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@misc{1773457,
author = {Araneda, Axel Alejandro},
language = {eng},
publisher = {arXiv:2105.14382},
title = {Asset volatility forecasting:The optimal decay parameter in the EWMA model},
url = {https://arxiv.org/abs/2105.14382},
year = {2021}
}
TY - GEN
ID - 1773457
AU - Araneda, Axel Alejandro
PY - 2021
TI - Asset volatility forecasting:The optimal decay parameter in the EWMA model
PB - arXiv:2105.14382
UR - https://arxiv.org/abs/2105.14382
N2 - The exponentially weighted moving average (EMWA) could be labeled as a competitive volatility estimator, where its main strength relies on computation simplicity, especially in a multi-asset scenario, due to dependency only on the decay parameter, λ. But, what is the best election for λ in the EMWA volatility model? Through a large time-series data set of historical returns of the top US large-cap companies; we test empirically the forecasting performance of the EWMA approach, under different time horizons and varying the decay parameter. Using a rolling window scheme, the out-of-sample performance of the variance-covariance matrix is computed following two approaches. First, if we look for a fixed decay parameter for the full sample, the results are in agreement with the RiskMetrics suggestion for 1-month forecasting. In addition, we provide the full-sample optimal decay parameter for the weekly and bi-weekly forecasting horizon cases, confirming two facts: i) the optimal value is as a function of the forecasting horizon, and ii) for lower forecasting horizons the short-term memory gains importance. In a second way, we also evaluate the forecasting performance of EWMA, but this time using the optimal time-varying decay parameter which minimizes the in-sample variance-covariance estimator, arriving at better accuracy than the use of a fixed-full-sample optimal parameter.
ER -
ARANEDA, Axel Alejandro. \textit{Asset volatility forecasting:The optimal decay parameter in the EWMA model}. arXiv:2105.14382, 2021.
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