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2 FindEcon Monograph Series : advances in financial market analysis

2 2006

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Modeling and Predicting Japanese Stock Returns Based on the ARFIMA-FIGARCH

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Nagayasu J.

2006

nr nr 2 Financial markets : principles of modeling forecasting and decision-making

209-220

Chapter 12 presents Japanese stock returns by modeling persistence in both their mean and volatility. Firstly, evidence is obtained of persistence in the Japanese stock mean and volatility. Secondly, it has been found that the models incorporating persistence and appropriate economic fundamentals produce more accurate forecasts than those from a linear model. For example, the long-term interest rate is found to be significant in the Japanese stock return equation. The positive relationship between the stock return and the long-term rate reported in this chapter is consistent with the Japanese experience when a rise in the nominal interest rate has been regarded as a sign of economic recovery, rather than a harbinger of higher inflation rates and a slowdown of its economic growth in the future. Thirdly, the forecasting accuracy of the mean of the stock return appears reliable, particularly in the long-term context, once the persistent characteristics and an appropriate determinant are properly considered in estimation models. The results may be encouraging for investors who make investment decisions based on statistical methods, and have some implications for portfolio formulation. (fragment of text)

Modeling the Realized Volatility with ARFIMA and Unobserved Component Models: Results from the Polish Financial Market

100%

Doman M.

2006

nr nr 2 Financial markets : principles of modeling forecasting and decision-making

121-135

Chapter 7 is focused on the notion of realized volatility in financial econometrics. The chapter presents an approach to the estimation of the daily realized volatility based on intraday returns. It also takes into account effects of market microstructure. The volatility has been modeled and predicted for stock index WIG20 and exchange rate USD/PLN using ARFIMA and unobserved component models. The findings are that modeling realized volatility with UC and ARFIMA models provides comparable volatility forecasts. (fragment of text)