Optimizes sparse mean-reverting portfolios for higher returns.
problem Finding optimal stock weights for mean-reverting portfolios.
method Transformed optimization problem into SDP, added constraints.
result Sparse mean-reverting portfolios provide higher returns with transaction costs.
Paper proposes an efficient MM method for optimizing mean-reverting portfolios in finance.
problem Optimizing mean-reverting portfolios in financial markets considering mean-reversion strength, variance, and investment constraints.
method Majorization-Minimization (MM) method.
result The proposed method significantly outperforms other methods in financial market simulations.
The paper designs mean-reverting portfolios with budget constraints.
problem Designing mean-reverting portfolios with a budget constraint.
method General problem formulation, optimization of mean-reversion criterion, consideration of portfolio variance, and investment budget constraint. Proposed specific problems and efficient algorithms.
result Our methods generate consistent profits and outperform traditional and benchmark methods.
Mean-reverting assets are one of the holy grails of financial markets: if such assets existed, they would provide trivially profitable investment strategies for any investor able to trade them, thanks to the knowledge that such assets oscillate predictably around their long term mean. The modus operandi of cointegratio…
Optimizes a portfolio with mean-reverting assets using Ornstein-Uhlenbeck process.
problem Design a portfolio with high mean reversion and low variance.
method Penalized OU-Likelihood Estimation with specialized algorithm.
result Parsimonious portfolio selection with desirable characteristics.
Solves optimal control for trading multiple mean-reverting assets.
problem How to construct a portfolio from mean-reverting assets.
method Optimal control problem for power utility agent.
result Nearly explicit solution with properties of optimal solution.
The paper analyzes optimal portfolio allocation under a fast mean-reverting fractional stochastic environment.
problem Optimal portfolio allocation under a fractional stochastic environment with long-range dependence.
method Analyzes the nonlinear optimal portfolio allocation problem using a stationary fractional Ornstein-Uhlenbeck process with fast mean-reverting.
result Establishes asymptotic optimality of zeroth order trading strategies and general utility functions within specific families of admissible strategies.
Optimizes portfolios in fast mean-reverting markets, achieving asymptotic efficiency.
problem Optimizing portfolios in markets with fast mean-reverting returns and volatility.
method Proposes a zeroth order strategy and uses singular perturbation method for asymptotic optimality.
result Shows asymptotic optimality of the proposed strategy under specific assumptions.
Survey on portfolio choice with small transaction costs using asymptotic methods.
problem Portfolio choice problems with small transaction costs.
method Derive dynamic programming equations, simplify in small-cost limit, and use policy iteration for complex models.
result Explicit solutions for various models, including mean-reverting returns and proportional costs.
Study on large portfolio losses with correlated volatility processes converging to a stochastic PDE.
problem Large portfolio losses with correlated volatility processes.
method Structural stochastic volatility model, mean-reverting diffusions, stochastic initial-boundary value problem.
result Convergence of empirical measure process to a stochastic PDE solution under certain conditions.
Paper proposes a new method for finding sparse mean reverting portfolios efficiently.
problem Finding sparse mean reverting portfolios from a large number of assets.
method Leverages H-SGDLM data to formulate a quasi-convex minimization problem with a normalisation constraint, solving it with a cyclical coordinate descent algorithm.
result Efficiently computes exact sparse solutions for large asset universes, demonstrating flexibility, speed, and scalability.
Investors can achieve optimal risk-reward trade-offs with bonds and stocks under mean-reverting stock returns.
problem Optimizing investment strategies with mean-reverting stock returns.
method Calculus of variations to derive the entire family of extremal strategies, not just the optimal ones.
result The value of the portfolio is effectively bounded from below, providing a 'guarantee' on the horizon.
Investors benefit from long horizons in a market with mean-reverting equity returns.
problem Optimal portfolio choice in a market with mean-reverting risk-free rate and equity risk-premium.
method Mean-variance optimization, Euler-Lagrange equation, Calculus of Variations, spectral problem.
result Optimal policies are characterized by eigenvalues of the lambda-matrix, leading to better risk-return trade-offs for long-term investors.
Study optimal portfolio in intraday electricity markets using Lévy-Ornstein-Uhlenbeck processes.
problem Maximizing expected terminal utility in a single risky asset market.
method Model power prices with mean-reverting additive process, solve HJB equation for logarithmic utility.
result Explicit solution for optimal strategy, numerical and analytical methods available.
Optimal portfolio design for statistical arbitrage in finance.
problem Designing optimal mean-reverting portfolios for statistical arbitrage.
method General problem formulation with investment leverage constraint, followed by successive convex approximation method.
result The proposed model and algorithms effectively construct portfolios with satisfactory mean reversion and variance properties.
Improves predictions by integrating forward-looking views into dynamic factor models.
problem Poor forecasts from historical data when dynamics change.
method Combines historical data with forward-looking views using a dynamic factor model.
result Derives optimal portfolio strategies influenced by both myopic and intertemporal factors.
We study the Merton problem of optimal consumption-investment for the case of two investors sharing a final wealth. The typical example would be a husband and wife sharing a portfolio looking to optimize the expected utility of consumption and final wealth. Each agent has different utility function and discount factor.…
Deep reinforcement learning improves trading performance with predictable returns.
problem Improving trading performance in financial markets with low signal-to-noise ratio.
method Investigates model-free deep reinforcement learning traders in a market with known mean-reverting factors.
result DRL agents outperform benchmarks in misspecified price dynamics and extreme events.
The paper optimizes portfolios using MACD signals derived from price history.
problem Optimizing risky asset portfolios with latent mean-reverting and momentum factors.
method Derives optimal strategies based on MACD signals from EMA processes.
result Establishes admissibility and verification of optimal strategies.
We extend the theory of asymmetric information in mispricing models for stocks following geometric Brownian motion to constant relative risk averse investors. Mispricing follows a continuous mean--reverting Ornstein--Uhlenbeck process. Optimal portfolios and maximum expected log--linear utilities from terminal wealth f…
The paper optimizes portfolios in a market with hidden drift and random expert opinions.
problem Optimizing portfolios in a market with hidden Gaussian drift and random expert signals.
method Modeling the hidden drift using Kalman filters and solving the utility maximization problem with dynamic programming.
result Derivation of optimal portfolio weights and utility maximization under the given market conditions.
Investigates portfolio selection with transaction costs and stochastic volatility, using deep learning for computation.
problem Optimal portfolio selection with transaction costs and stochastic volatility.
method Two-factor stochastic volatility model, option-implied utility function, deep learning policy iteration.
result Deep learning method effectively computes optimal investment decisions under transaction costs and stochastic volatility.
A new model reduces rating transition matrix estimation errors for small portfolios.
problem Estimating rating transition matrices for small portfolios leads to unreliable and unstable predictions.
method A sparse structural model with three parameters that assumes an autoregressive mean-reverting ability-to-pay process.
result The model produces well-behaved transition probabilities, reducing statistical degrees of freedom and improving reliability.
The paper analyzes a five-factor capital market model and facilitates exact simulation.
problem Analyzing and simulating a five-factor capital market model.
method Using a Vasicek interest rate model, mean-reverting excess return, and realized inflation with expectation, the paper derives the necessary distributional results and describes practical methods to overcome rank deficiency.
result Exact simulation from the model can be achieved by sampling from a seven-dimensional normal distribution.
Optimizes trading strategies with price impact, predictable returns, and stochastic volatility.
problem Dynamic portfolio optimization under complex market conditions.
method Multi-scale volatility expansion, singular and regular perturbations, asymptotic approximations.
result Improved portfolio strategy with reduced profit and loss (PnL) through corrections for small price impact.
Study fast mean-reversion in large portfolios of stochastic volatility models for accurate loss estimation.
problem Estimating loss from large portfolios of stochastic volatility models with fast mean-reversion.
method Analyzes SPDEs and convergence of stochastic initial-boundary value problems under fast mean-reversion of volatility.
result Accurate estimation of loss distribution using approximate constant volatility models.
The paper values perpetual callable American volatility options using a mean-reverting volatility model.
problem Valuation of callable American volatility put options.
method Modeling volatility dynamics as a mean-reverting 3/2 process and proposing a pricing formula.
result The value of perpetual callable American volatility put options is discussed under given conditions.
Study pairs trading strategy with uncertain drift and penalized risk.
problem Optimizing pairs trading strategy with uncertain drift and risk penalty.
method Model pairs trading as a Gaussian mean-reverting process with a Markov chain, use stochastic filtering theory, and solve for logarithmic utility function.
result Characterize optimal strategies and value functions under full and partial information, showing certainty equivalence principle.
The paper reconciles rough volatility with fast mean reverting models.
problem Empirical evidence of rough volatility contradicts fast mean reverting models.
method Analyzes mean reverting rough volatility and compares it to fast mean reverting Markov volatilities.
result Price impact of rough volatility coincides with fast mean reverting models.
Modified model prevents volatility from approaching zero.
problem Volatility in the Gatheral model can approach zero, making it statistically indistinguishable.
method Proposed a modified model with Skorokhod reflection to prevent volatility from approaching zero.
result The modified model prevents volatility from approaching zero, preserving the model's flexibility.
This paper analyzes portfolio optimization with multi-scale volatility.
problem Optimizing portfolio under multi-scale volatility in a stochastic environment.
method Zeroth-order strategy followed by first-order approximation via PDE analysis.
result Asymptotic optimality of the proposed strategy in specific families of controls.
This paper optimizes portfolios in a fast-reverting volatility environment.
problem Optimizing portfolios in a fast-reverting volatility environment.
method Fractional Brownian motions with Hurst index H, modeling fast or slow regimes with small parameters.
result Only one deterministic term of order √ε appears in the first order correction for the fast-varying rough environment.
The paper develops optimal strategies for high-dimensional statistical arbitrage using factor models and stochastic control.
problem Optimal strategies for high-dimensional statistical arbitrage in a factor model setting.
method Combines factor models with stochastic control to derive optimal strategies.
result Closed-form optimal strategies for market-neutral portfolios in a high-dimensional setting.
Proposes a new model to better handle correlation risk in credit risk calculations.
problem Empirical evidence shows correlation risk is significant in credit risk models.
method Introduces a stochastic correlation extension of the Vasicek model using circular diffusion.
result Demonstrates how correlation volatility and persistence affect joint default and survival probabilities.
Optimal timing strategy for mean-reverting price spreads.
problem Trading price spreads with mean-reverting characteristics.
method Sequential optimal stopping framework with refined signature method.
result Precise entry and exit timings that maximize gains.
This paper investigates optimal portfolio strategies in a financial market where the drift of the stock returns is driven by an unobserved Gaussian mean reverting process. Information on this process is obtained from observing stock returns and expert opinions. The latter provide at discrete time points an unbiased est…
We study the profitability of optimal mean reversion trading strategies in the US equity market. Different from regular pair trading practice, we apply maximum likelihood method to construct the optimal static pairs trading portfolio that best fits the Ornstein-Uhlenbeck process, and rigorously estimate the parameters.…
The paper models exchange rate risk premium using mean-reverting dynamics.
problem Empirical failure of uncovered interest parity (UIP).
method Modeling risk premium using Ornstein-Uhlenbeck (OU) process embedded in stochastic differential equation for exchange rate.
result The model shows strong predictive performance at short and long horizons, but underperforms at intermediate horizons.
Study how trading costs affect equilibrium returns in a model with heterogeneous investors.
problem How trading costs influence equilibrium returns in a model with mean-variance investors.
method Developed a continuous-time risk-sharing model with quadratic transaction costs, characterized equilibrium as solution of coupled forward-backward SDEs.
result Equilibrium returns are mean-reverting and higher for risk-averse net sellers or expanding asset supply.
Develops a statistical arbitrage strategy with stop-loss and leverage for energy markets.
problem Optimizing trading strategies in high-frequency energy markets with stop-loss and leverage.
method Analytical approach using mean-reverting processes and optimal trading strategies.
result Analytical expressions for expected First-Exit-Times and long-run returns of the strategy.
Study optimal trading strategies for mean-reverting spreads using integral equations.
problem Optimal timing for trading mean-reverting price spreads.
method Utilized local time-space calculus and nonlinear integral equations of Volterra-type.
result Derived optimal boundaries for trading strategies.
The paper studies efficient simulation methods for financial firm values under fast mean-reverting volatility.
problem Estimating the probability of firm default under fast mean-reverting stochastic volatility models.
method Approximations using ergodic averages and central limit theorem corrections for efficient simulation.
result Accuracy of approximations assessed through numerical simulation and payoff function estimation.
A Monte Carlo method for pairs trading on mean-reverting spreads with Lévy processes.
problem Trading on mean-reverting spreads with flexible models.
method Monte Carlo simulation with variance gamma and alpha-gamma driving processes.
result Optimal trading strategies are affected by model parameters and correlation.
The paper models default probabilities and total defaults in credit portfolios using a contagion process with self-exciting jumps.
problem Modeling default probabilities and total defaults in credit portfolios to mitigate credit risk.
method Developed a contagion process with self-exciting jumps to model credit events and derive closed-form expressions for default probabilities and total defaults.
result The proposed framework captures the feedback effect and can be used to price synthetic CDOs.
The log-periodic power law (LPPL) is a model of asset prices during endogenous bubbles. A major open issue is to verify the presence of LPPL in price sequences and to estimate the LPPL parameters. Estimation is complicated by the fact that daily LPPL returns are typically orders of magnitude smaller than measured price…
Study examines returns of Asian ADRs, finding mean-reverting patterns and developing trading strategies.
problem Analyzing returns of Asian ADRs in asynchronous markets.
method Dissected returns into intraday and overnight components, fitted to Ornstein-Uhlenbeck process, developed pairs trading strategies.
result Consistent positive payoffs in pairs trading strategies exploiting mean-reverting ADR-SPY spreads.
The paper solves complex swing option pricing equations with numerical methods.
problem Valuation of swing options with jumps under a mean-reverting model.
method Proposes second-order numerical methods to solve PIDEs convection-dominated and with nonlocal integral terms.
result Numerical methods confirm second-order convergence behavior.
Optimal purchasing policy for mean-reverting items with a finite deadline.
problem Minimizing cost of purchasing and holding mean-reverting items within a fixed time.
method Proved optimal policy as a time-variant threshold function, constructed with dynamic programming.
result Explicit equations for crossing time probability and overshoot expectation.