Bridging the gap between the distribution of realized (ECU) volatility and ARCH modelling (of the Euro): the GARCH‐NIG model

By Lars Forsberg, Tim Bollerslev. 2002

123 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/55efb5b5f45bfb25b037a6c290c874155afc616c

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Citations in the corpus

Some Like it Smooth, and Some Like it Rough: Untangling Continuous and Jump Components in Measuring, Modeling, and Forecasting Asset Return Volatility (2003, unknown): “…These individual models for Jt and Ct could then be used discussed above, along with existing reduced form models for the realized volatility (e.g., Forsberg and Bollerslev, 2002), suggests that a Gaussian time series model for log(Ct ) should fit the data for most markets fairly well.”
Realized GARCH: a joint model for returns and realized measures of volatility (2012, unknown): “Such models were estimated by Engle (2002) who used the realized variance, see also Forsberg and Bollerslev (2002).”
Option Valuation with Observable Volatility and Jump Dynamics (2015, unknown): “3Dynamic models for daily returns and volatility using high-frequency information have been developed in Forsberg and Bollerslev (2002), Engle and Gallo (2006), Bollerslev, Kretschmer, Pigorsch, and Tauchen 2 carry over to option valuation.”
Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility (2007, background): “However, as argued by Barndorff-Nielsen and Shephard (2002a) and Forsberg and Bollerslev (2002), the log-normal volatility distributions may be closely approximated by Inverse Gaussian distributions, which are formally closed under temporal aggregation.”
The Generalized Hyperbolic Skew Student’s t-Distribution (2006, methodology): “…has been used repeatedly for financial applications, both as the conditional distribution of a GARCH-model (Andersson, 2001; Jensen and Lunde, 2001; Forsberg and Bollerslev, 2002; Venter and de Jongh, 2002) and as the unconditional †Address for correspondence: The Norwegian Computing Center, P.O.…”
American Option Pricing Using GARCH Models and the Normal Inverse Gaussian Distribution (2008, unknown): “If it is further assumed that the mean specification in (1) is identically equal to zero and the volatility specification in (2) is given by ht = ω + βht−1 + αR2t−1 we essentially obtain the model used for the ECU/USD exchange rate in Forsberg & Bollerslev (2002).”
A Discrete-Time Model for Daily S & P500 Returns and Realized Variations: Jumps and Leverage Effects (2009, unknown): “This same idea has previously been used in the context of modeling daily returns by Forsberg and Bollerslev (2002).”
Exponential GARCH Modeling With Realized Measures of Volatility (2016, unknown): “…using realized measures in the evaluation of volatility models, Engle (2002) explored the idea of including the realized variance as a predetermined variable in the GARCH equation, and found it to be highly significant and greatly enhancing the empirical ﬁt, see also Forsberg and Bollerslev (2002).”
No-Arbitrage Semi-Martingale Restrictions for Continuous-Time Volatility Models subject to Leverage Effects, Jumps and i.i.d. Noise: Theory and Testable Distributional Implications (2007, background): “…return variability are truly Gaussian, (8) This result provides a rationalization for why financial returns normalized by volatility forecasts from standard models exhibit fat tails; see, e.g., Bollerslev (1987), Nelson (1991), Chib, Nardari and Shephard (2002), and Forsberg and Bollerslev (2002).”
Properties of Range-Based Volatility Estimators (2012, unknown): “As was empirically shown by Andersen, Bollerslev, Diebold, Labys (2000), Andersen, Bollerslev, Diebold, Ebens (2001), Forsberg and Bollerslev (2002) and Thamakos and Wang (2003) on different data sets, standardized returns (returns divided by their standard deviations) are approximately normally…”