Study on banking networks and leverage dependence in emerging countries.
problem Understanding interactions and leverage dependence among banks in emerging markets.
method Constructed financial networks based on leverage ratio dependence, analyzed using network analysis.
result Emerging banks form modular structures with synchronized dynamics and a large cluster at low leverage dependence.
This paper improves random feature sampling using empirical leverage scores.
problem Optimizing the number of features for kernel approximation and supervised learning.
method Uses empirical leverage scores to optimize feature sampling.
result Empirical sampling of random features using leverage scores outperforms vanilla Monte Carlo sampling.
We study in details the skew of stock option smiles, which is induced by the so-called leverage effect on the underlying -- i.e. the correlation between past returns and future square returns. This naturally explains the anomalous dependence of the skew as a function of maturity of the option. The market cap dependence…
Paper tackles leverage effect estimation from noisy data.
problem Estimating leverage effect from high-frequency data with microstructure noise.
method Holistic multi-scale framework operating directly on leverage effect, using Subsampling-and-Averaging Leverage Effect (SALE) and Multi-Scale Leverage Effect (MSLE) estimators.
result Holistic multi-scale framework achieves substantial efficiency gains over existing benchmarks.
The paper introduces a new spectral error bound for column subset selection.
problem Improving the reconstruction error in column subset selection.
method Developed a novel analysis of spectral norm reconstruction for a randomized algorithm, introducing a sampling-dependent error bound.
result A new sampling distribution with probabilities proportional to the square root of statistical leverage scores outperforms uniform and leverage-based sampling.
Optimizes leveraged staking strategies in decentralized finance.
problem Maximizing returns on staked assets in decentralized lending platforms.
method Developed a mathematical framework to optimize leveraged staking strategies, reducing the multi-market problem to convex allocation over market exposures.
result Rebalanced leveraged positions can achieve up to 6.2% APY, significantly higher than unleveraged staking.
Binary testing for softmax models requires many samples, similar to leverage score models.
problem Binary hypothesis testing for softmax models and leverage score models.
method Analyzing sample complexity and drawing analogies between models.
result Sample complexity is asymptotically \(O(ε^{-2})\), where \(ε\) is the distance between model parameters.
We introduce two types of ordinal pattern dependence between time series. Positive (resp. negative) ordinal pattern dependence can be seen as a non-paramatric and in particular non-linear counterpart to positive (resp. negative) correlation. We show in an explorative study that both types of this dependence show up in …
A new financial model merges long-range dependence and leverage effects.
problem Challenges posed by financial markets' stylized facts.
method Develops a fractional and mixed-fractional CEV model using fractional calculus.
result Analytical valuation formula for European Call options and Greeks.
Paper improves signal proportion estimation by accounting for variable dependence.
problem Traditional estimators assume independence, limiting applicability in real-world scenarios.
method Integrates arbitrary covariance dependence information using principal factor approximation.
result Method outperforms state-of-the-art estimators in accuracy and detection of weaker signals.
New method clusters multivariate time series using copulas and optimal transport.
problem Clustering multivariate time series with complex dependencies.
method Optimal copula transport for measuring intra- and inter-dependence.
result Defines robust multivariate dependence coefficient for clustering.
Based on a criterion of mathematical simplicity and consistency with empirical market data, a stochastic volatility model has been obtained with the volatility process driven by fractional noise. Depending on whether the stochasticity generators of log-price and volatility are independent or are the same, two versions …
New methods leverage labels as features to improve multi-label classification.
problem Inadequate base classifiers lead to inefficient label dependence analysis.
method Create neural-network inspired methods that treat labels as features, improving classification efficiency.
result Methods perform competitively and are scalable.
We present a simple agent-based model of a financial system composed of leveraged investors such as banks that invest in stocks and manage their risk using a Value-at-Risk constraint, based on historical observations of asset prices. The Value-at-Risk constraint implies that when perceived risk is low, leverage is high…
In modern portfolio theory, the balancing of expected returns on investments against uncertainties in those returns is aided by the use of utility functions. The Kelly criterion offers another approach, rooted in information theory, that always implies logarithmic utility. The two approaches seem incompatible, too loos…
Based on a criterium of mathematical simplicity and consistency with empirical market data, a stochastic volatility model has been obtained with the volatility process driven by fractional noise. Depending on whether the stochasticity generators of log-price and volatility are independent or are the same, two versions …
Efficiently approximates statistical leverage scores for faster KRR.
problem Accurately estimating statistical leverage scores for fast KRR.
method Analytic formula for statistical leverage scores, leveraging kernel spectral density.
result Linear time approximation with theoretical guarantees, significantly faster than existing methods.
New algorithms improve Bayesian inference for SV models with leverage.
problem Efficient Bayesian estimation of SV models with leverage.
method Derive novel algorithms for centered and non-centered parameterizations, combine samplers using ASIS.
result Stable sampling efficiency irrespective of parameterization.
Study examines asymmetry impacts on Japanese stock market volatility modeling and forecasting.
problem Understanding asymmetry's impact on modeling and forecasting realized volatility in Japanese stock markets.
method Employed heterogeneous autoregressive (HAR) models with three types of asymmetry: positive and negative realized semivariance, asymmetric jumps, and leverage effects.
result Leverage effects significantly influence realized volatility modeling and forecast performance in Japanese stock markets.
Excessive leverage, i.e. the abuse of debt financing, is considered one of the primary factors in the default of financial institutions. Systemic risk results from correlations between individual default probabilities that cannot be considered independent. Based on the structural framework by Merton (1974), we discuss …
The study models bank leverage dynamics and identifies the Basel leverage cycle.
problem Understanding the tradeoff between microprudential and macroprudential risks in banking.
method Developed a dynamical model with a bank and an investor, analyzing parameter spaces and calibrating to data.
result Identified the Basel leverage cycle with a period of 10-15 years, influenced by exogenous noise, bank size, and leverage target adjustment speed.
Methodology to measure non-linear correlations using copulas and clustering.
problem Measuring pairwise correlations between variables in datasets.
method Copulas for encoding dependence, optimal transport for geometry, clustering for summarizing patterns.
result Novel dependence coefficient parameterized by clusters centers.
We build a simple model of leveraged asset purchases with margin calls. Investment funds use what is perhaps the most basic financial strategy, called "value investing", i.e. systematically attempting to buy underpriced assets. When funds do not borrow, the price fluctuations of the asset are normally distributed and u…
The growth of the exhange-traded fund (ETF) industry has given rise to the trading of options written on ETFs and their leveraged counterparts {(LETFs)}. We study the relationship between the ETF and LETF implied volatility surfaces when the underlying ETF is modeled by a general class of local-stochastic volatility mo…
We propose a comprehensive treatment of the leverage effect, i.e. the relationship between returns and volatility of a specific asset, focusing on energy commodities futures, namely Brent and WTI crude oils, natural gas and heating oil. After estimating the volatility process without assuming any specific form of its b…
Enhanced deep learning model forecasts household leverage series accurately.
problem Forecasting household leverage series due to complex temporal-spatial dynamics.
method TSEN model with multiple RNN-based layers and an attention layer.
result Captures temporal-spatial dynamics and provides more accurate predictions.
Unified framework for simple question answering using subgraph ranking and joint-scoring.
problem Simple question answering with knowledge graphs is challenging.
method Unified framework focusing on subgraph selection and fact selection, with novel ranking and joint-scoring methods.
result Achieved state-of-the-art accuracy of 85.44% on SimpleQuestions dataset.
New framework relaxes independence assumption for graph-mixing dependencies.
problem Tackles limitations of existing generalization results for graph-mixing dependencies.
method Proposes a framework where dependencies decay with graph distance, derives generalization bounds leveraging online-to-PAC framework.
result Derives high-probability generalization guarantees that depend on mixing rate and graph's chromatic number.
CACTI improves tabular data imputation by leveraging missingness patterns and contextual information.
problem Tabular data imputation with improved accuracy and robustness.
method Masked autoencoding approach with median truncated copy masking and contextual information.
result Average R2 gain of 7.8% over the next best method across various datasets and missingness conditions. Estimates volatility of volatility and leverage effect using high-frequency options data.
problem Estimating volatility of volatility and leverage effect from high-frequency options data.
method Model-free estimators using characteristic function of price increments and spot volatility.
result Developed feasible inference methods for estimating volatility of volatility and leverage effect.
Quant GANs model financial time series using GANs with TCNs.
problem Modeling financial time series with stochastic processes.
method Quant GANs use a generator and discriminator with TCNs to capture long-range dependencies.
result Quant GANs generate financial time series with distributional and dependence properties in high fidelity.
Leveraged ETFs can outperform their targets in certain market conditions, contrary to the volatility drag hypothesis.
problem The long-term performance decay of leveraged ETFs due to volatility drag.
method Unified framework incorporating AR(1) and AR-GARCH models, continuous-time regime switching, and flexible rebalancing frequencies.
result Return dynamics, including return autocorrelation, volatility clustering, and regime persistence, determine LETF performance.
DOS improves language model generation by considering inter-token dependencies.
problem Lack of sequence-level information and inter-token dependencies in existing decoding strategies.
method Dependency-Oriented Sampler (DOS) that uses attention matrices to approximate inter-token dependencies.
result DOS consistently achieves superior performance on code generation and mathematical reasoning tasks.
Temporal Normalizing Flows enhance density estimation of time-dependent data.
problem Accurate and robust density estimation of time-dependent stochastic data.
method Leveraging normalizing flows for temporal data, tNFs estimate multi-scale distributions without prior scale knowledge.
result Temporal Normalizing Flows improve density estimation of time-dependent data, including multi-scale distributions.
Develops a statistical arbitrage strategy with stop-loss and leverage for energy markets.
problem Optimizing trading strategies in high-frequency energy markets with stop-loss and leverage.
method Analytical approach using mean-reverting processes and optimal trading strategies.
result Analytical expressions for expected First-Exit-Times and long-run returns of the strategy.
A new algorithm for efficient kernel Nyström approximation.
problem Efficiently approximating large kernel matrices for machine learning.
method Recursive sampling of landmark points using ridge leverage scores.
result Scalable and accurate kernel approximation with linear runtime.
SBO improves global optimization of noisy functions with random inputs.
problem Optimizing expensive, noisy functions with random inputs.
method Stratified Bayesian Optimization (SBO) that leverages strong dependence on influential scalar random inputs.
result SBO outperforms state-of-the-art benchmarks in numerical experiments.
Axient handles debt-free finality for leveraged binary event markets.
problem Managing debt and finality in leveraged event positions with uncertain outcomes.
method Axient separates leverage maturity from claim maturity, using a protocol to select smallest sale covering debt.
result Proves robust ex-ante debt clearing and debt-free-finality invariants, maximal residual spot exposure, and payout-vector invariance.
Improved gap-dependent bounds for reinforcement learning with linear approximations.
problem Achieving nearly minimax-optimal performance with linear function approximation.
method Developed and analyzed the LSVI-UCB++ algorithm and its concurrent variant.
result First gap-dependent regret bound for nearly minimax-optimal algorithm LSVI-UCB++.
The paper shows how the timing of prediction impacts model performance in healthcare.
problem The timing of prediction affects model performance in healthcare.
method The paper compares two prediction schemes: outcome-dependent and outcome-independent.
result An outcome-independent scheme outperforms an outcome-dependent scheme.
Predicts financial asset dependencies using spatiotemporal patterns.
problem Complex dependency structures in financial assets for risk mitigation.
method Proposes Asset Dependency Matrix (ADM) and Asset Dependency Neural Network (ADNN) with ConvLSTM for spatiotemporal asset dependency prediction.
result ADNN outperforms baselines in predicting asset dependencies and their applications.
Synapse arbitrates TSFMs to improve time series forecasting performance.
problem TSFMs vary in performance across different forecasting tasks, domains, and horizons.
method Synapse dynamically assigns and adjusts predictive weights based on TSFM performance.
result Synapse consistently outperforms other ensembling techniques and individual TSFMs.
THP model captures complex dependencies in event sequences efficiently.
problem Inability of existing models to capture long-term dependencies in event sequences.
method Transformer Hawkes Process (THP) model using self-attention mechanism.
result THP outperforms existing models in likelihood and event prediction accuracy.
Novel kernel-based SEMs improve edge detection in directed networks.
problem Detecting causal interactions in complex directed networks.
method Advocates nonlinear SEMs using kernels for nonlinear dependencies, proposing a convex regularized estimator with efficient optimization methods.
result Novel kernel-based approach outperforms linear SEMs in edge detection, revealing new regulatory edges.
This paper improves matrix completion by leveraging element importance and non-uniform sampling.
problem The challenge of completing low-rank matrices from noisy, subsampled measurements.
method Employing leverage scores to characterize element importance and devising a biased sampling procedure.
result Theoretical and empirical evidence shows that a smaller number of entries (about O(nrlog2(n))) can recover a low-rank matrix with noise. GPDFlow models extreme threshold exceedance with flexible dependence using normalizing flows.
problem Challenges in modeling multivariate threshold exceedance probabilities due to infinite parametrizations.
method GPDFlow uses normalizing flows to flexibly represent dependence without explicit parametric assumptions.
result GPDFlow significantly improves modeling accuracy and flexibility compared to traditional parametric methods.
Novel signature approach for pricing and hedging path-dependent options with market frictions.
problem Pricing and hedging path-dependent options with market frictions.
method Signature approach, mean-quadratic variation criterion, non-standard infinite-dimensional Riccati equations, time-augmented signature, non-Markovian stochastic control problem.
result Effective hedging strategies in frictional markets with low-truncated signature approximations.
Enhances reinforcement learning with partial state information.
problem Improving learning under partial observability with limited privileged signals.
method Introduced informed asymmetric actor-critic framework that uses arbitrary state-dependent privileged signals.
result Unbiased policy gradient estimates with arbitrary privileged signals.