New algorithm for risk-sensitive reinforcement learning with natural policy gradients.
problem Risk-sensitive reinforcement learning with downside risk constraints.
method Introduce a new Bellman equation to estimate the lower partial moment of returns, use natural policy gradients, and extend Reward Constrained Policy Optimization.
result Sample-efficient estimation of partial moments and effective risk-sensitive control.
A new game-theoretic approach balances downside risk with expected reward.
problem Traditional game theory views risk only from the upside perspective, ignoring downside risk.
method Introduces downside risk aware equilibria (DRAE) based on lower partial moments.
result Successfully finds equilibria that balance downside risk with expected reward.
Unified framework combines views and optimization for better portfolio management.
problem Optimizing portfolio weights with dynamic adjustment based on volatility.
method Dynamic sliding window adjusting horizon, factor estimates, BL posterior returns, and weights over time.
result Outperforms dynamic mean-variance optimization without BL views, providing stronger downside risk control.
Study optimizes portfolio to minimize relative drawdown duration, penalizing unfavorable performance states.
problem Minimizing relative drawdown duration in portfolio optimization relative to a benchmark.
method Introduces a benchmark-relative drawdown-duration criterion penalizing unfavorable performance states. Uses a one-dimensional Markovian representation and Hamilton-Jacobi-Bellman equation.
result Derives explicit projection-based characterization of the optimal feedback control and identifies geometric settings for unique strong solutions.
Instead of controlling "symmetric" risks measured by central moments of investment return or terminal wealth, more and more portfolio models have shifted their focus to manage "asymmetric" downside risks that the investment return is below certain threshold. Among the existing downside risk measures, the lower-partial …
Hedge Funds are considered as one of the portfolio management sectors which shows a fastest growing for the past decade. An optimal Hedge Fund management requires an appropriate risk metrics. The classic CAPM theory and its Ratio Sharpe fail to capture some crucial aspects due to the strong non-Gaussian character of He…
The study analyzes ETFs' portfolio optimization and tail-risk management.
problem Analyzing the performance of actively managed ETFs in managing risk and diversification.
method Daily Bloomberg data for 30 funds, evaluating various strategies under long-only and long-short constraints.
result Tangency-type portfolios generally outperform buy-and-hold benchmarks, while minimum-variance and CVaR-minimizing portfolios sacrifice upside for downside control.
We investigate optimal consumption and investment problems for a Black-Scholes market under uniform restrictions on Value-at-Risk and Expected Shortfall. We formulate various utility maximization problems, which can be solved explicitly. We compare the optimal solutions in form of optimal value, optimal control and opt…
Investigates JM for reducing downside risk in market regimes.
problem Mitigating downside risk during market downturns.
method Statistical jump model for identifying market regimes, optimizing penalty for state transitions.
result JM-guided strategies outperform traditional models in reducing risk and enhancing returns.
The downside risk of a portfolio of (equity)assets is generally substantially higher than the downside risk of its components. In particular in times of crises when assets tend to have high correlation, the understanding of this difference can be crucial in managing systemic risk of a portfolio. In this paper we genera…
New risk class penalizes loss deviations from mean on both sides.
problem Current risks are sensitive to loss tails on the upside and ignore the downside.
method Introduces a bi-directional risk class with flexible tail sensitivity.
result Derives high-probability learning guarantees without gradient clipping.
Model predicts global financial market risks and asset allocation.
problem Predicting downside risk and market regime shifts.
method Dynamic regime switching model based on GARCH-DCC-Copula.
result Significantly improves risk and alpha-based asset allocation strategies.
Study uses MLP models to predict large-cap US stocks, finding 2-3 hidden layers more flexible.
problem Predicting asset prices for large-cap US stocks.
method Applied MLP models with dynamic structure to factor models, focusing on firm characteristics.
result MLP models with 2-3 hidden layers more flexible in modeling factors, better for downside risk control.
Hybrid model combines risk measures for better portfolio allocation.
problem Optimizing portfolios with various risk measures.
method Mean-variance hybrid model combining spectral risk measure and quantile optimization.
result Hybrid model outperforms classical mean-variance model in risk allocation.
This paper discusses an alternative explanation for the empirical findings contradicting the positive relationship between risk (variance) and reward (expected return). We show that these contradicting results might be due to the false definition of risk-perception, which we correct by introducing Expected Downside Ris…
Benchmarking deep learning models for financial time series, focusing on risk-adjusted performance.
problem Optimizing risk-adjusted performance in financial time series prediction.
method Evaluation of various deep learning architectures including linear models, RNNs, transformers, state space models, and sequence representation approaches.
result Hybrid models like VSN with LSTM and xLSTM achieve the highest overall Sharpe ratio and superior downside adjusted characteristics.
Investigates optimal PPI strategies in jump-diffusion models to mitigate downside risk.
problem Gap risk in PPI strategies due to jumps in asset price dynamics.
method Optimization problem with S-shaped utility functions, solved via martingale approach in a jump-diffusion framework.
result Determines optimal PPI strategy to maximize expected utility of terminal wealth.
The paper introduces a new method for forecasting financial risk using quantile-based modeling.
problem Forecasting Value-at-Risk (VaR) and Expected Shortfall (ES) for financial returns.
method Semiparametric approach using restricted quantile regression to model the conditional scale of financial returns.
result The method provides robust, distribution-free estimates of extreme losses and captures risk dynamics.
Commodity ETFs' portfolio optimization under heavy-tailed returns.
problem Optimizing commodity ETF portfolios under heavy-tailed return behavior.
method Passive buy-and-hold vs. rolling-window optimized portfolios.
result Improved risk-adjusted performance with minimum-risk and CVaR-based portfolios.
This paper extends the results of the article [C. Klüppelberg and S. M. Pergamenchtchikov. Optimal consumption and investment with bounded downside risk for power utility functions. In Optimality and Risk: {\it Modern Trends in Mathematical Finance. The Kabanov Festschrift}, pages 133-169, 2009] to a jump-diffusion set…
Under Solvency II the computation of capital requirements is based on value at risk (V@R). V@R is a quantile-based risk measure and neglects extreme risks in the tail. V@R belongs to the family of distortion risk measures. A serious deficiency of V@R is that firms can hide their total downside risk in corporate network…
We introduce an equilibrium asset pricing model, which we build on the relationship between a novel risk measure, the Expected Downside Risk (EDR) and the expected return. On the one hand, our proposed risk measure uses a nonparametric approach that allows us to get rid of any assumption on the distribution of returns.…
Study finds significant premium for low-beta stocks in firm-level idiosyncratic return distributions.
problem Understanding the role of common idiosyncratic quantile factors in asset pricing.
method Quantile factor analysis to extract common idiosyncratic quantile factors with asymmetric pricing effects.
result Significant premium for innovations to the lower-tail factor: high-beta stocks outperform low-beta stocks by around 7-8% per year.
Climate-contingent finance helps adapt to uncertain climate risks.
problem Uncertainty in future climate scenarios makes proactive adaptation less feasible.
method Underwrite climate adaptation projects with repayment based on future climate scenarios.
result Optimal financing reduces over- and under-preparation risks.
Paper optimizes financial trading strategies under uncertain market conditions.
problem Guaranteeing robust positive expected profits in financial systems.
method Transformed semi-infinite constraints into structured policies and proposed a novel graphical approach.
result Demonstrated superior risk-adjusted returns and downside risk compared to conventional strategies.
QBVAR improves oil price forecasting across quantiles, especially for downside risk.
problem Forecasting oil prices across different quantiles for better risk assessment.
method Quantile Bayesian Vector Autoregression (QBVAR) model.
result QBVAR improves median forecasts by 2-5% and left-tail forecast improvements of 10-25% during crisis episodes.
We study the feasibility and noise sensitivity of portfolio optimization under some downside risk measures (Value-at-Risk, Expected Shortfall, and semivariance) when they are estimated by fitting a parametric distribution on a finite sample of asset returns. We find that the existence of the optimum is a probabilistic …
This paper optimizes decarbonized indices for financial tracking, balancing risk and environmental impact.
problem Balancing financial performance with environmental responsibilities in the context of climate risks.
method Develops decarbonized indices using mean-VaR and mean-ES optimization methods.
result Optimized indices reduce financial risk and carbon footprint, providing a balanced investment option.
Enhanced Transformer models predict ETF portfolio performance by optimizing covariance and semi-covariance matrices.
problem Static covariance estimates fail to capture dynamic market fluctuations and non-linear correlations.
method Transformer-based models for real-time covariance and semi-covariance predictions.
result Portfolios optimized with semi-covariance matrix outperform those with standard covariance matrix, especially in volatile conditions.
Optimal market making improves liquidity in prediction markets.
problem Efficient price discovery in prediction markets.
method Stochastic control framework for optimal market making.
result Optimal market quotes improve downside protection and profit.
Managing a portfolio to a risk model can tilt the portfolio toward weaknesses of the model. As a result, the optimized portfolio acquires downside exposure to uncertainty in the model itself, what we call "second order risk." We propose a risk measure that accounts for this bias. Studies of real portfolios, in asset-by…
Shorting IG ETFs can hedge bond portfolios during market drawdowns effectively.
problem Managing downside risk in bond portfolios during market crises.
method Constructing three signals (Momentum, Liquidity, Credit) to dynamically hedge short IG positions.
result Dynamic hedge removes when predicted hedged return mean reverts, achieving higher returns and Sortino ratios.
This paper optimizes cryptocurrency portfolios by clustering price correlations and improving risk-return profiles.
problem Volatility and regulatory uncertainty in cryptocurrency markets make portfolio construction challenging.
method The paper combines network analysis, price forecasting, and portfolio theory to identify stable groups of correlated cryptocurrencies.
result Predictive consensus-clustering portfolios maintain positive and stable performance up to a 14-day horizon, with favourable gain-loss asymmetry and tighter tail-risk control.
We investigate optimal consumption problems for a Black-Scholes market under uniform restrictions on Value-at-Risk and Expected Shortfall for logarithmic utility functions. We find the solutions in terms of a dynamic strategy in explicit form, which can be compared and interpreted. This paper continues our previous wor…
Russia-Ukraine conflict impacts global agricultural futures and spot markets' extreme risks.
problem Impact of Russia-Ukraine conflict on global agricultural futures and spot markets' extreme risks.
method Analytical framework for tail dependence, Copula-CoVaR method, ARMA-GARCH-skewed Student-t model.
result The outbreak of the conflict intensified risks in the wheat market the most and showed significant asymmetries in extreme risk spillovers.
QTMRL uses RL with multi-indicators to improve trading adaptability.
problem Traditional trading models fail in volatile markets due to rigid assumptions.
method Combines multi-indicators with RL for adaptive portfolio management.
result QTMRL outperforms baselines in profitability and risk control.
Variable annuities, as a class of retirement income products, allow equity market exposure for a policyholder's retirement fund with electable additional guarantees to limit the downside risk of the market. Management fees and guarantee insurance fees are charged respectively for the market exposure and for the protect…
Diversified risk parity strategies outperform equally-weighted portfolios in various asset universes.
problem Finding optimal portfolio allocations that balance risk and reward.
method Integrates various reward-risk measures and generic allocation rules into diversified risk parity.
result Diversified reward-risk parity strategies exhibit higher average returns, Sharpe ratios, and Calmar ratios compared to equally-weighted risk portfolios.
In this paper, we propose a novel investment strategy for portfolio optimization problems. The proposed strategy maximizes the expected portfolio value bounded within a targeted range, composed of a conservative lower target representing a need for capital protection and a desired upper target representing an investmen…
We implement momentum strategies using reward-risk measures as ranking criteria based on classical tempered stable distribution. Performances and risk characteristics for the alternative portfolios are obtained in various asset classes and markets. The reward-risk momentum strategies with lower volatility levels outper…
Investigates optimal PPI strategies to reduce carbon emissions while managing financial risk.
problem Optimizing portfolio insurance strategies to mitigate carbon emissions.
method Modelled risky assets using stochastic factor model with partial information, solved optimization problem using CRRA utility function.
result Optimal carbon penalized PPI strategies reduce carbon emissions without sacrificing financial performance.
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.
The paper develops a new framework for managing asymmetric volatility.
problem Managing asymmetric volatility to improve recovery and participation.
method Path-dependent framework for asymmetric volatility management.
result Skew engineering reduces harmful downside participation more than productive upside participation.
Paper proposes a deep hedging method for Bermudan swaptions to manage residual profit and loss.
problem Real-world market conditions differ from ideal assumptions in traditional hedging methods, leading to residual profit and loss.
method Deep hedging framework applied to Bermudan swaptions, allowing flexible risk measures and hedge strategies.
result Effective residual profit and loss management demonstrated through numerical analysis.
SBCA optimizes portfolios by fusing price data and text sentiment.
problem Insufficient integration of multi-modal information in traditional portfolio optimization models.
method Cross-modal BERT-driven Actor-Critic framework with gated fusion and constraint embedding.
result SBCA outperforms benchmarks in portfolio value, return, Sharpe ratio, and maximum drawdown.
Zero-Liquidation loans protect ETH borrowers from liquidation risks.
problem Risk of liquidation in DeFi lending protocols.
method Allows borrowers to repay in either USDC or pledged ETH, compensating liquidity providers with higher yield.
result More robust and less contagion-prone lending compared to traditional protocols.
Study uses RL to hedge financial derivatives, showing robust strategies outperform non-robust ones.
problem Risk mitigation and gain-seeking in hedging path-dependent financial derivatives.
method Robust risk-aware reinforcement learning (RL) with policy gradient approach.
result Robust hedging strategies outperform non-robust ones under varying data generating processes.
A robust machine learning approach forecasts U.S. Treasury yields, reducing risk for investors.
problem Noisy and uncertain U.S. Treasury yields pose risk to forecast users.
method Formulates yield curve forecasting as a distributionally robust problem, combining factor models and machine learning.
result Robust forecast combinations improve out-of-sample performance across different maturity periods.