We explore a decomposition in which returns on a large class of portfolios relative to the market depend on a smooth non-negative drift and changes in the asset price distribution. This decomposition is obtained using general continuous semimartingale price representations, and is thus consistent with virtually any ass…
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48 results for “price dispersion”
Skewness dispersion predicts future stock market returns, especially in months with monetary policy announcements.
problem Predicting future stock market returns using skewness dispersion.
method Cross-sectional analysis of firm-level realized skewness and stock market returns.
result Skewness dispersion is a significant predictor of future stock market returns, robust to various estimation methods.
Statistical and economic evaluation of forecasts in electricity markets: beyond RMSE and MAEq-fin.CP
This paper evaluates forecast quality in electricity markets beyond traditional accuracy measures.
problem Traditional accuracy measures fail to reflect the economic value of electricity price forecasts.
method Investigates four quality dimensions: accuracy, dispersion, association, and extremum identification.
result Dispersion- and association-based measures better capture forecast economic value.
The purpose of this paper is to study the generalized Fong--Vasicek two-factor interest rate model with stochastic volatility. In this model the dispersion of the stochastic short rate (square of volatility) is assumed to be stochastic as well and it follows a non-negative process with volatility proportional to the sq…
This paper investigates the impact of pre-existing offline data on online learning, in the context of dynamic pricing. We study a single-product dynamic pricing problem over a selling horizon of periods. The demand in each period is determined by the price of the product according to a linear demand model with unkn…
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.
The paper links labor income risk to stock returns using industry portfolio returns.
problem Understanding the impact of sectoral shifts on stock returns.
method Using cross-industry dispersion (CID) as a proxy for unemployment risk, the paper examines the relationship between stock returns and the sensitivity of returns to CID innovations.
result Stocks with high sensitivity to CID have lower expected returns, suggesting they are more exposed to sectoral shifts and unemployment risk.
New dispersion indices based on inaccuracy and divergence introduced for information measures.
problem Measuring variability in uncertainty measures.
method Introducing new dispersion indices based on Kerridge inaccuracy and Kullback-Leibler divergence.
result Properties, bounds, and examples of new dispersion indices presented.
Cross-sectional signatures of market panic were recently discussed on daily time scales in [1], extended here to a study of cross-sectional properties of stocks on intra-day time scales. We confirm specific intra-day patterns of dispersion and kurtosis, and find that the correlation across stocks increases in times of …
A new microeconomic model is presented that aims at a description of the long-term unit sales and price evolution of homogeneous non-durable goods in polypoly markets. It merges the product lifecycle approach with the price dispersion dynamics of homogeneous goods. The model predicts a minimum critical lifetime of non-…
New heat dispersion laws established for smooth compact manifolds.
problem Understanding heat dispersion in smooth compact manifolds.
method Established new heat dispersion laws through Theorem 1.1 and explored them further with Propositions 3.1 and 3.2.
result New heat dispersion laws for smooth compact manifolds.
MallowsPO enhances LLM fine-tuning with a dispersion index of human preferences.
problem Lack of diversity in human preferences in DPO.
method Developed a dispersion index based on Mallows' theory to characterize preference diversity.
result Demonstrated improved performance in various tasks using the dispersion index.
Geometric focusing affects dispersive estimates for Schrödinger and wave equations.
problem Long-time decay rate in dispersive estimates for Schrödinger and wave equations on non-trapping asymptotically conic manifolds and exact metric cones.
method Classifying the long-time decay rate in dispersive estimates for the Schrödinger and wave equations on non-trapping asymptotically conic manifolds and exact metric cones in terms of the intensity of geometric focusing.
result Each multiplicity of conjugate points within distance π on Y = ∂X0 leads to a |t|1/2-loss in the long-time decay order and a half-order shift in the regularity index in the dispersive estimate for the Schrödinger equation.
A Research on Cross-sectional Return Dispersion and Volatility of US Stock Market during COVID-19q-fin.ST
Study on stock market volatility and return dispersion during COVID-19.
problem Impact of COVID-19 on stock market volatility and return dispersion.
method Used Google index to proxy epidemic impact, modeled volatility, and analyzed influencing factors of log-return.
result Volatility significantly affected by epidemic and cross-sectional return dispersion, with positive coefficients.
In the recent years, banks have sold structured products such as worst-of options, Everest and Himalayas, resulting in a short correlation exposure. They have hence become interested in offsetting part of this exposure, namely buying back correlation. Two ways have been proposed for such a strategy : either pure correl…
The study examines Hawkes processes and their long-term behavior.
problem Understanding the long-term behavior of Hawkes processes.
method Proving functional limit theorems under various conditions on the dispersion of child events.
result Functional limit theorems hold for Hawkes processes with different levels of child event dispersion.
Urban dispersal events are processes where an unusually large number of people leave the same area in a short period. Early prediction of dispersal events is important in mitigating congestion and safety risks and making better dispatching decisions for taxi and ride-sharing fleets. Existing work mostly focuses on pred…
New framework controls statistical dispersion for high-stakes applications.
problem Understanding and controlling the dispersion of loss distributions in high-stakes applications.
method Simple yet flexible framework for distribution-free control of statistical dispersion measures.
result Proposed methods control statistical dispersion measures with societal implications.
Machine learning classifies surface wave dispersion curves from ambient noise.
problem Classifying surface wave dispersion curves from ambient noise.
method Convolutional neural network (U-net) with transfer learning and supervised learning.
result Machine classification nearly identical to human-picked phases.
News-based trading strategiesq-fin.TR
The marvel of markets lies in the fact that dispersed information is instantaneously processed and used to adjust the price of goods, services and assets. Financial markets are particularly efficient when it comes to processing information; such information is typically embedded in textual news that is then interpreted…
Risk in Network Economiesecon.GN
Network models assume unrealistic idiosyncratic risk, which can be mitigated by allowing for correlated shocks.
problem Network models assume idiosyncratic risk, which can be unrealistic and lead to incorrect predictions.
method Proposed a production-based asset pricing model to account for substitutability between trade partners and correlation in supply and demand shocks.
result Assets positively exposed to average propagation of upstream and downstream shocks earn lower average risk premia.
Bayesian model tackles spatial count data issues with flexible non-parametric techniques.
problem Challenges in traditional parametric models for spatial count data with unbalanced distributions and complex dependencies.
method Bayesian semi-parametric spatial dispersed count model combining non-parametric techniques and adapted count models.
result Demonstrates superior performance in managing dispersion and capturing intricate spatial patterns.
The paper finds upper bounds for Bermudan options with convex payoffs.
problem Finding robust bounds for Bermudan options with convex payoffs.
method Characterizing and simplifying the dual problem, solving under structural assumptions on measures.
result Additional randomisation is required for optimal model definition even when marginal laws are atom-free.
This paper presents a novel one-factor stochastic volatility model where the instantaneous volatility of the asset log-return is a diffusion with a quadratic drift and a linear dispersion function. The instantaneous volatility mean reverts around a constant level, with a speed of mean reversion that is affine in the in…
Bayesian Quadrature improves ensembling for neural networks with dispersed likelihood peaks.
problem Ensembling neural networks struggles with dispersed, narrow peaks in likelihood surfaces.
method Uses Bayesian Quadrature to construct weighted ensembles of architectures.
result Empirically outperforms state-of-the-art baselines in test likelihood, accuracy, and expected calibration error.
We consider time-domain digital backpropagation with chromatic dispersion filters jointly optimized and quantized using machine-learning techniques. Compared to the baseline implementations, we show improved BER performance and >40% power dissipation reductions in 28-nm CMOS.
Study on billiard trajectories with fixed bounces.
problem Counting periodic trajectories with specific bounces.
method Analyzes two-dimensional dispersive billiard systems.
result Asymptotic growth of primitive periodic trajectories.
Machine learning techniques have recently received significant attention as promising approaches to deal with the optical channel impairments, and in particular, the nonlinear effects. In this work, a machine learning-based classification technique, known as the Parzen window (PW) classifier, is applied to mitigate the…
Study contextual online pricing with biased offline data, achieving optimal regret bounds.
problem Contextual online pricing with biased offline data.
method Identify to measure data bias, use OFU policy and robust variant for unknown bias.
result Achieve minimax-optimal regret bounds for contextual pricing.
Develops a novel framework for pricing variance swaps in multi-asset stochastic volatility models.
problem Pricing variance swaps in multi-asset stochastic volatility models.
method Determinant-based instantaneous generalized variance, Heston and BNS stochastic volatility frameworks.
result Analytical pricing expressions for multi-asset Heston and BNS formulations.
Structure of a fourth-order dispersive flow equation through the generalized Hasimoto transformationmath.DG
Paper transforms a complex equation into simpler forms for analysis.
problem Analyzing a fourth-order dispersive flow equation on Kähler manifolds.
method Developed the generalized Hasimoto transformation to simplify the equation.
result Explicit expressions derived for three examples of compact Kähler manifolds.
Posterior Dispersion Indicesstat.ML
Probabilistic modeling is cyclical: we specify a model, infer its posterior, and evaluate its performance. Evaluation drives the cycle, as we revise our model based on how it performs. This requires a metric. Traditionally, predictive accuracy prevails. Yet, predictive accuracy does not tell the whole story. We propose…
Dynamical Optimization Theory of a Diversified Portfoliocond-mat.stat-mech
We propose and study a simple model of dynamical redistribution of capital in a diversified portfolio. We consider a hypothetical situation of a portfolio composed of N uncorrelated stocks. Each stock price follows a multiplicative random walk with identical drift and dispersion. The rules of our model naturally give r…
Reduced-order modeling for parameterized large-eddy simulations of atmospheric pollutant dispersionstat.ML
Reduced-order model improves LES for atmospheric pollutant dispersion.
problem Accurate near-field pollutant concentration tracking in urban areas.
method Combining POD and GPR for non-intrusive reduced-order modeling.
result Component-by-component optimization captures spatial scales in high-order modes.
Range-Based Volatility Estimators for Monitoring Market Stress: Evidence from Local Food Price Dataq-fin.ST
Study adapts OHLC volatility estimators for monitoring market stress in diverse settings.
problem Limited use of range-based volatility estimators in local commodity markets.
method Adapted OHLC volatility estimators to monitor market distress across various contexts.
result OHLC-based volatility indicators detect market disruptions missed by standard momentum indicators.
We consider a firm that sells products over periods without knowing the demand function. The firm sequentially sets prices to earn revenue and to learn the underlying demand function simultaneously. A natural heuristic for this problem, commonly used in practice, is greedy iterative least squares (GILS). At each ti…
Dropout Regularization in Extended Generalized Linear Models based on Double Exponential Familiesstat.ML
Dropout improves regularization in flexible models for rare features.
problem Understanding theoretical properties of dropout in generalized linear models.
method Theoretical analysis and application to adaptive smoothing with B-splines.
result Dropout prefers rare features in mean and dispersion parameters.
The standard deviation and Gini mean difference order based on tail behavior.
problem Ordering between standard deviation and Gini mean difference for real-valued risks.
method Analysis of the mean excess function of the pairwise difference .
result Dominance regimes of SD and GMD are determined by tail behavior of the distribution.
Pointwise dispersive estimates for Schrodinger and wave equations in a conical singular spacemath.AP
Study dispersive estimates for Schrödinger and wave equations on a cone with specific metric.
problem Pointwise decay estimates for Schrödinger and wave equations on a product cone.
method Modified Hadamard parametrix on with to prove dispersive estimates.
result Threshold of conjugate radius for pointwise dispersive estimates.
Study shows how high-budget agents can manipulate prediction markets.
problem Manipulation of prediction markets by high-budget agents.
method Agent-based simulations and analytic characterization of price dynamics.
result High-budget agents can temporarily shift prediction market prices.
Proposes a new portfolio optimization method considering reward, dispersion, and asymmetry.
problem Capturing fat-tails and asymmetry in asset return distributions.
method Market model with tempered stable distribution; extended mean-variance optimization.
result Closed-form solutions for VaR and CVaR; efficient frontier extended to three dimensions.
The uncertainty or the variability of the data may be treated by considering, rather than a single value for each data, the interval of values in which it may fall. This paper studies the derivation of basic description statistics for interval-valued datasets. We propose a geometrical approach in the determination of s…
We discuss a short-time existence theorem of solutions to the initial value problem for a third order dispersive flow for closed curves into a compact almost Hermitian manifold. Our equations geometrically generalize a physical model describing the motion of vortex filament. The classical energy method cannot work for …
Short-term incentives lead to riskier trading strategies.
problem Optimal execution with performance barriers.
method Analyzes the impact of short-term performance incentives on trading behavior.
result Short-term incentives result in more aggressive but less risky trading strategies in the short term, but poorer performance over long periods.
We study productivity dispersions across workers, firms and industrial sectors. Empirical study of the Japanese data shows that they all obey the Pareto law, and also that the Pareto index decreases with the level of aggregation. In order to explain these two stylized facts, we propose a theoretical framework built upo…
A nonlinear wave alternative for the standard Black-Scholes option-pricing model is presented. The adaptive-wave model, representing 'controlled Brownian behavior' of financial markets, is formally defined by adaptive nonlinear Schrödinger (NLS) equations, defining the option-pricing wave function in terms of the stock…
ChatGPT snapshots predict future stock returns.
problem Predicting future stock returns using pre-cutoff text.
method Extracted LLM outlook scores from OpenAI snapshots.
result Outlook scores positively correlate with future stock returns.
Improved peak detection in ChIP-seq data reduces over-dispersion.
problem Over-dispersion in ChIP-seq data reduces peak detection accuracy.
method Supervised segmentation models with alternative noise assumptions.
result Improved peak detection accuracy compared to natural assumptions.