Forecasting Volatility in Financial Markets

By Hong Zhang, S. Li. 2010

207 citations recorded by the source corpus.

In this paper, we analyze the stock of Tsingtao Brewery Co Ltd for the 8-year period, from July 31, 2001, to September 11, 2009, a total of 2003 trading days. Using the False Nearest Neighbors method, we obtain the embedding dimension m in the k-nearest neighbour Algorithm. In order to investigate the validity of this method, we apply the modified method to the daily adjusted opening values of the Tsingtao Brewery Co Ltd. We find that the prediction of experimental results is more accurate than traditional methods.

Subfields: Electrical and Electronic Engineering, Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/7cb416f270ca52f29bae803b886c6cd19b6d0299

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In Finance

1. Negar Fazlollahi
2. Korhan K. Gokmenoglu

Citations in the corpus

• Forecasting Volatility Using High-Frequency Data (2011, unknown): “This (false) conclusion was based on the empirical observation that GARCH models were unable to explain much of the variability in squared returns when evaluated out-of-sample, despite the fact that GARCH models had good in-sample fit, see e.g. Jorion (1995) and Figlewski (1997).”
• The Interactions among Gold, Oil, and Stock Market: Evidence from S&P500 (2015, background): “We consider OVX and GVZ as index series because they imply market uncertainty based on both historical volatility information and investors’ expectation over future market conditions (Poon and Granger, 2003; Ji and Fan, 2012).”