arXiv 29 May 2021 · Econometrics · 4 citations (OpenAlex)
arXiv:2105.14382 · PDF · DOI · OpenAlex · Extracted main text
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, $\lambda$. But, what is the best election for $\lambda$ 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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | RiskMetrics (1996) Technical Document, J.P.Morgan/Reuters, New York, 1996 | 1.000 | 5 | 4 | 100% |
| 2 | Francis X. Diebold and Roberto S. Mariano (1995) Comparing predictive accuracy | 0.737 | 3 | 2 | 100% |
| 3 | Bernard Bollen (2015) What should the value of lambda be in the exponentially weighted moving average volatility model? | 0.644 | 2 | 2 | 100% |
| 4 | Valeriy Zakamulin (2015) A test of covariance-matrix forecasting methods | 0.644 | 2 | 2 | 100% |
| 5 | Tim Bollerslev (2010) Glossary to ARCH (GARCH) | 0.405 | 1 | 1 | 100% |
| 6 | Torben G. Andersen, Tim Bollerslev, and Steve Lange (1999) Forecasting financial market volatility: Sample frequency vis-a-vis forecast horizon | 0.405 | 1 | 1 | 100% |
| 7 | Marco Bee (2012) Dynamic value-at-risk models and the peaks-over-threshold method for market risk measurement: an empirical investigation during… | 0.405 | 1 | 1 | 100% |
| 8 | Tim Bollerslev (1986) Generalized autoregressive conditional heteroskedasticity | 0.405 | 1 | 1 | 100% |
| 9 | Jie Ding and Nigel Meade (2010) Forecasting accuracy of stochastic volatility, GARCH and EWMA models under different volatility scenarios | 0.405 | 1 | 1 | 100% |
| 10 | Robert F. Engle and Tim Bollerslev (1986) Modelling the persistence of conditional variances | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 16 scored citations.