The paper develops stochastic models for mortality rates using infinite dimensional processes.
problem Uncertainty in demographic projections of future mortality rates.
method Forward mortality models driven by Wiener process and Poisson random measure.
result Consistency conditions for forward mortality improvements and mortality rates.
Introduces new performance criteria for investment under distorted probabilities.
problem Reconciling time-consistent performance with probability distortions.
method Two definitions of forward rank-dependent criteria, equivalence established; characterization of viable probability distortion processes.
result Characterization of optimal wealth process and new distorted measure.
This paper revisits optimal investment strategies for defined contribution pension schemes using forward preferences.
problem Optimal investment strategies derived from backward models are not time-consistent and sub-optimal in real scenarios.
method Introduces forward preferences and solves optimal investment strategies for defined contribution pension schemes.
result Constructs optimal investment strategies for defined contribution pension schemes using forward preferences.
Optimal investment and risk control strategies for insurers are derived using a time-consistent approach.
problem Optimal investment and risk control for insurers under mean-variance criterion.
method Introducing a deterministic forward auxiliary process to formulate a time-consistent problem.
result Optimal strategy and value function obtained in closed-form for the new problem.
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.
SSFN self-estimates network size with low complexity and consistent performance.
problem Designing a self-estimating feed-forward network with low complexity and consistent performance.
method Joint optimization for layer and node estimation, low computational complexity, and use of lossless flow property and convex optimization.
result Consistent performance across Monte-Carlo trials and monotonically non-increasing cost with network growth.
This paper is dedicated to the construction of high-order (in both space and time) finite-difference schemes for both forward and backward PDEs and PIDEs, such that option prices obtained by solving both the forward and backward equations are consistent. This approach is partly inspired by Andreasen & Huge, 2011 who re…
We combine forward investment performance processes and ambiguity averse portfolio selection. We introduce the notion of robust forward criteria which addresses the issues of ambiguity in model specification and in preferences and investment horizon specification. It describes the evolution of time-consistent ambiguity…
Study of portfolio management under relative performance concerns using mean field games.
problem Portfolio management problems under relative performance concerns.
method Forward utilities of CARA type, mean field games, best response and equilibrium strategies.
result Solve forward-utility finite player game and mean-field game under asset specialization.
Classifies shapes of yield curves in the Svensson family.
problem Classifying shapes of yield curves in the Svensson family.
method Complete classification of shapes using mathematical analysis.
result Certain complex shapes cannot appear after a deterministic time horizon.
Model interest rates and energy futures with regime-switching dynamics.
problem Modeling interest rates and energy futures with regime-switching dynamics.
method HJM model with Markov-chain modulated forward rates, proving affine structure for term structure.
result Explicit solutions for forward curves in many cases.
Enhances swaption modeling with rough stochastic volatility.
problem Modeling swaption volatility in post-LIBOR markets.
method Introduces rough stochastic volatility into FMM and rigorously justifies the freezing approximation.
result Establishes a new framework connecting FMM to rough Bergomi for forward swap rates.
Two-dimensional transition rates improve life insurance reserve calculations.
problem Calculating life insurance reserves with Markov assumptions.
method Introducing two-dimensional forward and backward transition rates.
result Two-dimensional transition rates enable more accurate reserve calculations.
This paper offers a new class of models of the term structure of interest rates. We allow each instantaneous forward rate to be driven by a different stochastic shock, constrained in such a way as to keep the forward rate curve continuous. We term the process followed by the shocks to the forward curve ``stochastic str…
In this paper a simple model for the evolution of the forward density of the future value of an asset is proposed. The model allows for a straightforward initial calibration to option prices and has dynamics that are consistent with empirical findings from option price data. The model is constructed with the aim of bei…
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.
We consider a market model that consists of financial investors and producers of a commodity. Producers optionally store some production for future sale and go short on forward contracts to hedge the uncertainty of the future commodity price. Financial investors take positions in these contracts in order to diversify t…
Proposes a new framework for discount models.
problem Arbitrage-free dynamic framework for discount models.
method Derives general consistency conditions for factor models.
result Alternative to Heath--Jarrow--Morton framework for forward rates.
Algorithm optimizes electricity procurement costs by 1.65%.
problem Minimizing energy cost while covering forecast consumption.
method Deep learning forecasting and deviation indicator.
result Reduction of 1.65% in costs compared to uniform policy.
Paper introduces FoMoH for optimization without backpropagation.
problem Optimizing machine learning models without backpropagation.
method Second-order hyperplane search, forward-mode stochastic gradient method, hyper-dual numbers, FoMoH.
result Developed a novel optimization algorithm that avoids backpropagation.
Study shows Nelson-Siegel curves fit well with Ho-Lee and Hull-White models.
problem Fitting observed interest rate term structures with interest rate models.
method Examined Nelson-Siegel curves in the context of Ho-Lee and Hull-White models.
result Extended Nelson-Siegel curves emerge from the forward curve process of the models.
Adaptive Group Lasso selects important features in neural networks.
problem Lack of interpretability in neural networks.
method Adaptive Group Lasso for feature selection.
result Consistent feature selection for neural networks with theoretical guarantee.
Designs a Heath-Jarrow-Morton framework for forward contracts in power and gas markets.
problem Designing a framework for forward contracts in power and gas markets.
method Heath-Jarrow-Morton framework, affine functions, Girsanov kernel, measure changes.
result Validates measure changes for forward contracts in power and gas markets.
In this article we consider the problem of giving a robust, model-independent, lower bound on the price of a forward starting straddle with payoff ∣FT1−FT0∣ where 0<T0<T1. Rather than assuming a model for the underlying forward price (Ft)t≥0, we assume that call prices for maturities $T_0<T_1…
A new diffusion model improves time-series forecasting by preserving seasonal patterns.
problem Improving time-series forecasting accuracy, especially for seasonal data.
method A forward diffusion process that decomposes signals into spectral components, altering only the diffusion process.
result The method maintains high signal-to-noise ratios for dominant frequencies, improving long-term pattern recovery.
Push-forward models struggle to fit multimodal distributions due to high Lipschitz constants.
problem Expressivity of push-forward generative models in fitting multimodal distributions.
method Analyzing the Lipschitz constant and its relation to the total variation distance and Kullback-Leibler divergence.
result Push-forward models require high Lipschitz constants to approximate multimodal distributions, leading to a trade-off between expressivity and stability.
Blade uses diffusion priors to accurately and calibratedly infer complex systems.
problem Derivative-free Bayesian inversion for high-dimensional, nonlinear problems with costly forward models.
method Blade employs an ensemble of interacting particles and diffusion models as priors, querying forward models only through evaluations.
result Blade produces well-calibrated posterior samples that existing methods cannot, improving with more iterations and particles.
A new model for short rates using pure-jump processes.
problem Modeling short rates with bounded behavior and affine bond prices.
method Sum of pure-jump Ornstein-Uhlenbeck processes for mean-reversion, with affine bond price representations.
result The model can be market-consistently calibrated and has an explicit option pricing formula.
Beam search improves feature selection for better model performance.
problem Improving feature selection for better model performance.
method Proposed beam search as a generalization of forward selection for feature selection.
result Beam search can outperform forward selection, especially with correlated features.
The paper addresses pricing interest rate derivatives in markets with volatility uncertainty.
problem Pricing interest rate derivatives under uncertainty about volatility.
method Modeling volatility uncertainty with G-Brownian motion and defining forward sublinear expectation.
result Developed robust pricing formulas for interest rate derivatives.
Develops a forward variable selection method for interpretable random forest models.
problem Interpreting high-dimensional non-parametric models like random forests.
method Forward variable selection using CRPS as loss function, with hypothesis testing at each step.
result Method selects a smaller set of variables that optimizes predictive performance.
New model adds persistent memory to self-attention layers for improved performance.
problem Improving transformer performance by removing feed-forward layers.
method Augmenting self-attention layers with persistent memory vectors.
result The model outperforms standard transformers on language modeling benchmarks.
We consider forward-backward greedy algorithms for solving sparse feature selection problems with general convex smooth functions. A state-of-the-art greedy method, the Forward-Backward greedy algorithm (FoBa-obj) requires to solve a large number of optimization problems, thus it is not scalable for large-size problems…
Diffusion-GAN uses diffusion to improve GAN training stability and realism.
problem Stability and realism issues in training GANs.
method Diffusion-GAN employs a forward diffusion chain to generate Gaussian-mixture distributed instance noise, with adaptive diffusion process and timestep-dependent discriminator.
result Diffusion-GAN produces more realistic images with higher stability and data efficiency.
Paper introduces a flexible HJM framework for consistent electricity prices.
problem Consistent modeling of intraday, spot, futures, and option prices.
method Flexible HJM-type framework with economic interpretations.
result Allows existing spot price models to be used in HJM setting.
Study time-inconsistent consumption-investment in incomplete markets with general discount functions.
problem Time-inconsistent consumption-investment problems in incomplete markets.
method Coupled forward-backward stochastic differential equation approach.
result Uniqueness of open-loop equilibrium pair proved.
A new VIS approach improves log-likelihood estimation in latent variable models.
problem Challenges in achieving high log-likelihood with VI for complex posterior distributions.
method Uses forward χ2 divergence to optimize proposal distribution for better log-likelihood estimation. result Consistently outperforms state-of-the-art baselines in log-likelihood and parameter estimation.
Unique optimal strategy identified for state-dependent risk aversion.
problem Consistency of optimal portfolio choice for varying risk aversion.
method Analysis of state-dependent exponential utilities in arbitrage-free markets.
result Uniqueness of optimal strategy across any time horizon.
Model explains yield curve dynamics using order flow shocks.
problem Understanding the yield curve's fluctuations and their relation to order flows.
method Relates exogenous shocks to order flow surprises, creating a microstructural model that incorporates price and order flow dynamics.
result The model explains yield curve dynamics with fewer parameters and generates liquidity-dependent correlations.
We prove the Fundamental Theorem of Asset Pricing for a discrete time financial market where trading is subject to proportional transaction cost and the asset price dynamic is modeled by a family of probability measures, possibly non-dominated. Using a backward-forward scheme, we show that when the market consists of a…
We explore the robust replication of forward-start straddles given quoted (Call and Put options) market data. One approach to this problem classically follows semi-infinite linear programming arguments, and we propose a discretisation scheme to reduce its dimensionality and hence its complexity. Alternatively, one can …
A new algorithm SLA reduces bias in sampling from measures using Langevin dynamics.
problem Reducing bias in sampling from complex target measures using Langevin dynamics.
method Proposed symmetrized Langevin algorithm (SLA) to correct bias in ULA.
result SLA is consistent for Gaussian target measures, while ULA is not.
In this paper, we establish a market model for the term structure of forward inflation rates based on the risk-neutral dynamics of nominal and real zero-coupon bonds. Under the market model, we can price inflation caplets as well as inflation swaptions with a formula similar to the Black's formula, thus justify the cur…
PFP-BNNs offer a fast, deterministic approach to Bayesian neural networks.
problem Limited uncertainty handling in traditional neural networks restricts their use in safety-critical settings.
method Probabilistic Forward Pass (PFP) approximates Stochastic Variational Inference (SVI) for efficient BNNs.
result PFP-BNNs achieve up to 4200x speedup over SVI-BNNs while maintaining similar accuracy and uncertainty.
New model for pricing volatility derivatives considering rough volatility and jumps.
problem Modeling instantaneous volatility with rough volatility and jumps.
method Generalized fractional Ornstein-Uhlenbeck process with Lévy subordinator and sinusoidal-composite Lévy process.
result Pricing-hedging formulae for power-type derivatives on average forward variance are derived.
This paper addresses the problem of neighborhood selection for Gaussian graphical models. We present two heuristic algorithms: a forward-backward greedy algorithm for general Gaussian graphical models based on mutual information test, and a threshold-based algorithm for walk summable Gaussian graphical models. Both alg…
The paper models term structures under volatility uncertainty using G-Brownian motion.
problem Modeling term structures with volatility uncertainty.
method Modeling instantaneous forward rates as a diffusion process driven by G-Brownian motion.
result Derives a sufficient condition for the absence of arbitrage under volatility uncertainty.
Edge devices adapt pre-trained models to local data without backpropagation.
problem Adapting pre-trained models to edge devices' local data distributions.
method Feed-forward latent domain adaptation using cross-attention.
result Consistent improvements over ERM baselines and domain-supervised adaptation.