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Forecasting volatility in the New Zealand stock market

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  • Jun Yu
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Abstract

This study evaluates the performance of nine alternative models for predicting stock price volatility using daily New Zealand data. The competing models contain both simple models such as the random walk and smoothing models and complex models such as ARCH-type models and a stochastic volatility model. Four different measures are used to evaluate the forecasting accuracy. The main results are the following: (1) the stochastic volatility model provides the best performance among all the candidates; (2) ARCH-type models can perform well or badly depending on the form chosen: the performance of the GARCH(3,2) model, the best model within the ARCH family, is sensitive to the choice of assessment measures; and (3) the regression and exponentially weighted moving average models do not perform well according to any assessment measure, in contrast to the results found in various markets.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Applied Financial Economics.

Volume (Year): 12 (2002)
Issue (Month): 3 ()
Pages: 193-202

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Handle: RePEc:taf:apfiec:v:12:y:2002:i:3:p:193-202

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Cited by:
  1. Oleg Korenok & Stanislav Radchenko, 2005. "The smooth transition autoregressive target zone model with the Gaussian stochastic volatility and TGARCH error terms with applications," Working Papers 0505, VCU School of Business, Department of Economics.
  2. Jian Zhou & Zhixin Kang, 2011. "A Comparison of Alternative Forecast Models of REIT Volatility," The Journal of Real Estate Finance and Economics, Springer, vol. 42(3), pages 275-294, April.
  3. Carmen Broto & Esther Ruiz, 2004. "Estimation methods for stochastic volatility models: a survey," Journal of Economic Surveys, Wiley Blackwell, vol. 18(5), pages 613-649, December.
  4. Ibrahim Onour, . "Exploring Stability of Systematic Risk: Sectoral Portfolio Analysis," API-Working Paper Series 1002, Arab Planning Institute - Kuwait, Information Center.
  5. Sabur Mollah & Asma Mobarek, 2009. "Market volatility across countries – evidence from international markets," Studies in Economics and Finance, Emerald Group Publishing, vol. 26(4), pages 257-274, October.
  6. Ortas, Eduardo & Moneva, José M. & Salvador, Manuel, 2012. "Does socially responsible investment equity indexes in emerging markets pay off? Evidence from Brazil," Emerging Markets Review, Elsevier, vol. 13(4), pages 581-597.
  7. Jun Yu & Zhenlin Yang & Xibin Zhang, 2002. "A Class of Nonlinear Stochastic Volatility Models and Its Implications on Pricing Currency Options," Monash Econometrics and Business Statistics Working Papers 17/02, Monash University, Department of Econometrics and Business Statistics.
  8. Stavros Degiannakis & Evdokia Xekalaki, 2007. "Assessing the performance of a prediction error criterion model selection algorithm in the context of ARCH models," Applied Financial Economics, Taylor & Francis Journals, vol. 17(2), pages 149-171.
  9. Bulla, Jan & Bulla, Ingo, 2006. "Stylized facts of financial time series and hidden semi-Markov models," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2192-2209, December.
  10. Johansson, Anders C., 2009. "Asian Sovereign Debt and Country Risk," Working Paper Series 2009-11, China Economic Research Center, Stockholm School of Economics.
  11. Ana Filipa Carvalho & Jose Sa da Costa & Jose Assis Lopes, 2006. "A systematic modelling strategy for futures markets volatility," Applied Financial Economics, Taylor & Francis Journals, vol. 16(11), pages 819-833.
  12. Sadorsky, Perry, 2006. "Modeling and forecasting petroleum futures volatility," Energy Economics, Elsevier, vol. 28(4), pages 467-488, July.
  13. Onour, Ibrahim, 2009. "Natural Gas markets:How Sensitive to Crude Oil Price Changes?," MPRA Paper 14937, University Library of Munich, Germany.
  14. Chortareas, Georgios & Jiang, Ying & Nankervis, John. C., 2011. "Forecasting exchange rate volatility using high-frequency data: Is the euro different?," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1089-1107, October.
  15. Georgios Chortareas & John Nankervis & Ying Jiang, 2007. "Forecasting Exchange Rate Volatility with High Frequency Data: Is the Euro Different?," Money Macro and Finance (MMF) Research Group Conference 2006 79, Money Macro and Finance Research Group.
  16. Onour, Ibrahim, 2008. "Forward-Looking Beta Estimates:Evidence from an Emerging Market," MPRA Paper 14992, University Library of Munich, Germany.
  17. Guidi, Francesco, 2010. "Modelling and forecasting volatility of East Asian Newly Industrialized Countries and Japan stock markets with non-linear models," MPRA Paper 19851, University Library of Munich, Germany.
  18. Vo, Minh T., 2009. "Regime-switching stochastic volatility: Evidence from the crude oil market," Energy Economics, Elsevier, vol. 31(5), pages 779-788, September.
  19. Cheng, Ai-ru (Meg) & Gallant, A. Ronald & Ji, Chuanshu & Lee, Beom S., 2008. "A Gaussian approximation scheme for computation of option prices in stochastic volatility models," Journal of Econometrics, Elsevier, vol. 146(1), pages 44-58, September.
  20. Dr. Ibrahim Onour, . "The Global Financial Crisis and Equity Markets in Middle East Oil Exporting Countries," API-Working Paper Series 1009, Arab Planning Institute - Kuwait, Information Center.

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