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Hedging performance using google Trends–Evidence from the indian forex options market

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  • Chi, Tsung-Li
  • Liu, Hung-Tsen
  • Chang, Chia-Chien

Abstract

This study examines whether Google Trends can improve hedging performance by incorporating 15 search queries provided by Bulut (2018) into the exchange rate volatility model and hedging strategy in the Indian options market. Results show that Google Trends can enhance volatility prediction and that price-related search queries have the best forecasting ability. The hedging performances of USD-INR options incorporating 15 search queries or “PCA5” perform better than existing investor sentiment indices. An asymmetric effect of search queries is also found for hedging performance. Additionally, the constructed index “PCA5”, which contains all the information from 15 search queries, also achieves a better hedging effect compared to other search queries, except for price-related search queries ("prices" and "cheap") and liquidity-related search queries ("cash" and "credit").

Suggested Citation

  • Chi, Tsung-Li & Liu, Hung-Tsen & Chang, Chia-Chien, 2023. "Hedging performance using google Trends–Evidence from the indian forex options market," International Review of Economics & Finance, Elsevier, vol. 85(C), pages 107-123.
  • Handle: RePEc:eee:reveco:v:85:y:2023:i:c:p:107-123
    DOI: 10.1016/j.iref.2023.01.003
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    1. Nikolaos Askitas & Klaus F. Zimmermann, 2009. "Google Econometrics and Unemployment Forecasting," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 55(2), pages 107-120.
    2. Morana, Claudio, 2009. "On the macroeconomic causes of exchange rate volatility," International Journal of Forecasting, Elsevier, vol. 25(2), pages 328-350.
    3. Arratibel, Olga & Furceri, Davide & Martin, Reiner & Zdzienicka, Aleksandra, 2011. "The effect of nominal exchange rate volatility on real macroeconomic performance in the CEE countries," Economic Systems, Elsevier, vol. 35(2), pages 261-277, June.
    4. Yang, Chih-Yuan & Jhang, Ling-Jhen & Chang, Chia-Chien, 2016. "Do investor sentiment, weather and catastrophe effects improve hedging performance? Evidence from the Taiwan options market," Pacific-Basin Finance Journal, Elsevier, vol. 37(C), pages 35-51.
    5. Bakshi, Gurdip & Cao, Charles & Chen, Zhiwu, 1997. "Empirical Performance of Alternative Option Pricing Models," Journal of Finance, American Finance Association, vol. 52(5), pages 2003-2049, December.
    6. Zhi Da & Joseph Engelberg & Pengjie Gao, 2015. "Editor's Choice The Sum of All FEARS Investor Sentiment and Asset Prices," The Review of Financial Studies, Society for Financial Studies, vol. 28(1), pages 1-32.
    7. Levent Bulut, 2018. "Google Trends and the forecasting performance of exchange rate models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(3), pages 303-315, April.
    8. Jerry Coakley & George Dotsis & Xiaoquan Liu & Jia Zhai, 2014. "Investor sentiment and value and growth stock index options," The European Journal of Finance, Taylor & Francis Journals, vol. 20(12), pages 1211-1229, December.
    9. Engle, Robert F. & Gallo, Giampiero M., 2006. "A multiple indicators model for volatility using intra-daily data," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 3-27.
    10. Gurdip Bakshi & Nikunj Kapadia, 2003. "Delta-Hedged Gains and the Negative Market Volatility Risk Premium," The Review of Financial Studies, Society for Financial Studies, vol. 16(2), pages 527-566.
    11. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    12. Stambaugh, Robert F. & Yu, Jianfeng & Yuan, Yu, 2012. "The short of it: Investor sentiment and anomalies," Journal of Financial Economics, Elsevier, vol. 104(2), pages 288-302.
    13. Seabold,Skipper & Coppola,Andrea, 2015. "Nowcasting prices using Google trends : an application to Central America," Policy Research Working Paper Series 7398, The World Bank.
    14. De Long, J Bradford & Andrei Shleifer & Lawrence H. Summers & Robert J. Waldmann, 1990. "Noise Trader Risk in Financial Markets," Journal of Political Economy, University of Chicago Press, vol. 98(4), pages 703-738, August.
    15. Pami Dua & Partha Sen, 2006. "Capital Flow Volatility And Exchange Rates-- The Case Of India," Working papers 144, Centre for Development Economics, Delhi School of Economics.
    16. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    17. Anita Mirchandani, 2013. "Analysis of Macroeconomic Determinants of Exchange Rate Volatility in India," International Journal of Economics and Financial Issues, Econjournals, vol. 3(1), pages 172-179.
    18. Tao Chen & Erin Pik Ki So & Liang Wu & Isabel Kit Ming Yan, 2015. "The 2007–2008 U.S. Recession: What Did The Real-Time Google Trends Data Tell The United States?," Contemporary Economic Policy, Western Economic Association International, vol. 33(2), pages 395-403, April.
    19. Han, Liyan & Xu, Yang & Yin, Libo, 2018. "Does investor attention matter? The attention-return relationships in FX markets," Economic Modelling, Elsevier, vol. 68(C), pages 644-660.
    20. Smith, Geoffrey Peter, 2012. "Google Internet search activity and volatility prediction in the market for foreign currency," Finance Research Letters, Elsevier, vol. 9(2), pages 103-110.
    21. Chen, Liming & Du, Ziqing & Hu, Zhihao, 2020. "Impact of economic policy uncertainty on exchange rate volatility of China," Finance Research Letters, Elsevier, vol. 32(C).
    22. Charles J. Corrado & Thomas W. Miller, Jr., 2005. "The forecast quality of CBOE implied volatility indexes," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 25(4), pages 339-373, April.
    23. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
    24. Amihud, Yakov, 2002. "Illiquidity and stock returns: cross-section and time-series effects," Journal of Financial Markets, Elsevier, vol. 5(1), pages 31-56, January.
    25. Jordan Wilcoxson & Lendie Follett & Sean Severe, 2020. "Forecasting Foreign Exchange Markets Using Google Trends: Prediction Performance of Competing Models," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 21(4), pages 412-422, October.
    26. Naccarato, Alessia & Falorsi, Stefano & Loriga, Silvia & Pierini, Andrea, 2018. "Combining official and Google Trends data to forecast the Italian youth unemployment rate," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 114-122.
    27. Yu, Jianfeng & Yuan, Yu, 2011. "Investor sentiment and the mean-variance relation," Journal of Financial Economics, Elsevier, vol. 100(2), pages 367-381, May.
    28. Bing Han, 2008. "Investor Sentiment and Option Prices," The Review of Financial Studies, Society for Financial Studies, vol. 21(1), pages 387-414, January.
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