Fixed income is where quantitative finance’s modeling tradition runs deepest and data runs thinnest. This hub covers term-structure models (affine, HJM-lineage, and their ML successors), yield-curve forecasting, bond risk premia, and credit — spread dynamics, default modeling, and corporate bond market structure.
Two things distinguish strong papers here. First, honest treatment of the small-sample problem: there are only a handful of independent interest-rate cycles in any dataset, so out-of-sample claims rest on far less evidence than equity research enjoys, and papers that acknowledge regime dependence (pre/post-2008 zero-rate era, the 2022 repricing) age better. Second, attention to market plumbing — corporate bond research that ignores dealer intermediation costs and stale quotes produces backtests no desk can trade. The intersection with machine learning is growing fast, but the base rates favor parsimonious models in this asset class more than anywhere else.
Related hubs: Risk Management, Options & Derivatives, Factor Investing.
Explainers from the corpus: ARDL models: appropriate use vs p-hacking → · risk measures beyond VaR →
In the financial services industry, forecasting the risk factor distribution conditional on the history and the current market environment is the key to market risk modeling in general and value at risk (VaR) model in particular. As one of the most widely adopted VaR models in commercial banks, Hist
For $n$ assets and discrete-time rebalancing, the probability to complete a given schedule of investments and withdrawals is maximized over progressively measurable portfolio weight functions. Applications consider two assets, namely the S&P Composite Index and an inflation-protected bond. The maxim
In credit risk analysis, survival models with fixed and time-varying covariates are widely used to predict a borrower’s time-to-event. When the time-varying drivers are endogenous, modelling jointly the evolution of the survival time and the endogenous covariates is the most appropriate approach, al
This paper develops a copula-based time-series framework for modelling sovereign credit rating activity and its dependence dynamics, with extensions incorporating climate risk. We introduce a mixed-difference transformation that maps discrete annual counts of sovereign rating actions into a continuo
This paper studies the impact of funding market frictions on bond prices and market-wide liquidity. Using proprietary transaction-level data on all gilt-backed repo and reverse-repo trades, we demonstrate how the market power of individual dealers and their linkages generate frictions. Specifically,
Credit risk models are a critical decision-support tool for financial institutions, yet tightening data-protection rules (e.g., GDPR, CCPA) increasingly prohibit cross-border sharing of borrower data, even as these models benefit from cross-institution learning. Traditional default prediction suffer
This paper proposes a novel approach for Asset-Liability Management (ALM) by employing continuous-time Reinforcement Learning (RL) with a linear-quadratic (LQ) formulation that incorporates both interim and terminal objectives. We develop a model-free, policy gradient-based soft actor-critic algorit
We extend the Lindquist-Rachev (LR) option-pricing framework–which values derivatives in markets lacking a traded risk-free bond–by introducing common Levy jump dynamics across two risky assets. The resulting endogenous “shadow” short rate replaces the usual risk-free yield and governs discounting
Recent studies have identified long-range dependence as a key feature in the dynamics of both mortality and interest rates. Building on this insight, we develop a novel bi-variate stochastic framework based on mixed fractional Brownian motions to jointly model their long-memory behavior and instanta
Money launderers exploit the weaknesses in detection systems by purposefully placing their ill-gotten money into multiple accounts, at different banks. That money is then layered and moved around among mule accounts to obscure the origin and the flow of transactions. Consequently, the money is integ
Predicting corporate default risk has long been a crucial topic in the finance field, as bankruptcies impose enormous costs on market participants as well as the economy as a whole. This paper aims to forecast frailty correlated default models with subjective judgements on a sample of U.S. public no
The Nelson-Siegel-Svensson (NSS) interest rate curve model yields a separable nonlinear least-squares problem whose inner linear block is often ill-conditioned because the basis functions become nearly collinear. We analyze this instability via an exact orthogonal reparametrization of the design mat
We study U.S. Treasury yield curve forecasting under distributional uncertainty and recast forecasting as an operations research and managerial decision problem. Rather than minimizing average forecast error, the forecaster selects a decision rule that minimizes worst case expected loss over an ambi
The estimation of fill probabilities for trade orders represents a key ingredient in the optimization of algorithmic trading strategies. It is bound by the complex dynamics of financial markets with inherent uncertainties, and the limitations of models aiming to learn from multivariate financial tim
We amend and extend the Chiarella model of financial markets to deal with arbitrary long-term value drifts in a consistent way. This allows us to improve upon existing calibration schemes, opening the possibility of calibrating individual monthly time series instead of classes of time series. The te
This paper investigates the dynamic interdependencies between the European insurance sector and key financial markets-equity, bond, and banking-by extending the Generalized Forecast Error Variance Decomposition framework to a broad set of performance and risk indicators. Our empirical analysis, base
United States (US) IG bonds typically trade at modest spreads over US Treasuries, reflecting the credit risk tied to a corporation’s default potential. During market crises, IG spreads often widen and liquidity tends to decrease, likely due to increased credit risk (evidenced by higher IG Credit Def
In this paper we derive a new direct inversion method to simulate squared Bessel processes. Since the transition probability of these processes can be represented by a non-central chi-square distribution, we construct an efficient and accurate algorithm to simulate non-central chi-square variables.
In the analysis of commodity futures, it is commonly assumed that futures prices are driven by two latent factors: short-term fluctuations and long-term equilibrium price levels. In this study, we extend this framework by introducing a novel state-space functional regression model that incorporates
We study a multivariate autoregressive stochastic volatility model for the first 3 principal components (level, slope, curvature) of 10 series of zero-coupon Treasury bond rates with maturities from 1 to 10 years. We fit this model using monthly data from 1990. Unlike classic models with hidden stoc