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A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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0111 · Apr 201819922001200920172026
6 results for BN-S

Refined BN-S model improves crude oil hedging with machine learning.

problem Finding optimal hedging strategy for commodity markets.
method Implemented a refined Barndorff-Nielsen and Shephard model with machine learning algorithms.
result The refined model performs better than the classical BN-S model.

New model predicts dynamic volatility in uncertain financial markets.

problem Predicting dynamic volatility in financial markets with uncertainty.
method Generalized Barndorff-Nielsen and Shephard (BN-S) model considering delay and fuzziness.
result Effective prediction of dynamic volatility with improved performance.

The paper introduces a new stochastic volatility model with long-term memory and jumps.

problem Developing a model for variance and volatility swaps with long-term memory and jumps.
method Fractional Barndorff-Nielsen and Shephard model incorporating long-term memory and jumps.
result Arbitrage-free prices for variance and volatility swaps derived for the new model.

Batch Normalization (BN) is capable of accelerating the training of deep models by centering and scaling activations within mini-batches. In this work, we propose Decorrelated Batch Normalization (DBN), which not just centers and scales activations but whitens them. We explore multiple whitening techniques, and find th…

2018-04-23abs ↗pdf ↗

Improved stock index analysis using fuzzy parameters and machine learning.

problem Analyzing the S&P 500 stock index with long-term dependence.
method Combining fuzzy theory and machine learning to modify the Barndorff-Nielsen and Shephard model.
result The new model effectively captures the stochastic dynamics of the stock index time series.