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.
Genetic Algorithm improves Nelson-Siegel-Svensson model calibration for interest rates.
A new model explains relative spreads between economies using dynamic Nelson-Siegel and functional regression.
Investment strategies derived from commodity futures curves exploit dynamics in price movements.
This study models Burundi's bond market yield curve using Nelson-Siegel and Svensson models.
Yield curve modeling is an essential problem in finance. In this work, we explore the use of Bayesian statistical methods in conjunction with Nelson-Siegel model. We present the hierarchical Bayesian model for the parameters of the Nelson-Siegel yield function. We implement the MAP estimates via BFGS algorithm in rstan…
Nelson and Siegel curves are widely used to fit the observed term structure of interest rates in a particular date. By the other hand, several interest rate models have been developed such their initial forward rate curve can be adjusted to any observed data, as the Ho-Lee and the Hull and White one factor models. In t…
A robust machine learning approach forecasts U.S. Treasury yields, reducing risk for investors.
We orthogonalize the NSS model to condition and diagnose its ill-conditioned parameters.
In this work we introduce Heath-Jarrow-Morton (HJM) interest rate models driven by fractional Brownian motions. By using support arguments we prove that the resulting model is arbitrage free under proportional transaction costs in the same spirit of Guasoni [Math. Finance 16 (2006) 569-582]. In particular, we obtain a …
Neural network model improves robustness of mortgage bond yield curve estimation.
We derive an equation of motion for interest-rate yield curves by applying a minimum Fisher information variational approach to the implied probability density. By construction, solutions to the equation of motion recover observed bond prices. More significantly, the form of the resulting equation explains the success …
Recent literature seek to forecast implied volatility derived from equity, index, foreign exchange, and interest rate options using latent factor and parametric frameworks. Motivated by increased public attention borne out of the financialization of futures markets in the early 2000s, we investigate if these extant mod…
The paper contributes to the rare literature modeling term structure of crude oil markets. We explain term structure of crude oil prices using dynamic Nelson-Siegel model, and propose to forecast them with the generalized regression framework based on neural networks. The newly proposed framework is empirically tested …
Classifies shapes of yield curves in the Svensson family.
The term structure of interest rates or yield curve is a function relating the interest rate with its own term. Nonlinear regression models of Nelson-Siegel and Svensson were used to estimate the yield curve using a sample of historical data supplied by the National Stock Exchange of Costa Rica. The optimization proble…
Paper uses RL for dynamic swaption hedging, outperforming traditional methods.
Deep learning framework for bond and yield curve forecasting with no-arbitrage constraints.
Study on liquidity dynamics in Uniswap v3 pools using statistical methods.
Bayesian model predicts interest rates with short-term accuracy and long-term stability.
The study constructs models for SOFR term rates using futures data.
Machine learning models outperform traditional econometric methods for forecasting term structure of government bonds