Analyzing Bitcoin?s Inflation-Hedging Capacity Through Stochastic and Econometric Models

References

[1] [A¨ıt-Sahalia and Jacod(2009)] A¨ıt-Sahalia, Y., & Jacod, J. (2009). Estimating the degree of activity of jumps in high-frequency data. The Annals of Statistics, 37(5A), 2202-2244.

[2] [Bachelier(1900)] Bachelier, L. (1900). The´orie de la spe´culation. Gauthier- Villars.

[3] [Baur and Dimpfl(2018)] Baur, D. G., & Dimpfl, T. (2018). Asymmetric volatility in cryptocurrencies. Economics Letters, 173, 148-151.

[4] [Black and Scholes(1973)] Black, F., & Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of Political Economy, 81(3), 637-654.

[5] [Bollerslev(1986)] Bollerslev, T. (1986). Generalized autoregressive condi- tional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.

[6] [Bouri et al.(2017)] Bouri, E., Molna´r, P., Azzi, G., Roubaud, D., & Hagfors, L. I. (2017). On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier? Finance Research Letters, 20, 192-198.

[7] [Conlon et al.(2021)] Conlon, T., Corbet, S., & McGee, R. J. (2021). Inflation and cryptocurrencies revisited: A time-scale analysis. Economics Letters, 206, 109996.

[8] [Conrad et al.(2018)] Conrad, C., Custovic, A., & Ghysels, E. (2018). Long-and short-term cryptocurrency volatility components: A GARCH- MIDAS analysis. Journal of Risk and Financial Management, 11(2), 23.

[9] [Cont(2001)] Cont, R. (2001). Empirical properties of asset returns: Stylized facts and statistical issues. Quantitative Finance, 1(2), 223-236.

[10] [Corbet et al.(2020)] Corbet, S., Larkin, C., & Lucey, B. (2020). The con- tagion effects of the COVID-19 pandemic: Evidence from gold and cryptocurrencies. Finance Research Letters, 35, 101554.

[11] [Dyhrberg(2016)] Dyhrberg, A. H. (2016). Hedging capabilities of Bitcoin.

[12] Is it the virtual gold? Finance Research Letters, 16, 139-144. [Engle(1982)] Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1007.

[13] [Engle(2002)] Engle, R. F. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business & Economic Statistics, 20(3), 339-350.

[14] [Fisher(1930)] Fisher, I. (1930). The theory of interest. Macmillan.

[15] [Glosten et al.(1993)] Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The Journal of Finance, 48(5), 1779-1801.

[16] [Katsiampa(2017)] Katsiampa, P. (2017). Volatility estimation for Bitcoin: A comparison of GARCH models. Economics Letters, 158, 3-6.

[17] [Merton(1973)] Merton, R. C. (1973). Theory of rational option pricing. The Bell Journal of Economics and Management Science, 4(1), 141-183.

[18] [Merton(1976)] Merton, R. C. (1976). Option pricing when underlying stock returns are discontinuous. Journal of Financial Economics, 3(1-2), 125- 144.

[19] [Nelson(1991)] Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347-370.

[20] [Samuelson(1965)] Samuelson, P. A. (1965). Proof that properly anticipated prices fluctuate randomly. Industrial Management Review, 6(2), 41-49.

@article{1712369,
      author = {Folorunso Tobi Emmanuel, Ini Adinya},
      title = {Analyzing Bitcoin?s Inflation-Hedging Capacity Through Stochastic and Econometric Models},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {5},
      pages = {2567-2575},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1712369.pdf},
      abstract = {This study comprehensively investigates Bitcoin?s efficacy as a hedge against inflation through an integrated stochastic modeling and econometric framework. Utilizing daily Bitcoin and gold prices (2015-2024) and monthly U.S. CPI inflation data, we model Bitcoin?s extreme volatility characteristics using Geometric Brownian Motion (GBM), Merton Jump-Diffusion, and GARCH-family specifications. The inflation-hedging effectiveness is quantitatively assessed using correlation analysis, regression frameworks, and hedging metrics. Results demonstrate that Bitcoin exhibits weak and statistically insignificant correlation with inflation (? = ?0.014, p = 0.4876), comparable to gold?s performance (? = 0.025, p = 0.2228). Among all models, ARIMA- EGARCH provides the best fit (AIC = -8841.98). Hedging effectiveness measures reveal minimal variance reduction for both Bitcoin (0.0202%) and gold (0.0624%). The findings challenge Bitcoin?s classification as ?digital gold? and suggest it behaves primarily as a speculative asset rather than a reliable inflation hedge during the study period.},
      keywords = {Bitcoin, Inflation Hedge, GARCH Models, Jump Diffusion, Stochastic Processes, Financial Econometrics, Cryptocurrency.},
      month = {November},
      doi = {https://doi.org/10.64388/IREV9I5-1712369}
  }