Yield-factor volatility models

Pérignon, Christophe; Smith, Daniel R.

doi:10.1016/j.jbankfin.2006.11.016

Cited by 14 publications

(16 citation statements)

References 56 publications

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“…When volatility is allowed to switch between low-and high-volatility regimes, the estimated elasticity increases significantly for each of the three factors. A similar phenomenon is reported by Pérignon and Smith (2007).…”

Section: Midiivariate Model Estimatessupporting

confidence: 88%

“…However, the elasticity parameter from the levels equation (YL = 1.52) is clearly lower than the analogous estimate under the univariate equation (1.95). Pérignon and Smith (2007) report a similar reduction in elasticity for US interest rates. Table 3 also provides evidence that the volatility of the slope and curvature factors is sensitive to the short-rate level (Fu)'' Clearly, explicitly modelling contemporaneous relationships between the yield factors is relevant to the ongoing debate over the magnitude of the level effect in short-term interest rates.…”

Section: Midiivariate Model Estimatesmentioning

confidence: 69%

“…Christiansen and Lund (2002). Brandt and Chapman (2003), and Pérignon and Smith (2007) for similar proxies of yield factors. Diebold and Li (2006) use a similar definition of factors using iho Nelson and Siegel (1987) model of the yield curve.…”

Section: Datamentioning

confidence: 99%

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An Empirical Investigation of the Level Effect in Australian Interest Rates

Gray

Smith

2008

Australian Journal of Management

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An extensive literature examines the dynamics of interest rates, with particular attention given to the positive relationship between interest-rate volatility and the level of interest rates-the so-called level effect. This paper examines the interaction bet^'een the estimated level effect and competing parameterisations of interest-rate volatility for the Australian yield curve. We adopt a new methodology that estimates elasticity in a multivariate setting that explicitly accommodates the correlations that exist between various yield factors. Results show that significant correlations exist betM>een the residuals of yield factors and that such correlations do indeed impact on model estimates. Within the multivariate setting, the level of the short rate is shown to be a crucial determinant of the conditional volatility of all three yield factors. Measures of model fit suggest that, in addition to the usual level effect, the incorporation of GARCH effects and possible regime shifts is important.

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Section: Midiivariate Model Estimatessupporting

confidence: 88%

Section: Midiivariate Model Estimatesmentioning

confidence: 69%

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An Empirical Investigation of the Level Effect in Australian Interest Rates

Gray

Smith

2008

Australian Journal of Management

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“…It is currently used in the literature to test the specification of both univariate and multivariate GARCH models (see, e.g. Brenner et al, 1996 who test univariate GARCH models andPe´rignon andSmith, 2007 for tests of multivariate GARCH models).…”

Section: Diagnostic Testsmentioning

confidence: 99%

“…Parameter instability is evidence of model miss-specification and standard econometric theory no longer applies. It is particularly curious that relatively few structural break tests are conducted given the popularity of regime-shifting GARCH models (Gray, 1996;Dueker, 1997;Klaassen, 2002;Haas et al, 2004;Pe´rignon and Smith, 2007).…”

Section: Introductionmentioning

confidence: 99%

Testing for structural breaks in GARCH models

Smith¹

2008

Applied Financial Economics

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We study the ability of traditional diagnostic tests and LM and CUSUM structural break tests to detect a range of different types of breaks in GARCH models. We find that Wooldridge's (1990) robust LM tests for autocorrelation and ARCH have no power to detect structural breaks in GARCH models. However, CUSUM- and LM-based structural break tests have excellent size when the data is Gaussian, but the CUSUM tests tend to overreject even in quite large samples when returns have fat tails. However, the LM-based tests have approximately the correct size and exhibit impressive power to detect a range of breaks in the dynamics of conditional volatility. We apply these tests to a range of financial time series using returns starting only in 1990 and find that many GARCH models that pass standard specification tests fail the structural break tests.

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Estimation of volatility causality in structural autoregressions with heteroskedasticity using independent component analysis

Hải

2017

Stat Papers

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Yield-factor volatility models

Cited by 14 publications

References 56 publications

An Empirical Investigation of the Level Effect in Australian Interest Rates

An Empirical Investigation of the Level Effect in Australian Interest Rates

Testing for structural breaks in GARCH models

Estimation of volatility causality in structural autoregressions with heteroskedasticity using independent component analysis

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