The article provides formulas to hedge impermanent loss in decentralized markets.
problem Impermanent loss in concentrated liquidity provision in decentralized markets.
method Analytical characterizations and static replication formulas using European calls or puts.
result Static replication formulas accurately hedge impermanent loss.
Neural networks can find financial arbitrage opportunities without needing market models.
problem Finding arbitrage opportunities in financial markets without using market models.
method Used neural networks to solve convex semi-infinite programs and detect arbitrage opportunities.
result Neural networks can detect model-free static arbitrage strategies in financial markets.
DGRCL integrates dynamic and static graph relations for financial market prediction.
problem Capturing the evolving nature of stock markets while considering both temporal changes and static relational structures.
method Dynamic Graph Representation with Contrastive Learning (DGRCL) framework, including Embedding Enhancement (EE) and Contrastive Constrained Training (CCT) modules.
result DGRCL significantly outperforms state-of-the-art TGL baselines on NASDAQ and NYSE datasets.
Study optimal semi-static hedging for illiquid markets using dynamic cash and static quoted derivatives.
problem Optimal pricing of exotic derivatives in illiquid markets with bid-ask spreads.
method Use Galerkin method and integration quadratures to approximate hedging problem as convex optimization, solved by interior point method.
result Semi-static hedging improves pricing and reduces transaction costs compared to static or dynamic trading alone.
Optimal trade execution strategies show adaptive methods reduce costs.
problem Optimal trade execution with short-term price predictive signals.
method Comparison of static and adaptive strategies with transient and instantaneous market impacts.
result Adaptive strategies significantly reduce transaction costs compared to static strategies.
With model uncertainty characterized by a convex, possibly non-dominated set of probability measures, the agent minimizes the cost of hedging a path dependent contingent claim with given expected success ratio, in a discrete-time, semi-static market of stocks and options. Based on duality results which link quantile he…
In incomplete financial markets not every contingent claim can be replicated by a self-financing strategy. The risk of the resulting shortfall can be measured by convex risk measures, recently introduced by Föllmer, Schied (2002). The dynamic optimization problem of finding a self-financing strategy that minimizes the …
We consider a continuous-time financial market that consists of securities available for dynamic trading, and securities only available for static trading. We work in a robust framework where a set of non-dominated models is given. The concept of semi-static completeness is introduced: it corresponds to having exact re…
Study optimizes investment strategies in volatile markets using machine learning and Bayesian techniques.
problem Enhancing portfolio management in volatile markets.
method Market segmentation into ten volatility-based states, real-time asset allocation adjustments using Bayesian Markov switching model.
result Dynamic portfolio achieves significantly higher risk-adjusted returns and total returns.
Based on the daily data of American and Chinese stock markets, the dynamic behavior of a financial network with static and dynamic thresholds is investigated. Compared with the static threshold, the dynamic threshold suppresses the large fluctuation induced by the cross-correlation of individual stock prices, and leads…
Different approaches to defining dynamic market risk measures are available in the literature. Most are focused or derived from probability theory, economic behavior or dynamic programming. Here, we propose an approach to define and implement dynamic market risk measures based on recursion and state economy representat…
The paper finds optimal strategies for hedging in incomplete markets using derivatives.
problem Optimal static hedging in incomplete markets with two underlying assets and vanilla options.
method Formulated as a utility maximization problem, solved through variational methods and fixed point analysis.
result Semi-analytical solutions for exponential, power/logarithmic, and quadratic utilities, with convergence to a fixed point for exponential utility.
This paper studies the problem of maximizing expected utility from terminal wealth in a semi-static market composed of derivative securities, which we assume can be traded only at time zero, and of stocks, which can be traded continuously in time and are modeled as locally-bounded semi-martingales. Using a general util…
Paper presents a machine learning algorithm for hedging ETF options, outperforming static hedging methods.
problem Semi-static hedging of ETF options with transaction costs and varying market conditions.
method Data-driven machine learning algorithm considering transaction costs, automated portfolio management, and PnL attribution analysis.
result The static hedging approach outperforms dynamic hedging methods in terms of profit and loss.
The paper redefines semi-static hedging as derivatives and calculates hedging errors.
problem The costs of maintaining hedging portfolios and the limitations of semi-static hedging.
method New integral representations, approximations, and efficient numerical methods for calculating Wiener-Hopf factors and Laplace-Fourier inversion.
result The hedging error of static hedging portfolios can be larger than variance-minimizing portfolios.
Develops a robust framework for pricing and hedging in discrete-time markets.
problem Pricing and hedging of derivative securities in markets with dynamically and statically traded assets.
method Abstract Fundamental Theorem of Asset Pricing and Pricing--Hedging Duality, minimal measurability assumptions, scenario-based approach.
result Includes model-independent results and extends classical probabilistic approaches.
QLBS and RLOP methods improve option pricing and hedging performance.
problem Improving option pricing and hedging performance under market frictions.
method Incorporates risk aversion and trading costs into QLBS, proposes RLOP approach.
result RLOP outperforms in dynamic hedging by reducing shortfall probability.
Unified framework for fixed-income pricing and liability replication.
problem Static arbitrage and discount curve construction.
method Model-free framework for static fixed-income pricing and liability replication.
result Existence of strictly positive discount curves reproducing market prices and least-cost super-replicating portfolios.
Paper mathematically extends timing risk hedging for barrier options.
problem Timing risk in barrier options under multi-dimensional models.
method Semi-static hedge using barrier options and asymptotic expansions.
result Higher order semi-static hedges can reduce hedging cost by over 90%.
A new method simulates implied volatility surfaces for multiple assets.
problem Generating consistent market scenarios for multiple asset implied volatilities.
method Combining functional data analysis and neural SDEs with a penalty for model misspecification.
result Simulated market scenarios are consistent with historical features and lie within the sub-manifold of essentially free static arbitrage.
QLAMMP optimizes fees on AMMs using Q-Learning.
problem Static AMMs cannot adapt to market changes, leading to high slippage.
method Developed a Q-Learning Agent (QLAMMP) to learn optimal fee rates.
result QLAMMP consistently outperforms static AMMs under various market conditions.
We propose a flexible framework for hedging a contingent claim by holding static positions in vanilla European calls, puts, bonds, and forwards. A model-free expression is derived for the optimal static hedging strategy that minimizes the expected squared hedging error subject to a cost constraint. The optimal hedge in…
We consider a financial market where stocks are available for dynamic trading, and European and American options are available for static trading (semi-static trading strategies). We assume that the American options are infinitely divisible, and can only be bought but not sold. In the first part of the paper, we work w…
Dynamic retirement glidepaths evolve over time based on some measure such as the retiree's funded status or current market valuations. Conversely, static glidepaths are fixed at a starting point and selected under the assumption that they will not change. In practice, new static glidepaths may be derived periodically m…
Optimal fees protect passive LPs in AMMs under varying market conditions.
problem Adverse selection losses in AMMs are not offset by static trading fees.
method Dynamic reduced-form model with parallel AMM and CEX, large-scale simulations, real market data analysis.
result Optimal AMM fees are stable under normal conditions but high in volatile periods to protect LPs.
It turns out that in the bivariate Black-Scholes economy Margrabe type options exhibit symmetry properties leading to semi-static hedges of rather general barrier options. Some of the results are extended to variants obtained by means of Brownian subordination. In order to increase the liquidity of the hedging instrume…
The paper develops a Fourier-based method for optimal hedging in stochastic volatility models.
problem Optimal hedging in financial markets with stochastic volatility.
method Fourier representation in a semimartingale factor model.
result A tractable formula for expected squared hedging error and optimal strategy.
FinSMART uses reinforcement learning to analyze financial sentiment, outperforming existing methods.
problem Limited adaptability of financial sentiment analysis to evolving market conditions.
method Market-aligned reinforcement learning framework that optimizes sentiment signals using realized market outcomes.
result Significantly outperforms existing state-of-the-art methods in profitability and sentiment signal quality.
We report a statistical analysis of the Island ECN (NASDAQ) order book. We determine the static and dynamic properties of this system, and then analyze them from a physicist's viewpoint using an equivalent particle system obtained by treating orders as massive particles and price as position. We identify the fundamenta…
A new framework forecasts implied volatility surfaces by separating learning and refinement stages.
problem Forecasting implied volatility surfaces is challenging due to stochastic future surfaces and static no-arbitrage constraints.
method Decoupled generative refinement framework using a conditional diffusion model and SAAM for surface refinement.
result The framework improves forecasting accuracy and reduces static no-arbitrage violations.
Investigates fund separations and stability for long-term optimal investments.
problem Optimizing long-term investments in an incomplete market with risky and safe assets.
method Analyzes three market models with different state variable processes to find optimal portfolios and prove convergence stability.
result Dynamic optimal portfolios converge to static portfolios over time, with vanishing sensitivities in the long run.
Dynamic VWAP execution improves by 10-15% in liquid markets.
problem Improving VWAP execution in dynamic markets.
method Recurrent Neural Networks (RNNs) for capturing temporal market dynamics, dynamic adjustment mechanism.
result Significant performance gains in liquid markets (10-15%) over traditional methods.
A semi-static approach efficiently replicates and prices callable interest rate derivatives.
problem Efficiently replicating and pricing callable interest rate derivatives under dynamic market conditions.
method Proposes a semi-static hedging algorithm that updates the replication portfolio on a finite number of instances, rather than continuously.
result The hedging error can be made arbitrarily small with a sufficiently large replication portfolio, and closed-form error margins are determined.
This work analyzes impermanent loss in decentralized markets and provides a hedging strategy.
problem Impermanent loss in automated market makers (AMMs).
method Analytical derivation of a static replication formula using European options, and numerical example with real data.
result Guaranteed hedging coverage for all final prices within a predefined interval.
In this article, we consider European options of type h(XT1,XT2,…,XTn) depending on several underlying assets. We study how such options can be valued in terms of simple vanilla options in non-specified market models. We consider different approaches related to static hedging and derive several pricing f…
It is well known that any sufficiently regular one-dimensional payoff function has an explicit static hedge by bonds, forward contracts and lots of vanilla options. We show that the natural extension of the corresponding representation leads to a static hedge based on the same instruments along with traffic light optio…
Simulates risk-neutral markets using neural spline flows.
problem Creating realistic risk-neutral market simulations.
method Developed a low-dimensional martingale representation and used neural spline flows for sampling.
result The calibrated simulator is closest to historical data with respect to Kullback-Leibler divergence.
In a discrete-time market, we study model-independent superhedging, while the semi-static superhedging portfolio consists of {\it three} parts: static positions in liquidly traded vanilla calls, static positions in other tradable, yet possibly less liquid, exotic options, and a dynamic trading strategy in risky assets …
Prediction markets show considerable promise for developing flexible mechanisms for machine learning. Here, machine learning markets for multivariate systems are defined, and a utility-based framework is established for their analysis. This differs from the usual approach of defining static betting functions. It is sho…
New trading strategies yield gains on average in various market scenarios.
problem Developing trading strategies that consistently yield positive gains in different market conditions.
method Introducing generalized statistical arbitrage concepts and profitable strategies based on information systems.
result Constructed profitable generalized strategies with good performance on simulated and real market data.
Simple model uses time series momentum to outperform benchmarks in equity and bond markets.
problem Finding systematic excess returns in various markets.
method Time series momentum applied to multiple investable indices without complex parameter estimation.
result Significant outperformance in equity and bond markets, nearly doubling returns.
We study continuous time Bertrand oligopolies in which a small number of firms producing similar goods compete with one another by setting prices. We first analyze a static version of this game in order to better understand the strategies played in the dynamic setting. Within the static game, we characterize the Nash e…
Simulates multi-asset spot and option markets using normalizing flows.
problem High-dimensionality of market call prices and dynamic preservation across simulators.
method Normalizing flows for efficient low-dimensional representations, conditional invertibility for joint distribution calibration.
result Calibrated simulators maintain dynamics of each underlying and accurately represent market call prices.
Proposes a dynamic matching algorithm for two-sided online markets.
problem Dynamic preferences in two-sided online matching platforms.
method Dynamic Matching Bandit Algorithm with statistical preference ranking estimation.
result Agent-optimal stable matching result with logarithmic regret bound.
New models optimize quotes for automated market makers considering various price dynamics and demand variability.
problem Optimizing quotes for automated market makers in volatile price environments.
method Advanced models incorporating stochastic volatility, jumps, Hawkes processes, and Markov-modulated Poisson processes.
result Optimal quotes can be computed using numerical methods tailored to each model.
\begin{abstract} The aim of this paper is to study the spanning power of options in a static financial market that allows non-integrable assets. Our findings extend and unify the results in [8,9,18] for Lp-models. We also apply the spanning power properties to the pricing problem. In particular, we show that prices …
The study examines robust decision-making in volatile financial markets, finding action robustness is more impactful than uncertainty tolerance.
problem Sequential decision making in high-frequency markets under evolving uncertainty.
method Analyzes two dimensions of robustness: uncertainty tolerance and action robustness, using simulations and empirical evidence.
result Action robustness has a larger impact on profitability than uncertainty tolerance, and excessive robustness can reduce profitability in illiquid markets.
Predict stock movement with news headlines using BERT embeddings.
problem Predicting stock price movement after financial news.
method Fine-Tuned Contextualized-Embedding Recurrent Neural Network (FT-CE-RNN) using BERT.
result Obtains state-of-the-art results on stock movement prediction task.