nep-for New Economics Papers
on Forecasting
Issue of 2007‒09‒02
two papers chosen by
Rob J Hyndman
Monash University

  1. Adaptive Pointwise Estimation in Time-Inhomogeneous Time-Series Models By Cizek, P.; Haerdle, W.; Spokoiny, V.
  2. The Information Content of Option Implied Volatility Surrounding the 1997 Hong Kong Stock Market Crash By Joseph K.W. Fung

  1. By: Cizek, P.; Haerdle, W.; Spokoiny, V. (Tilburg University, Center for Economic Research)
    Abstract: This paper offers a new method for estimation and forecasting of the linear and nonlinear time series when the stationarity assumption is violated. Our general local parametric approach particularly applies to general varying-coefficient parametric models, such as AR or GARCH, whose coefficients may arbitrarily vary with time. Global parametric, smooth transition, and changepoint models are special cases. The method is based on an adaptive pointwise selection of the largest interval of homogeneity with a given right-end point by a local change-point analysis. We construct locally adaptive estimates that can perform this task and investigate them both from the theoretical point of view and by Monte Carlo simulations. In the particular case of GARCH estimation, the proposed method is applied to stock-index series and is shown to outperform the standard parametric GARCH model.
    Keywords: adaptive pointwise estimation;autoregressive models;conditional heteroscedasticity models;local time-homogeneity
    JEL: C13 C14 C22
    Date: 2007
  2. By: Joseph K.W. Fung (Hong Kong Baptist University)
    Abstract: This study examines the information conveyed by options, and examines their implied volatility at the time of the 1997 Hong Kong stock market crash. The paper determines the efficiency of implied volatility as a predictor of future volatility by comparing it to other candidate leading indicators. These include volume and open interest of index options and futures, as well as the arbitrage basis of index futures. Using monthly, non-overlapping data, the study reveals that implied volatility is superior to those variables in forecasting future realized volatility. The paper also demonstrates that a simple signal extraction model could have produced useful warning signals prior to periods of extreme volatility.
    Date: 2005–12

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