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FMC2/MACSI Colloquium. A MEM-based Analysis of Volatility Spillovers in European Financial Markets. Lena Golubovskaja NUI Maynooth. Contents. 1. Introduction. 2. Theoretical Developments. The MEM Models. 3. Empirical Results. 4. Analysis of Volatility.

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Theoretical Developments

The MEM Models


Empirical Results


analysis of volatility
Analysis of Volatility
  • Financial volatility has been extensively investigated for more than twenty-five years
  • Financial integration => cross-country transmission mechanisms
  • Strong empirical regularities about GARCH models
channels of financial contagion
Channels of Financial Contagion

Source: Árvai, Driessen, and Ötker-Robe (2009)

chou wu and yung 2010
Chou, Wu, and Yung (2010)

The relationships between the U.S. and European Stock Price Ranges


B: Pre-subprime,

2001/02 – 2007/06

C: Post-subprime

2007/07 – 2010/01

A: Entire Period


Whether volatility spillover effect exist between the European markets?


Do we have a symmetric or assymetric volatility mechanism among the markets?


Who is the main volatility transmitter during the subprime mortgage crisis?


why multiplicative error models
Why Multiplicative Error Models?
  • Modelling non-negative time series
    • A lot of information available in financial markets is positive valued: ultra-high frequency data (within a time interval: range, volume, number of trades, number of buys/sells, durations) daily volatility estimators (realized volatility, daily range, absolute returns)
    • Time series exhibit persistence: GARCH–type models
germany daily range 1991 2011
Germany: Daily Range, 1991-2011

Autocorrelation 0.737

range based volatility
Range-based Volatility

Parkinson (1980) showed that that the range is an unbiased estimator of the volatility parameter in a diffusion process. The intuition behind his finding is that the price range of intraday gives more information regarding the future volatility than two arbitrary points in this series (the closing prices).

are these days the same
Are These Days the Same?

Can we use this information to measure volatility better?

volatility proxy
Volatility Proxy
  • The daily range
  • Theoretical relative efficiency gain 2.5-5 (Parkinson, 1980)
  • Directly observable from the data
  • It is well approximated as Gaussian
  • Robust to the microstructure noise (Brandt and Diebold, 2006)
  • Extension of GARCH approach to modelling the expected value of processes with positive support (Engle, 2002; Engle and Gallo, 2006)
  • Autoregressive Conditional Duration is a special case
  • Ease of estimation
  • Possibility of expanding the information set
the base model
The Base Model


  • a non-negative univariate process
  • the information about the process up to time t-1
  • MEM for is specified as
  • is a nonnegative predictable process, depending on a vector of unknown parameters θ
  • is a conditionally stochastic i.i.d. process, with density having non-negative support, mean 1 and unknown variance
range density
Range Density


The Netherlands

the specification of
The specification of
  • Base(1,1) specification
  • Extended specification

- the return of stock i at time t-1.

- a dummy variable to test the leverage effect.

  • The contribution of to the log likelihood function is
gamma ged
Gamma  GED
  • A useful relationship is between the Gamma distribution and the Generalized Error Distribution (GED):
  • Thus the conditional density of hlthas a correspondence in a conditional density of hlt


forecasting performance
Forecasting Performance





summing up
Summing Up
  • We find evidence of dependence across European markets over full sample and over post-subprime
  • MEM is a flexible class of models to estimate conditional expectations of non-negative processes
  • Captures a wide range of features suggested by data structure
  • Challenge: handle a large panel of data