Quantile Autoregression

By Roger W. Koenker, Zhijie Xiao. 2006

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

Citations in the corpus

Autoregressive Quantile Networks for Generative Modeling (2018, methodology): “…as in Equation 1, then the joint c.d.f. can be written as Furthermore, for τ joint = =1 τ i , we can write the joint-quantile function of X as This approach has been used previously by Koenker & Xiao (2006), who introduced a quantile autoregression model for quantile regression on time-series.”
Robust Spectral Analysis (2011, unknown): “Now let (εt)t∈Z be a sequence of independent Uniform(0, 1) variables and consider the second-order quantile autoregressive (QAR(2)) process of Koenker and Xiao (2006), Xt = β0(εt) + β1(εt)Xt−1 + β2(εt)Xt−2 = E(β1(ε0))Xt−1 + E(β2(ε0))Xt−2 + Yt, where Yt = β0(εt) + [β1(εt) − E(β1(ε0))]Xt−1 + [β2(εt)…”
Testing for Granger-causality in quantiles (2018, methodology): “…any√ T-consistent estimator of θT(τ ) satisfying somemild conditions (described in the next section) such as the quantile regression estimator by Koenker and Bassett (1978), the quantile autoregressive estimator by Koenker and Xiao (2006), and the CAViaR estimator by Engle and Manganelli (2004).”
Quantile Spectral Processes: Asymptotic Analysis and Inference (2014, unknown): “Skaug and Tjøstheim (1993) and Hong (1999) used joint distribution functions to test for serial independence at given lag.”
Causality in quantiles and dynamic stock return–volume relations (2009, background): “; see Koenker (2005) and Koenker and Xiao (2006).2 Given a linear model for conditional quantiles, testing (3) amounts to testing”
Stock Return Autocorrelations Revisited: A Quantile Regression Approach (2012, unknown): “The basic model is simply a first-order quantile autoregressive model (QAR(1), see Koenker and Xiao, 2006).”
Copula-Based Nonlinear Quantile Autoregression (2009, unknown): “Specification of, say, the copula function may be investigated based on parameter constancy over quantiles, along the lines of Koenker and Xiao (2006).”
Finite Sample Inference for Quantile Regression Models (2006, background): “Condition A6 is satisfied if (Yi,Xi, Zi, i = 1, ..., n) are i.i.d., but more generally allows rather rich dynamics, e.g. of the kinds considered in Koenker and Xiao (2004a) and Koenker and Xiao (2004b).”
A joint quantile and expected shortfall regression framework (2017, unknown): “Forecasts of the VaR and ES for the HAR model are obtained from the volatility forecasts and by assuming a Gaussian return distribution.”
GARCH Models (2010, unknown): “Dynamic VaR models were proposed by Koenker and Xiao (2006) (quantile autoregressive models), Engle and Manganelli (2004) (conditional autoregressive VaR), Gouriéroux and Jasiak (2008) (dynamic additive quantile).”
Quantile coherency: A general measure for dependence between cyclical economic variables (2015, unknown): “Koenker and Xiao (2006) and Zhu et al. (2018) established conditions that ensure that quantile regressions, similar to (6.”
ROBUST BACKTESTING TESTS FOR VALUE-AT-RISK MODELS (2011, methodology): “To test the performance of this and alternative VaR measures the Basel Accord (1996) set a statistical device denoted backtesting that consisted of out-of-sample comparisons between the actual trading results with internally model-generated risk measures.”
Quantile Correlations and Quantile Autoregressive Modeling (2012, unknown): “Based on the above two simulation experiments, we suggest using the direct method in the modified three-stage procedure, together with the bandwidth 0.6h B (also recommended by Koenker and Xiao 2006), for practical application.”
Quantile Connectedness: Modeling Tail Behavior in the Topology of Financial Networks (2022, methodology): “We follow the precedent of Koenker and Xiao (2006) and assume that the optimal lag order for the conditional mean model remains valid at every conditional quantile.”
Quantile Connectedness: Modelling Tail Behaviour in the Topology of Financial Networks (2018, unknown): “Following Koenker and Xiao (2006), we assume that the optimal lag order for the conditional mean model remains valid at every conditional quantile.”
Optimally Harnessing Inter-Day and Intra-Day Information for Daily Value-at-Risk Prediction (2013, unknown): “…returns rt satisfies that P (rt+1 ≤ V aRt+1,α | =t) = α, almost surely (a.s.), α ∈ (0, 1), ∀t ∈ Z. (15) This conditional moment restriction has been used in various theoretical papers (Christoffersen et al., 2001; Engle and Manganelli, 2004; Gourieroux and Jasiak, 2006; Koenker and Xiao, 2006).”