The paper investigates the singularity and extendibility of inflationary spacetimes.
problem The existence and extendibility of initial curvature singularities in inflationary spacetimes.
method Classification and rigorous extendibility criteria derivation for quasi-de Sitter spacetimes.
result Past-eternal inflationary scenarios are most likely physically singular, except in very special initial conditions.
New findings show cosmological constant as initial condition for non-isotropic spacetimes.
problem Cosmological constant as initial condition in non-isotropic spacetimes.
method Generalized previous results to non-isotropic spacetimes.
result Quasi de Sitter expansion for early universe, potential for inflationary scenarios.
A proof is given that the maximal Fermi coordinate chart for any comoving observer in a broad class of Robertson-Walker spacetimes consists of all events within the cosmological event horizon, if there is one, or is otherwise global. Exact formulas for the metric coefficients in Fermi coordinates are derived. Sharp uni…
Investigates optimal life insurance and annuity decisions in inflationary economies.
problem Optimal consumption and investment decisions in an inflationary economy with money illusion.
method Formulated as a random horizon utility maximization problem, derived optimal strategy.
result Money illusion increases life insurance demand for young adults and reduces annuity demand for retirees.
Study shows inflation in 3+1D cosmologies with bounded scalar potential and specific symmetry.
problem Understanding inflation in 3+1D cosmologies with specific constraints.
method Mean curvature flow and asymptotic analysis of metric variations, stress-energy tensor, and inflaton field dynamics.
result Inflation occurs in 3+1D cosmologies with specific constraints, demonstrating it is possible with inhomogeneous initial conditions.
Milne-like spacetimes are a class of FLRW models which admit C0 spacetime extensions through the big bang. The boundary of a Milne-like spacetime can be identified with a null cone in the extension. We find that the comoving observers all emanate from a single point in the extension. This suggests that something phy…
The stability of money value is an important requisite for a functioning economy, yet it critically depends on the actions of participants in the market themselves. Here we model the value of money as a dynamical variable that results from trading between agents. The basic trading scenario can be recast into an Ising t…
The paper proposes a new model for financial order books without assuming prices or quantities.
problem Understanding the geometry of financial order books without assuming prices or quantities.
method Modeling financial order books as an inflationary relational system without metric, temporal, or price coordinates. Observable quantities arise through spectral embeddings of the graph Laplacian.
result Projected supply and demand are constrained to gamma-like functional forms, which can be observed as integrated-gamma cumulative profiles in high-frequency data.
The paper analyzes global inflation's systemic nature and its impact on equity markets.
problem Understanding the systemic nature of global inflation and its financial market implications.
method Data-driven study using eigenvalue analysis, inner-product optimization, and time-varying portfolio optimization.
result Countries with high centrality in global inflation are identified, and the robustness of equity indices and sectors during inflationary periods are explored.
Inflationary flows use DBMs for accurate Bayesian inference.
problem Calibrated uncertainty quantification in Bayesian inference.
method Inflationary flows leverage DBMs to map data to a Gaussian latent space.
result Inflationary flows produce accurate, identifiable posterior distributions.
Study uses social network data to analyze regional inflation trends.
problem Analyzing inflation trends using social media data.
method BERT neural networks for identifying pro-inflationary and disinflationary keywords.
result Models can visualize and classify inflationary keywords in different contexts.
The main goal of this paper is to define a 1-1 correspondence between between substitution tilings constructed by inflation and the arithmetic of positional representation in the underlying real vector space. It introduces a generalization of inflationary tessellations to equivalence classes of tiles. Two tiles belong …
The aim of this paper is to compare statistical properties of a bubble period with those of the anti-bubble period in stock markets. We investigate the statistical properties of daily data for the Nikkei 225 index in the 28-year period from January 1975 to April 2003, corresponded to the periods of bubbles and anti-bub…
This paper is intended as an investigation of the statistical properties of {\it absolute log-returns}, defined as the absolute value of the logarithmic price change, for the Nikkei 225 index in the 28-year period from January 4, 1975 to December 30, 2002. We divided the time series of the Nikkei 225 index into two per…
New cosmological models with changing curvature slices.
problem Cosmological models with varying and sign-changing curvature.
method Constructing globally hyperbolic spacetimes with slices of constant curvature that can change sign.
result Shows at least one comoving observer disappearing in finite time.
Bitcoin reacts negatively to inflation surprises, contrary to belief.
problem Bitcoin's ability to hedge inflation is questioned.
method Examined cryptocurrency responses to macroeconomic news announcements.
result Bitcoin's price decreases by 24 bps in response to inflationary surprises.
This paper analyzes microstructure dynamics in coupled markets using CFMMs.
problem Quantifying contributions of CFMMs to market dynamics in coupled markets.
method Examined constant function market makers (CFMMs) in coupled markets, focusing on basket inflation/deflation.
result CFMMs contribute significantly to basket inflation/deflation in coupled markets.
In general relativity, an IDEAL (Intrinsic, Deductive, Explicit, ALgorithmic) characterization of a reference spacetime metric g0 consists of a set of tensorial equations T[g]=0, constructed covariantly out of the metric g, its Riemann curvature and their derivatives, that are satisfied if and only if g is loc…
The analysis of dollar inflation performed by the authors through the approximation of empirical data for 1913-2012 with a power-law function with an accelerating log-periodic oscillation superimposed over it has made it possible to detect a quasi-singularity point around the 17th of December, 2012. It is demonstrated …
We present a novel analysis extending the recent work of Mizuno et al. [2002] on the hyperinflations of Germany (1920/1/1-1923/11/1), Hungary (1945/4/30-1946/7/15), Brazil (1969-1994), Israel (1969-1985), Nicaragua (1969-1991), Peru (1969-1990) and Bolivia (1969-1985). On the basis of a generalization of Cagan's model …
Research in deep learning for multi-speaker source separation has received a boost in the last years. However, most studies are restricted to mixtures of a specific number of speakers, called a specific scenario. While some works included experiments for different scenarios, research towards combining data of different…
Over the last two decades, many unexpected relations between exotic smoothness, e.g. exotic R4, and quantum field theory were found. Some of these relations are rooted in a relation to superstring theory and quantum gravity. Therefore one would expect that exotic smoothness is directly related to the quan…
Two new methods score stress test scenarios for risk managers.
problem Comparing and evaluating stress test scenarios for risk managers.
method Inspired by Archer-Mouy-Selmi, two methodologies for scoring stress test scenarios.
result New methods can compare and evaluate stress test scenarios.
Investigates cryptocurrency maturity through collective dynamics and diversification.
problem Determining if cryptocurrency market exhibits similar mathematical properties to equity market.
method Adjusts focus to retail cryptocurrency investors' behavioral patterns, contrasting with equity market.
result Identifies ideal portfolio size and spread across cryptocurrencies, revealing signatures of maturity.
Extracts representative scenarios from large data panels.
problem Creating representative scenarios from large data panels.
method Two novel algorithms: one identifies new scenarios, the other selects known important data points.
result Efficient algorithms for consistent scenario-based modeling and multi-dimensional numerical integration.
Methodology measures financial impacts using existing credit loss infrastructure.
problem Measuring the impact of financial scenarios on expected credit losses.
method Captures scenario effects through changes in default probabilities; uses existing provisioning infrastructure.
result Methodology validated through standardized climate scenario exercise in Canada and Quebec.
REDS improves scenario discovery from few simulations, reducing costs by 50-75%.
problem Discovering scenarios in data spaces resulting from simulations with limited computational resources.
method Uses an intermediate machine learning model to label data for subgroup discovery methods.
result Reduces the number of simulations required by 50-75% on average.
Proposes a new framework for environmental CVA with robust wrong-way risk.
problem Limited operational implementations of translating environmental scenarios into CVA.
method Three components: hazard rate mapping, tail generators, and KL divergence-based wrong-way risk bound.
result Nature CVAs can vary significantly across different ecosystem generators.
This paper formed part of a preliminary research report for a risk consultancy and academic research. Stochastic Programming models provide a powerful paradigm for decision making under uncertainty. In these models the uncertainties are represented by a discrete scenario tree and the quality of the solutions obtained i…
Statistical depth metrics help identify risky power grid scenarios.
problem Identifying extreme scenarios for risk mitigation in power grid planning.
method Functional depth metrics for sub-selecting outlying scenarios.
result The proposed approach effectively identifies risky scenarios for operational risk mitigation.
Two autoencoding models learn latent traffic scene representations.
problem Learning latent representations of traffic scenarios.
method CNN and RNN models for spatio-temporal and temporal data, incorporating permutation invariance.
result Latent scenario embeddings can be used for clustering and similarity retrieval.
A new method using energy distance for ensemble and scenario reduction.
problem Solving complex dynamic and stochastic programs, especially in energy systems.
method Proposes a new method based on energy distance for ensemble and scenario reduction.
result Reduced scenario sets exhibit better statistical properties for energy distance than Wasserstein distance.
Develops a method for reverse stress testing in multivariate scenarios.
problem Reconstructing a multivariate stress scenario from a single exogenous shock.
method Maximizing conditional density under three distributional assumptions.
result Simulated scenarios are economically coherent and reproduce risk-reward asymmetry.
CMTS synthesizes near-miss driving scenarios for safer autonomous driving tests.
problem Lack of near-miss driving data for testing autonomous driving algorithms.
method Generative model conditioned on road maps, using Variational Bayesian methods.
result Synthesized data covers more near-miss scenarios, improving trajectory prediction and risk handling.
Method generates plausible financial stress scenarios using large deviations.
problem Misleading risk management by overlooking or overemphasizing implausible scenarios.
method Exploits large-deviations principle to concentrate risk factors near most likely stress configurations.
result Can generate informative stress scenarios even with limited historical data.
Generates multimodal safety-critical scenarios for robustness evaluation of decision-making algorithms.
problem Lack of comprehensive evaluation of neural network robustness under real-world scenarios.
method Proposes a flow-based multimodal scenario generator using weighted likelihood maximization and gradient-based sampling.
result Demonstrates improved testing efficiency and multimodal modeling capability compared to traditional methods.
Adaptive framework generates challenging adversarial scenarios for autonomous vehicles.
problem Lack of efficient and adaptable evaluation methods for autonomous vehicles.
method Adaptive evaluation framework using ensemble models and nonparametric Bayesian clustering.
result Adversarial scenarios significantly degrade tested autonomous vehicles' performance.
We treat the so-called scenario approach, a popular probabilistic approximation method for robust minmax optimization problems via independent and indentically distributed (i.i.d) sampling from the uncertainty set, from various perspectives. The scenario approach is well-studied in the important case of convex robust o…
Algorithm reduces historical expected shortfall computation by focusing on worst-case scenarios.
problem Computing the historical expected shortfall efficiently and accurately.
method Multi-step algorithm using Monte Carlo simulations to identify and reduce the number of worst-case scenarios.
result Non-asymptotic bounds for the L p-error of the expected shortfall estimator are derived.
Risk measures such as Expected Shortfall (ES) and Value-at-Risk (VaR) have been prominent in banking regulation and financial risk management. Motivated by practical considerations in the assessment and management of risks, including tractability, scenario relevance and robustness, we consider theoretical properties of…
Paper proposes a copula method to generate unfavorable VaR scenarios.
problem Creating unfavorable VaR scenarios for insurance models.
method Patchwork copulas to create unfavorable VaR scenarios with given marginal distributions.
result Demonstrated with a 19-dimensional real-life insurance losses data set.
Study optimal timing to divest from assets with uncertain future scenarios.
problem Optimal timing to divest from assets with uncertain future scenarios.
method Smooth model of decision making under ambiguity aversion, optimal stopping problem with learning.
result Proves a minimax result reducing the problem to standard optimal stopping problems with learning.
Researchers validate ML scenario generators by checking dependencies and detecting memorization effects.
problem Validation of machine learning-based scenario generators differs from classical methods due to data-driven dependencies.
method Two novel validation aspects: checking dependencies and detecting memorization effects. Novel memorization ratio introduced.
result Validation methods successfully detect dependencies and memorization effects in ML-based scenario generators.
Standard artificial neural networks suffer from the well-known issue of catastrophic forgetting, making continual or lifelong learning difficult for machine learning. In recent years, numerous methods have been proposed for continual learning, but due to differences in evaluation protocols it is difficult to directly c…
This paper presents a method for testing the decision making systems of autonomous vehicles. Our approach involves perturbing stochastic elements in the vehicle's environment until the vehicle is involved in a collision. Instead of applying direct Monte Carlo sampling to find collision scenarios, we formulate the probl…
We define scenarios, propose different methods of aggregating them, discuss their properties and benchmark them against quadrant requirements.
In this paper we propose a problem-driven scenario generation approach to the single-period portfolio selection problem which use tail risk measures such as conditional value-at-risk. Tail risk measures are useful for quantifying potential losses in worst cases. However, for scenario-based problems these are problemati…
The paper proposes an efficient nested simulation design using likelihood ratio method.
problem Designing nested simulations with fixed outer scenarios and minimizing simulation effort.
method Proposes a bi-level optimization problem to decide inner replications and pooling strategies.
result Optimized design achieves $\cO(Γ^{-1})$ mean squared error of estimators.