Dynamic risk factor model improves portfolio performance in high dimensions.
problem Dynamic portfolio allocation in high-dimensional financial markets.
method Time-varying sparsity on factor loadings, sequential learning of parameters and volatilities.
result Significant portfolio performance improvements and higher utility gains.
This study analyzes dynamic connectedness in global supply chain infrastructure portfolios, identifying key risk factors and extreme events.
problem Understanding dynamic connectedness in global supply chain infrastructure portfolios under various risk factors and extreme events.
method Time-varying parameter vector autoregression (TVP-VAR) model to study spillover and interconnectedness of risk factors.
result Risk shocks influence dynamic connectedness between portfolios and risk factors, and extreme events affect investment outcomes.
Paper uses machine learning to uncover nonlinear dynamics in CAT bond pricing.
problem Traditional linear models miss nonlinear relationships in CAT bond pricing.
method Advanced machine learning techniques applied to CAT bond transaction records.
result Machine learning enhances CAT bond pricing accuracy and reveals complex risk interactions.
The paper finds stocks with higher dynamic network risk have lower returns.
problem Understanding and pricing short-term and long-term dynamic network risk in stock returns.
method Examined the relationship between stock sensitivities to dynamic network risk and expected returns, using economic theory and empirical analysis.
result A one-standard deviation increase in long-term network risk loadings associates with a 7.66% drop in annualized expected returns.
This study develops a multi-factor framework where not only market risk is considered but also potential changes in the investment opportunity set. Although previous studies find no clear evidence about a positive and significant relation between return and risk, favourable evidence can be obtained if a non-linear rela…
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.
The paper analyzes market risk factors for a mining company using a VAR model with stable distribution.
problem Understanding mid- and long-term dynamics of market risk factors for a mining company.
method Two-dimensional vector autoregressive (VAR) model with α-stable distribution, identifying two regimes.
result Derives dynamics of copper price in PLN, crucial for company risk exposure.
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.
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.
Develops a dynamic latent-factor model for high-dimensional asset characteristics.
problem Estimating asset pricing tests with high-dimensional data.
method Dynamic latent-factor model with Double Selection Lasso regularization.
result The inflation-mimicking portfolio in the crypto asset class has positive risk compensation.
This study examines the evolving causal structure of equity risk factors.
problem Redundancy and risk contagion in multi-factor strategies during financial crises.
method Causal structure learning methods applied to US equity market data over 29 years.
result Statistically significant sparsifying trend of causal structure during normal times, but densification during financial stress.
One primary task of population health analysis is the identification of risk factors that, for some subpopulation, have a significant association with some health condition. Examples include finding lifestyle factors associated with chronic diseases and finding genetic mutations associated with diseases in precision he…
DPLS improves asset pricing by capturing non-linear risk factor structures.
problem Estimating asset pricing models with non-linear risk factor structures.
method Deep Partial Least Squares (DPLS) for dynamic and flexible factor modeling.
result DPLS models outperform linear models in asset pricing, capturing non-linear risk factor interactions.
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.
Framework for quantifying uncertainty in dynamic processes.
problem Quantifying uncertainty in dynamic stochastic processes.
method Define dynamic uncertainty sets and dynamic robust risk measures.
result Dynamic robust risk measures are time-consistent under specific uncertainty sets.
We discuss a general dynamic replication approach to counterparty credit risk modeling. This leads to a fundamental jump-process backward stochastic differential equation (BSDE) for the credit risk adjusted portfolio value. We then reduce the fundamental BSDE to a continuous BSDE. Depending on the close out value conve…
The study assesses carbon risk in investment portfolios and proposes new management strategies.
problem The impact of carbon risk on stock pricing and portfolio construction.
method Developed a BMG risk factor and estimated time-varying carbon beta using a multi-factor model.
result Carbon risk can be incorporated into portfolio construction to reduce unrewarded financial risks.
Dynamic portfolio strategy using generative model with attention mechanism.
problem Dynamic modeling of multivariate stock returns with tail-side properties.
method Dynamic generative factor model using Attention-GRU network for dynamic learning and forecasting.
result The proposed model leads to wiser investments with higher reward-risk ratios and lower tail risks.
We analyze the counterparty risk embedded in CDS contracts, in presence of a bilateral margin agreement. First, we investigate the pricing of collateralized counterparty risk and we derive the bilateral Credit Valuation Adjustment (CVA), unilateral Credit Valuation Adjustment (UCVA) and Debt Valuation Adjustment (DVA).…
Since the introduction of risk-based solvency regulation, pro-cyclicality has been a subject of concerns from all market participants. Here, we lay down a methodology to evaluate the amount of pro-cyclicality in the way finnancial institutions measure risk, and identify factors explaining this pro-cyclical behavior. We…
Enhances systemic risk analysis by incorporating debt valuation factors.
problem Systemic risk in financial networks due to bank failures.
method Incorporates debt valuation factors into existing risk analysis frameworks.
result Additional debt valuation factors substantially influence risk assessment outcomes.
RVRAE combines deep learning and dynamic factor models for better stock returns prediction.
problem Improving stock returns prediction in volatile markets.
method Combines dynamic factor modeling with variational recurrent autoencoder (VRAE). Uses prior-posterior learning for optimal factor model.
result RVRAE outperforms traditional methods in predicting stock returns and estimating variances.
Realized GARCH model explains VIX and VRP dynamics.
problem Understanding VIX and VRP dynamics in financial markets.
method Developed Realized GARCH model with two shocks.
result Realized GARCH model outperforms conventional GARCH models.
Robo-advisors estimate clients' risk aversion using interactive questionnaires.
problem Estimating risk aversion of non-expert clients using adaptive questionnaires.
method Model risk aversion with cost functions and spectral risk measures. Use inverse reinforcement learning to design questions maximizing distinguishing power.
result Designing questions by maximizing distinguishing power achieves satisfactory accuracy in learning risk aversion with fewer than 50 questions.
The study analyzes the differences between physical and risk-neutral correlation estimates for equity baskets.
problem Analyzing the differences between physical and risk-neutral correlation estimates for equity baskets.
method Assumed equicorrelation, reduced dimensionality, approximated ICS from implied volatilities, analyzed dynamics using dynamic semiparametric factor model.
result Proposed profitability improvement schemes based on implied correlation forecasts.
We consider insurance derivatives depending on an external physical risk process, for example a temperature in a low dimensional climate model. We assume that this process is correlated with a tradable financial asset. We derive optimal strategies for exponential utility from terminal wealth, determine the indifference…
We found that factors decay over time, with momentum fitting best.
problem Understanding how factors decay over time and their impact on performance.
method Derived a hyperbolic decay model for factors, tested against linear and exponential alternatives.
result Momentum exhibits hyperbolic decay, outperforming linear and exponential models.
Develops a new framework for joint portfolio risk forecasting.
problem Joint portfolio risk forecasting, especially for Value-at-Risk and Expected Shortfall.
method Semi-parametric multivariate framework with dynamic conditional correlation modeling.
result The proposed model outperforms existing approaches in risk forecasting.
We provide a new dynamic approach to scenario generation for the purposes of risk management in the banking industry. We connect ideas from conventional techniques -- like historical and Monte Carlo simulation -- and we come up with a hybrid method that shares the advantages of standard procedures but eliminates severa…
We develop a methodology for index tracking and risk exposure control using financial derivatives. Under a continuous-time diffusion framework for price evolution, we present a pathwise approach to construct dynamic portfolios of derivatives in order to gain exposure to an index and/or market factors that may be not di…
A new framework improves volatility forecasting for financial markets.
problem Static factor models fail to capture evolving volatility co-movements.
method Time-varying factor model integrating dynamic cross-sectional factors.
result Framework demonstrates strong performance in AI-driven models and pairs trading.
Investor optimizes portfolio under dynamic risk preferences.
problem Optimizing investment under uncertain future risk attitudes.
method Developed a general equilibrium framework and solved for subgame-perfect equilibrium policies.
result Equilibrium policies include a novel hedging component to counteract anticipated risk aversion changes.
A risk-averse agent hedges her exposure to a non-tradable risk factor U using a correlated traded asset S and accounts for the impact of her trades on both factors. The effect of the agent's trades on U is referred to as cross-impact. By solving the agent's stochastic control problem, we obtain a closed-form expr…
A new framework for asset pricing based on modelling the information available to market participants is presented. Each asset is characterised by the cash flows it generates. Each cash flow is expressed as a function of one or more independent random variables called market factors or "X-factors". Each X-factor is ass…
Introduces factor risk measures to assess risk relative to multiple factors.
problem Measuring risk relative to multiple factors.
method Introduces a double-argument mapping as a risk measure to assess risk relative to a vector of factors.
result Characterizes various types of factor risk measures including distortion, quantile, linear, and coherent measures.
The article uses dynamic factor allocation to improve portfolio performance by integrating regime-switching signals.
problem Improving portfolio performance through dynamic factor allocation.
method The authors apply the sparse jump model (SJM) to identify bull and bear market regimes for individual factors, then fine-tune hyperparameters using a hypothetical single-factor long-short strategy. These regime inferences are incorporated into the Black-Litterman framework to dynamically adjust allocations among indices.
result The constructed multi-factor portfolio significantly improves the information ratio (IR) relative to the market, raising it from 0.05 to approximately 0.4.
While the investors' responses to price changes and their price forecasts are well accepted major factors contributing to large price fluctuations in financial markets, our study shows that investors' heterogeneous and dynamic risk aversion (DRA) preferences may play a more critical role in the dynamics of asset price …
This paper proposes non-stationary factor models for financial stress in the UK.
problem Managing financial vulnerabilities in the UK's complex financial system.
method Creation of non-stationary factor models to capture financial stress.
result Non-stationary factor models can better capture financial stress, especially tail events.
We study the dynamics of correlation and variance in systems under the load of environmental factors. A universal effect in ensembles of similar systems under the load of similar factors is described: in crisis, typically, even before obvious symptoms of crisis appear, correlation increases, and, at the same time, vari…
The location-based social network, Foursquare, reflects the human activities of a city. The mobility dynamics inferred from Foursquare helps us understanding urban social events like crime In this paper, we propose a directed graph from the aggregated movement between regions using Foursquare data. We derive region ris…
The article constructs a forward utility for markets with multiple default risks.
problem Characterizing forward performance processes in a market with multiple default risks.
method Using Jacod-Pham decomposition and recursive BSDEs, the article constructs a forward utility and proves its existence and uniqueness.
result The article identifies the risk-sensitive long-run growth rate of the optimal wealth process in a stochastic factor model with ergodic dynamics.
New approach uses MST and copula-DCC-GARCH for systemic risk analysis in European insurance sector.
problem Analyzing systemic risk in European insurance sector through indirect connections.
method Combining copula-DCC-GARCH model and Minimum Spanning Trees (MST) for interlinkage dynamics analysis.
result Proposed approach useful for systemic risk analysis in insurance sector, with MST topological indicators as predictors.
WRSE predicts dynamic survival distributions in ICU patients.
problem Dynamic assessment of ICU patient mortality risk.
method Non-parametric weighted-resolution ensemble model combining binary classifiers.
result Competitive results with state-of-the-art models, reducing training time.
Paper presents a neural network method for efficient xVA computation and risk management.
problem High-dimensional counterparty credit risk valuation and management.
method Neural network-based BSDE solver for coupled system of BSDEs for xVA.
result Efficient computation of xVA for high-dimensional portfolios.
We present an extension of the Johansen-Ledoit-Sornette (JLS) model to include an additional pricing factor called the "Zipf factor", which describes the diversification risk of the stock market portfolio. Keeping all the dynamical characteristics of a bubble described in the JLS model, the new model provides additiona…
The study forecasts portfolio volatility using cointegrated asset dynamics.
problem Forecasting volatility in portfolios with high accuracy.
method Developed HVR/DVR ratios and used Vector Error Correction Model (VECM) to forecast volatility.
result VECM forecasts of portfolio volatility have lower MAPE than covariance-based forecasts.
Modeling counterparty risk is computationally challenging because it requires the simultaneous evaluation of all the trades with each counterparty under both market and credit risk. We present a multi-Gaussian process regression approach, which is well suited for OTC derivative portfolio valuation involved in CVA compu…
In this paper we consider Fourier transform techniques to efficiently compute the Value-at-Risk and the Conditional Value-at-Risk of an arbitrary loss random variable, characterized by having a computable generalized characteristic function. We exploit the property of these risk measures of being the solution of an ele…