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arXiv research

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.

168,742 papers · 148 categories

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295786114 · May 202619922001200920172026
48 results for fixed-income markets

Study uses weak transport for non-convex costs in fixed-income markets.

problem Characterizing optimal caplet pricing in fixed-income markets.
method Introduced weak optimal transport for non-convex costs, reduced general costs to convex problems.
result Established robust super-replication results for fixed-income markets.

Improved AMM protocol supports diverse loan maturities in DeFi.

problem Challenges in designing AMMs for fixed-income lending with time-related complexities.
method Generalized BondMM protocol to support arbitrary maturities.
result BondMM-A protocol demonstrates superior performance in interest rate stability and financial robustness.

We present cross and time series analysis of price fluctuations in the U.S. Treasury fixed income market. By means of techniques borrowed from statistical physics we show that the correlation among bonds depends strongly on the maturity and bonds' price increments do not fulfill the random walk hyphoteses.

2000-03-02abs ↗pdf ↗

Global fixed income returns span across multiple maturities and economies, that is, they naturally reside on multi-dimensional data structures referred to as tensors. In contrast to standard "flat-view" multivariate models that are agnostic to data structure and only describe linear pairwise relationships, we introduce…

2019-08-06abs ↗pdf ↗

A simplified model for fixed income portfolio optimisation.

problem Modeling interest rates and credit risk in fixed income portfolios.
method Proposes a two-factor model for the time evolution of the efficient frontier.
result The efficient frontier is mainly controlled by linear constraints, with standard deviation less important.

In this paper we introduce a class of information-based models for the pricing of fixed-income securities. We consider a set of continuous- time information processes that describe the flow of information about market factors in a monetary economy. The nominal pricing kernel is at any given time assumed to be given by …

2009-11-09abs ↗pdf ↗

We review the main changes in the interbank market after the financial crisis started in August 2007. In particular, we focus on the fixed income market and we analyse the most relevant empirical evidences regarding the divergence of the existing basis between interbank rates with different tenor, such as Libor and OIS…

2013-01-27abs ↗pdf ↗

Paper uses PCA to analyze Chinese sovereign bonds and discusses bond immunization.

problem Analyzing factors affecting Chinese sovereign bond yield changes.
method Applied Principal Component Analysis (PCA) on bond yield data.
result Identified principal factors influencing Chinese sovereign bond yield changes.

Model predicts risk-adjusted returns across various financial markets.

problem Stationary models fail in predicting risk-adjusted returns due to market regime changes.
method Asset-independent regime-switching model using hidden Markov models.
result Accurately detects bull, bear, and high volatility periods for improved risk-adjusted returns.

The instability of historical risk factor correlations renders their use in estimating portfolio risk extremely questionable. In periods of market stress correlations of risk factors have a tendency to quickly go well beyond estimated values. For instance, in times of severe market stress, one would expect with certain…

2001-08-14abs ↗pdf ↗

Enhanced synthetic dataset improves asset allocation analysis.

problem Lack of realistic synthetic data for fixed income portfolio construction.
method Improved CorrGAN model for synthetic correlation matrices and Encoder-Decoder model for additional data conditioning.
result Synthetic dataset enhances portfolio construction and asset allocation analysis.

Framework for transferring discount curve estimates across fixed-income product classes.

problem Challenges in estimating discount curves from sparse or noisy data.
method Proposes a vector-valued kernel ridge regression (KR) framework with economic regularization.
result Transfer learning tightens confidence intervals and improves extrapolation performance.

Paper proposes an alternative method to price American options using HJM approach.

problem Price American options efficiently and accurately.
method Utilizes HJM technique to model term structure of volatility for equity markets.
result Proposes a new value function, stopping criteria, and stopping time for American options.

Empirical evidence suggests that fixed income markets exhibit unspanned stochastic volatility (USV), that is, that one cannot fully hedge volatility risk solely using a portfolio of bonds. While [1] showed that no two-factor Cox-Ingersoll-Ross (CIR) model can exhibit USV, it has been unknown to date whether CIR models …

2017-05-08abs ↗pdf ↗

A new model explains relative spreads between economies using dynamic Nelson-Siegel and functional regression.

problem Analyzing and predicting relative spreads between economies in fixed income markets.
method State-space functional regression model incorporating dynamic Nelson-Siegel model and kernel PCA.
result The new model outperforms the dynamic Nelson-Siegel model in explaining relative spreads.

The general problem of asset pricing when the discount rate differs from the rate at which an asset's cash flows accrue is considered. A pricing kernel framework is used to model an economy that is segmented into distinct markets, each identified by a yield curve having its own market, credit and liquidity risk charact…

2018-01-15abs ↗pdf ↗

In fixed income sector, the yield curve is probably the most observed indicator by the market for trading and fifinancing purposes. A yield curve plots interest rates across different contract maturities from short end to as long as 30 years. For each currency, the corresponding curve shows the relation between the lev…

2018-08-10abs ↗pdf ↗

Study uses ML to analyze how interest rates affect fund returns, finding gradient boosting is effective.

problem Understanding how interest rate changes impact fund returns.
method Combines Machine Learning and causal inference, using Double Machine Learning framework.
result Gradient boosting is useful for predicting fund returns, showing a significant negative effect of interest rate increases.

In this paper we show how to hedge a zero coupon bond with a smaller amount of initial capital than required by the classical risk neutral paradigm, whose (trivial) hedging strategy does not suggest to invest in the risky assets. Long dated zero coupon bonds we derive, invest first primarily in risky securities and whe…

2016-08-16abs ↗pdf ↗

Neural network model improves robustness of mortgage bond yield curve estimation.

problem Overfitting and instability in traditional yield curve estimation methods for small mortgage bond markets.
method Neural network framework with a new loss function for smoothness and stability.
result Empirical results show more robust and stable yield curve estimates compared to existing methods.

In this paper we present a rigorously motivated pricing equation for derivatives, including general cash collateralization schemes, which is consistent with quoted market bond prices. Traditionally, there have been differences in how instruments with similar cash flow structures have been priced if their definition fal…

2014-06-22abs ↗pdf ↗

The paper addresses pricing interest rate derivatives in markets with volatility uncertainty.

problem Pricing interest rate derivatives under uncertainty about volatility.
method Modeling volatility uncertainty with G-Brownian motion and defining forward sublinear expectation.
result Developed robust pricing formulas for interest rate derivatives.

Machine learning models outperform traditional econometric methods for forecasting term structure of government bonds

problem Forecasting the term structure of government bonds
method Combining traditional econometric models with neural network architectures
result Neural network models consistently outperform traditional models in both forecasting accuracy and portfolio performance

This article provides the mathematical foundation for stochastically continuous affine processes on the cone of positive semidefinite symmetric matrices. This analysis has been motivated by a large and growing use of matrix-valued affine processes in finance, including multi-asset option pricing with stochastic volatil…

2009-10-01abs ↗pdf ↗

A heat kernel approach is proposed for the development of a general, flexible, and mathematically tractable asset pricing framework in finite time. The pricing kernel, giving rise to the price system in an incomplete market, is modelled by weighted heat kernels which are driven by multivariate Markov processes and whic…

2012-11-05abs ↗pdf ↗

DeepUnifiedMom uses deep learning to create better momentum portfolios.

problem Lack of unified momentum portfolios across different time frames.
method Multi-task learning with multi-gate mixture of experts.
result DeepUnifiedMom outperforms benchmark models in diverse asset classes.