Publication details
A Fuzzy Framework for Realized Volatility Prediction: Empirical Evidence From Equity Markets
| Authors | |
|---|---|
| Year of publication | 2026 |
| Type | Article in Periodical |
| Magazine / Source | JOURNAL OF FORECASTING |
| MU Faculty or unit | |
| Citation | |
| web | https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.70082 |
| Doi | https://doi.org/10.1002/for.70082 |
| Keywords | forecast combination; fuzzy clustering; fuzzy time series; HAR; realized variance; volatility forecasting |
| Attached files | |
| Description | This study introduces a realized volatility fuzzy time series (RV-FTS) model that applies a fuzzy c-means clustering algorithm toestimate time-varying c latent volatility states and their corresponding membership degrees. These memberships are used to con-struct a fuzzified volatility estimate as a weighted average of cluster centroids. The final volatility forecast is generated throughan exponentially weighted moving average (EWMA) mechanism that combines the most recent fuzzified volatility estimate withthe previous forecast, governed by the smoothing parameter ?. The two hyperparameters are estimated using a rolling-windowcross-validation approach. Our empirical study is based on volatility forecasts for 14 major stock market indices, covering morethan 20 years of data. We predict 1- to 22-day-ahead volatility and compare the RV-FTS model with nine standard volatility modelbenchmarks: generalized autoregressive conditional heteroscedasticity (GARCH), ARFIMA, AR, heterogeneous autoregressive(HAR), EWMA, and random forest models, as well as conditional combination forecasts. We find that, in the short-term, day-ahead setting, the RV-FTS model tends to outperform the benchmark models under the mean squared error loss and performssimilarly to the best models under the QLIKE loss. The conditional combination forecast shows that across all markets and mul-tiple forecast horizons, there are periods when the weight of the RV-FTS model in the conditional combination of eight modelsreaches 50% or more. The volatility timing strategy also shows that the RV-FTS model leads to higher cost- and risk-adjusted returns compared with a benchmark volatility model. |
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