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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.

168,786 papers · 148 categories

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2.8%5.6%8.3%11.1% · May 202619922001200920172026
48 results for NDF tenor selection

WATTNet models FX trading tenor selection using spatio-temporal data.

problem NDF tenor selection in FX trading with long-term planning.
method WaveATTentionNet (WATTNet) for spatio-temporal modeling of multivariate time series.
result Significant positive ROI in all NDF markets, outperforming baselines.

Machine learning is essentially the sciences of playing with data. An adaptive data selection strategy, enabling to dynamically choose different data at various training stages, can reach a more effective model in a more efficient way. In this paper, we propose a deep reinforcement learning framework, which we call \em…

2017-02-28abs ↗pdf ↗

A new model for pricing ultra-short-term options with complex volatility patterns.

problem Complex pricing of ultra-short-term options due to oscillations in implied volatility.
method Edgeworth++ model with nonparametric stochastic volatility and deterministic shift extension.
result Fast and accurate closed-form option pricing for ultra-short-term options.

We develop an optimal currency hedging strategy for fund managers who own foreign assets to choose the hedge tenors that maximize their FX carry returns within a liquidity risk constraint. The strategy assumes that the offshore assets are fully hedged with FX forwards. The chosen liquidity risk metric is Cash Flow at R…

2019-03-15abs ↗pdf ↗

In the LIBOR market model, forward interest rates are log-normal under their respective forward measures. This note shows that their distributions under the other forward measures of the tenor structure have approximately log-normal tails.

2010-08-12abs ↗pdf ↗

Regulations impose idiosyncratic capital and funding costs for holding derivatives. Capital requirements are costly because derivatives desks are risky businesses; funding is costly in part because regulations increase the minimum funding tenor. Idiosyncratic costs mean no single measure makes derivatives martingales f…

2013-11-01abs ↗pdf ↗

We introduce simple cost and risk proxy metrics that can be attached to Treasury issuance strategy to complement analysis of the resulting portfolio weighted-average maturity (WAM). These metrics are based on mapping issuance fractions to their long-term, asymptotic portfolio implications for cost and risk under mechan…

2018-02-09abs ↗pdf ↗

We propose a general framework for modeling multiple yield curves which have emerged after the last financial crisis. In a general semimartingale setting, we provide an HJM approach to model the term structure of multiplicative spreads between FRA rates and simply compounded OIS risk-free forward rates. We derive an HJ…

2014-06-17abs ↗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 ↗

In the context of multi-curve modeling we consider a two-curve setup, with one curve for discounting (OIS swap curve) and one for generating future cash flows (LIBOR for a give tenor). Within this context we present an approach for the clean-valuation pricing of FRAs and CAPs (linear and nonlinear derivatives) with one…

2014-01-21abs ↗pdf ↗

Cash collateral is perfect in that it provides simultaneous counterparty credit risk protection and derivatives funding. Securities are imperfect collateral, because of collateral segregation or differences in CSA haircuts and repo haircuts. Moreover, the collateral rate term structure is not observable in the repo mar…

2017-02-14abs ↗pdf ↗

Neural-SDE models improve option hedging with lower errors and robustness.

problem Improving option hedging strategies using machine learning.
method Derive sensitivity-based and minimum-variance-based hedging strategies using neural-SDE market models.
result Neural-SDE models achieve lower hedging errors and are more robust than traditional models.

Model explains yield curve dynamics using order flow shocks.

problem Understanding the yield curve's fluctuations and their relation to order flows.
method Relates exogenous shocks to order flow surprises, creating a microstructural model that incorporates price and order flow dynamics.
result The model explains yield curve dynamics with fewer parameters and generates liquidity-dependent correlations.

A repurchase agreement lets investors borrow cash to buy securities. Financier only lends to securities' market value after a haircut and charges interest. Repo pricing is characterized with its puzzling dual pricing measures: repo haircut and repo spread. This article develops a repo haircut model by designing haircut…

2016-04-19abs ↗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 ↗

Proposes a new model to handle negative interest rates using CIR framework.

problem Negative interest rates and their impact on financial markets.
method Develops a new model based on Cox-Ingersoll-Ross (CIR) framework without shifting market rates.
result The model accurately reproduces market term structures and swaption prices.

Paper uses VAEs to model yield curves without arbitrage violations.

problem Forecasting yield curves across diverse macroeconomic regimes leads to arbitrage violations.
method Proposes a two-stage architecture with CVAEsT+LS and Neural SDEs penalized by No-Arbitrage PDE.
result Significantly reduces forecasting errors and overcomes HJM model limitations.

Develops a novel SABR DNN for accurate volatility surface calibration.

problem Inaccurate SABR model approximation for high volatility, long maturities, and out-of-the-money options.
method A specialized Artificial Deep Neural Network (DNN) architecture trained on a large dataset of interest rate volatility surfaces.
result Arbitrage-free calibration of real market volatility surfaces and Cap/Floor prices for any maturity and strike.

We orthogonalize the NSS model to condition and diagnose its ill-conditioned parameters.

problem The ill-conditioning of the NSS model's design matrix.
method Exact orthogonal reparametrization via QR decomposition.
result Orthogonalization isolates the conditioning structure and maintains fit uncertainty.

The study proposes a new interest rate model that captures long-term periodicity in U.S. Treasury yields.

problem The conventional Hull-White model fails to adequately capture long-term economic cycles in interest rates.
method The study introduces a sinusoidal Hull-White model with a time-varying mean reversion speed.
result The proposed model improves bond pricing and interest rate derivative valuation, especially for longer maturities.

Develops a three-currency HJM framework for Brazilian credit markets, finding significant credit spread differences between indexed segments.

problem Identifies and quantifies differences in corporate credit spreads between two parallel segments of the Brazilian bond market.
method Uses a Heath-Jarrow-Morton framework to model corporate credit as a separate economy, linking it to nominal and real economies through synthetic rates.
result Empirically finds a 640 basis point average difference in credit spreads between CDI-indexed and IPCA-indexed segments, stable through market cycles.

Proposes PEMI for online selective conformal prediction with asymmetric rules.

problem Challenges of handling asymmetric selection mechanisms in online selective conformal prediction.
method PEMI: permutation-based framework for selective conformal prediction with arbitrary asymmetric selection rules.
result Achieves exact selection-conditional coverage for any asymmetric selection mechanism and any prediction model.

The paper addresses errors in online selective conformal prediction and proposes new strategies to ensure valid inference.

problem Online selective conformal prediction's exchangeability issues and false coverage rate control problems.
method Evaluation and correction of existing calibration selection strategies, proposing new ones that preserve exchangeability.
result Novel calibration selection strategies ensure both selection-conditional coverage and FCR control.

This work develops scalable model selection methods with fast update and selection.

problem Efficient model selection for large pools of candidate models.
method Isolated model embedding, which supports asymptotically fast update and selection.
result Standardized Embedder achieves competitive model selection performances.

Online method selects candidates from data streams, ensuring irreversible decisions.

problem Conformal selection's incompatibility with irreversible decisions in online scenarios.
method Online Conformal Selection with Accept-to-Reject Changes (OCS-ARC) incorporating online Benjamini-Hochberg procedure.
result OCS-ARC controls FDR at or below nominal level, improving selection power.