Does Anything Beat 5-Minute RV? A Comparison of Realized Measures Across Multiple Asset Classes

By Lily Y. Liu, Andrew J. Patton, Kevin Sheppard. 2015

539 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/137e4d26f6c33ac00ad7ff1ae581591f544a601f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Stavros Degiannakis *
2. George Filis
3. T. Walther
4. Tony Klein
5. Tetsuya Takaishi
6. Vipin Arora
7. D. Broadstock
8. Ioannis Chatziantoniou

In Finance

1. George Filis
2. Stavros Degiannakis *
3. T. Walther
4. Tony Klein
5. Tetsuya Takaishi
6. R. Brooks

In Global and Planetary Change

1. Tetsuya Takaishi
2. Tony Klein

Citations in the corpus

• Forecasting realized volatility of agricultural commodities (2020, background): “(2017), and Luo et al. (2019) who maintain that the jump component as well as the introduction of time-varying HAR coefficients help improving forecast accuracy of agricultural commodities price volatility.”
• Rough volatility of Bitcoin (2019, methodology): “e volatility signature plot(Andersen et al., 2000). Here, we use a moderate 5-min sampling frequency to avoid strong bias at high-frequency by maintaining reasonable accuracy(Bandi and Russell, 2006; Liu et al., 2015). To obtain a more accurate RV, we could introduce a modication factor, such as the”
• Two are better than one: Volatility forecasting using multiplicative component GARCH‐MIDAS models (2020, unknown): “See, for example, Laurent, Rombouts, and Violante (2013) and Liu, Patton, and Sheppard (2015).”
• Forecasting crude oil price volatility (2018, unknown): “Liu, Patton, and Sheppard (2012) among others, also …nd that the 5-minute sampling frequency outperforms most other realized volatility measures across multiple asset classes including equities and interest rates.”
• Forecasting the volatility of Bitcoin: The importance of jumps and structural breaks (2020, unknown): “5 Liu et al (2015) show that it is difficult to beat the standard 5-minute realized variance when forecasting volatility and the use of the 5-minute frequency is consistent with other studies such as Sévi (2014); Wen (2016), Behrendt and Schmidt (2018).”