nep-ets New Economics Papers
on Econometric Time Series
Issue of 2021‒04‒26
three papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Under the same (Chole)sky: DNK models, timing restrictions and recursive identification of monetary policy shocks By Giovanni Angelini; Marco M. Sorge
  2. GARCH-UGH: A bias-reduced approach for dynamic extreme Value-at-Risk estimation in financial time series By Hibiki Kaibuchi; Yoshinori Kawasaki; Gilles Stupfler
  3. Semiparametric GARCH models with long memory applied to Value at Risk and Expected Shortfall By Sebastian Letmathe; Yuanhua Feng; André Uhde

  1. By: Giovanni Angelini; Marco M. Sorge
    Abstract: Recent structural VAR studies of the monetary transmission mechanism have voiced concerns about the use of recursive identification schemes based on short-run exclusion restrictions. We trace out the effects on impulse propagation of informational constraints embodying classical Cholesky-timing restrictions in otherwise standard Dynamic New Keynesian (DNK) models. By reinforcing internal propagation mechanisms and enlarging a model's equilibrium state space, timing restrictions may produce a non-trivial moving average component of the equilibrium representation, making finite order VARs a poor approximation of true adjustment paths to monetary impulses, albeit correctly identified. They can even serve as an independent source of model-based nonfundamentalness, thereby hampering shock identification via VAR methods. This notwithstanding, restricted DNK models are shown to feature (i) invertible equilibrium representations for the observables and (ii) fast-converging VAR coefficient matrices under empirically tenable parameterizations. This alleviates concerns about identification and lag truncation bias: low-order Cholesky-VARs do well at uncovering the transmission of monetary impulses in a truly Cholesky world.
    JEL: C3 E3
    Date: 2021–04
  2. By: Hibiki Kaibuchi; Yoshinori Kawasaki; Gilles Stupfler
    Abstract: The Value-at-Risk (VaR) is a widely used instrument in financial risk management. The question of estimating the VaR of loss return distributions at extreme levels is an important question in financial applications, both from operational and regulatory perspectives; in particular, the dynamic estimation of extreme VaR given the recent past has received substantial attention. We propose here a two-step bias-reduced estimation methodology called GARCH-UGH (Unbiased Gomes-de Haan), whereby financial returns are first filtered using an AR-GARCH model, and then a bias-reduced estimator of extreme quantiles is applied to the standardized residuals to estimate one-step ahead dynamic extreme VaR. Our results indicate that the GARCH-UGH estimates are more accurate than those obtained by combining conventional AR-GARCH filtering and extreme value estimates from the perspective of in-sample and out-of-sample backtestings of historical daily returns on several financial time series.
    Date: 2021–04
  3. By: Sebastian Letmathe (Paderborn University); Yuanhua Feng (Paderborn University); André Uhde (Paderborn University)
    Abstract: In this paper new semiparametric GARCH models with long memory are in- troduced. The estimation of the nonparametric scale function is carried out by an adapted version of the SEMIFAR algorithm (Beran et al., 2002). Recurring on the revised recommendations by the Basel Committee to measure market risk in the banks' trading books (Basel Committee on Banking Supervision, 2013), the semi- parametric GARCH models are applied to obtain rolling one-step ahead forecasts for the Value at Risk (VaR) and Expected Shortfall (ES) for market risk assets. In addition, standard regulatory traffic light tests (Basel Committee on Banking Supervision, 1996) and a newly introduced traffic light test for the ES are carried out for all models. The practical relevance of our proposal is demonstrated by a comparative study. Our results indicate that semiparametric long memory GARCH models are an attractive alternative to their conventional, parametric counterparts.
    Keywords: Semiparametric, long memory, GARCH models, forecasting, Value at Risk, Expected Shortfall, traffic light test, Basel Committee on Banking Supervision
    JEL: C14 C51 C52 G17 G32
    Date: 2021–04

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