8 papers
Amortized Inference for Sampling Distributions Where the Bootstrap Fails
Akash Deep
Efron's bootstrap is the default tool for estimating the sampling distribution of a statistic, yet it is provably inconsistent for maxima of bounded-support distributions, means un…
Local Gaussian Correlation in the Tails: A Scarcity Diagnostic, an Optimal Local Bandwidth, and the Limits of Adaptivity
Akash Deep, Gagan Deep
Local Gaussian correlation (LGC) measures dependence locally, making it a natural tool for tail dependence and financial contagion, but its estimates degrade in the joint tails, wh…
Memory, Roughness, and Information Persistence in Financial Markets: A Structural Approach to Volatility Forecasting
Akash Deep, Nicholas Appiah, Svetlozar T. Rachev
This paper studies the joint role of long-memory dynamics,rough-volatility behavior, and persistence-based forecasting features in equity volatility modeling. We combine semiparame…
Full-Field Damage Monitoring in Architected Lattices Using In situ Electrical Impedance Tomography
Akash Deep, Andrea Samore, Alistair McEwan +2
Electrical impedance tomography (EIT) enables non-invasive, spatially continuous reconstruction of internal conductivity distributions, providing full field sensing beyond conventi…
Interpretable Hypothesis-Driven Trading:A Rigorous Walk-Forward Validation Framework for Market Microstructure Signals
Gagan Deep, Akash Deep, William Lamptey
We develop a rigorous walk-forward validation framework for algorithmic trading designed to mitigate overfitting and lookahead bias. Our methodology combines interpretable hypothes…
Probability Weighting Meets Heavy Tails: An Econometric Framework for Behavioral Asset Pricing
Akash Deep, Svetlozar T. Rachev, Frank J. Fabozzi
We develop an econometric framework integrating heavy-tailed Student's distributions with behavioral probability weighting while preserving infinite divisibility. Using 432{,}7…