This project implements a research paper that introduces a novel volatility estimator derived from multiple periods of high, low, open, and close prices within a historical time series. The proposed estimator offers several significant advantages over traditional methods, making it a valuable tool for financial analysts and researchers.
Unbiased in the Continuous Limit The new estimator is designed to be unbiased in the continuous limit, ensuring that the volatility estimates are accurate and reliable over long periods.
Unlike many traditional volatility estimators, this novel method is not affected by drift. This means that the estimator can provide accurate volatility measurements even in the presence of trends or long-term movements in the price series.
The estimator effectively manages jumps in the opening prices, providing consistent and stable volatility estimates even when there are significant overnight changes in the market.
One of the most significant advantages of this new estimator is its low variance. Among all estimators with similar characteristics, this method exhibits the lowest variance, making it a more precise tool for volatility measurement.
When applied to real-life time series data, the new estimator demonstrates a notable improvement in accuracy compared to the conventional close-to-close estimator. This enhancement makes it particularly useful for practical financial analysis and risk management.
The implementation of the estimator involves the following steps:
Gather historical time series data, including high, low, open, and close prices for the desired periods.
Estimator Calculation: Apply the novel estimator formula to the collected data to compute the volatility.
Comparison and Analysis: Compare the results of the new estimator with those obtained from traditional methods to evaluate its performance.
To use this estimator, follow the steps below:
Install Dependencies: Ensure that you have the required dependencies installed. These might include libraries such as pandas for data manipulation and numpy for numerical calculations. Prepare Data: Prepare your historical time series data with columns for high, low, open, and close prices. Run the Estimator: Use the provided functions or scripts to calculate the volatility using the new estimator. Analyze Results: Analyze the results and compare them with traditional estimators to see the improvements in accuracy and variance.