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- W251640478 abstract "The increasing availability of intraday financial data has led to improvements in daily volatility forecasting through long-memory models of realized volatility. This paper demonstrates the merit of the non-parametric Nearest Neighbor (NN) approach for S&P 100 realized variance forecasting. A priori the NN approach is appealing because it can reproduce complex dynamic dependencies while largely avoiding misspecification and parameter estimation uncertainty, unlike model-based methods. We evaluate the forecasts through straddle trading profitability metrics and using conventional statistical accuracy criteria. The ranking of individual forecasts confirms that statistical accuracy does not have a one-to-one mapping into profitability. In turbulent markets, the NN forecasts lead to higher risk-adjusted profitability even though the model-based forecasts are statistically superior. In both calm and turbulent market conditions, the directional combination of NN and model-based forecasts is more profitable than any of the individual forecasts." @default.
- W251640478 created "2016-06-24" @default.
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- W251640478 date "2013-01-01" @default.
- W251640478 modified "2023-09-23" @default.
- W251640478 title "A Model-Free Approach to Forecasting Realized Volatility of the S&P 100 Stock Index: Does it Pay?" @default.
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- W251640478 doi "https://doi.org/10.2139/ssrn.2258457" @default.
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