Forecasting ability of GARCH vs Kalman filter method: evidence from daily UK time‐varying beta

Choudhry, Taufiq; Wu, Hao

doi:10.1002/for.1096

Cited by 49 publications

(27 citation statements)

References 52 publications

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“…The KF procedure outperforms the other methods more than 40% of the time during the crisis period. This superior forecasting performance by the KF backs the findings of Faff et al (); Choudhry and Wu () and Mergner and Bulla (). In order to test the robustness of our results we also apply different non‐overlapping forecast periods during the pre‐crisis (January 2006) and the crisis (January 2010) eras.…”

Section: Resultssupporting

confidence: 84%

“…The following analysis relies heavily on Bodurtha and Mark () and Choudhry and Wu (). The conditional CAPM in excess returns may be given as

E ((), r_{i, t} true| {I_{t}}_{prefix- 1}) = {β_{i I t}}_{prefix- 1} E ((), r_{m, t} true| {I_{t}}_{prefix- 1})

where

{β_{i I t}}_{prefix- 1} = c o v ((), R_{i, t}, {R_{m}}_{, t} true| {I_{t}}_{prefix- 1}) true/ v a r ((), {R_{m}}_{, t} true| {I_{t}}_{prefix- 1}) = c o v ((), r_{i, t}, {r_{m}}_{, t} true| {I_{t}}_{prefix- 1}) true/ v a r ((), {r_{m}}_{, t} true| {I_{t}}_{prefix- 1})

and E (| I t −1 ) is the mathematical expectation conditional on the information set available to the economic agents in the last period ( t − 1), I t −1 .…”

Section: The (Conditional) Capm and The Time‐varying Betamentioning

confidence: 99%

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Forecasting the Daily Time‐Varying Beta of European Banks During the Crisis Period: Comparison Between GARCH Models and the Kalman Filter

Zhang

Choudhry

2016

Journal of Forecasting

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This intention of this paper is to empirically forecast the daily betas of a few European banks by means of four generalized autoregressive conditional heteroscedasticity (GARCH) models and the Kalman filter method during the pre‐global financial crisis period and the crisis period. The four GARCH models employed are BEKK GARCH, DCC GARCH, DCC‐MIDAS GARCH and Gaussian‐copula GARCH. The data consist of daily stock prices from 2001 to 2013 from two large banks each from Austria, Belgium, Greece, Holland, Ireland, Italy, Portugal and Spain. We apply the rolling forecasting method and the model confidence sets (MCS) to compare the daily forecasting ability of the five models during one month of the pre‐crisis (January 2007) and the crisis (January 2013) periods. Based on the MCS results, the BEKK proves the best model in the January 2007 period, and the Kalman filter overly outperforms the other models during the January 2013 period. Results have implications regarding the choice of model during different periods by practitioners and academics. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Resultssupporting

confidence: 84%

“…The following analysis relies heavily on Bodurtha and Mark () and Choudhry and Wu (). The conditional CAPM in excess returns may be given as

E ((), r_{i, t} true| {I_{t}}_{prefix- 1}) = {β_{i I t}}_{prefix- 1} E ((), r_{m, t} true| {I_{t}}_{prefix- 1})

where

{β_{i I t}}_{prefix- 1} = c o v ((), R_{i, t}, {R_{m}}_{, t} true| {I_{t}}_{prefix- 1}) true/ v a r ((), {R_{m}}_{, t} true| {I_{t}}_{prefix- 1}) = c o v ((), r_{i, t}, {r_{m}}_{, t} true| {I_{t}}_{prefix- 1}) true/ v a r ((), {r_{m}}_{, t} true| {I_{t}}_{prefix- 1})

and E (| I t −1 ) is the mathematical expectation conditional on the information set available to the economic agents in the last period ( t − 1), I t −1 .…”

Section: The (Conditional) Capm and The Time‐varying Betamentioning

confidence: 99%

Forecasting the Daily Time‐Varying Beta of European Banks During the Crisis Period: Comparison Between GARCH Models and the Kalman Filter

Zhang

Choudhry

2016

Journal of Forecasting

Self Cite

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“…We find that if the number of static factors and dynamic factors are carefully determined, the method proposed can improve the forecasting ability of mortality. Choudhry and Wu (2008) investigate the forecasting ability of different bivariate GARCH models and the Kalman filter method. They find that measures of forecast errors overwhelmingly support the Kalman filter approach and the GARCH-GJR model appears to provide somewhat more accurate forecasts than the BEKK GARCH model.…”

Section: Discussionmentioning

confidence: 99%

Dynamic mortality factor model with conditional heteroskedasticity

Quan-sheng

2009

Insurance: Mathematics and Economics

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“…a and b for the t th ( t = 1,…, n ). Here, w is equal to 1 when using MAE as the measure and to 2 when using MSE as the measure, as described in Choudhry and Wu (). Here, given the null hypothesis of there being no different levels of modelling (forecasting) accuracy between the two different models, DB follows a t distribution with n − 1 degrees of freedom.…”

Section: Methodsmentioning

confidence: 99%

Nonlinearities in the CAPM: Evidence from Developed and Emerging Markets

Neslihanoğlu

Sogiakas

McColl

et al. 2016

Journal of Forecasting

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This paper examines the forecasting ability of the non-linear specifications of the market model. We propose a conditional Two-moment market model with a time-varying systematic covariance (beta) risk in the form of a mean reverting process of the state space model via Kalman Filter algorithm. In addition, we account for the systematic component of co-skewness and co-kurtosis by considering 2 Serdar Neslihanoglu et al.higher moments. The analysis is implemented using data from the stock indices of several developed and emerging stock markets. The empirical findings favour the time-varying market model approaches which outperform linear model specifications, both in terms of model fit and predictability. Precisely, higher moments are necessary for datasets which involve structural changes and/or market inefficiencies which are common in most of the emerging stock markets.

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Forecasting ability of GARCH vs Kalman filter method: evidence from daily UK time‐varying beta

Cited by 49 publications

References 52 publications

Forecasting the Daily Time‐Varying Beta of European Banks During the Crisis Period: Comparison Between GARCH Models and the Kalman Filter

Forecasting the Daily Time‐Varying Beta of European Banks During the Crisis Period: Comparison Between GARCH Models and the Kalman Filter

Dynamic mortality factor model with conditional heteroskedasticity

Nonlinearities in the CAPM: Evidence from Developed and Emerging Markets

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