Paper introduces a new method for calibrating ESGs to both historical and forward-looking data.
problem Lack of a generally accepted methodology for calibrating ESGs to forward-looking information.
method Conditional Scenario Simulator framework for consistent calibration of economic and financial variables.
result Framework can embed various financial and macroeconomic models and demonstrate practical examples in frequentist and Bayesian settings.
Combines historical and market data for better portfolio selection.
problem Improving portfolio selection through diverse information integration.
method Bayesian learning via Gaussian mixture model to harmonize historical and market data.
result The method enhances forecasting accuracy and robustness across various capital markets.
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.
This paper introduces forward-looking measures of the network connectedness of fears in the financial system, arising due to the good and bad beliefs of market participants about uncertainty that spreads unequally across a network of banks. We argue that this asymmetric network structure extracted from call and put tra…
New approach uses 'forward-looking' counterfactuals for treatment choice.
problem Using traditional 'retrospective' counterfactuals in treatment choice leads to counterintuitive results.
method Introduces 'counterfactual treatment choice' for forward-looking counterfactuals.
result Mismatches between interventional and forward-looking counterfactuals can lead to counterintuitive results.
Extends SABR model for pricing RFR caplets.
problem Pricing backward RFR caplets in a post-Libor market.
method Closed-form effective SABR parameters for backward RFR caplets.
result Closed-form solution for backward RFR caplets.
FORK improves model-free reinforcement learning performance.
problem Improving model-free reinforcement learning performance.
method Introducing a new forward-looking Actor (FORK) for Actor-Critic algorithms.
result FORK significantly improves performance in various environments.
Enhanced hedging for S&P 500 options using volatility surface data.
problem Optimizing hedging strategies for S&P 500 options with transaction costs.
method Deep policy gradient reinforcement learning with volatility surface feedback.
result Outperforms conventional hedging methods in simulations and backtesting.
Paper improves SVaR estimation for stress testing under macro scenarios using a hybrid GPR-HS framework.
problem Numerical instability in traditional SVaR estimation under extreme shocks.
method Extends GPR-HS framework to forward-looking stress scenarios with SACS for stable covariance.
result Stable SVaR ranges from -2.1020% to -2.2231%, preserving coherence property.
Paper forecasts stock correlations using a hybrid model combining graph neural networks and transformers.
problem Improving stock correlation forecasts for better portfolio management.
method Hybrid model combining Transformer and graph attention networks for forecasting residual deviations from historical data.
result The hybrid model reduces correlation forecasting error compared to rolling-window estimates.
A new framework assesses liquidity risk in perpetual futures exchanges.
problem Measuring and predicting liquidation execution risk in perpetual futures markets.
method Slippage-at-Risk (SaR) framework, comprising three metrics: cross-sectional slippage quantile, expected slippage, and aggregate dollar-denominated tail slippage.
result SaR provides a forward-looking assessment of liquidation execution risk, predictive of systemic stress.
The paper discusses scalable learning for wireless data-driven systems.
problem Expanding data volume and model complexity limit centralized learning solutions.
method Discusses scalable architecture and local learning strategies.
result Promising research directions in scalable data-driven wireless communications.
The paper introduces a new short rate model with memory components.
problem Modeling short rate dynamics with past values.
method Integrates memory (delay) components into Merton or Vasiček models.
result Analytical solutions for bond prices and forward rates.
DBNs improve VaR forecasting compared to traditional models, but SVaR forecasts are conservative.
problem Forecasting VaR and SVaR using dynamic Bayesian networks.
method DBN framework applied to S&P 500 index returns, comparing to autoregressive models and historical simulation.
result DBNs achieve comparable VaR forecasting accuracy to historical simulation models, but SVaR forecasts remain conservative.
In this study we suggest a portfolio selection framework based on option-implied information and multivariate non-Gaussian models. The proposed models incorporate skewness, kurtosis and more complex dependence structures among stocks log-returns than the simple correlation matrix. The two models considered are a multiv…
Generative Networks outperform traditional methods in PiT ESG generation.
problem Generating economic scenarios quickly and flexibly for sudden changes.
method Comparison of nonparametric, parametric, and generative models.
result Conditional Variational Autoencoder (CVAE) performs best.
We study online learning settings in which experts act strategically to maximize their influence on the learning algorithm's predictions by potentially misreporting their beliefs about a sequence of binary events. Our goal is twofold. First, we want the learning algorithm to be no-regret with respect to the best fixed …
The MSPI predicts market stress with machine learning.
problem Estimating the probability of high market stress.
method L1-regularized logistic regression on stock fragility signals.
result MSPI tracks major stress episodes and improves accuracy.
GO-CBED optimizes experiments for specific causal queries, improving efficiency.
problem Efficiently infer causal relationships with limited resources.
method Goal-oriented Bayesian framework that maximizes expected information gain on user-specified causal quantities.
result GO-CBED outperforms existing methods in various causal tasks, especially with limited budgets.
New core inflation measure predicts future headline inflation.
problem Creating a better measure of core inflation for timely policy decisions.
method Assemblage Regression, a nonnegative ridge regression that optimizes subcomponent weights.
result Significant improvements in forecasting medium-term inflation developments.
According to the volatility feedback effect, an unexpected increase in squared volatility leads to an immediate decline in the price-dividend ratio. In this paper, we consider the properties of stock price dynamics and option valuations under the volatility feedback effect by modeling the joint dynamics of stock price,…
Analyst reports contain valuable information for investment decisions.
problem Investment value in analyst reports is not fully understood or utilized.
method Embedded analyst reports with LLMs and ML forecasts of future returns.
result Portfolios formed on analyst report narratives outperform numerical forecasts and established factors.
Study affine models for alternative risk-free rates and derive caplet pricing formulas.
problem Valuation of caplets/floorlets in models for alternative risk-free rates.
method Affine process for RFRs, explicit valuation formulas for various derivatives.
result Explicit formulas for caplet/floorlet pricing in affine models for RFRs.
We implement a systematic asset allocation model using the Historical Simulation with Flexible Probabilities (HS-FP) framework developed by Meucci. The HS-FP framework is a flexible non-parametric estimation approach that considers future asset class behavior to be conditional on time and market environments, and deriv…
We construct the term structure of the (forward-looking, US market) equity risk premium from SPX option chains. The method is "model-light". Risk-neutral probability densities are estimated by fitting N-component Gaussian mixture models to option quotes, where N is a small integer (here 4 or 5). These densities are…
The Financial Chaos Index models stock market volatility across three regimes based on mutual price fluctuations.
problem Capturing regime-dependent volatility in stock markets.
method Developed a regime-switching framework using the Financial Chaos Index (FCIX) and elastic net regression.
result Identified three market regimes: low-chaos, intermediate-chaos, and high-chaos, each with distinct volatility characteristics.
Learning customer preferences from an observed behaviour is an important topic in the marketing literature. Structural models typically model forward-looking customers or firms as utility-maximizing agents whose utility is estimated using methods of Stochastic Optimal Control. We suggest an alternative approach to stud…
Develops a new volatility model for prediction markets.
problem Volatility forecasting in prediction markets differs from standard asset markets.
method Combines Wright-Fisher and Glosten-Milgrom mechanisms to model binary prediction markets.
result Structural model outperforms standard ARCH/GARCH models in volatility forecasting.
Develops a new volatility model for prediction markets.
problem Volatility forecasting in prediction markets differs from standard asset markets.
method Combines Wright-Fisher and Glosten-Milgrom mechanisms to model binary prediction markets.
result Structural model outperforms standard ARCH/GARCH models in volatility forecasting.
GeomHerd predicts herding behavior before market prices move, using Ricci curvature of agent interaction graphs.
problem Quantifying herding behavior in markets that lags behind actual price movements.
method Develops a geometric framework to track coordination on agent interaction graphs, bypassing lag in price-correlation statistics.
result GeomHerd anticipates herding long before market baselines, with significant lead times in predictions.
Improved probabilistic forecasts using behavioral transformations.
problem Improving accuracy and consistency of probabilistic asset price forecasts.
method Behavioral transformation of fundamental expectations to disentangle sentiment-induced biases.
result Substantial forecast gains across various models and risk-preferences.
We study a variant of the martingale optimal transport problem in a multi-period setting to derive robust price bounds of a financial derivative. On top of marginal and martingale constraints, we introduce a time-homogeneity assumption, which restricts the variability of the forward-looking transitions of the martingal…
Model predicts default risk based on company's financial forecasts and credit conditions.
problem Estimating the risk of a company defaulting on its financial obligations.
method Developed an equilibrium model linking interest rates to corporate performance and credit supply.
result Estimates idiosyncratic default risk and provides forward-looking probability of default (PD).
Geotechnics adopts data-driven methods from materials informatics.
problem Soil complexity and lack of comprehensive data.
method Leveraging deep learning and transfer learning for feature extraction.
result Revolutionary potential of advanced computational tools in geotechnics.
In the paper, we propose a new calculation scheme for American options in the framework of a forward backward stochastic differential equation (FBSDE). The well-known decomposition of an American option price with that of a European option of the same maturity and the remaining early exercise premium can be cast into t…
New method recovers BSDE from financial data without ergodicity.
problem Discovering probabilistic laws from financial data.
method Stochastic SINDy method under risk-neutral measure.
result Recovery of BSDE from limited financial data.
Dynamic Black-Litterman integrates expert views with portfolio optimization over varying time horizons.
problem Incorporating expert views with varying horizons in portfolio optimization.
method Exploiting graphical structure, deriving conditional distribution of asset returns, and using affine factor models.
result Explicit expression for optimal dynamic investment policy and hedging demand analysis.
Develops ML tool for macroeconomic forecasting with clear interpretations.
problem Forecasting and understanding macroeconomic parameters over time.
method Macroeconomic Random Forest (MRF) algorithm, Generalized Time-Varying Parameters (GTVPs).
result Clear forecasting gains and accurate predictions of unemployment and inflation.
Real Estate Investment Trusts (REITs) are the only truly liquid assets related to real estate investments. We study the behavior of U.S. REITs over the past three decades and document their return characteristics. REITs have somewhat less market risk than equity; their betas against a broad market index average about .…
In this paper, we address one of the main puzzles in finance observed in the stock market by proponents of behavioral finance: the stock predictability puzzle. We offer a statistical model within the context of rational finance which can be used without relying on behavioral finance assumptions to model the predictabil…
New measure predicts Dutch housing market downturns.
problem Understanding causes of Dutch housing boom and bust.
method Modelled household lending capacity using bank formulas.
result New measure outperforms traditional measures in forecasting housing prices.
The quest for diversification has led to an increasing number of complex funds with a high number of strategies and non-linear payoffs. The new generation of Alternative Risk Premia (ARP) funds are an example that has been very popular in recent years. For complex funds like these, a Reverse Stress Test (RST) is regard…
Study proposes a novel local explanation method for deep learning classifiers in process mining.
problem Lack of interpretability in deep learning models for process mining.
method Defines local regions using latent space representations and visualizes explanations.
result Deep learning classifier achieves high performance and local explanations increase user trust.
This paper suggests claim history will be deprecated in future auto insurance rates.
problem The role of historical claim records in auto insurance rates.
method Proposes a new risk variable elimination method and real-time road risk model design.
result Claim history will be considered a 'noise' factor and deprecated in Pay-How-You-Drive models.
This paper introduces a new market-based carbon risk measure for portfolio optimization.
problem The challenge of measuring and managing carbon risk in investment portfolios.
method Develops a market-based carbon risk measure and applies it to minimum variance portfolio construction.
result Market-based carbon risk measures can complement fundamental-based approaches in portfolio optimization.
The paper proposes a new SDF scaled by time-varying volatility from S&P 500 options.
problem Estimating the SDF from option prices and predicting the equity premium.
method Utilizes S&P 500 options data to recover a stable, non-monotonic SDF.
result The SDF exhibits a hump on the put side, which transitions into a W-shape with maturity.
Index tracking is a popular form of asset management. Typically, a quadratic function is used to define the tracking error of a portfolio and the look back approach is applied to solve the index tracking problem. We argue that a forward looking approach is more suitable, whereby the tracking error is expressed as expec…
Model explains deleveraging risks in non-custodial stablecoins.
problem Deleveraging risks in non-custodial stablecoins during market crises.
method Developed a stochastic model incorporating speculators' profit optimization and collateral liquidation costs.
result Identified deflationary deleveraging spirals and higher price variance in unstable domains.