Measuring Downside Risk - Realised Semivariance

By Ole E. Barndorff-Nielsen, Silja Kinnebrock, N. Shephard. 2008

418 citations recorded by the source corpus.

Subfields: Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/bd5204b1cf4a1c5470d524a96bdd62fa9d9fc369

How researchers see this paper

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

In Finance

In Management Science and Operations Research

Citations in the corpus

Realized GARCH: a joint model for returns and realized measures of volatility (2012, neutral): “The structure of the QMLE analysis is very similar to that of the standard GARCH model, see Bollerslev and Wooldridge (1992), Lee and Hansen (1994), Lumsdaine (1996), Jensen and Rahbek (2004a,b), and Straumann and Mikosch (2006).”
Forecasting the realized volatility of the oil futures market: A regime switching approach (2017, result): “Our results also show that the same findings are valid for another volatility benchmark - realized kernel (RK) (e.g., Barndorff-Nielsen et al., 2008) and different forecasting windows.”
Forecasting Volatility of the U.S. Oil Market (2014, neutral): “The RV time series over the sample period is also stationary according to both the Dickey Fuller test and the Phillips-Perron test.3 Barndorff-Nielsen et al. (2008) found for that a decomposition of the RV into two separate measures; one based on positive and and another one on negative intra-daily…”
Exploiting the errors: A simple approach for improved volatility forecasting (2016, methodology): “Building on the semivariation measures of Barndorff-Nielsen et al. (2010), the SHAR model decomposes the total variation in the standard HAR model into the variation due to negative and positive intraday returns, respectively.”
Good Volatility, Bad Volatility: Signed Jumps and The Persistence of Volatility (2015, methodology): “We use this decomposition of realized volatility extensively in our empirical analysis below.6 Barndorff-Nielsen et al. (2010) show that, like realized variance, the limiting behavior of realized semivariance includes variation due to both the continuous part of the price process and the jump…”
Discrete-Time Volatility Forecasting With Persistent Leverage Effect and the Link With Continuous-Time Volatility Modeling (2012, neutral): “Recently, Barndorﬀ-Nielsen et al. (2010) proposed the realized semivariance as the sum of square negative returns to capture the impact on volatility of downward price pressures.”
Tails, Fears and Risk Premia (2010, positive): “These findings are consistent with the empirical results in Barndorff-Nielsen et al. (2010) and Todorov and Tauchen (2011). They also support a two-factor stochastic volatility model, as in, e.g., Todorov (2010), in which one of the factors is highly persistent, while the other factor is short lived”