CONDITIONAL HETEROSKEDASTICITY IN ASSET RETURNS: A NEW APPROACH

By Daniel B. Nelson. 1991

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Citations in the corpus

Frontiers in VaR forecasting and backtesting (2016, methodology): “Among the many alternative asymmetric GARCH models, the Exponential GARCH (EGARCH) model of Nelson (1991) has often been shown as having an adequate performance when forecasting the VaR; see, for example, Bali and Theodosiou (2007).”
New frontiers for arch models (2002, methodology): “The most influential models were the first: the GARCH model of Bollerslev(1986), and the EGARCH of Nelson(1991).”
The Relationship between Stock Returns and Volatility in International Stock Markets (2005, methodology): “We are 1 The use of the stock return is consistent with Baillie and DeGennarro (1990), Nelson (1991), Choudhry (1996), and Lee et al. (2001), among others.”
The Reaction of Asset Prices to Macroeconomic Announcements in New EU Markets: Evidence from Intraday Data (2009, unknown): “Rather, we allow for generalized error distribution (GED) following Nelson (1991).”
Modelling and Estimation of Volatility Using ARCH/GARCH Models in Jordan's Stock Market (2016, methodology): “Moreover, to break the rigidness of the GARCH specifications, Nelson (1991) contributed a new model through Exponential GARCH (EGARCH) which supported that variance of return was affected differently by positive and negative excess returns.”
Dynamics of Trade-By-Trade Price Movements: Decomposition and Models (2003, unknown): “This is the familiar dynamic leverage effect (see Nelson (1991)) and so its presence is not surprising.”
Uncertainty and Business Cycles: Exogenous Impulse or Endogenous Response? (2015, unknown): “…nature have typically been associated with increases in returns on quintessential safe-haven assets, the archetypal example being gold.10 8See, for example, French, Schwert, and Stambaugh (1987); Schwert (1989); Nelson (1991); Campbell and Hentschel (1992); Engle and Ng (1993), and Whaley (2000).”
Modeling Conditional Volatility of the Indian Stock Markets (2005, methodology): “One method proposed to capture such asymmetric effects is Nelson’s (1991) EGARCH model : Log(ht)= ω + β.”
Small sample properties of GARCH estimates and persistence (2006, background): “7 See Nelson (1991), who raises this point.”
Value-at-risk and expected shortfall in cryptocurrencies’ portfolio: a vine copula–based approach (2019, unknown): “Although well-known models, such as GARCH and EGARCH (Nelson 1991), can be obtained as special cases of GAS model by using pð Þ as the Gaussian density and appropriate choices of St and ft.”
On Leverage in a Stochastic Volatility Model (2005, background): “In the ARCH literature, often the conditional variance is specified to be a function of the size as well as the sign of return (Glosten, Jagannathan and Runkle, 1993 and Nelson, 1991).”
Empirical analysis of GARCH models in value at risk estimation (2006, methodology): “Besides the GARCH model of Bollerslev (1986) and its variants (Engle and (Bollerslev, 1986; Nelson, 1991)) which can capture the time-varying volatility feature, several long memory GARCH models were proposed to incorporate the long memory volatility property in financial time series; see for exampl”