Deep learning predicts market sensitivities for cost-effective index tracking.
problem Costly and impractical replication of index funds.
method Learning to predict market sensitivities using deep learning models.
result Significant reduction in prediction errors compared to historical methods.
Constructs portfolios based on Hellinger distance to normal, finding market invariance.
problem Finding a market invariant for portfolio construction.
method Uses Hellinger distance to normal distribution for portfolio construction and analysis.
result Minimum Hellinger distance varies drastically between markets, suggesting market invariance.
This paper calculates second order price sensitivities for markets affected by financial crises.
problem Accurate risk management in financial derivative markets, especially during financial crises.
method Derives explicit formulas for second order price sensitivities under a depressed market model.
result Improved hedging strategies during financial crunches are possible with the derived formulas.
Risk-averse trading policies learned from simulated market interactions.
problem Minimizing execution cost in limit order book markets with market impact.
method Risk-sensitive Q-learning applied to Markov Decision Process in a market simulator.
result Derived decision-tree-based execution policies that minimize cost variance.
The paper analyzes binary option markets with exogenous information and price sensitivity.
problem Analyzing binary option markets with exogenous information and price sensitivity.
method Derive and analyze a continuous model of binary option markets with exogenous information, using Filippov surfaces and general assumptions on purchasing rules.
result Price always converges when exogenous information is constant, and price sensitivity affects price lag vs. information.
Improves SA-CCR model to be more consistent and risk-sensitive.
problem Inconsistent and risk-insensitive SA-CCR model.
method Cashflow decomposition in a 3-Factor Gaussian Market Model.
result Makes SA-CCR self-consistent and risk-sensitive.
Framework improves ETF volatility forecasting by adapting to market conditions.
problem Challenges in volatility forecasting due to shifting market conditions and varying model performance.
method Risk-sensitive specialist routing using online risk-sensitive evaluation and state-dependent gating.
result Reduces forecast loss by 24% and underprediction loss by 22% compared to rolling-best baseline.
Market efficiency at least requires the absence of weak arbitrage opportunities, but this is not sufficient to establish a situation where the market is sensitive, i.e., where it "fully reflects" or "rapidly adjusts to" some information flow including the evolution of asset prices. By contrast, No Weak Arbitrage togeth…
Study sensitivity of utility maximization to market changes.
problem Sensitivity of utility maximization to market price of risk changes.
method Obtained second-order expansion of value function, first-order terminal wealth approximation, constructed trading strategies, reduced approximation to Kunita-Watanabe decomposition.
result Reduced sensitivity analysis to a Kunita-Watanabe decomposition.
A general market model with memory is considered in terms of stochastic functional differential equations. We aim at representation formulae for the sensitivity analysis of the dependence of option prices on the memory. This implies a generalization of the concept of delta.
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.
This paper improves robot traders' market impact sensitivity.
problem Market impact in automated trading systems.
method Critiqued existing methods, introduced MLOFI, and demonstrated new algorithms.
result New imbalance-sensitive trader-agents exhibit market impact effects.
The paper analyzes robustness and sensitivity of rough Volterra stochastic volatility models.
problem Analyzing the robustness and sensitivity of stochastic volatility models.
method Statistical tests and empirical analysis on Apple Inc. equity options.
result Comparison of different models' robustness and sensitivity to option data structure.
Model dynamic customer sensitivities across categories.
problem Dynamic heterogeneity in customer sensitivities to marketing elements.
method Hierarchical dynamic factor model with Bayesian nonparametric Gaussian processes.
result Dynamic heterogeneity can be explained by a few global trends.
Hedging methods to mitigate the exposure of variable annuity products to market risks require the calculation of market risk sensitivities (or "Greeks"). The complex, path-dependent nature of these products means these sensitivities typically must be estimated by Monte Carlo simulation. Standard market practice is to m…
Researchers quantify risk exposure and sensitivities in financial markets under model uncertainty.
problem Optimizing investment and pricing under model uncertainty in financial markets.
method Distributionally robust optimization, Wasserstein ball, first-order sensitivity analysis.
result Sensitivities of value function, investment policy, and marginal prices to model uncertainty can be non-monotonic.
The paper defines price sensitivity and liquidity in CFMMs and links it to curvature.
problem Understanding the relationship between CFMM curvature and market performance.
method Proposes a definition of price sensitivity and liquidity, and links it to CFMM curvature.
result Curvature of CFMMs affects market performance and liquidity provider incentives.
By incorporating market impact and asymmetric sensitivity into the evolutionary minority game, we study the coevolutionary dynamics of stock prices and investment strategies in financial markets. Both the stock price movement and the investors' global behavior are found to be closely related to the phase region they fa…
Study optimizes interbank lending and borrowing to reduce systemic risk.
problem Optimizing lending and borrowing in interbank markets to mitigate systemic risk.
method Risk-sensitive mean field games with common noise, convex analysis, Fokker-Planck equations, first hitting time method.
result Risk-averse behavior reduces individual and systemic bank risks.
The paper analyzes how sensitive long-term utility of optimal portfolios is to changes in market models.
problem Sensitivity of long-term expected utility of optimal portfolios to market model changes.
method Analyzes utility maximization problem with long-time horizon under incomplete market given by a factor model, focusing on eigenpairs of operators.
result Eigenpairs determine long-term sensitivity of optimal expected utility to market model changes.
Study quantos in energy markets using HJM framework and Malliavin calculus.
problem Analyzing sensitivity of energy quanto options.
method Using HJM framework and Malliavin calculus, derive delta and cross-gamma formulas.
result Extension of existing work on quanto options in energy markets.
Study proposes a new resilience metric for stock market performance analysis.
problem Quantifying resilience cycles in stock market performance.
method Systems-oriented approach with Robustness Range and Elasticity Threshold.
result New metric quantifies non-linear resilience cycles in stock markets.
This paper tests for chaos in energy futures markets using the butterfly effect.
problem Determining chaos in energy futures time series.
method Reread existing literature on energy markets, introduced a coefficient to measure determinism and reliability.
result Maximum reliability level was approximately 56%, not strong evidence of chaos.
Study validates SV models with jump component and long memory parameter, using robustness and sensitivity analysis.
problem Validation of SV models with jump component and long memory parameter.
method Robustness and sensitivity analysis using bootstrapping and Monte-Carlo filtering on market data.
result Validation of SV models with jump component and long memory parameter.
A minimal model of a market of myopic non-cooperative agents who trade bilaterally with random bids reproduces qualitative features of short-term electric power markets, such as those in California and New England. Each agent knows its own budget and preferences but not those of any other agent. The near-equilibrium pr…
The paper analyzes Reliability Options in electricity markets, deriving pricing formulas and simulating real market scenarios.
problem Determining the value of Reliability Options in electricity markets under various price regimes.
method The paper derives closed-form pricing formulae and simulates real market scenarios using data from the Italian power market. Sensitivity analyses are performed to highlight the impact of different parameters.
result The value of Reliability Options is influenced by the level and volatility of power and strike prices, mean reversion speeds, and correlation coefficients.
Study risk-sensitive market making with entropy regularization for better quote control.
problem Risk-sensitive market making with exponential utility and penalties.
method Entropy-regularized certainty-equivalent Bellman policies for discrete-time market dynamics.
result Proves convergence and performance bounds for entropy-regularized policies.
New model uses symmetries and scaling laws to predict consumer advertising response.
problem Understanding consumer response to advertising efforts.
method Introduces a physics-based mathematical model to describe consumer response dynamics.
result The model better captures nonlinearities in advertising effects and provides new parameters for audience engagement.
In this article we consider a game theoretic approach to the Risk-Sensitive Benchmarked Asset Management problem (RSBAM) of Davis and Lleo \cite{DL}. In particular, we consider a stochastic differential game between two players, namely, the investor who has a power utility while the second player represents the market …
Unified market making controls risk, arbitrage, and volatility surfaces.
problem Market making risk, arbitrage, and volatility surface consistency.
method Constrained RL and stochastic control for risk-sensitive execution and hedging.
result Agent achieves positive P&L with zero calendar and butterfly violations.
Paper discusses how automatic differentiation aids financial markets in computing derivatives.
problem Computing derivatives for financial market participants to understand exposure to market moves.
method Uses automatic differentiation and adjoint algorithmic differentiation (AAD) to compute financial sensitivities.
result Demonstrates the limitations of AAD and the need for specialized tools in financial contexts.
Study shows financial network resilience highly sensitive to its topology.
problem Systemic risk sensitivity to network topology.
method Examined various realistic network topologies, including density and block structures.
result Systemic risk properties are extremely sensitive to network features.
A deep reinforcement learning method for cost-sensitive portfolio selection.
problem Non-stationary price series and complex asset correlations make feature learning hard, and practical cost constraints are not considered.
method A two-stream portfolio policy network and a cost-sensitive reward function are developed using deep reinforcement learning.
result The method achieves superior performance in profitability, cost-sensitivity, and representation abilities.
Two machine learning methods detect insider trading from investor activity data.
problem Detecting insider trading from trading activity data is challenging.
method Two unsupervised machine learning methods: clustering and group identification.
result Identifies potential insider trading rings around price sensitive events.
This paper simplifies hedge ratios in financial models using pathwise algorithmic differentiation.
problem Expensive and unstable computation of hedge ratios from pathwise sensitivities.
method Develops reduced stochastic hedge ratios of the form φ_j^r = Σ_j^r ξ_j^q X_q, retaining sensitivity tensor through empirical averages.
result Two coefficient criteria are introduced to minimize pathwise residuals and satisfy moment equations.
We study a risk sensitive control version of the lifetime ruin probability problem. We consider a sequence of investments problems in Black-Scholes market that includes a risky asset and a riskless asset. We present a differential game that governs the limit behavior. We solve it explicitly and use it in order to find …
Study improves MACD trading strategy with volume and price adjustments.
problem Signal lag and false signals in traditional MACD trading rules.
method Develops VP-MACD framework with sensitivity calibration.
result Proposed framework outperforms baseline MACD in profitability and risk-adjusted return.
Study optimal portfolio allocation in credit markets with default contagion.
problem Risk-sensitive portfolio optimization in a regime-switching credit market with default contagion.
method Investigate recursive infinite-dimensional nonlinear dynamical programming equations (DPEs) and develop a verification theorem for optimal feedback strategies.
result Established existence and uniqueness of classical solutions to the recursive DPEs and constructed approximating problems to converge to the original system.
In the paper portfolio optimization over long run risk sensitive criterion is considered. It is assumed that economic factors which stimulate asset prices are ergodic but non necessarily uniformly ergodic. Solution to suitable Bellman equation using local span contraction with weighted norms is shown. The form of optim…
Study reveals finite-size effects and sensitivity to random numbers in Levy-Levy-Solomon model.
problem Finite-size effects and sensitivity to random numbers in Levy-Levy-Solomon model.
method Simulations and analysis of Levy-Levy-Solomon model with different random number generators and stopping criteria.
result Low-quality pseudo random number generators significantly impact simulation results.
The paper analyzes log-optimal portfolios in markets with random time events.
problem Analyzing log-optimal portfolios in markets with random events.
method Examined a market model with two information flows, F and G, and addressed log-optimal portfolio existence and sensitivity.
result Identified necessary and sufficient conditions for log-optimal portfolio existence, types of risks induced by random time, and factors affecting sensitivity.
Predicts stock market crashes using rational bubble model.
problem Financial market crashes prediction.
method White box model based on rational bubble theory.
result Successfully predicts major crashes in Dow Jones and Bitcoin markets.
Case study shows impact of co-optimizing energy and reserve for wind energy.
problem Impact of lack of co-optimization of energy and reserve in high wind penetration scenarios.
method Developed two models with and without co-optimization, calibrated with Spanish market parameters.
result Models show significant differences in energy and reserve management.
Biondi et al. (2012) develop an analytical model to examine the emergent dynamic properties of share market price formation over time, capable to capture important stylized facts. These latter properties prove to be sensitive to regulatory regimes for fundamental information provision, as well as to market confidence c…
Paper improves ISDA margin calculation using LSMC.
problem Efficiently calculating initial margin for financial contracts.
method Extends Least Squares Monte-Carlo (LSMC) technique.
result Improved efficiency in estimating margin sensitivities.
The paper offers a new model for variable annuities with surrender risk.
problem Modeling variable annuities with surrender risk and market consistency.
method Hybrid model with Lévy processes, time-inhomogeneous, and dependence between financial and surrender risks.
result Explicit analytical formulas and practical numerical procedures for variable annuity valuation.
The study examines VIX-linked fees for GMWBs using explicit solution simulation methods.
problem Decreasing the sensitivity of insurer's liability to volatility risk in GMWBs.
method Explicit weak solution for VA account value and Monte Carlo simulations.
result VIX-linked fees decrease the sensitivity of the insurer's liability to volatility risk.
Study analyzes market co-movements in critical mineral investments using change point detection and cross-sectional analysis.
problem Market dynamics in critical mineral investments during significant global events.
method Combines change-point detection (PELT algorithm) with cross-sectional analysis on ESG-ranked ETFs.
result Investors herded during market downturns and shifted to anti-herding after positive news and geopolitical shocks.