Risk Analysis

Alexander, Bob

doi:10.4135/9781452275956.n288

Cited by 13 publications

(27 citation statements)

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“…OLS estimation only provides a prediction of the conditional mean, but finding several quantile regression lines gives more comprehensive idea of the joint distribution of the data. Alexander (2008) pointed out that LQR is a natural extension of OLS where the optimization objective of minimizing the residual sum of squares is replaced by an asymmetric objective. We illustrate the LQR regression model in terms of dependent variable r RVt and independent variable r IVIXt .…”

Section: Linear Quantile Regressionmentioning

confidence: 99%

The information content of implied volatility index (India VIX)

Shaikh

Padhi

2013

Glob Bus Perspect

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This paper examines the predictive ability of India VIX as the best forecast of realized return volatility. This study takes into account the implied volatility index (India VIX), also known as the investors fear gauge index. We have employed OLS, 2SLS procedure and quantile regression to study the predictive power of implied volatility index. Empirical results show that India VIX is the best estimate of future realized return volatility. The Hausman specification test analyzes that implied volatility is measured with the errors; consequently, instrumental variable technique is used. The 2SLS estimation procedure shows that 2SLS estimates are more consistent than the simple OLS. Finally, the 2SLS procedure explains that historical volatility does not contain valuable information what already contained in the implied volatility. The implied volatility best subsumes the marketwide information to explain the future volatility. This study explains that Asian implied volatility indices also subsumes the information regarding the future volatility like the US and European volatility indices.

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Section: Linear Quantile Regressionmentioning

confidence: 99%

The information content of implied volatility index (India VIX)

Shaikh

Padhi

2013

Glob Bus Perspect

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“…Esta parametrização BEKK permite a estimação de menos parâmetros, comparado com as representações VECH tradicionais. Outra vantagem, segundo Alexander (2008) e Francq e Zakoian (2010), é a não imposição sobre a forma das covariâncias condicionais, como por exemplo, nos modelos de correlação condicional constante (CCC).…”

Section: Estimando O Arcabouço Linearizado De Depreciação Cambialunclassified

Previsão do câmbio real-dólar sob um arcabouço de apreçamento de ativos

Matos¹,

Beviláqua²,

Filho³

2012

Rev. Bras. Econ.

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Este artigo estima via componentes principais uma série temporal para o Fator Estocástico de Desconto contendo informações sobre os retornos de fundos cambiais e de renda fixa operando no Brasil, visando utilizá-lo na modelagem do câmbio nominal mensal Real Brasileiro/Dólar Americano através de um Generalized Autoregressive Conditional Heteroskedasticity-in-Mean (GARCH-M) bivariado. Este exercício empírico baseado na teoria de apreçamento de ativos segue metodologicamente Chong et alii (2002) e Da Costa et alii (2010), sendo possível evidenciar um erro de previsão de 5,27% para depreciação cambial e um acerto do sinal da variação cambial em 57,5% dos meses, durante o período de janeiro de 2000 a dezembro de 2009. Estes resultados sugerem à literatura que não se deve desprezar a variação temporal dos momentos condicionais de segunda ordem.
We use principal components to extract a time series for the Stochastic Discount Factor based on returns on Brazilian mutual funds that invest in fixed-income securities and foreign currency. This factor is then used to model the Brazilian Real/American Dolar Exchange rate using a Bivariate Generalized Autoregressive Conditional Heteroskedasticity-in-Mean (GARCH-M). Our empirical exercise based on asset pricing theory follows the methodology used in Chong, Chung and Ahmad (2002) and Da Costa et alii (2010). Our foreign currency depreciation forecasting error is 5,27% and we are able to say the correct variation sign in 57,5% of the time, during the January 2000 -December 2009 period. These results suggest to the literature that one should not omit the time varying second order conditional moments

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“…Estimation and inference in the OGARCH model are discussed in detail in Alexander (2001Alexander ( , 2008. The OGARCH model is also sometimes referred to as a principal component -24-MGARCH model.…”

Section: Nm-garch (Normal Mixture Garch)mentioning

confidence: 99%

“…Numerous surveys of the burgeon ARCH literature also exist; e.g., Andersen and Bollerslev (1998), Andersen, Bollerslev, Christoffersen and Diebold (2006), Bauwens, Laurent and Rombouts (2006), Bera and Higgins (1993), Bollerslev, Chou and Kroner (1992), Bollerslev, Engle and Nelson (1994), Degiannakis and Xekalaki (2004), Diebold (2004), Diebold and Lopez (1995), Engle (2001Engle ( , 2004, Engle and Patton (2001), Pagan (1996), Palm (1996), and Shephard (1996). Moreover, ARCH models have now become standard textbook material in econometrics and finance as exemplified by, e.g., Alexander (2001Alexander ( , 2008, Brooks (2002), Campbell, Lo and MacKinlay (1997), Chan (2002), Christoffersen (2003), Enders (2004), Franses and van Dijk (2000), Gourieroux and Jasiak (2001), Hamilton (1994), Mills (1993), Poon (2005), Singleton (2006), Stock and Watson (2005), Tsay (2002), and Taylor (2004). So, why another survey type paper?…”

Section: Introductionmentioning

confidence: 99%

Volatility and predictability of the Russian ruble exchange rate

Borochkin¹

2017

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Risk Analysis

Cited by 13 publications

References 0 publications

The information content of implied volatility index (India VIX)

The information content of implied volatility index (India VIX)

Previsão do câmbio real-dólar sob um arcabouço de apreçamento de ativos

Volatility and predictability of the Russian ruble exchange rate

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