A mem based analysis of volatility spillovers in european financial markets
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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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A MEM-based Analysis of Volatility Spillovers in European Financial Markets

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A mem based analysis of volatility spillovers in european financial markets

FMC2/MACSI Colloquium

A MEM-based Analysis of Volatility Spillovers in European Financial Markets

Lena Golubovskaja

NUI Maynooth


Contents

Contents

1

Introduction

2

Theoretical Developments

The MEM Models

3

Empirical Results

4


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


Hypothesis

Hypothesis

Whether volatility spillover effect exist between the European markets?

1

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

2

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

3


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)


A mem based analysis of volatility spillovers in european financial markets

MEM

  • 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

Assumptions

  • 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


A gamma assumption for

A Gamma Assumption for

With and

=>


Range density

Range Density

France

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.


Estimation

Estimation

  • 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

    where


Cross autocorrelation matrices

Cross-autocorrelation matrices


Estimation results

Estimation Results

t-stats


Forecasting performance

Forecasting Performance

France

Germany

Netherlands

Spain


Forecasting

Forecasting


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


Thank you

Thank You!


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