Trading strategy uses Hoeffding's Inequality to predict financial regime change.
problem Predicting financial regime change for trading strategies.
method Applies Hoeffding's Inequality to trading performance data.
result Early warning of financial regime change can be detected.
Modular pipeline improves stock portfolio prediction robustness under regime changes.
problem Overfitting in deep learning models for non-stationary datasets.
method Modular machine learning pipeline with GBDT models and online learning techniques.
result GBDT models with dropout show high performance, robustness, and generalisability.
Study identifies change points in piecewise constant reward functions with fixed exploration budget.
problem Locating abrupt changes in piecewise constant reward functions under bandit feedback.
method Fixed exploration budget, piecewise constant bandit problem, lower bounds, near optimal algorithms.
result Established lower bounds and near matching upper bounds for both small and large budgets.
This study examines how ChiNext IPOs' initial returns are influenced by regulation regime changes.
problem Investors' behavior and pricing of ChiNext IPOs under different regulation regimes.
method Analysis of three time periods with two different regulation regimes and three sets of listing day trading restrictions.
result Regulation regime changes significantly impact ChiNext IPO pricing and overreaction.
Study shows registration regime improves IPO pricing efficiency in China.
problem Improving IPO pricing efficiency in China's entrepreneurial boards.
method Examined the impacts of regime changes and rule implementations on IPO pricing efficiency.
result The registration regime is most efficient in Chinese IPO pricing.
Paper optimizes change-point detection using learned distributions from training sequences.
problem Optimal change-point detection with unknown pre- and post-change distributions.
method Designs a change-point estimator using training sequences and test sequences.
result Optimal confidence width characterized as a function of undetected error.
The rBergomi model is improved with a regime switching change of measure to match market VIX smiles.
problem The rBergomi model produces flat VIX smiles, not matching market observations.
method A regime switching stochastic change of measure is applied to the rBergomi model, using an inhomogeneous fractional Ornstein-Uhlenbeck equation and an efficient Monte Carlo method.
result The model produces upward sloping VIX smiles, aligning with market observations.
Unified framework detects change-points and estimates parameters in nonlinear systems with regime switching.
problem Detecting change-points and estimating parameters in nonlinear dynamical systems with regime transitions.
method Residual-loss anomaly analysis of physics-informed neural networks, two-stage strategy.
result The method outperforms traditional approaches in change-point localization and parameter estimation accuracy.
In this paper, we study the modeling and the classification of functional data presenting regime changes over time. We propose a new model-based functional mixture discriminant analysis approach based on a specific hidden process regression model that governs the regime changes over time. Our approach is particularly a…
Bayesian method detects change points in time series data.
problem Detecting significant regime shifts in time series data.
method Bayesian autoregressive model with time-varying parameters.
result Enhanced estimate accuracy and forecasting power.
Model predicts risk-adjusted returns across various financial markets.
problem Stationary models fail in predicting risk-adjusted returns due to market regime changes.
method Asset-independent regime-switching model using hidden Markov models.
result Accurately detects bull, bear, and high volatility periods for improved risk-adjusted returns.
Financial markets change their behaviours abruptly. The mean, variance and correlation patterns of stocks can vary dramatically, triggered by fundamental changes in macroeconomic variables, policies or regulations. A trader needs to adapt her trading style to make the best out of the different phases in the stock marke…
ProteuS generates synthetic financial data with regime changes for testing drift detection.
problem Simulating concept drift in financial markets for model evaluation.
method ARMA-GARCH models fitted to ETF data, generating synthetic time series with predefined regime changes.
result Generated datasets reveal the complexity of detecting and adapting to market regime changes.
The paper improves energy contract pricing models by incorporating jumps and varying parameters.
problem Inaccurate pricing of energy contracts using the Black-Scholes-Merton model.
method Integrates regime switching and time-changed Levy processes with a two-state Markov chain.
result Improved accuracy in pricing energy contracts through a new model.
New method clusters financial time series into volatility regimes.
problem Finding the number of volatility regimes in nonstationary financial time series.
method Change point detection and clustering of segment distributions.
result Optimized trading strategy based on learned volatility regimes.
Statistical approaches for Functional Data Analysis concern the paradigm for which the individuals are functions or curves rather than finite dimensional vectors. In this paper, we particularly focus on the modeling and the classification of functional data which are temporal curves presenting regime changes over time.…
RegimeFolio optimizes portfolios by adapting to changing market regimes.
problem Non-stationary markets with shifting volatility regimes.
method Explicitly models volatility regimes with sector-specific ensemble forecasting and adaptive mean-variance allocation.
result Significant improvement in return and robustness compared to conventional methods.
Bayesian VAR and Elliptical Black-Litterman models improve portfolio optimization during regime changes and heavy-tailed returns.
problem Portfolio optimization under market regime changes and heavy-tailed returns.
method BAVAR-BLED algorithm combining BAVAR and Black-Litterman models with Elliptical Distributions.
result Significant outperformance of state-of-the-art methods in Sharpe, Sortino ratios, and total returns.
Paper proposes BOCPD for real-time order flow and market impact prediction.
problem Persistent order flow patterns in financial markets.
method Bayesian online change-point detection (BOCPD) with score-driven approach.
result Model outperforms existing models in predicting order flow and market impact.
This paper presents the construction of a particle filter, which incorporates elements inspired by genetic algorithms, in order to achieve accelerated adaptation of the estimated posterior distribution to changes in model parameters. Specifically, the filter is designed for the situation where the subsequent data in on…
New method uses topological data analysis for better change point detection.
problem Detecting change points in time series data.
method Integrates topological data analysis with existing nonparametric change point detection methods.
result Enhanced detection accuracy of change point locations.
A new jump diffusion regime-switching model is introduced, which allows for linking jumps in asset prices with regime changes. We prove the existence and uniqueness of the solution to the risk-sensitive asset management criterion maximisation problem in this setting. We provide an ODE for the optimal value function, wh…
Study improves S&P 500 volatility forecasting through regime-switching methods.
problem Accurate prediction of S&P 500 volatility for risk management and investment.
method Regime-switching methods including soft Markov switching, spectral clustering, and coefficient-based clustering.
result Coefficient-based clustering algorithm outperformed other models during all time periods.
Detects changes in global financial networks before crashes.
problem Financial contagion and crashes across global markets.
method Sequential change point detection in dynamic networks.
result Can detect changes in network behavior before stock market crashes.
This paper introduces a novel model-based clustering approach for clustering time series which present changes in regime. It consists of a mixture of polynomial regressions governed by hidden Markov chains. The underlying hidden process for each cluster activates successively several polynomial regimes during time. The…
Framework models multiscale dynamics with Bayesian learning for regime changes.
problem Analyzing complex interactions between fast and slow processes.
method Hierarchical state-space modeling with Sequential Monte Carlo.
result Bayesian approach accurately tracks state transitions and identifies switching dynamics.
Mixture model-based clustering, usually applied to multidimensional data, has become a popular approach in many data analysis problems, both for its good statistical properties and for the simplicity of implementation of the Expectation-Maximization (EM) algorithm. Within the context of a railway application, this pape…
The paper develops methods to accurately locate change points in high-dimensional mean shift models.
problem Locating change points in high-dimensional mean shift models.
method Locally refitted least squares estimator, component-wise and simultaneous rates of estimation.
result Asymptotic validity of component-wise and simultaneous confidence intervals for change point parameters.
Modeling regime shifts in co-evolving time series with interactions and time-dependency.
problem Discovering and modeling regime shifts in multiple time series with relationships and time-dependent behaviors.
method Modeling interactions and time-dependency in co-evolving time series using a mapping grid and dynamic network representation for regime identification and time-dependent Cox regression for regime transition probabilities.
result A principled approach for modeling interactions and time-dependency in co-evolving time series.
The present paper aims at locating the breakings of the integration process of an international system observed during about 50 years in the 19th century. A historical study could link them to special events, which operated as exogenous shocks on this process. The indicator of integration used is the spread between the…
This paper improves risk control for financial markets by calibrating VaR forecasts using conformal methods.
problem Nonstationary and regime-dependent losses in financial markets.
method Regime-weighted conformal risk control (RWC) for VaR forecasting.
result RWC improves regime-conditional stability in some settings with modest conservativeness changes.
Dynamic text classification with changing labels using learned metrics.
problem Traditional text classifiers struggle with dynamic label sets.
method Replacing fixed output layer with a learned metric space for nearest-neighbor classification.
result Learning a non-Euclidean metric improves performance in low data regimes.
Model for dynamic relational data with regime changes.
problem Handling abrupt changes in dynamic relational data.
method Factorized fusion shrinkage model with global-local shrinkage priors.
result Posterior distribution attains minimax optimal rate up to logarithmic factors.
The paper extends MS models with TVTP to U.S. Treasury yields, finding reliable regime dynamics but challenging TVTP identification.
problem Identifying time-varying transition probabilities in Markov-switching models for U.S. Treasury yields.
method Developed a comprehensive MS model with TVTP, including simulations and an R package for estimation.
result Regime means, variances, and transition probabilities are reliably identified, but TVTP coefficients are harder to estimate.
We have developed a statistical technique to test the model assumption of binary regime switching extension of the geometric Brownian motion (GBM) model by proposing a new discriminating statistics. Given a time series data, we have identified an admissible class of the regime switching candidate models for the statist…
This paper considers the optimal portfolio selection problem in a dynamic multi-period stochastic framework with regime switching. The risk preferences are of exponential (CARA) type with an absolute coefficient of risk aversion which changes with the regime. The market model is incomplete and there are two risky asset…
Algorithm detects changes online using expert tracking.
problem Online change point detection in nonparametric settings.
method Sequential score function estimation and tracking the best expert approach.
result Algorithm performs well in artificial and real-world data.
New framework for regression trees with multivariate response and dynamic mean vectors.
problem Characterizing and implementing regression trees for multivariate responses.
method High dimensional model with dynamic mean vectors over multi-dimensional change axes.
result Optimal rate of convergence and asymptotic valid confidence intervals for change points.
The private car license plates issued in Shanghai are bestowed the title of "the most expensive sheet iron all over the world", more expensive than gold. A citizen has to bid in an monthly auction to obtain a license plate for his new private car. We perform statistical analysis to investigate the influence of the mini…
Improves decision complexity in hybrid environments.
problem Complexity in hybrid decision-making problems.
method General extension of DEC framework, model aggregation approach.
result Improved regret bounds for linear Q*/V* MDPs.
The article detects market regimes from covariance matrices using VLSTAR and clustering models.
problem Market regime switching is hard to detect due to time-varying correlation coefficients.
method The article applies VLSTAR and unsupervised hierarchical clustering on monthly realized covariance matrices.
result VLSTAR outperforms clustering in detecting market regimes.
BRPC online Bayesian calibration handles gradual and abrupt system changes.
problem Aligning model outputs with field observations in evolving systems.
method Bayesian Recursive Projected Calibration (BRPC) for streaming data under simulator mismatch and nonstationarity.
result Improves calibration accuracy under gradual changes and robustness under abrupt regime shifts.
In this paper, we consider the optimal dividend problem for a company. We describe the surplus process of the company by a diffusion model with regime switching. The aim of the company is to choose a dividend policy to maximize the expected total discounted payments until ruin. In this article, we consider a hybrid div…
A new method for pricing European options in changing market conditions.
problem Lack of closed-form solutions for pricing European options in regime-switching models.
method Physics-informed residual learning (PIRL) for efficient option pricing.
result PIRL eliminates the need for retraining and offers near-instantaneous pricing.
A new approach is presented to describe the change in the statistics of the log return distribution of financial data as a function of the timescale. To this purpose a measure is introduced, which quantifies the distance of a considered distribution to a reference distribution. The existence of a small timescale regime…
Paper addresses uncertainty in model generalization under regime shifts.
problem Uncertainty in model generalization under regime changes.
method Proposes a framework to quantify and separate regime mismatch and sensitivity.
result Obtains exact decomposition and minimax lower bound for regime-aware models.
Patients with epilepsy can manifest short, sub-clinical epileptic "bursts" in addition to full-blown clinical seizures. We believe the relationship between these two classes of events---something not previously studied quantitatively---could yield important insights into the nature and intrinsic dynamics of seizures. A…
We consider option pricing in a regime-switching diffusion market. As the market is incomplete, there is no unique price for a derivative. We apply the good-deal pricing bounds idea to obtain ranges for the price of a derivative. As an illustration, we calculate the good-deal pricing bounds for a European call option a…