nep-ets New Economics Papers
on Econometric Time Series
Issue of 2012‒10‒20
eight papers chosen by
Yong Yin
SUNY at Buffalo

  1. The macroeconomic forecasting performance of autoregressive models with alternative specifications of time-varying volatility By Todd E. Clark; Francesco Ravazzolo
  2. Asymmetry with respect to the memory in stock market volatilities By Lönnbark, Carl
  3. Occurrence of long and short term asymmetry in stock market volatilities By Lönnbark, Carl
  4. Can we use seasonally adjusted indicators in dynamic factor models? By Maximo Camacho; Yuliya Lovcha; Gabriel Perez-Quiros
  5. Detecting outliers in time series By Ardelean, Vlad
  6. Stock returns and implied volatility: A new VAR approach By Lee, Bong Soo; Ryu, Doojin
  7. Regime switches in the volatility and correlation of financial institutions By Kris Boudt; Jon Danielsson; Siem Jan Koopman; Andre Lucas
  8. New Non-Linearity Test to Circumvent the Limitation of Volterra Expansion By Bai, Zhidong; Hui, Yongchang; Wong, Wing-Keung

  1. By: Todd E. Clark (Federal Reserve bank of Cleveland); Francesco Ravazzolo (Norges Bank (Central Bank of Norway) and BI Norwegian Business School)
    Abstract: This paper compares alternative models of time-varying macroeconomic volatility on the basis of the accuracy of point and density forecasts of macroeconomic variables. In this analysis, we consider both Bayesian autoregressive and Bayesian vector autoregressive models that incorporate some form of time-varying volatility, precisely stochastic volatility (both with constant and time-varying autoregressive coefficients), stochastic volatility following a stationary AR process, stochastic volatility coupled with fat tails, GARCH and mixture of innovation models. The comparison is based on the accuracy of forecasts of key macroeconomic time series for real-time post War-II data both for the United States and United Kingdom. The results show that the AR and VAR specifications with widely-used stochastic volatility dominate models with alternative volatility specifications, in terms of point forecasting to some degree and density forecasting to a greater degree.
    Keywords: Stochastic volatility, GARCH, forecasting
    JEL: E17 C11 C53
    Date: 2012–10–09
  2. By: Lönnbark, Carl (Department of Economics, Umeå University)
    Abstract: The empirically most relevant stylized facts when it comes to modeling time varying financial volatility are the asymmetric response to return shocks and the long memory property. Up till now, these have largely been modeled in isolation though. To more flexibly capture asymmetry also with respect to the memory structure we introduce a new model and apply it to stock market index data. We find that, although the effect on volatility of negative return shocks is higher than for positive ones, the latter are more persistent and relatively quickly dominate negative ones.
    Keywords: Financial econometrics; GARCH; news impact; nonlinear; risk prediction; time series
    JEL: C12 C51 C58 G10 G15
    Date: 2012–10–03
  3. By: Lönnbark, Carl (Department of Economics, Umeå University)
    Abstract: We introduce the notions of short and long term asymmetric effects in volatilities. With short term asymmetry we mean the conventional one, i.e. the asymmetric response of current volatility to the most recent return shocks. However, there may be asymmetries in the way the effect of past return shocks propagate over time as well. We refer to this as long term asymmetry. We propose a model that enables the study of such a feature. In an empirical application using stock market index data we found evidence of the joint presence of short and long term asymmetric effects.
    Keywords: Financial econometrics; GARCH; memory; nonlinear; risk prediction; time series
    JEL: C22 C51 C58 G15 G17
    Date: 2012–10–03
  4. By: Maximo Camacho (Universidad de Murcia); Yuliya Lovcha (Universidad de Navarra); Gabriel Perez-Quiros (Banco de España)
    Abstract: We examine the short-term performance of two alternative approaches to forecasting using dynamic factor models. The first approach extracts the seasonal component of the individual indicators before estimating the dynamic factor model, while the alternative uses the nonseasonally adjusted data in a model that endogenously accounts for seasonal adjustment. Our Monte Carlo analysis reveals that the performance of the former is always comparable to or even better than that of the latter in all the simulated scenarios. Our results have important implications for the factor models literature because they show that the common practice of using seasonally adjusted data in this type of model is very accurate in terms of forecasting ability. Drawing on fi ve coincident indicators, we illustrate this result for US data
    Keywords: Dynamic factor models, seasonal adjustment, short-term forecasting
    JEL: E32 C22 E27
    Date: 2012–10
  5. By: Ardelean, Vlad
    Abstract: In parametric time series analysis there is the implicit assumption of no aberrant observations, so-called outliers. Outliers are observations that seem to be inconsistent with the assumed model. When these observations are included to estimate the model parameters, the resulting estimates are biased. The fact that markets have been affected by shocks (i.e. East Asian crisis, Dot-com bubble, sub-prime mortgage crisis) make the assumption that no outlier is present questionable. This paper addresses the problem of detecting outlying observations in time series. Outliers can be understood as a short transient change of the underlying parameters. Unfortunately tests designed to detect structural breaks cannot be used to find outlying observations. To overcome this problem a test normally used to detect structural breaks is modified. This test is based on the cumulative sum (CUSUM) of the squared observations. In comparison to a likelihood-ratio test neither the underlying model nor the functional form of the outliers have to be specified. In a simulation study the finite sample behaviour of the proposed test is analysed. The simulation study shows that the test has reasonable power against a variety of alternatives. Moreover, to illustrate the behaviour of the proposed test we analyse the returns of the Volkswagen stock. --
    Keywords: GARCH processes,Detection of outliers,CUSUM-type test
    Date: 2012
  6. By: Lee, Bong Soo; Ryu, Doojin
    Abstract: This study re-examines the return-volatility relationship and dynamics under a new VAR framework. By analyzing two model-free implied volatility indices - VIX (the U.S.) and VKOSPI (Korea) - and their corresponding stock market indices, we found an asymmetric volatility phenomenon in both developed and emerging markets. However, the VKOSPI, a recently published implied volatility index, shows impulse response dynamics that are clearly distinct from those for the VIX, an implied volatility index for the developed market. --
    Keywords: asymmetric volatility,vector autoregression,VIX,VKOSPI
    JEL: G10 G15
    Date: 2012
  7. By: Kris Boudt (KU Leuven; Lessius; V.U. University Amsterdam); Jon Danielsson (London School of Economics); Siem Jan Koopman (V.U. University Amsterdam; Tinbergen Institute); Andre Lucas (V.U. University Amsterdam; Tinbergen Institute)
    Abstract: We propose a parsimonious regime switching model to characterize the dynamics in the volatilities and correlations of US deposit banks' stock returns over 1994-2011. A first innovative feature of the model is that the within-regime dynamics in the volatilities and correlation depend on the shape of the Student t innovations. Secondly, the across-regime dynamics in the transition probabilities of both volatilities and correlations are driven by macro-financial indicators such as the Saint Louis Financial Stability index, VIX or TED spread. We find strong evidence of time-variation in the regime switching probabilities and the within-regime volatility of most banks. The within-regime dynamics of the equicorrelation seem to be constant over the period.
    Date: 2012–10
  8. By: Bai, Zhidong; Hui, Yongchang; Wong, Wing-Keung
    Abstract: In this article we propose a quick, efficient, and easy method to detect whether a time series Yt possesses any nonlinear feature. The advantage of our proposed nonlinearity test is that it is not required to know the exact nonlinear features and the detailed nonlinear forms of Yt. Our proposed test could also be used to test whether the model, including linear and nonlinear, hypothesized to be used for the variable is appropriate as long as the residuals of the model being used could be estimated. Our simulation results show that our proposed test is stable and powerful while our illustration on Wolf's sunspots numbers is consistent with the findings from existing literature.
    Keywords: linearity; nonlinearity; U-statistics; Volterra expansion
    JEL: C32 C14 C01
    Date: 2012–08–01

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