We study the dynamics of the linear and non-linear serial dependencies in financial time series in a rolling window framework. In particular, we focus on the detection of episodes of statistically significant two- and three-point correlations in the returns of several leading currency exchange rates that could offer so…
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48 results for “rolling window”
BAWS adapts window size for financial risk forecasting.
problem Adaptive selection of look-back window for financial risk modeling.
method Data-driven online learning method using bootstrap-based adaptive window selection (BAWS).
result BAWS improves risk forecasting, especially in data with structural changes.
Modeling cryptocurrency volatility and jumps with SVCJ model.
problem Understanding the dynamics and volatility of cryptocurrency markets.
method Stochastic volatility with correlated jumps (SVCJ) model with rolling-window parameter estimates.
result Cryptocurrency volatility stabilizes during bullish periods and increases during bearish periods.
Pairs trading strategy improved using Ornstein-Uhlenbeck process.
problem Improving pairs trading strategy effectiveness.
method Used Ornstein-Uhlenbeck process to model stock price spreads.
result OU model captures signals and trends effectively but underperforms compared to naive model.
This paper evaluates different methods to estimate S&P 500 volatility.
problem Accurately estimating the volatility of the S&P 500 index.
method Historical volatility, GARCH model, and implied volatility methods were compared.
result Implied volatility is the best estimator of real volatility.
Model predicts volatility and dependencies in EUA and energy prices.
problem Analyzing uncertainty and dependencies in European carbon and energy prices.
method Probabilistic multivariate conditional time series model with VECM-Copula-GARCH structure.
result Forecasting performance evaluated in an extensive rolling-window study.
Rolling Diffusion improves video prediction by progressively corrupting frames based on their temporal position.
problem Improving video prediction accuracy by accounting for temporal dynamics.
method A sliding window denoising process that assigns more noise to frames that appear later in a sequence.
result Rolling Diffusion outperforms standard diffusion models in tasks with complex temporal dynamics.
This paper investigates bias in resampled backtests for financial portfolios, finding it often negligible.
problem Bias in resampled backtests for financial portfolio evaluation.
method Investigation of bias in rolling-window mean-variance portfolios using resampling techniques.
result The bias in Sharpe Ratio estimates from IID resampling is often a fraction of estimation noise, making it tolerable.
Study on liquidity dynamics in Uniswap v3 pools using statistical methods.
problem Characterize liquidity in Uniswap v3 pools.
method Functional principal component analysis (FPCA) and dynamic factor methods.
result Liquidity dynamics in Uniswap v3 pools are well-captured by a low-order Legendre polynomial basis.
Paper proposes deep learning model for dynamic stock repurchase forecasting.
problem Complex temporal dependencies in corporate financial conditions.
method Hybrid Temporal Convolutional Network (TCN) and Attention-based LSTM.
result Model significantly outperforms static baselines in stock repurchase forecasting.
ERDM integrates rolling forecasts with diffusion models for complex dynamics.
problem Forecasting complex dynamics with rolling forecasts and diffusion models.
method Adapting EDM components for rolling forecasts, introducing novel loss weighting, efficient initialization, and hybrid architecture.
result ERDM outperforms diffusion-based baselines in 2D Navier-Stokes simulations and ERA5 weather forecasting.
New Bayesian method for estimating portfolio VaR and CVaR that adapts to volatility changes.
problem Estimating VaR and CVaR of portfolios in volatile markets.
method Volatility-sensitive Bayesian estimation using conjugate priors and rolling window sizes.
result The new method provides better risk estimation, especially during turbulent periods.
New ARIMA framework improves forecast accuracy for economic and financial time series.
problem Improving forecast accuracy for nonlinear dynamics in time series data.
method Projection-based ARIMA framework using Galerkin basis expansions.
result Galerkin-SARIMA matches or improves forecast accuracy compared to classical ARIMA/SARIMA.
The study forecasts hourly intraday electricity prices using ensemble methods.
problem Weak-form efficiency of hourly German Intraday Continuous Market prices.
method Probabilistic forecasting with ensemble trajectories, generalized additive model, and lasso penalty.
result The mixture model outperforms benchmarks in forecasting price distribution and volatility.
Paper proposes SPO paradigm for better portfolio optimization in real markets.
problem Real-world trading frictions and constraints affect portfolio optimization quality.
method SPO paradigm with decision-focused training using surrogate loss and linear predictors.
result Decision-focused training improves risk-adjusted performance and robustness.
Paper tests for time-varying entropy in stock prices, finding periods of inefficiency.
problem Testing for time-varying entropy in stock price dynamics.
method Unbiased approximation of Shannon entropy variance, optimal rolling window selection, hypothesis testing.
result Existence of periods of market inefficiency for meme stocks.
New method improves stock return prediction in non-stationary markets.
problem Tackles the challenge of predicting stock returns in non-stationary environments.
method Jointly optimizes model class and training window size using a tournament procedure.
result Consistently outperforms standard benchmarks by 14-23% in out-of-sample .
Study compares GARCH, EWMA, and IV models for GBP/USD and EUR/GBP currency pairs.
problem Predicting 20-day variation in GBP/USD and EUR/GBP currency pairs.
method Applied GARCH, EWMA, and IV models to GBP/USD and EUR/GBP pairs data.
result GARCH models outperform other models in predicting volatility for EUR/GBP, while GARCH with rolling window for GBP/USD.
Chronos models improve financial forecasting by integrating multivariate data.
problem Improving financial forecasting accuracy using multivariate data.
method Evaluation of Chronos-2 on multivariate and univariate financial forecasting models.
result Multivariate forecasts consistently outperform univariate forecasts, especially for interest rates.
A novel algorithm predicts customized allergy seasons using multi-variate triple-regression.
problem Predicting customized allergy seasons for individual patients.
method Triple-regression algorithm with pre-processing and three-stage regressions.
result Improved forecasting accuracy and reduced uncertainty.
A deep learning approach for pricing convertible bonds with path-dependent reset and call provisionsq-fin.PR
Deep learning models price convertible bonds with complex reset and call features.
problem Pricing convertible bonds with path-dependent reset and call provisions.
method Formulated as a PPDE, deep learning approximates conditional expectations.
result Deep learning produces stable and accurate prices across various model specifications.
HAR model outperforms ML in stock forecasting with correct fitting schemes.
problem Realized volatility forecasting using machine learning techniques.
method Investigated the role of fitting schemes in HAR model performance, focusing on training window and re-estimation frequency.
result HAR model consistently outperforms ML models when using a correctly specified fitting approach.
XGBoost predicts NEPSE Index log returns with low error and high directional accuracy.
problem Forecasting daily log-returns in the NEPSE Index with high accuracy.
method XGBoost machine learning, feature engineering, hyperparameter optimization, walk-forward validation.
result Optimal XGBoost configuration achieves lowest log-return RMSE and MAE.
Simple GBRT model improved by window-based input transformation outperforms state-of-the-art deep learning models.
problem Improving performance of traditional forecasting models for time series data.
method Transformed GBRT model input structure to include target values and external features, forming one input instance per training window.
result Simple GBRT model with window-based input transformation outperformed state-of-the-art deep learning models on nine datasets.
Energy price forecasting is a relevant yet hard task in the field of multi-step time series forecasting. In this paper we compare a well-known and established method, ARMA with exogenous variables with a relatively new technique Gradient Boosting Regression. The method was tested on data from Global Energy Forecasting …
Paper forecasts stock correlations using a hybrid model combining graph neural networks and transformers.
problem Improving stock correlation forecasts for better portfolio management.
method Hybrid model combining Transformer and graph attention networks for forecasting residual deviations from historical data.
result The hybrid model reduces correlation forecasting error compared to rolling-window estimates.
The Empirical Mode Decomposition (EMD) provides a tool to characterize time series in terms of its implicit components oscillating at different time-scales. We apply this decomposition to intraday time series of the following three financial indices: the S\&P 500 (USA), the IPC (Mexico) and the VIX (volatility index US…
Nonlinear Evidence of Investor Heterogeneity: Retail Cash Flows as Drivers of Market Dynamicsq-fin.GN
Study reveals investor heterogeneity in Korean equity market cash flows.
problem Investor heterogeneity and its impact on market dynamics.
method Detrended fluctuation analysis (DFA) on aggregated cash flows.
result Persistence in cash flows varies by investor type, with retail flows showing strong persistence.
Recently, there has been a surge of interest in the use of machine learning to help aid in the accurate predictions of financial markets. Despite the exciting advances in this cross-section of finance and AI, many of the current approaches are limited to using technical analysis to capture historical trends of each sto…
Enhanced GARCH model uses autoencoder for volatility forecasting.
problem Selecting optimal realised volatility estimator for forecasting.
method Proposes an autoencoder-enhanced Realised GARCH model combining multiple realised measures.
result The model outperforms traditional linear methods in one-step-ahead rolling volatility forecasting.
Explains rolling of symmetric spaces on flat spaces.
problem Clarifying the difference between two types of rolling.
method Detailed explanation and illustrative examples.
result Theoretical results complemented with examples.
AutoQuant addresses cryptocurrency backtesting fragility by modeling execution costs and improving strategy selection.
problem Fragile backtests of cryptocurrency perpetual futures ignoring microstructure frictions and execution costs.
method Execution-centric framework with Bayesian optimization, double screening, and strict T+1 semantics.
result Fee-only and zero-cost backtests overestimate returns, highlighting the importance of modeling execution costs.
TCP provides well-calibrated prediction intervals for nonstationary time series.
problem Nonstationary time series forecasting with well-calibrated prediction intervals.
method Temporal Conformal Prediction (TCP) couples a modern quantile forecaster with a rolling split-conformal calibration layer.
result TCP achieves near-nominal coverage, providing slightly wider intervals than Historical Simulation.
Study on rolling Stiefel manifolds with specific metrics.
problem Intrinsic and extrinsic rolling of Stiefel manifolds with -metrics.
method Investigation of intrinsic rolling of normal naturally reductive homogeneous spaces, derivation of ODEs for rolling, and explicit solutions.
result Explicit solutions for intrinsic and extrinsic rolling of Stiefel manifolds.
We report on time-varying network connectedness within three banking systems: North America, the EU, and ASEAN. The original method by Diebold and Yilmaz is improved by using exponentially weighted daily returns and ridge regularization on vector autoregression (VAR) and forecast error variance decomposition (FEVD). We…
A new model for curves on manifolds using rolling operations.
problem Modeling curves on manifolds without explicit parametrization.
method Using rolling operations to construct Gaussian processes on manifolds.
result Conditions for the rolling of mean to equal Fréchet mean and estimators of parameters.
Study on rolling of 2D and 3D manifolds, identifying orbit dimensions.
problem Understanding rolling dynamics of 2D and 3D manifolds with constraints.
method Modeling rolling as a control affine system on a fibered space Q, analyzing reachable sets.
result Identified possible dimensions of non-open rolling orbits: 2, 5, 6, 7.
The Blaschke rolling disk theorem is extended to non-convex domains.
problem Classical inclusion principle for non-convex domains.
method Geometric conditions based on curvature, algorithm for decomposition.
result Necessary and sufficient conditions for rolling disks in non-convex domains.
Expected Shortfall (ES) is the average return on a risky asset conditional on the return being below some quantile of its distribution, namely its Value-at-Risk (VaR). The Basel III Accord, which will be implemented in the years leading up to 2019, places new attention on ES, but unlike VaR, there is little existing wo…
Long-term lane change prediction model predicts maneuvers with 75% accuracy.
problem Predicting long-term lane changes for safer autonomous driving.
method Introduced three models: logistic regression, MLP, and RNN. Used NGSIM dataset with new labeling scheme.
result Developed model predicts 75% of lane changes with an average advanced time of 8.05 seconds.
When Does Order Flow Matter? State-Dependent L2 Liquidity-State Transitions in Crypto Futuresq-fin.TR
Study shows pre-event L2 liquidity state predicts crypto futures liquidity better than event labels.
problem Understanding how crypto futures liquidity changes over time.
method Combining L2 order book data, trade-flow records, and macro-event windows to define discrete liquidity-state transitions and evaluate models.
result Pre-event L2 liquidity state predicts post-event liquidity regimes better than event labels, and order flow adds value only when layered on top of the state model.
Local equivalence found between maximally symmetric rolling and flat Cartan distributions.
problem Establishing local equivalence between maximally symmetric rolling and flat Cartan distributions.
method Using complex parametrisation of su(2), a change of coordinates maps the maximally symmetric rolling -distribution to the flat Cartan distribution.
result Local equivalence between maximally symmetric rolling and flat Cartan distributions established.
Generalized Blaschke rolling theorem for curved spaces.
problem Extending classical theorem to curved spaces.
method Generalization to Riemannian manifolds with bounded curvature.
result Sharp results in arbitrary dimensions, new even in constant curvature spaces.
Rolling two hyperboloid surfaces is described using a Monge normal form.
problem Describing the rolling motion of hyperboloid surfaces.
method Parametrization of using unimodular fractional linear transformations.
result Found a Monge normal form for the rolling of hyperboloid surfaces.
Recurrent auto-encoder model summarises sequential data through an encoder structure into a fixed-length vector and then reconstructs the original sequence through the decoder structure. The summarised vector can be used to represent time series features. In this paper, we propose relaxing the dimensionality of the dec…
Shapes can roll downhill following any curve, but often return to initial orientation after crossing multiple copies.
problem How to design shapes that roll downhill along a given curve and its translations.
method Analyzing the geometric properties and motion of shapes on inclined planes.
result Most curves allow shapes to roll downhill following them and their translations, but some require crossing multiple copies.
In the present work we define the rolling of one pseudo-Riemannian manifold over another without slipping and twisting. We compare the definition of the rolling without slipping and twisting of two manifolds isometrically embedded into a pseudo-Euclidean space with the rolling defined only by the intrinsic data, namely…
Rollings of reductive homogeneous spaces are studied using intrinsic curves.
problem Investigate rollings of reductive homogeneous spaces without slip and twist.
method An intrinsic point of view, considering rollings as curves in the configuration space tangent to a certain distribution.
result Explicit solutions for rollings of over are obtained for specific cases.