In this paper we investigate model-independent bounds for exotic options written on a risky asset. Based on arguments from the theory of Monge-Kantorovich mass-transport we establish a dual version of the problem that has a natural financial interpretation in terms of semi-static hedging. In particular we prove that th…
On-device research index
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.
169,341 papers · 148 categories
Trend · papers per month
48 results for “asset-based model”
Models value assets based on non-devaluation, creating global valuation formulas.
problem Valuation of assets that can potentially lose value.
method Conditioning on non-devaluation, using each asset as a numéraire, and aggregating local valuation rules.
result Global arbitrage-free valuation formulas can be derived from local rules.
Model predicts stock price dynamics using quantum gauge theory.
problem Predicting short-term stock price movements.
method Path integral model based on quantum gauge theory.
result Model accurately predicts stock price distributions.
Paper proposes a simple method for deriving lifetime PD forecasts.
problem Deriving accurate lifetime PD forecasts with minimal data.
method Classical asset-based credit portfolio model with autoregressive process for systematic factor, Bayesian methodology for macroeconomic judgments.
result Endogenous derivation of lifetime PD forecasts without exogenous macroeconomic forecasts.
The paper proposes a class of financial market models which are based on inhomogeneous telegraph processes and jump diffusions with alternating volatilities. It is assumed that the jumps occur when the tendencies and volatilities are switching. We argue that such a model captures well the stock price dynamics under per…
Unified HS and related methods with explicit modeling assumptions.
problem Lack of clear assumptions in HS methods for Value-at-Risk.
method Explicitly defined parametric model for asset returns and extraction of innovation process.
result HS and related methods require more assumptions than commonly acknowledged.
New model uses financial news to predict stock returns.
problem Predicting stock returns based on financial news.
method Derive company embedding vectors from news, select basis assets, and use statistical methods.
result NEUS model outperforms Fama-French 5-factor model.
MTS-CycleGAN adapts multivariate time series data for ironmaking industry.
problem Creating a domain invariant dataset from multivariate time series data of different blast furnaces.
method Adversarial-based deep mapping learning network (CycleGAN) with LSTM-based AutoEncoder and discriminator.
result MTS-CycleGAN successfully translates multivariate time series data between different blast furnaces.
Study the impact of overfitting on linear predictive models' performance.
problem Overfitting reduces the out-of-sample performance of linear predictive trading strategies.
method Computed in- and out-of-sample means and variances of PnLs to derive replication ratios.
result Replication ratio diminishes for complex strategies with many assets.
Valuation of Music Catalogsq-fin.PR
The study uses historical revenue data to forecast music catalog cashflows and multipliers.
problem Valuation of music catalogs based on historical revenue data.
method Risk-neutral approach using discounted cashflows formula.
result Ask prices are close to multipliers justified by median song cashflows, while best bids are near multipliers justified by bottom decile cashflows.
The risk premium is one of main concepts in mathematical finance. It is a measure of the trade-offs investors make between return and risk and is defined by the excess return relative to the risk-free interest rate that is earned from an asset per one unit of risk. The purpose of this article is to determine upper and …
Microstructure model explains leverage effect and rough volatility.
problem Understanding leverage effect and rough volatility in financial markets.
method Built a simple microscopic model using Hawkes processes to encode market microstructure features.
result Microscopic model demonstrates leverage effect and rough volatility in the long run.
Paper tackles risk-aware portfolio selection using multi-armed bandit.
problem Sequential portfolio selection under uncertainty.
method Incorporates risk-awareness into multi-armed bandit, constructs portfolio through asset filtering and risk minimization.
result Achieves balance between risk and return.
This paper calculates risk-dependent centrality of Brazilian stocks, showing rankings vary with external risk and crisis events.
problem Understanding asset rankings in the Brazilian stock market under varying external risks.
method Computed risk-dependent centrality (RDC) for Brazilian stocks traded from 2008 to 2020, analyzing volatility and returns.
result Asset rankings based on RDC vary with external risk and crisis events, with higher volatility in crisis periods.
Graph theory improves portfolio optimization for diversified investments.
problem Standard portfolio optimization ignores data structure, leading to suboptimal results.
method Introduces portfolio cut paradigm to incorporate graph theory into portfolio optimization.
result Graph-theoretic portfolio partitioning allows for robust and tractable asset allocation schemes.
Quantum-inspired method optimizes portfolio selection.
problem Optimizing asset allocation in finance.
method Combining quantum-inspired and conventional optimization methods.
result Faster and more accurate portfolio optimization solutions.
MOT: A Mixture of Actors Reinforcement Learning Method by Optimal Transport for Algorithmic Tradingq-fin.TR
MOT uses RL with OT to adapt to different market conditions for algorithmic trading.
problem Adapting to varying market conditions in algorithmic trading.
method MOT uses multiple actors with disentangled representation learning and Optimal Transport to model different market patterns.
result MOT outperforms in real futures market data with excellent profit capabilities and risk balancing.
A model for choosing crypto assets based on security and stability.
problem Optimal selection of crypto assets considering security and stability.
method A recommender app-like system that presents pairs of crypto assets and collects investor preferences.
result A variety of possible outcomes for crypto asset investments and adoption.
AI helps simplify complex ship finance processes.
problem Complexity in ship finance due to data and regulatory requirements.
method Integrates large language models for document comprehension, information extraction, and workflow automation.
result AI-assisted systems can support maritime finance professionals in managing complex information and reporting requirements.
Quanto Option Pricing on a Multivariate Levy Process Model with a Generative Artificial Intelligenceq-fin.MF
Study uses AI to price exotic options with a new Levy process model.
problem Pricing exotic options with a non-Gaussian Levy process model.
method Introduced a new multivariate Levy process model and used a generative AI model to estimate the probability density function.
result Developed a method to price quanto options using a trained generative AI model.
Unified model learns from both time-series and cross-sectional momentum features.
problem Separate time-series and cross-sectional momentum strategies do not consider concurrent relationships.
method Spatio-Temporal Momentum strategies using neural networks to combine both types of momentum.
result Simple neural network with single fully connected layer generates trading signals for all assets.
A new portfolio model DEWSP improves Sharpe ratio by 0.24% to 5.15%.
problem High sensitivity of optimized portfolios to estimation errors.
method Deep learning algorithms predict returns for top-N ranked assets, then equally weight them.
result DEWSPs provide an improvement rate of 0.24% to 5.15% in terms of monthly Sharpe ratio compared to HEWSPs.
Motivated by empirical data, we develop a statistical description of the queue dynamics for large tick assets based on a two-dimensional Fokker-Planck (diffusion) equation, that explicitly includes state dependence, i.e. the fact that the drift and diffusion depends on the volume present on both sides of the spread. "J…
Optimal hedging strategies identified for markets with fast-varying volatility.
problem No perfect hedge in markets with fast-varying stochastic volatility.
method Analyzes various delta-type hedging strategies and their performance in a specific asymptotic regime of rapid mean reversion.
result Identifies the `practitioners' delta hedging scheme as optimal in the considered regime of rapid mean reversion.
We consider a statistical model for pairs of traded assets, based on a Cointegrated Vector Auto Regression (CVAR) Model. We extend standard CVAR models to incorporate estimation of model parameters in the presence of price series level shifts which are not accurately modeled in the standard Gaussian error correction mo…
Optimal portfolios are found for a wide range of utility functions under hyperbolic returns.
problem Portfolio optimization under expected utility criterion for large portfolios.
method Analytical expressions for optimal portfolios under hyperbolic return distributions and various utility functions.
result The two-fund separation holds true for a broad class of utility functions.
Study on Bitcoin price fluctuations revealing power-law behavior with 2 < α < 2.5.
problem Characterizing the complexity and volatility of cryptocurrency markets.
method Analysis of Bitcoin returns over various time intervals and exchanges.
result Empirical evidence of power-law behavior with scaling exponent 2 < α < 2.5.
Paper develops a risk scoring framework for tokenized RWA markets.
problem Tokenized assets may not reflect true risk due to illiquidity and concentration.
method Develops a risk scoring framework based on observable indicators.
result Assets with limited transfer activity and concentrated ownership have high empirical risk.
Optimizes financial portfolios to minimize systemic risk.
problem Minimizing systemic risk in financial markets.
method Network optimization to rearrange overlapping portfolios.
result Systemic risk can be reduced by more than a factor of two without harming individual banks.
Intermediating DFMM Asset (IDA)q-fin.GN
IDA makes DFMM's asset tradeable, enhancing cross-chain finance efficiency.
problem Making DFMM's asset tradeable to improve cross-chain finance efficiency.
method Introducing IDA as a tradeable asset, leveraging DFMM's robust liquidity and dynamic AMM.
result IDA enhances cross-chain finance efficiency through tradeable asset and dynamic AMM.
A novel graphical matching approach improves pairs trading by reducing portfolio variance and risk-adjusted returns.
problem Common pairs trading methods lead to high portfolio variance and low risk-adjusted returns due to focusing on highly cointegrated assets.
method Model all assets and their cointegration levels with a weighted graph. Select pairs as a maximum weighted matching to ensure no shared assets and lower portfolio variance.
result The matching-based strategy shows a significant improvement in risk-adjusted performance, with a gross Sharpe ratio of 1.23.
This paper explains tax policy for crypto assets in a rapidly evolving tech landscape.
problem Rapid technological changes in crypto assets create regulatory and tax policy blind spots.
method Explains principles of crypto assets, their technology, and tax issues.
result Tax policies are lagging behind innovation in blockchain and crypto.
Study develops time-continuous models and probabilistic descriptions for agent-based economic market models.
problem Formulating and describing agent-based economic market models in a time-continuous and probabilistic manner.
method Derived time-continuous formulations, discussed impact of time-scaling, proved stability, presented probabilistic descriptions using kinetic theory.
result Time-continuous formulations and probabilistic descriptions for agent-based economic market models.
Hybrid model combines interpretable and black-box models for better transparency and performance.
problem Balancing interpretability and predictive performance in machine learning models.
method Proposes a Hybrid Predictive Model (HPM) integrating an interpretable model with a black-box model, using principled objective functions and customized training algorithms.
result Hybrid models achieve an efficient trade-off between transparency and predictive performance.
The paper introduces BCART models for aggregate claim amount, improving frequency-severity and joint modeling.
problem Modeling aggregate claim amount with frequency-severity and joint dependencies.
method Developed three types of BCART models: frequency-severity, sequential, and joint models. Used various distributions for claim severity data.
result Weibull distribution outperforms gamma and lognormal for right-skewed, heavy-tailed claim severity data.
Boosts generative models by combining multiple meta-models.
problem Challenges in creating a single generative model that accurately represents complex data.
method Cascades multiple meta-models (like RBM and VAE) to create a stronger generative model.
result Derives a decomposable variational lower bound for training and evaluating the boosted model.
The paper uses model-based trees to create interpretable surrogate models for complex machine learning models.
problem Interpreting complex machine learning models.
method Using model-based trees to partition feature space and create interpretable models.
result Model-based trees generate optimal surrogate models that balance interpretability and performance.
Study on limits of community detection in various network models.
problem Limits of community detection in network models.
method Analysis of several network models including Stochastic Block Model, Exponential Random Graph Model, Latent Space Model, Directed Preferential Attachment Model, and Directed Small-world Model.
result Information-theoretic limits for recovery of node labels in network models.
Gauge Flow Modelscs.LG
Gauge Flow Models use a learnable Gauge Field in Generative Flow Models.
problem Improving generative model performance.
method Integrates a learnable Gauge Field into Flow ODEs.
result Gauge Flow Models outperform traditional Flow Models in Flow Matching experiments.
The study examines how model predictions hold up under model extensions.
problem Model predictions may not be robust under model extensions, limiting their applicability.
method The study uses causal ordering to assess robustness of qualitative model predictions and characterizes model extensions that preserve predictions.
result Conditions and techniques are provided to assess robustness of model predictions under model extensions.
MALC combines interpretable linear models with black-box models for better predictions and transparency.
problem Combining interpretability with black-box models for better predictions.
method Formulates MALC as a convex optimization problem and uses accelerated proximal gradient method for training.
result MALC provides an efficient frontier balancing prediction accuracy and transparency.
Alternative approach to model selection using transformation analysis.
problem Over-simplistic models lead to erroneous interpretations.
method Step-wise complexity reduction to identify simpler, better-interpretable models.
result Transformation models improve model fit and interpretability.
Revises Bayesian model averaging for foundation models.
problem Ensemble pre-trained and lightly-finetuned foundation models for improved classification performance.
method Introduces trainable linear classifiers and computationally cheaper model averaging scheme (OMA).
result Ensembled models can better predict on various datasets.
Paper introduces symmetric divergence link models for probability distributions.
problem Symmetric divergence measures for probability distributions.
method Two general classes of link models: one for survival functions and another for cumulative probability distribution functions.
result Advantages of symmetric divergence measures over asymmetric measures for model averaging and feature assessment.
The paper tests stock return models and uses LSTM to predict stock returns.
problem Validating stock return models and predicting stock returns.
method Used Fama-French three-factor, four-factor, and five-factor models; also used LSTM model.
result Fama-French five-factor model shows better validity for stock returns.
New method to handle credit portfolio model uncertainties.
problem Model risk in credit portfolio models.
method Demonstrates comprehensive yet easy-to-implement approach to uncertainty in model parameters.
result Comprehensive method to deal with model uncertainties.
A new neural network model predicts multi-symbol tokens over multiple scales.
problem Language modeling with improved flexibility and performance.
method A learned dictionary of multi-symbol tokens using BPE compression.
result The model outperforms LSTM on language modeling tasks, especially for smaller models.
Researchers review challenges in interpreting additive models, especially neural additive models.
problem Challenges in interpreting additive models, particularly neural additive models.
method Review of generalized additive models and discussion of nonidentifiability.
result Challenges in claiming interpretability or suitability for safety-critical applications of additive models.