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Stock returns and inflation: a tale of two periods in India

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Abstract

The relationship between stock returns and inflation has been tested in relation to hedge ability of stocks against inflation. The study covers a period of over five decades (1960–2014) and examines both pre- and post-structural economic reform experiences in India. This empirical analysis exploits the techniques of both Discrete and Continuous wavelet transforms. In the methodological framework of Wavelets, first it analyses the individual volatility characteristics of stock returns and inflation using wavelet power spectra, followed by their relationship at different time horizons. The empirical evidence suggests the two variables to be independent across time horizons lending support to stocks as instrument of hedge against inflation. The results are robust to techniques and sub-samples, as well.

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Notes

  1. Countries like India have experienced double-digit average annual inflation several times in the past, including the years 1992, 1998, 2009, 2010 and 2013.

  2. Kumar at al. (2002), Raju and Ghosh. (2004), are a few to note.

  3. TradingEconomics.com https://tradingeconomics.com/india/stock-market.

  4. The context of these hypotheses has been discussed in the subsequent text.

  5. Please see Fama (1981) for detail theoretical argument on proxy hypothesis.

  6. The horizon is the time-interval over which rates of return and inflation are measured. It is also known as holding period.

  7. This topic falls under the domain of future research and as an extension to current study.

  8. Existing studies are mostly concentrated on post-reform experience.

  9. Wavelet decompositions distort data at the two ends of a time series. In order to preserve the originality of data for 2008–2012 (crisis period), the data has been extended by two more years to June 2014.

  10. It includes 318 manufactured products, 98 primary articles, and 19 fuel and power items. Since 2014 the RBI, however, has adopted CPI as the key measure of inflation.

  11. The methodology is heavily drawn from Tiwari et al. (2014).

  12. For more details reader can refer to Aguiar-Conraria et al. (2008).

  13. Further decomposition has been avoided; with successive decomposition the energy embedded in successive lower frequencies tends to decline.

  14. The corresponding estimated equation is Stock returns = α + β Inflation + µ.

  15. Please refer analysis of Figs. 2 and 3 presented in Sect. 4.3—results from continuous wavelet transform (CWT). Since wavelet power spectra forms a part of CWT process, we prefer to report it under CWT results section.

  16. To this end, we have avoided the interpretation of phase angles within cross wavelet power spectra.

  17. Frequency and time are inversely related. Therefore, higher frequency components of a time series represent lower time scales (short run) and lower frequency components represent higher time scales (long run).

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Authors and Affiliations

  1. MICA - The School of Ideas, Ahmedabad, India

    Niyati Bhanja

  2. School of Economics, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India

    Arif Billah Dar

Authors
  1. Niyati Bhanja
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  2. Arif Billah Dar
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Corresponding author

Correspondence to Arif Billah Dar.

Appendix

Appendix

MODWT decomposition of DRS_CPI, DRS_WPI, INF_CPI and INF_CPI

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Bhanja, N., Dar, A.B. Stock returns and inflation: a tale of two periods in India. Econ Change Restruct 52, 413–438 (2019). https://doi.org/10.1007/s10644-018-9231-z

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