Bootstrap Methods for Time Series

Härdle, Wolfgang Karl; Horowitz, Joel L.; Kreiß, Jens-Peter

doi:10.1111/j.1751-5823.2003.tb00485.x

Cited by 215 publications

(121 citation statements)

References 60 publications

(83 reference statements)

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“…Given a sample of size T, the idea of the sieve bootstrap is to fi t a sequence of AR models of order p(T), where p(T) → ∞ as T → ∞, and then to construct a 'new' bootstrap realization generated from the re-sampled residuals (Grenander, 1981). The asymptotic properties of the sieve bootstrap are studied by Bühlmann (1997), Bickel and Bühlmann (1999), Härdle et al (2003), Politis (2003) and Lahiri (2003). Recently, the sieve bootstrap has been gaining popularity for constructing prediction intervals for linear processes.…”

Section: Sieve Bootstrap Procedures Of Garch(p Q) Processmentioning

confidence: 99%

Computationally efficient bootstrap prediction intervals for returns and volatilities in ARCH and GARCH processes

Chen

Gel

Balakrishna

et al. 2010

Journal of Forecasting

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We propose a novel, simple, effi cient and distribution-free re-sampling technique for developing prediction intervals for returns and volatilities following ARCH/GARCH models. In particular, our key idea is to employ a Box-Jenkins linear representation of an ARCH/GARCH equation and then to adapt a sieve bootstrap procedure to the nonlinear GARCH framework. Our simulation studies indicate that the new re-sampling method provides sharp and well calibrated prediction intervals for both returns and volatilities while reducing computational costs by up to 100 times, compared to other available re-sampling techniques for ARCH/GARCH models. The proposed procedure is illustrated by an application to Yen/U.S. dollar daily exchange rate data.

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Section: Sieve Bootstrap Procedures Of Garch(p Q) Processmentioning

confidence: 99%

Computationally efficient bootstrap prediction intervals for returns and volatilities in ARCH and GARCH processes

Chen

Gel

Balakrishna

et al. 2010

Journal of Forecasting

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“…Therefore, the result for block length equal to 4 will be reported. See Härdle, Horowitz and Kreiss (2003) and references therein for details of block bootstrap method for time series.…”

Section: Finite Sample Properties Of Local Historical Averagementioning

confidence: 99%

Forecasting Equity Premium: Global Historical Average Versus Local Historical Average and Constraints

Lee

Ullah

2015

Journal of Business & Economic Statistics

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The equity premium, return on equity minus return on risk-free asset, is expected to be positive. We consider imposing such positivity constraint in local historical average (LHA) in nonparametric kernel regression framework. It is also extended to the semiparametric single index model when multiple predictors are used. We construct the constrained LHA estimator via an indicator function which operates as 'model-selection' between the unconstrained LHA and the bound of the constraint (zero for the positivity constraint). We smooth the indicator function by bagging (Breiman 1996a), which operates as 'model-averaging' and yields a combined forecast of unconstrained LHA forecasts and the bound of the constraint. The local combining weights are determined by the probability that the constraint is binding. Asymptotic properties of the constrained LHA estimators without and with bagging are established, which show how the positive constraint and bagging can help reduce the asymptotic variance and mean squared errors. Monte Carlo simulations are conducted to show the finite sample behavior of the asymptotic properties. In predicting U.S. equity premium, we show that substantial nonlinearity can be captured by LHA and that the local positivity constraint can improve out-of-sample prediction of the equity premium.

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“…6 The block bootstrap was first introduced by Künsch [24]. A recent survey of alternative time series bootstrapping methods is Härdle et al [23].…”

Section: The Block Bootstrapmentioning

confidence: 99%

Intra-Financial Lending, Credit, and Capital Formation

Montecino

Epstein

2014

SSRN Journal

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This paper examines the e↵ects of intra-financial lending -claims between financial institutions -on aggregate investment and credit to the non-financial sector in the United States. Building on Montecino, Epstein, and Levina (2014) we document a large growth in intra-financial assets beginning in the early 1980s. Using a vector autoregression model, we find that intra-financial lending is negatively related to gross capital formation and present evidence that this operates through a credit channel. However, we also find evidence of a structural break around the year 2000. Rolling impulse response functions suggest the presence of two alternative regimes over the post-war period: a "capital diversion" regime in which credit to the non-financial sector and intra-financial lending are substitutes, as well as a financial bubble regime in which credit and intra-financial lending are complements. In the latter case, credit to the non-financial sector and intra-financial lending appear to feed each other in an unsustainable bubble process. In neither case do we find macroeconomic evidence in support of the financial e ciency view that increased intra-financial lending reflect financial innovations associated with more e cient risk bearing, liquidity provision, and credit allocation.JEL classification: G01, G10, G20

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Bootstrap Methods for Time Series

Cited by 215 publications

References 60 publications

Computationally efficient bootstrap prediction intervals for returns and volatilities in ARCH and GARCH processes

Computationally efficient bootstrap prediction intervals for returns and volatilities in ARCH and GARCH processes

Forecasting Equity Premium: Global Historical Average Versus Local Historical Average and Constraints

Intra-Financial Lending, Credit, and Capital Formation

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