Asymmetric Predictability of Conditional Variances

Conrad, Jennifer; Gültekin, Mustafa; Kaul, Gautam

doi:10.1093/rfs/4.4.597

Cited by 180 publications

(129 citation statements)

References 29 publications

Supporting

Mentioning

109

Contrasting

Unclassified

Order By: Relevance

“…Elegimos la cartera equiponderada como cartera de mercado porque es la cartera empleada habitualmente en los trabajos de valoración de activos (4) (véase, por ejemplo, Rubio [1988] y Nieto [2004] para el mercado de capitales español) y los resultados usando las carteras equiponderada y ponderada son muy similares (véase, por ejemplo, Rubio [1988]). Y agrupamos los activos en carteras por sector y tamaño para captar el diferente impacto de los distintos efectos por sector (Moskowiz y Grinblatt [1999] observan un componente momentum específico a nivel industria) y por tamaño (Conrad, Gultekin y Kaul [1991] defienden una predictibilidad de la volatilidad condicional asimétrica de empresas grandes a pequeñas).…”

Section: Carteras De Mercado Y De Activos Factores Y Variable Instruunclassified

Los factores tamaño,book-to-marketymomentumen el mercado de capitales español: explicaciones racionales y efecto en la formación del precio

Font-Belaire

Grau-Grau

2007

Spanish Journal of Finance and Accounting / Revista Española de

View full text Add to dashboard Cite

Los factores tamaño, book-to-marketRESUMEN ¿Pueden el efecto tamaño, book-to-market y momentum explicar el riesgo de los activos en el Mercado de Capitales Español? Este artículo intenta analizar la validez de las dos hipótesis racionales estudiadas por la literatura y el impacto de estos efectos sobre los riesgos y primas al riesgo de mercado. Nuestros resultados destacan la importancia del análisis condicional de los riesgos y primas de mercado para capturar la naturaleza de estos efectos, y dan soporte a las explicaciones racionales de los mismos como predictores del ciclo económico y cuantificadores del riesgo asociado a las estrategias de inversión de las empresas.PALABRAS CLAVE Anomalías; Riesgos y primas al riesgo variables en el tiempo; Rendimientos variables en el tiempo.ABSTRACT Can size, book-to-market and momentum be risk factors that explain the returns in the Spanish Capital Market? This article attempts to get an answer to this issue studying two competitive rational hypothesis, and their impact on beta and risk premium. Our findings stress the importance of the role played by the conditional analysis of beta and risk prices as one way to capture those effects, and give support to rational explanations based on the predictive power of size, book-to-market and momentum for predict economic growth, and explaining risks associated to the firms' strategies of investment.

show abstract

Section: Carteras De Mercado Y De Activos Factores Y Variable Instruunclassified

Los factores tamaño,book-to-marketymomentumen el mercado de capitales español: explicaciones racionales y efecto en la formación del precio

Font-Belaire

Grau-Grau

2007

Spanish Journal of Finance and Accounting / Revista Española de

View full text Add to dashboard Cite

show abstract

“…As noted by Barndorff-Nielsen and Shephard (2004), failing to remove the jump component of quadratic volatility is likely to diminish the serial correlation coefficient of volatility. 27 Indeed, the average serial correlation of the bi-power covariation measures (detailed below), essentially quadratic volatility measures purged of return shocks, have much higher serial correlation coefficients with the average across all variances and covariances increasing to 0.55. The Geweke and Porter-Hudak (1983, hereafter GPH) statistics estimate the degree of fractional integration.…”

Section: Constructing the Realized Covariance Matrixmentioning

confidence: 99%

Multivariate Realized Stock Market Volatility

Bauer

Vorkink

2006

SSRN Journal

View full text Add to dashboard Cite

We present a new matrix-logarithm model of the realized covariance matrix of stock returns. The model uses latent factors which are functions of both lagged volatility and returns. The model has several advantages: it is parsimonious; it does not require imposing parameter restrictions; and, it results in a positive-definite covariance matrix. We apply the model to the covariance matrix of size-sorted stock returns and find that two factors are sufficient to capture most of the dynamics.We also introduce a new method to track an index using our model of the realized volatility covariance matrix.

show abstract

“…Studies document volatility spillover from large to small firms (Conrad, Gultekin andKaul 1991, andReyes 2001), although bad news may cause spillover in the reverse direction as well (Pardo and Torro 2003).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Volatility spillover patterns appear to be widespread in financial markets. There is evidence for spillovers between equity markets (see for example Hamao, Masulis andNg 1990, andIto 1994), bond markets (Christiansen 2003), futures contracts (Abhyankar 1995, Pan andHsueh 1998), exchange rates Lin 1990, andBaillie andBollerslev 1990), equities and exchange rates (Apergis and Rezitis 2001), various industries (Kaltenhauser 2002), size-sorted portfolios (Conrad, Gultekin and Kaul 1991), commodities (Apergis and Rezitis 2003), and swaps (Eom, Subrahmanyam and Uno 2002). Despite the interest that investors might have in these pervasive spillover effects, we are not aware of any study that investigates the question of their impact on efficient asset allocation.…”

Section: Introductionmentioning

confidence: 99%

Valuing volatility spillovers

Milunovich

Thorp

2006

Global Finance Journal

View full text Add to dashboard Cite

We show that volatility spillovers are large enough to matter to investors. We demonstrate that standard deviations of returns to mean-variance portfolios of European equities fall by 1-1.5% at daily, weekly, and monthly rebalancing horizons when volatility spillovers are included in covariance forecasts. We estimate the conditional second moment matrix of (synchronized) daily index returns for the London, Frankfurt and Paris stock markets via two asymmetric dynamic conditional correlation models (A-DCC): the unrestricted model includes volatility spillovers and the restricted model does not. We combine covariance forecasts from the restricted and unrestricted models with a wide range of assumed returns relatives via a polar coordinates method, and compute out-of-sample realized portfolio returns and variances for testing. Diebold-Mariano tests confirm that most risk reductions are statistically significant. Stochastic dominance tests indicate that portfolios accounting for volatility spillover would be preferred by risk averse agents.

show abstract

Asymmetric Predictability of Conditional Variances

Cited by 180 publications

References 29 publications

Los factores tamaño,book-to-marketymomentumen el mercado de capitales español: explicaciones racionales y efecto en la formación del precio

Los factores tamaño,book-to-marketymomentumen el mercado de capitales español: explicaciones racionales y efecto en la formación del precio

Multivariate Realized Stock Market Volatility

Valuing volatility spillovers

Contact Info

Product

Resources

About