus-Rebalancing (LVR). While theoretical literature quantifies these costs, the interplay between realistic blockchain microstructure and endogenous pricing mechanisms remains under-explored. This paper develops a g…

Daniele Maria Di Nosse, Fabrizio Lillo
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arXiv · arXiv · 2012

Funding Liquidity, Debt Tenor Structure, and Creditor's Belief: An Exogenous Dynamic Debt Run Model

We propose a unified structural credit risk model incorporating both insolvency and illiquidity risks, in order to investigate how a firm's default probability depends on the liquidity risk associated with its financing structure. We assume the firm finances its risky assets by mainly issuing short- and long-term debt. Short-term debt can have either a discrete or a more realistic staggered tenor structure. At rollov…

Gechun Liang, Eva Lütkebohmert, Wei Wei
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Wiki Entities · 36
AI Systems

Activation Function

An activation function is the pointwise nonlinearity between linear layers. Without it, stacked layers collapse to a single linear map.

AI Systems

Agent Workflow

An agent workflow is a structured loop that plans, calls tools or models, observes results, and repeats until a stop condition — a pipeline with memory, contracts, and failure handling rather than a single completion.

AI Systems

Attention Mechanism

Attention builds a weighted average of values, with weights from a compatibility function of queries and keys. It lets a model focus on relevant parts of a context instead of a single fixed vector.

AI Systems

Autoencoder

An autoencoder learns to reconstruct its input through a bottleneck, producing a compressed latent that can be used for denoising, retrieval, or as a generative seed.

AI Systems

Batch Normalization

Batch normalization re-centers and re-scales layer inputs using mini-batch statistics, then learns a scale and shift, reducing internal covariate shift and allowing higher learning rates.

AI Systems

Beam Search

Beam search is a heuristic decoder that keeps the k best partial sequences at each step instead of greedily taking only the top token — the classic seq2seq inference method.

AI Systems

BERT

BERT is a bidirectional Transformer encoder trained with masked language modeling and next-sentence prediction, then fine-tuned on downstream NLP tasks.

AI Systems

Byte Pair Encoding

BPE grows a vocabulary by repeatedly merging the most frequent adjacent pairs, starting from characters or bytes, until a target vocab size is reached.

AI Systems

Chain of Thought

Chain-of-thought prompting asks the model to emit intermediate reasoning steps before the answer, which reliably lifts arithmetic, symbolic, and multi-hop tasks.

AI Systems

CLIP

CLIP jointly trains an image encoder and a text encoder so matched image–caption pairs are close in a shared space, enabling zero-shot visual classification by text prompts.

AI Systems

Contrastive Learning

Contrastive learning pulls representations of related pairs together and pushes unrelated pairs apart. It is the pretraining idea behind SimCLR, CLIP, and many embedding models.

AI Systems

Convolutional Neural Network

A CNN shares a local kernel across spatial (or temporal) positions, building translation-equivariant features. It is the inductive bias that cracked modern computer vision.

AI Systems

Cross-Entropy Loss

Cross-entropy measures how well a predicted distribution q matches a target distribution p. For one-hot labels it reduces to −log q(y), the usual classification and language-model loss.

AI Systems

Dropout

Dropout randomly zeroes hidden units during training so the net cannot rely on any single co-adaptation, then scales weights at test time (or uses inverted dropout).

AI Systems

Embedding

An embedding is a learned dense vector for an object (token, sentence, image, user) such that geometry supports retrieval, clustering, or as input to a downstream model.

AI Systems

Fine-Tuning

Fine-tuning continues training a pretrained model on a narrower distribution so the same weights specialize — classification heads, instruction following, or a desk domain.

AI Systems

Gated Recurrent Unit

GRU is a lighter gated RNN with reset and update gates, often matching LSTM quality at lower cost on medium-length sequences.

AI Systems

GPT

GPT is a decoder-only Transformer trained to predict the next token. Scale plus this objective produced in-context learning and the current foundation-model product line.

AI Systems

Hallucination

Hallucination is fluent generation that is not supported by the source or the world — a likelihood-trained model completing a pattern, not a database lookup.

AI Systems

In-Context Learning

In-context learning is when a frozen language model improves at a task from examples placed in the prompt, without weight updates.

AI Systems

Knowledge Distillation

Knowledge distillation trains a smaller student to match a teacher’s output distribution (soft labels), transferring behavior without copying every weight.

AI Systems

Learning Rate Schedule

A learning-rate schedule is the planned path of η_t — warmup, cosine, step decay — that often matters more than the architecture headline on a given run.

AI Systems

Long Short-Term Memory

LSTM is a gated RNN whose cell state can carry information across many steps, with input, forget, and output gates trained by gradient descent.

AI Systems

LoRA

LoRA fine-tunes a frozen model by learning low-rank adapters on selected weight matrices, cutting trainable parameters and storage versus full fine-tunes.

AI Systems

Mixture of Experts

MoE routes each token (or example) to a sparse subset of specialist feed-forward experts, raising parameter count without paying dense FLOPs on every token.

AI Systems

Neural Network

A neural network is a layered function approximator: units compute a weighted sum, apply a nonlinearity, and pass the result forward so the whole stack can learn a mapping from inputs to outputs.

AI Systems

Perceptron

The perceptron is the original trainable linear classifier: a weighted sum plus a threshold. It is the atom of neural nets, and it cannot learn XOR without a hidden layer.

AI Systems

Policy Gradient

Policy gradient methods optimize a parameterized policy π_θ directly by ascending the gradient of expected return, rather than via an action-value table.

AI Systems

Prompt Injection

Prompt injection is an attack that inserts instructions into retrieved or user-supplied text so the model obeys the attacker instead of the developer’s system policy.

AI Systems

Q-Learning

Q-learning is an off-policy TD method that learns action values Q(s, a) toward the greedy target r + γ max_a′ Q(s′, a′), without needing the behavior policy to be optimal.

AI Systems

Regularization

Regularization is any constraint that trades train fit for expected live error: weight decay, dropout, early stopping, data augmentation, or a simpler hypothesis class.

AI Systems

Reinforcement Learning

Reinforcement learning trains a policy to maximize expected return by interacting with an environment: states, actions, rewards, and (usually) a discount factor.

AI Systems

Reinforcement Learning from Human Feedback

RLHF fits a reward model to human (or AI) preference comparisons, then optimizes a language model against that reward, usually with a KL penalty back to a reference policy.

AI Systems

Residual Network

A ResNet learns a residual f(x) added back to x, so extra layers can default to identity. That skip connection made 100+ layer nets trainable.

AI Systems

Retrieval-Augmented Generation

RAG retrieves relevant documents first, then conditions a language model on that evidence so answers can be grounded, cited, and updated without retraining.

AI Systems

Scaling Laws

Scaling laws are empirical power laws relating language-model loss to parameter count, data, and compute, used to plan pretraining rather than guess.

Option Blackboard · 5
code

Gamma Regime Scanner

Code-linked scanner pattern that maps front-vol stress, ATM IV, and dealer gamma context into Blackboard + Options Lab actions.

vol-structure

IV Rank & Surface Filter

Vol-structure note for classifying rich vs cheap ATM IV and surface dispersion before choosing credit or debit templates.

code

Put/Call Flow Watch

Scanner code fragment for put/call ratio and open-interest activity on the ATM chain window.

strategy

Defined-Risk Bridge Playbook

Strategy bridge from Blackboard scanner cards into Options Lab templates without implying live broker execution.

greeks

Greeks Dynamics Sandbox

Educational IV × Greeks × surface sandbox already mounted on the Blackboard — synthetic regimes for pedagogy beside the live scanner.

Encyclopedia · 24
Rates · Foundations

3M10Y Treasury Curve

The 3M10Y Treasury curve compares 10-year Treasury yields with 3-month Treasury bill yields and is closely watched as a recession and policy-cycle indicator.

AI Systems · Foundations

Activation Function

An activation function is the pointwise nonlinearity between linear layers. Without it, stacked layers collapse to a single linear map.

Microstructure · Foundations

Adverse Selection

Adverse selection is the expected loss a liquidity provider takes when the other side is informed — the Glosten–Milgrom reason spreads exist even with no inventory.

AI Systems · Foundations

Agent Workflow

An agent workflow is a structured loop that plans, calls tools or models, observes results, and repeats until a stop condition — a pipeline with memory, contracts, and failure handling rather than a single completion.

CTA · Foundations

Agricultural CTA

Grains and oilseeds — corn, soy, wheat, and their products — where weather, USDA prints, and harvest calendars sit on top of generic trend.

Quant · Foundations

Alpha

Alpha is return not explained by the risk factors you chose — a residual, not a personality.

Strategies · Foundations

Alpha Cloning — Following 13F Filings

Copy (with a lag) the disclosed long holdings of selected 13F filers — a delayed clone of someone else’s book.

Systems · Foundations

Alpha Decay

Alpha Decay — Speed at which a signal loses predictive power as capital competes for it.

Equity · Foundations

Amortization

Amortization is the write-down of an intangible (or the scheduled paydown of a loan) — two different words sharing a calendar.

Financial Crises · Foundations

Archegos 2021

Archegos was a family-office total-return-swap blow-up in March 2021: concentrated longs, huge hidden leverage across prime brokers, and a week of block sales that hit ViacomCBS and others.

Financial Crises · Foundations

Argentine Crisis 2001

Argentina’s 2001–02 collapse ended the convertibility 1:1 peg with default, corralito, and a violent real devaluation — a political-economy crisis of an overvalued peg.

Equity · Foundations

Asset

An asset is a present economic resource controlled by an entity from which future cash or service is expected — the left-hand side of the balance sheet.

Fixed Income · Foundations

Asset Swap Spread

Asset Swap Spread — Spread between bond yield and floating leg, linking credit and funding markets.

Credit · Foundations

Asset-Backed Security

An ABS is a bond paid from a pool of receivables — cards, auto, equipment — sliced into tranches with a waterfall.

CTA · Foundations

ATR Unit Sizing

Size each new futures position so that 1 ATR move equals a fixed fraction of equity — the Turtle risk unit, still the cleanest per-trade language.

AI Systems · Foundations

Attention Mechanism

Attention builds a weighted average of values, with weights from a compatibility function of queries and keys. It lets a model focus on relevant parts of a context instead of a single fixed vector.

AI Systems · Foundations

Autoencoder

An autoencoder learns to reconstruct its input through a bottleneck, producing a compressed latent that can be used for denoising, retrieval, or as a generative seed.

Commodities · Foundations

Backwardation

Backwardation is a futures curve that falls with tenor — nearby richer than deferred, usually a tightness / convenience-yield story.

Banking · Foundations

Balance Sheet Constraint Dealer

Balance Sheet Constraint Dealer — Dealer SLR/balance-sheet limits reducing intermediation.

Banking · Foundations

Bank CDS Index

Bank CDS Index tracks the cost of insuring major bank credit risk and serves as a real-time indicator of banking-system stress and confidence.

Liquidity · Foundations

Bank Reserve Balances

Bank reserve balances reflect the quantity of reserves held by banks at the Federal Reserve and are central to understanding liquidity distribution and financial system stability.

Credit · Foundations

Bankruptcy

Bankruptcy is a court process that stays creditors and restructures or liquidates claims when a firm cannot meet its obligations as they come due.

Financial Crises · Foundations

Barings 1995

Barings Bank was wiped out in 1995 by Nick Leeson’s hidden Nikkei futures losses in Singapore — a rogue-trader plus failed control story, not a macro crisis.

AI Systems · Foundations

Batch Normalization

Batch normalization re-centers and re-scales layer inputs using mini-batch statistics, then learns a scale and shift, reducing internal covariate shift and allowing higher learning rates.

Cards · 6
StrategyCard

USD Funding Stress as Equity Vol Trigger

A funding-stress monitor can warn before equity volatility reprices.

SignalCard

Corporate Yield Spreads and Bond Liquidity

A liquidity premium monitor can separate default risk from liquidity-driven spread widening.

ExecutionCard

Shock Regime Execution

A card for handling fast markets, wide spreads, liquidity air pockets, and broken execution assumptions.

SignalCard

TACO Repricing Window

Tracks policy reversal probability, market repricing windows, and timing mismatch between rhetoric and positioning.

MethodCard

Paper to Strategy Transfer

A reusable card for converting research papers into buildable strategy or monitoring objects.

StrategyCard

Dual-Blade Entry Filter

A serial decision card combining direction filter and time-window filter before execution.

Local Modules · 1
PapersResearch

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