Improved reSGLD accelerates convergence in non-convex learning problems.
problem Inefficient swaps due to noisy energy estimators in reSGLD.
method Variance reduction for noisy energy estimators, theoretical analysis, and numerical experiments.
result Exponential acceleration in convergence for non-convex learning problems.
Data-aware activation function customization reduces neural network error.
problem Current neural networks lack consideration for specific activation functions.
method Linear algebraic explanation and Diaconis-Shahshahani Approximation Theorem criteria for activation functions.
result Using an even activation function like seagull can reduce neural network error by orders of magnitude.
In this paper, the valuation of European and path-dependent options in foreign exchange (FX) markets is considered when the currency exchange rate evolves according to the Heston model combined with the Cox-Ingersoll-Ross dynamics for the stochastic domestic and foreign short interest rates. The mixed Monte Carlo/PDE m…
New clustering algorithms for sensor networks reduce data exchange.
problem Minimize data exchange in decentralized sensor networks.
method Propose two clustering algorithms working on compressed data without prior cluster count.
result Reduce data exchange by at least 2x compared to K-means and DB-Scan.
Develops efficient time series prediction intervals.
problem Constructing reliable prediction intervals for time series data.
method Introduces exttt{EnbPI} algorithm for time series data.
result Demonstrates superior performance compared to existing methods.
A new algorithm for time series prediction intervals.
problem Non-exchangeability in time series data.
method Adaptive re-estimation of non-conformity scores.
result Significant reduction in interval width compared to existing methods.
This paper investigates - on the basis of the Cont-Bouchaud model - whether a Tobin tax can stabilize foreign exchange markets. Compared to earlier studies, this paper explicitly recognizes that a transaction tax-induced reduction in market depth may increase the price responsiveness of a given order. We find that the …
Central bank strategy to maintain currency exchange rate within limits.
problem Maintaining a currency exchange rate within a target zone despite adverse economic trends.
method Modeling the problem with a continuous-time market impact model and solving it as a stochastic control problem.
result Optimal strategy minimizes accumulated inventory of foreign currency.
Novel privatization framework for high-dimensional variable selection with differential privacy.
problem High-dimensional controlled variable selection with rigorous FDR control under differential privacy constraints.
method Gaussian Johnson-Lindenstrauss Transformation for privatizing the knockoff matrix.
result The proposed private variable selection procedure maintains statistical power even under strict privacy budgets.
Paper introduces CLVR to reduce price volatility in AMM exchanges.
problem Intra-block price volatility in AMM exchanges.
method CLVR constructs an ordering to minimize price volatility with low computation cost.
result CLVR minimizes price volatility with a small computation cost and can be externally verified.
Blockchain data improves asset predictions by 4x compared to LSTM.
problem Improving asset value predictions using blockchain data.
method Modern Deep Learning techniques applied to blockchain account distribution histograms and spatial dataset modeling.
result Error reduction of 4 times with blockchain data compared to LSTM approach. Study optimizes Bitcoin futures hedging to reduce liquidation risk.
problem Optimizing hedging strategies to minimize liquidation risk in Bitcoin futures.
method Derived a semi-closed form optimal hedging strategy considering spot and futures extreme returns, loss aversion, leverage, and collateral management.
result Optimal strategy reduces both hedged portfolio variance and liquidation probability.
In this paper, we use reduction by extended actions to give a construction of transitive Courant algebroids from string classes. We prove that T-duality commutes with the reductions and thereby determine global conditions for the existence of T-duals in heterotic string theory. In particular we find that T-duality exch…
Study compares models for pricing multi-strike quanto call options with SV, SC, and SER.
problem Pricing multi-strike quanto call options with stochastic volatility, correlation, and exchange rates.
method Comparative analysis of SV, SC, and SER models; Monte Carlo simulation; Milstein scheme; antithetic variates; correlation risk parameters.
result GARCH-Jump SV, Weibull SC, and Ornstein Uhlenbeck (OU) SER model combination performs best.
FlowScan models exchangeable data sets with flexible flow transformations.
problem Density estimation for exchangeable, non-i.i.d. data.
method Combines invertible flow transformations with a sorted scan.
result Achieves new state-of-the-art performance on point cloud and image set modeling.
Many models of market dynamics make use of the idea of wealth exchanges among economic agents. A simple analogy compares the wealth in a society with the energy in a physical system, and the trade between agents to the energy exchange between molecules during collisions. However, while in physical systems the equiparti…
Unified framework for representation and causal structure learning using exchangeable data.
problem Identifying latent representations or causal structures in non-i.i.d. data.
method Identifiable Exchangeable Mechanisms (IEM) framework for representation and structure learning.
result New insights and identifiability results for causal structure and representation learning.
IUS framework predicts EUR/USD exchange rate with improved accuracy.
problem Accurate forecasting of EUR/USD exchange rate.
method Combines large language models for sentiment analysis, deep learning for forecasting, and feature selection.
result Optuna-optimized Bi-LSTM model reduces MAE and RMSE by 10.69% and 9.56% respectively.
Optimal crypto order execution using cross-exchange signals.
problem Maximizing order execution efficiency in cryptocurrency markets.
method Reinforcement learning applied to cross-exchange data.
result Cross-exchange signals improve optimal execution outcomes.
BBE simulates betting exchanges to generate synthetic data for AI research.
problem Lack of real data for AI/ML in betting exchanges.
method Agent-based simulation model of a sports-betting exchange.
result Generates large, high-resolution synthetic data for AI/ML.
EBPs model exchangeable data with flexible distributions.
problem Current energy-based models restrict set cardinality and limited distribution forms.
method Introduced Energy-Based Processes (EBPs) that extend energy models to exchangeable data with neural network parameterizations.
result EBPs can express more flexible distributions over sets without cardinality restrictions.
XSPNs combine SPNs and MEVMs for efficient inference in data with repeated parts.
problem Efficient inference in data with repeated interchangeable parts.
method Introducing Exchangeability-Aware Sum-Product Networks (XSPNs) that combine SPNs and MEVMs.
result XSPNs can be more accurate than conventional SPNs when data contains repeated parts.
New method for robust prediction valid under non-exchangeable data.
problem Non-exchangeability in data violates robustness of common CP methods.
method Introduces efficient CP approach for general non-exchangeable data.
result Produces provably valid confidence sets for non-exchangeable data.
We analyze high-resolution foreign exchange data consisting of 20 million data points of USD-JPY for 13 years to report firm statistical laws in distributions and correlations of exchange rate fluctuations. A conditional probability density analysis clearly shows the existence of trend-following movements at time scale…
Faster Ethereum slots boost CEX-DEX arbitrage by 535% and 203%.
problem Reducing Ethereum slot time impacts CEX-DEX arbitrage opportunities.
method Developed a trading model to simulate and compare agent behavior under different slot times.
result Faster slot times increase CEX-DEX arbitrage activity and returns.
Proposes non-exchangeable conformal risk control for better uncertainty bounds.
problem Handling non-exchangeable data in black-box models for better risk control.
method Leverages and extends split conformal prediction and monotone loss function approaches.
result Allows controlling expected value of any monotone loss function for non-exchangeable data.
Paper argues for using functional theory of randomness for better understanding of data exchangeability and conformal prediction.
problem Understanding relationships between IID data assumptions and data exchangeability.
method Translation of conformal prediction results into the language of functional theory of randomness.
result Every confidence predictor valid for IID data can be transformed to a conformal predictor without losing much predictive efficiency.
This thesis explores supervised classification methods using Bayesian and exchangeability theories.
problem Assigning objects into predefined classes using training data and auxiliary information.
method Bayesian inductive theories and exchangeabilities (de Finetti and partition exchangeability).
result Optimal classifiers for different scenarios of object features and categories.
Mobile training improves speech recognition for users with unique speech characteristics.
problem Limited generalization of speaker-independent speech recognition models for users with very different speech characteristics.
method Securely training personalized end-to-end speech recognition models on mobile devices, splitting gradient computation to reduce memory usage.
result On-device personalization achieved 58.1% relative word error rate reduction compared to 63.7% in a server environment, with 18.7% performance degradation.
Study uses exchangeable GPs for staggered-adoption policy evaluation in panel data.
problem Evaluating the impact of staggered treatments in panel data settings.
method Exchangeable multi-task Gaussian processes (GPs) with flexible kernels.
result Flexible tool for policy evaluation in panel data settings.
We report on the occurrence of an anomaly in the price impacts of small transaction volumes following a change in the fee structure of an electronic market. We first review evidence for the existence of a master curve for price impact on the Johannesburg Stock Exchange (JSE). On attempting to re-estimate a master curve…
Synthetic data can be used to ask more questions and accelerate discovery with provable validity guarantees.
problem Valid inference with synthetic data
method Task exchangeability
result Provable validity guarantees for synthetic data inference
The paper develops concentration inequalities for structured random data, extending beyond independent terms.
problem Developing concentration inequalities for structured weighted sums of random data, including tensors and matrix-valued data.
method The paper develops Hoeffding and Bernstein bounds for structured weighted sums under exchangeability, extending beyond the classical framework of independent terms.
result The paper develops a sharper concentration bound for combinatorial sums of matrix arrays.
A new AMM design reduces impermanent loss and retains more liquidity.
problem Inefficiencies in conventional AMM designs lead to liquidity loss and user engagement issues in DEXs.
method Proposes a dual-mechanism framework: a power-law invariant BMM and dynamic rebate system.
result Reduces impermanent loss by 36% and retains 3.98x more liquidity during price volatility.
Optimizes cryptocurrency exchanges' risk management by reducing positions based on leverage.
problem Managing risk in cryptocurrency futures exchanges during large price moves.
method Formulates ADL as an optimization problem to minimize risk of loss, using a water-filling rule to equalize leverage.
result The optimal ADL policy minimizes maximum leverage among participants, providing a transparent and implementable benchmark.
Study finds recurring patterns in cryptocurrency volatility and liquidity.
problem Recurring patterns in volatility and liquidity of major cryptocurrencies.
method Data from two centralized exchanges and a decentralized exchange analyzed for patterns.
result Systematic patterns in volatility and liquidity across different timeframes.
Study lead-lag relationships in foreign exchange markets using three approaches.
problem Lack of research on lead-lag relationships in foreign exchange markets.
method Three approaches: lagged correlations, lagged partial correlations, and Granger causality.
result Statistically significant lead-lag relationships found in some exchange rate pairs.
The team predicts foreign exchange rates using clustering and attention models.
problem Complexity and unexpected events in foreign exchange markets.
method Clustering and attention models applied to historical data.
result Improved event-driven price prediction for oversold scenarios.
We introduce an autoregressive-type model with self-modulation effects for a foreign exchange rate by separating the foreign exchange rate into a moving average rate and an uncorrelated noise. From this model we indicate that traders are mainly using strategies with weighted feedbacks of the past rates in the exchange …
Study shows cooperation can reduce investment risk and price gaps.
problem Investment risk and price gaps in cooperative markets.
method Introduced Collective Arbitrage and Collective Super-replication, established asset pricing theorems.
result Reduction of price intervals through collective super-replication.
New measures capture tail dependence and non-exchangeability in financial data.
problem Underestimation of tail dependence and inability to capture non-exchangeable tail dependence.
method Tail copulas and novel tail dependence measures (MTCM, ATCM) are proposed.
result Captures non-exchangeable tail dependence and provides analytical forms for various copulas.
We first show that there are in fact triangular arbitrage opportunities in the spot foreign exchange markets, analyzing the time dependence of the yen-dollar rate, the dollar-euro rate and the yen-euro rate. Next, we propose a model of foreign exchange rates with an interaction. The model includes effects of triangular…
Directed graphs occur throughout statistical modeling of networks, and exchangeability is a natural assumption when the ordering of vertices does not matter. There is a deep structural theory for exchangeable undirected graphs, which extends to the directed case via measurable objects known as digraphons. Using digraph…
Decentralized detection avoids sharing data, controls false discoveries.
problem Global false discovery rate control in decentralized novelty detection.
method Quantized surrogate models for low-precision sharing, preserving exchangeability.
result Quantized composite scores maintain competitive statistical power with reduced communication.
Speed bumps reduce but do not fully eliminate investment in fast trading technology.
problem Limiting low-latency trading to curb investment in fast trading technology.
method Built an experimental trading platform to test the effects of speed bumps on investment in fast trading technology.
result Asymmetric speed bumps reduce investment in speed by only 20%, and increasing the magnitude further reduces investment by 8.33%. Symmetric speed bumps have no effect on investment levels.
New classifiers converge under large data, simplifying complex models.
problem Complex predictive models under large datasets.
method Convergence of simultaneous and marginal classifiers under partition exchangeability.
result Asymptotic convergence of classifiers with large data reduces computational complexity.
Framework handles both exchangeable and non-exchangeable event sequences without tuning.
problem Handling both exchangeable and non-exchangeable event sequences efficiently.
method Parametric Hawkes-process-inspired conditional probability mass function with variational inference.
result Competitive computational and predictive performance against state-of-the-art methods.
New neural model processes 2D data with long-range dependencies efficiently.
problem Limited receptive field of convolutions for complex 2D tasks.
method Proposes Matrix Shuffle-Exchange network with O(logn) layers and O(n2logn) complexity. result Exceeds convolutional and graph neural network baselines in long-range dependency modeling.