Develops forecast hedging for improved calibration of forecasts.
problem Improving the accuracy of forecasted frequencies.
method Combines deterministic and stochastic approaches to forecast hedging.
result Ensures expected track record can only improve.
Paper develops a robust hedging framework to reduce market risk and uncertainty.
problem Managing uncertainty and risk exposure in portfolio management.
method Combines high-frequency realized variance, covariance measures, and autoregressive models for multi-step volatility forecasting. Uses a box-uncertainty robust optimization scheme to derive a closed-form solution for the robust hedge ratio.
result Robust hedge ratios are more stable and entail lower turnover than standard dynamic hedges, improving downside protection and risk-adjusted performance.
The paper proposes a method to improve forecast combination accuracy using portfolio theory.
problem Improving forecast accuracy by combining multiple forecasts.
method Generates forecast combinations using a portfolio analogy, allowing negative weights for hedging.
result Demonstrates improved performance in weighted random forest forecasts.
Optimizes forecast distributions for financial risk management.
problem Improving risk management through better forecast distributions.
method Optimizes forecast distributions using scoring rules relevant to financial risk management.
result Tail-focused predictive distributions yield better outcomes in hedging strategies involving VIX futures.
LLMs improve stock price forecasting from financial news and reports.
problem Predicting stock prices with high accuracy and robustness.
method Analyzing financial news, reports, and transcripts using LLMs.
result LLMs can improve stock price forecasting but face practical challenges.
This paper improves dynamic hedging accuracy using genetic programming to forecast implied volatilities.
problem Improving the accuracy of dynamic hedging using implied volatilities.
method The paper uses genetic programming to forecast implied volatilities and tests the performance of these forecasts in dynamic hedging strategies.
result Genetic programming-generated implied volatilities improve hedging accuracy compared to static training methods.
This thesis proposes a derivatives hedging framework using deep learning and reinforcement learning.
problem Traditional hedging models fail in complex, uncertain markets due to assumptions like continuous trading and zero transaction costs.
method Integrates deep learning and reinforcement learning, using a spatiotemporal attention-based Transformer for probabilistic forecasting and hedging.
result The proposed method significantly outperforms traditional approaches in U.S. and Chinese financial markets.
Risk aversion is a key element of utility maximizing hedge strategies; however, it has typically been assigned an arbitrary value in the literature. This paper instead applies a GARCH-in-Mean (GARCH-M) model to estimate a time-varying measure of risk aversion that is based on the observed risk preferences of energy hed…
MELO predicts electricity loads by adapting to shifts without external indicators.
problem Adapting to non-stationary prediction challenges in online settings.
method MELO combines multiple forgetting factors and aggregation rules to adaptively predict.
result MELO reduces RMSE by 34.7% compared to base predictors and external covariates.
Enhances hedging strategies using deep neural networks.
problem Optimizing risks and returns in financial hedging.
method Integrates deep neural networks and random forest classifiers to find optimal hedging strategies.
result Improved hedging strategies with lower costs and risks.
DRL optimizes asset managers' hedging timing based on market conditions.
problem Optimal timing for hedging strategies given market conditions.
method Deep Reinforcement Learning framework with contextual information, lagged observations, and robust testing.
result Our approach achieves superior returns and lower risk compared to standard methods.
Ensemble of MFESNs improves macroeconomic forecasting.
problem Improving accuracy in macroeconomic forecasting.
method Hedge and Follow-the-Leader schemes applied to MFESNs.
result Ensemble models outperform individual models in forecasting.
A key problem in location-based modeling and forecasting lies in identifying suitable spatial and temporal resolutions. In particular, judicious spatial partitioning can play a significant role in enhancing the performance of location-based forecasting models. In this work, we investigate two widely used tessellation s…
In the framework of prediction with expert advice, we consider a recently introduced kind of regret bounds: the bounds that depend on the effective instead of nominal number of experts. In contrast to the Normal- Hedge bound, which mainly depends on the effective number of experts but also weakly depends on the nominal…
Research forecasts electricity spot prices using stochastic volatility models.
problem Forecasting day-ahead electricity prices in a spot market.
method Exploring and enriching a baseline stochastic volatility model with exogenous regressors.
result A better fitting model confirmed by out-of-sample forecasts.
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.
Study cash-flow forecasting for derivatives, aligning with replication strategy and addressing timing frictions.
problem Inconsistencies in cash-flow forecasting under different measures and stochastic payment times.
method Use discounting sensitivities (funding-curve hedge ratios) for replication and propose a liquidity valuation adjustment.
result Aligns forecasting with replication strategy and avoids measure-mixing issues.
Combining forecasts of 16 ED causes improves accuracy and stability.
problem Forecasting accuracy and stability for ED admissions is poor due to model uncertainty and limited data.
method High-dimensional forecast combinations of 16 cause-specific ED forecasts using extensive covariates.
result Forecast combinations yield forecast accuracies of 3.81%-23.54% across causes, outperforming individual models in 50% of scenarios.
Three adaptive methods improve financial forecasting and portfolio management.
problem Improving financial forecasting and portfolio management in volatile markets.
method Dynamic Model Selection (DMS), Adaptive Ensemble (AE), Dynamic Asset Allocation (DAA).
result Adaptive methods outperform long-only benchmarks in US market returns.
PolyModel theory and iTransformer improve hedge fund portfolio construction.
problem Sparse financial time series data makes portfolio construction challenging.
method Identify asset pool, select risk factors, create quantitative and classical measures, and use iTransformer for trend capture.
result Improved Sharpe ratio and annualized return compared to benchmarks.
Paper proposes novel hedging strategies using LSTM models for diversified investment portfolios.
problem Hedging risky asset portfolios in turbulent financial markets.
method Four diverse models (LSTM, ARIMA-GARCH, momentum, contrarian) generate price forecasts for diversified AIS.
result LSTM-based strategies outperform other models, with Bitcoin being the best diversifier for S&P 500 index.
Paper optimizes stock option forecasting using ML models and improved trading strategies.
problem Improving accuracy of stock option predictions and trading decisions.
method Application of Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Quasi-Reversibility Method (QRM).
result Optimized stock option investment results through improved trading strategies and model combination.
Paper proposes Multi-Transformer for more accurate stock volatility forecasts.
problem Accurate equity risk models needed for effective risk management.
method Introduces Multi-Transformer neural network architecture, adapted from Transformer models.
result Empirical results show Multi-Transformer leads to more accurate risk measures.
Study compares volatility models for Bitcoin, finds GARCH and EGARCH outperform.
problem Evaluating which volatility models best predict Bitcoin spot and option prices.
method Used HIST, EMA ARCH, GARCH, and EGARCH models on Bitcoin spot price series.
result GARCH and EGARCH models outperform other models in both in-sample and out-of-sample forecasts.
This paper improves volatility forecasting using dynamic subset selection in genetic programming.
problem Improving accuracy of implied volatility forecasting.
method Dynamic training-subset selection methods applied to genetic programming.
result Dynamic subset selection improves predictive accuracy of genetic programming models.
Accurate taxi demand-supply forecasting is a challenging application of ITS (Intelligent Transportation Systems), due to the complex spatial and temporal patterns. We investigate the impact of different spatial partitioning techniques on the prediction performance of an LSTM (Long Short-Term Memory) network, in the con…
This paper provides an insight to the time-varying dynamics of the shape of the distribution of financial return series by proposing an exponential weighted moving average model that jointly estimates volatility, skewness and kurtosis over time using a modified form of the Gram-Charlier density in which skewness and ku…
Contracts for Difference (CfDs) are forwards on the spread between an area price and the system price. Together with the system price forwards, these products are used to hedge the area price risk in the Nordic electricity market. The CfDs are typically available for the next two months, three quarters and three years.…
Unified kernel for prediction markets reduces belief variance forecast error.
problem Lack of standardized tools for quoting and hedging belief risk in prediction markets.
method Logit jump-diffusion model with risk-neutral drift, calibration pipeline, and coherent derivative layer.
result Model reduces forecast error compared to diffusion-only and probability-space baselines.
Due to the limited predictability of wind power and other stochastic generation, trading this energy in competitive electricity markets is challenging. This paper derives revenue-maximising and risk-constrained strategies for stochastic generators participating in electricity markets with a single-price balancing mecha…
Paper uses RL for dynamic swaption hedging, outperforming traditional methods.
problem Dynamic hedging of swaptions using reinforcement learning.
method Design agents with three objective functions to adapt hedging strategies dynamically.
result Deep hedging strategies using two swaps outperform traditional methods, even with model misspecification.
Study develops efficient nested deep hedging method for derivatives pricing.
problem Hedging derivatives in market frictions using multiple options.
method Nested deep hedging approach with efficient learning techniques.
result Reduces arbitrage opportunities and improves hedging risks.
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.
Study tests if deep hedging differs from delta hedging in a GARCH market model.
problem Whether deep hedging includes speculative components in a GARCH market.
method Tested in a GARCH-based market model, comparing deep hedging and delta hedging.
result The difference between deep hedging and delta hedging is speculative if risk measure does not prioritize adverse outcomes.
Paper proposes a natural hedging framework with graphical assessment for longevity risk management.
problem Lack of a unified framework for natural hedging and graphical risk assessment.
method Structured natural hedging framework integrated with a graphical risk metric.
result Demonstrates flexibility, interpretability, and practical value for longevity risk management.
This article analyzes the relationship between co-persistence and hedging which indicates co-persistence ratio is just the long-term hedging ratio. The new method of exhaustive search algorithm for deriving co-persistence ratio is derived in the article. And we also develop a new hedging strategy of combining co-persis…
This paper examines the volatility and covariance dynamics of cash and futures contracts that underlie the Optimal Hedge Ratio (OHR) across different hedging time horizons. We examine whether hedge ratios calculated over a short term hedging horizon can be scaled and successfully applied to longer term horizons. We als…
Deep learning enhances options hedging performance.
problem Improving delta hedging for options using neural networks.
method Learning residuals between hedging function and implied Black-Scholes delta using neural networks.
result Deep learning significantly improves hedging performance, often by more than 100%.
Adversarial deep hedging learns to hedge without specifying asset price models.
problem Lack of effective underlying asset models for deep hedging.
method Adversarial learning framework where a hedger and a generator compete to improve hedging performance.
result Adversarial deep hedging achieves competitive performance without explicit asset process modeling.
New method reduces training time for deep hedging networks.
problem Challenges in training deep hedging networks with large batch sizes.
method Integrates topological features to reduce batch sizes.
result Practical training of deep hedging models without sacrificing performance.
A hybrid approach detects financial market regime switches using PCA and k-means.
problem Detecting regime switches in financial markets for trend forecasting.
method Dimensionality reduction with PCA and clustering with k-means.
result Trading strategies based on detected regimes show improved performance.
Deep Hedging learns optimal strategies for various risk levels.
problem Finding optimal hedging policies for diverse risk aversions.
method Continuous Reinforcement Learning with actor-critic algorithm.
result Demonstrated effectiveness in a stochastic volatility model.
Geometric structure reveals optimal investment and hedging products.
problem Optimal design of investment and hedging products.
method Investigation of geometric structure in risks and returns using a simple formula.
result Duality between hedging and investment with geometric interpretation of rationality.
The paper compares traditional regression with modern neural network methods for financial hedging and risk compression.
problem Finding optimal hedge ratios and managing portfolio risk using traditional regression methods has limitations.
method The paper introduces regularization techniques and common factor analyses using neural networks to improve upon regression methods.
result Neural network methods provide better performance in hedge ratio estimation and risk compression compared to traditional regression.
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.
Proposes deep hedging for index options using implied volatility surface.
problem Managing risk in index option portfolios with complex dynamics.
method Integrates surface-informed decisions with multiple hedging instruments, accounting for transaction costs and variance risk premium.
result Consistently outperforms traditional hedging strategies across various market conditions.
NDI aims to forecast future natural disasters risk for insurers.
problem Increasing intensity and frequency of natural disasters.
method Develops a Natural Disasters Index (NDI) based on NOAA data.
result NDI forecasts future natural disasters risk for insurers.
This report was originally written as an industry white paper on Hedge Funds. This paper gives an overview to Hedge Funds, with a focus on risk management issues. We define and explain the general characteristics of Hedge Funds, their main investment strategies and the risk models employed. We address the problems in H…