The Nasdaq Volatility Index During and After the Bubble

Simon, David P.

doi:10.3905/jod.2003.319213

Cited by 95 publications

(66 citation statements)

References 18 publications

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“…Simon (2003) suggested that daily variations of VXN implied volatility index respond asymmetrically to positive and negative returns variations of the Nasdaq 100 Index both during and after the bursting of the Internet bubble respectively. Similarly, Giot (2005) also showed that there is a strong negative relationship between contemporary variations of two implied volatility indices VIX, VXN and returns of the underlying stocks indices S & P 100 and Nasdaq 100.…”

Section: Literature Reviewmentioning

confidence: 99%

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Dynamics of the Relationship between Implied Volatility Indices and Stock Prices Indices: The Case of European Stock Markets

Emna

Myriam

2017

Asian Economic and Financial Review

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Section: Literature Reviewmentioning

confidence: 99%

“…In particular, implied volatility indices are often considered to be forward-looking measures of the expected market volatility (see Whaley (1993); Simon (2003); Giot (2005) etc..).…”

Section: Introductionmentioning

confidence: 99%

Dynamics of the Relationship between Implied Volatility Indices and Stock Prices Indices: The Case of European Stock Markets

Emna

Myriam

2017

Asian Economic and Financial Review

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“…The VIX, the most well-known volatility index of the US market, plays a successful role as a market indicator and fear gauge measure. Numerous articles that examine the fitting and forecasting ability of the US' implied volatility index demonstrate its superiority over historical volatilities (Banerjee et al, 2007;Becker et al, 2007;Carr and Wu, 2006;Corrado and Miller, 2005;Frijns et al, 2010;Jiang and Tian, 2007;Konstantinidi et al, 2008;Simon, 2003). Some studies also investigate implied volatility indices for quantifying market risk and for risk management purposes (Giot, 2005;Kim and Ryu, 2015b).…”

Section: Introductionmentioning

confidence: 99%

Effects of the US Stock Market Return and Volatility on the VKOSPI

2015

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The KOSPI (Korea Composite Stock Price Index) 200 options are one of the most actively traded derivatives in the world. This paper empirically examines (a) the statistical properties of the Korea's representative implied volatility index (VKOSPI) derived from the KOSPI 200 options and (b) the macroeconomic and financial variables that can predict the implied volatility process of the index, using augmented heterogeneous autoregressive (HAR) models with exogenous covariates. The results suggest that the elaborate HAR framework is proficient at describing the dynamics of the VKOSPI and that some domestic macroeconomic variables explain the VKOSPI. More importantly, we find that the stock market return and implied volatility index of the US market (i.e., the S&P 500 spot return and the VIX from the S&P 500 options) play a key role in predicting the level of the VKOSPI and explaining its dynamics, and their explanatory power dominates that of domestic macro-finance variables. Further, while the domestic stock market return does not predict the VKOSPI, the US stock market return does so rather well. When two global factors, both the US stock market return and the US implied volatility index, are incorporated into the HAR framework, the model exhibits the best performance in terms of both in-sample fitting and out-of-sample forecasting ability. JEL C22 C50 G14 G15

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“…In particular, mean VIBEX-NEW MAPE in 1998-2000and 2003-2006 is 10 more units in percentage than the ones for BMK22. Nevertheless, we have to remember that BMK22 is the past value of DT22 (future IBEX-35 volatility), and it is clear that DT22 shows persistence over time, which could explain this result.…”

Section: Volatility Indicators and Future Realized Volatilitymentioning

confidence: 99%

“…As we mentioned, Whaley (2000), Giot (2005), Simon (2003), and Skiadopoulos (2004) find that, for different financial markets, the stock market return magnitude, associated with a given change in the volatility index, depends on the sign of this change. To search for possible evidence of an asymmetry, we extend model (9) to model (10) by defining a dummy variable D …”

Section: The Return-volatility Relationship: Statistical Analysismentioning

confidence: 99%

The information content in a volatility index for Spain

González-Pérez

Novales

2010

SERIEs

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A model-free methodology is used for the first time to estimate a daily volatility index (VIBEX-NEW) for the Spanish financial market. We use a public data set of daily option prices to compute this index and show that daily changes in VIBEX-NEW display a negative, tight contemporaneous relationship with IBEX daily returns, contrary to other common volatility indicators, as an implied volatility indicator or a GARCH(1,1) conditional volatility model. This relationship is approximately symmetric to the sign on VIBEX-NEW changes and asymmetric to the IBEX-35 returns sign, which make it clearly a suitable volatility index for the Spanish stock market. We also examine the relationship between current VIBEX-NEW and future IBEX-35 volatility. Our results suggest that VIBEX-NEW can be used to produce IBEX-35 volatility forecasts at least as good as historical and conditional volatility measures. A feasible volatility correction methodology is proposed to achieve it.Keywords Model-based volatility index · Model-free volatility index · Risk · Leverage effect · Forecasting volatility JEL Classification C53 · G13 · G15

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The Nasdaq Volatility Index During and After the Bubble

Cited by 95 publications

References 18 publications

Dynamics of the Relationship between Implied Volatility Indices and Stock Prices Indices: The Case of European Stock Markets

Dynamics of the Relationship between Implied Volatility Indices and Stock Prices Indices: The Case of European Stock Markets

Effects of the US Stock Market Return and Volatility on the VKOSPI

The information content in a volatility index for Spain

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