Forecasting of Realised Volatility with the Random Forests Algorithm

Chuong Luong · Nikolai Dokuchaev

57 citations0/0 files storedJournal of risk and financial management

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Financial Risk and Volatility Modeling Forecasting Techniques and Applications Stock Market Forecasting Methods

Abstract

The paper addresses the forecasting of realised volatility for financial time series using the heterogeneous autoregressive model (HAR) and machine learning techniques. We consider an extended version of the existing HAR model with included purified implied volatility. For this extended model, we apply the random forests algorithm for the forecasting of the direction and the magnitude of the realised volatility. In experiments with historical high frequency data, we demonstrate improvements of forecast accuracy for the proposed model.

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References

1973The Pricing of Options and Corporate Liabilities 1986Generalized autoregressive conditional heteroskedasticity 1982Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation 1991Conditional Heteroskedasticity in Asset Returns: A New Approach 1993On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks 1993A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options 2003Modeling and Forecasting Realized Volatility 1976The pricing of commodity contracts

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