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Hedge Profits in Fat-Tail with AI

Zou, Shibo LU (2025) NEKP01 20251
Department of Economics
Abstract
The fundamental way to optimize hedging transactions is to improve trading technology. This thesis uses AI to process the fat-tail part of financial data and optimizes the classic hedge error loss problem. Fat-tail data indicates large price changes, contains a lot of information, and has a large degree of data dispersion, which is suitable for AI processing. Investors will only trade when they see obvious changes in prices, just as fishermen will only lift their fishing rods when they see the float rise and fall. The data in the peak part has a small amount of information because no one trades, and its theoretical and practical value is relatively low.
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author
Zou, Shibo LU
supervisor
organization
course
NEKP01 20251
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Hedge, AI, Fat Tail, Commodity Future, Option
language
English
id
9198896
date added to LUP
2025-09-12 11:18:36
date last changed
2025-09-12 11:18:36
@misc{9198896,
  abstract     = {{The fundamental way to optimize hedging transactions is to improve trading technology. This thesis uses AI to process the fat-tail part of financial data and optimizes the classic hedge error loss problem. Fat-tail data indicates large price changes, contains a lot of information, and has a large degree of data dispersion, which is suitable for AI processing. Investors will only trade when they see obvious changes in prices, just as fishermen will only lift their fishing rods when they see the float rise and fall. The data in the peak part has a small amount of information because no one trades, and its theoretical and practical value is relatively low.}},
  author       = {{Zou, Shibo}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Hedge Profits in Fat-Tail with AI}},
  year         = {{2025}},
}