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Reinforcement Learning Model for Dynamic Hedging Strategies #932
first of all we will define a custom OpenAI Gym environment for our dynamic hedging problem.
Steps to Implement:
Data Collection:
Collecting historical financial news articles and their corresponding sentiment scores.
Feature Engineering:
Create features representing the sentiment scores and integrate them with the existing options data features.
Model Training:
Train the enhanced model on the combined dataset of options and sentiment features.
Evaluate the model's performance with and without sentiment features to assess the impact.
RND Estimation:
Using the enhanced model to estimate the risk-neutral density from new options data, incorporating current market sentiment.
Analyze the improvement in RND estimation accuracy due to sentiment integration.
@Niketkumardheeryan sir pls assign it me I'm Gssoc'24 contributor
first of all we will define a custom OpenAI Gym environment for our dynamic hedging problem. Steps to Implement:
Data Collection: Collecting historical financial news articles and their corresponding sentiment scores.
Feature Engineering: Create features representing the sentiment scores and integrate them with the existing options data features.
Model Training: Train the enhanced model on the combined dataset of options and sentiment features. Evaluate the model's performance with and without sentiment features to assess the impact. RND Estimation:
Using the enhanced model to estimate the risk-neutral density from new options data, incorporating current market sentiment. Analyze the improvement in RND estimation accuracy due to sentiment integration.
@Niketkumardheeryan sir pls assign it me I'm Gssoc'24 contributor