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Finansal Piyasalar ve Bitcoin Bağımlılığı: Copula-Garch Yaklaşımı

Yıl 2020, Cilt 25, Sayı 98, 35 - 63, 26.06.2020
https://doi.org/10.36484/liberal.662625

Öz

Bu makale, Bitcoin ile kritik finansal göstergeler arasındaki ilişkiyi Copula-GARCH yöntemini kullanarak incelemeyi amaçlamaktadır. Araştırmada, Bitcoin ve ABD 10-Yıllık Tahvil Verim, Altın Piyasa, ABD Doları Endeksi, S&P 500, FTSE 100 ve NIKKEI 225'in kapanış fiyatları kullanılmaktadır. Bildiğimiz kadarıyla, bu konuyu ampirik olarak inceleyen ilk makale budur. Analiz sonuçları, Bitcoin ve önde gelen finansal göstergeler arasında güçlü bir karşılıklı bağımlılık olmadığını göstermektedir. Bu bulgular, hem uzun vadeli hem de kısa vadeli stratejilerde alım satım faaliyetlerinde politika yapıcılara, bankalara, finansal yatırımcılara ve risk yöneticilerine fayda sağlayacak yeni bilgiler sunmaktadır.

Kaynakça

  • Albulescu, C. T., Aubin, C., Goyeau, D., Tiwari, A. K. (2018). Extreme co-movements and dependencies among major international exchange rates: A copula approach. The Quarterly Review of Economics and Finance.
  • Ardia, D., Bluteau, K., & Rüede, M. (2019). Regime changes in Bitcoin GARCH volatility dynamics. Finance Research Letters, 29, 266-271.
  • Azari, A. (2019). Bitcoin Price Prediction: An ARIMA Approach. arXiv preprint arXiv:1904.05315.
  • Baek, C., Elbeck, M. (2015). Bitcoins as an investment or speculative vehicle? A first look. Applied Economics Letters, 22(1), 30-34.
  • Balcilar, M., Bouri, E., Gupta, R., & Roubaud, D. (2017). Can volume predict Bitcoin returns and volatility? A quantiles-based approach. Economic Modelling, 64, 74-81.
  • Barber, S., Boyen, X., Shi, E., Uzun, E. (2012, February). Bitter to better—how to make bitcoin a better currency. In International Conference on Financial Cryptography and Data Security (pp. 399-414). Springer, Berlin, Heidelberg.
  • Bariviera, A. F. (2017). The inefficiency of Bitcoin revisited: A dynamic approach. Economics Letters, 161, 1-4.
  • Baur, D., Hong, K., Lee, A. (2015). Bitcoin–Currency or Asset?.
  • Baur, D. G., Dimpfl, T., & Kuck, K. (2018). Bitcoin, gold and the US dollar–A replication and extension. Finance Research Letters, 25, 103-110.
  • Brooks, C., Burke, S. P. (2003). Information criteria for GARCH model selection. The European journal of finance, 9(6), 557-580.
  • Bohr, J., Bashir, M. (2014, July). Who uses bitcoin? an exploration of the bitcoin community. In Privacy, Security and Trust (PST), 2014 Twelfth Annual International Conference on(pp. 94-101). IEEE.
  • Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327.
  • Bollerslev, T. (2009). Glossary to ARCH (GARCH. In Volatility and Time Series Econometrics: Essays in Honour of Robert F. Engle.
  • Bonneau, J., Miller, A., Clark, J., Narayanan, A., Kroll, J. A., Felten, E. W. (2015, May). Sok: Research perspectives and challenges for bitcoin and cryptocurrencies. In Security and Privacy (SP), 2015 IEEE Symposium on (pp. 104-121). IEEE.
  • Bouri, E., Molnár, 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.
  • Bouri, E., Gupta, R., Tiwari, A. K., & Roubaud, D. (2017). Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions. Finance Research Letters, 23, 87-95.
  • Böhme, R., Christin, N., Edelman, B., Moore, T. (2015). Bitcoin: Economics, technology, and governance. Journal of Economic Perspectives, 29(2), 213-38.
  • Chaim, P., & Laurini, M. P. (2018). Volatility and return jumps in bitcoin. Economics Letters, 173, 158-163.
  • Cheah, E. T., Fry, J. (2015). Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin. Economics Letters, 130, 32-36.
  • Cherubini, U., Luciano, E., Vecchiato, W. (2004). Copula methods in finance. John Wiley & Sons.
  • Dastgir, S., Demir, E., Downing, G., Gozgor, G., & Lau, C. K. M. (2018). The causal relationship between Bitcoin attention and Bitcoin returns: Evidence from the Copula-based Granger causality test. Finance Research Letters.
  • Demir, E., Gozgor, G., Lau, C. K. M., Vigne, S. A. (2018). Does economic policy uncertainty predict the Bitcoin returns? An empirical investigation. Finance Research Letters.
  • Dyhrberg, A. H. (2016a). Bitcoin, gold and the dollar–A GARCH volatility analysis. Finance Research Letters, 16, 85-92.
  • Dyhrberg, A. H. (2016b). Hedging capabilities of bitcoin. Is it the virtual gold? Finance Research Letters, 16, 139-144.
  • Du, J., Lai, K. K. (2017). Modeling Dependence between European Electricity Markets with Constant and Time-varying Copulas. Procedia computer science, 122, 94-101.
  • Embrechts, P., McNeil, A., Straumann, D. (2002). Correlation and dependence in risk management: properties and pitfalls. Risk management: value at risk and beyond, 176223.
  • Engle, R. F., Bollerslev, T. (1986). Modelling the persistence of conditional variances. Econometric reviews, 5(1), 1-50.
  • Engle, R. F., Kroner, K. F. (1995). Multivariate simultaneous generalized ARCH. Econometric theory, 11(1), 122-150.
  • Eyal, I., Sirer, E. G. (2014, March). Majority is not enough: Bitcoin mining is vulnerable. In International conference on financial cryptography and data security (pp. 436-454). Springer, Berlin, Heidelberg.
  • Garay, J., Kiayias, A., Leonardos, N. (2015, April). The bitcoin backbone protocol: Analysis and applications. In Annual International Conference on the Theory and Applications of Cryptographic Techniques (pp. 281-310). Springer, Berlin, Heidelberg.
  • Georgoula, I., Pournarakis, D., Bilanakos, C., Sotiropoulos, D. N., Giaglis, G. M. (2015). Using time-series and sentiment analysis to detect the determinants of bitcoin prices.
  • Grinberg, R. (2012). Bitcoin: An innovative alternative digital currency. Hastings Sci. & Tech. LJ, 4, 159.
  • Gronwald, M. (2014). The Economics of Bitcoins--Market Characteristics and Price Jumps.
  • Hale, G., Krishnamurthy, A., Kudlyak, M., Shultz, P. (2018). How Futures Trading Changed Bitcoin Prices. FRBSF Economic Letter, 2018, 12.
  • Holub, M., & Johnson, J. (2018). The Impact of the Bitcoin Bubble of 2017 on Bitcoin's P2P Market. Finance Research Letters.
  • Hughes, L., Dwivedi, Y. K., Misra, S. K., Rana, N. P., Raghavan, V., & Akella, V. (2019). Blockchain research, practice and policy: Applications, benefits, limitations, emerging research themes and research agenda. International Journal of Information Management, 49, 114-129.
  • Jiang, Y., Nie, H., & Ruan, W. (2018). Time-varying long-term memory in Bitcoin market. Finance Research Letters, 25, 280-284.
  • Jondeau, E., Rockinger, M. (2006). The copula-garch model of conditional dependencies: An international stock market application. Journal of international money and finance, 25(5), 827-853.
  • Karame, G. O., Androulaki, E., Capkun, S. (2012, October). Double-spending fast payments in bitcoin. In Proceedings of the 2012 ACM conference on Computer and communications security (pp. 906-917). ACM.
  • Katsiampa, P. (2017). Volatility estimation for Bitcoin: A comparison of GARCH models. Economics Letters, 158, 3-6.
  • Katsiampa, P. (2018). Volatility co-movement between Bitcoin and Ether. Finance Research Letters.
  • Kroll, J. A., Davey, I. C., Felten, E. W. (2013, June). The economics of Bitcoin mining, or Bitcoin in the presence of adversaries. In Proceedings of WEIS (Vol. 2013, p. 11).
  • Lahmiri, S., Bekiros, S., & Salvi, A. (2018). Long-range memory, distributional variation and randomness of bitcoin volatility. Chaos, Solitons & Fractals, 107, 43-48.
  • McNally, S., Roche, J., & Caton, S. (2018, March). Predicting the price of Bitcoin using Machine Learning. In 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP) (pp. 339-343). IEEE.
  • Mitchell, H., McKenzie, M. D. (2003). GARCH model selection criteria. Quantitative Finance, 3(4), 262-284.
  • Munim, Z. H., Shakil, M. H., & Alon, I. (2019). Next-Day Bitcoin Price Forecast. Journal of Risk and Financial Management, 12(2), 103.
  • Nadarajah, S., Chu, J. (2017). On the inefficiency of Bitcoin. Economics Letters, 150, 6-9.
  • Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system.
  • Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the Econometric Society, 347-370.
  • Nelson, R. B. (1999). An Introduction to Copulas, Lectures Notes in Statistics Vol. 39.
  • O'Dwyer, K. J., Malone, D. (2014). Bitcoin mining and its energy footprint.
  • Pal, D., & Mitra, S. K. (2019). Hedging bitcoin with other financial assets. Finance Research Letters, 30, 30-36.
  • Pant, D. R., Neupane, P., Poudel, A., Pokhrel, A. K., & Lama, B. K. (2018, October). Recurrent Neural Network Based Bitcoin Price Prediction by Twitter Sentiment Analysis. In 2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS) (pp. 128-132). IEEE.
  • Patton, A. J. (2006). Modelling asymmetric exchange rate dependence. International economic review, 47(2), 527-556.
  • Poon, S. H., Granger, C. W. (2003). Forecasting volatility in financial markets: A review. Journal of economic literature, 41(2), 478-539.
  • Reid, F., Harrigan, M. (2013). An analysis of anonymity in the bitcoin system. In Security and privacy in social networks (pp. 197-223). Springer, New York, NY.
  • Ron, D., Shamir, A. (2013, April). Quantitative analysis of the full bitcoin transaction graph. In International Conference on Financial Cryptography and Data Security (pp. 6-24). Springer, Berlin, Heidelberg.
  • Sklar, M. (1959). Fonctions de repartition an dimensions et leurs marges. Publ. Inst. Statist. Univ. Paris, 8, 229-
  • Symitsi, E., & Chalvatzis, K. J. (2018). Return, volatility and shock spillovers of Bitcoin with energy and technology companies. Economics Letters, 170, 127-130.
  • Urquhart, A. (2016). The inefficiency of Bitcoin. Economics Letters, 148, 80-82.231.
  • Urquhart, A. (2017). Price clustering in Bitcoin. Economics Letters, 159, 145-148.
  • Urquhart, A., & Zhang, H. (2019). Is Bitcoin a hedge or safe haven for currencies? An intraday analysis. International Review of Financial Analysis, 63, 49-57.
  • Van Wijk, D. (2013). What can be expected from the BitCoin. Erasmus Universiteit Rotterdam.
  • Wu, S., Tong, M., Yang, Z., & Derbali, A. (2019). Does gold or Bitcoin hedge economic policy uncertainty?. Finance Research Letters, 31, 171-178.
  • Yermack, D. (2015). Is Bitcoin a real currency? An economic appraisal. In Handbook of digital currency (pp. 31-43).

The Interdependence of Bitcoin and Financial Markets: A Copula-Garch Approach

Yıl 2020, Cilt 25, Sayı 98, 35 - 63, 26.06.2020
https://doi.org/10.36484/liberal.662625

Öz

This paper aims to examine the relationship between Bitcoin and preeminent financial indicators using Copula-GARCH method. In the study, we use closing prices of Bitcoin and US 10-Year Bond Yield, Gold Spot US Dollar, US Dollar Index, S&P 500, FTSE 100 and NIKKEI 225. To our knowledge, our paper is the first to examine this issue empirically. Analysis results show that there is no strong interdependence between Bitcoin and preeminent financial indicators. These findings provide new information that will benefit policy makers, banks, financial investors, and risk managers in trading activities for both long-term and short-term strategies.

Kaynakça

  • Albulescu, C. T., Aubin, C., Goyeau, D., Tiwari, A. K. (2018). Extreme co-movements and dependencies among major international exchange rates: A copula approach. The Quarterly Review of Economics and Finance.
  • Ardia, D., Bluteau, K., & Rüede, M. (2019). Regime changes in Bitcoin GARCH volatility dynamics. Finance Research Letters, 29, 266-271.
  • Azari, A. (2019). Bitcoin Price Prediction: An ARIMA Approach. arXiv preprint arXiv:1904.05315.
  • Baek, C., Elbeck, M. (2015). Bitcoins as an investment or speculative vehicle? A first look. Applied Economics Letters, 22(1), 30-34.
  • Balcilar, M., Bouri, E., Gupta, R., & Roubaud, D. (2017). Can volume predict Bitcoin returns and volatility? A quantiles-based approach. Economic Modelling, 64, 74-81.
  • Barber, S., Boyen, X., Shi, E., Uzun, E. (2012, February). Bitter to better—how to make bitcoin a better currency. In International Conference on Financial Cryptography and Data Security (pp. 399-414). Springer, Berlin, Heidelberg.
  • Bariviera, A. F. (2017). The inefficiency of Bitcoin revisited: A dynamic approach. Economics Letters, 161, 1-4.
  • Baur, D., Hong, K., Lee, A. (2015). Bitcoin–Currency or Asset?.
  • Baur, D. G., Dimpfl, T., & Kuck, K. (2018). Bitcoin, gold and the US dollar–A replication and extension. Finance Research Letters, 25, 103-110.
  • Brooks, C., Burke, S. P. (2003). Information criteria for GARCH model selection. The European journal of finance, 9(6), 557-580.
  • Bohr, J., Bashir, M. (2014, July). Who uses bitcoin? an exploration of the bitcoin community. In Privacy, Security and Trust (PST), 2014 Twelfth Annual International Conference on(pp. 94-101). IEEE.
  • Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327.
  • Bollerslev, T. (2009). Glossary to ARCH (GARCH. In Volatility and Time Series Econometrics: Essays in Honour of Robert F. Engle.
  • Bonneau, J., Miller, A., Clark, J., Narayanan, A., Kroll, J. A., Felten, E. W. (2015, May). Sok: Research perspectives and challenges for bitcoin and cryptocurrencies. In Security and Privacy (SP), 2015 IEEE Symposium on (pp. 104-121). IEEE.
  • Bouri, E., Molnár, 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.
  • Bouri, E., Gupta, R., Tiwari, A. K., & Roubaud, D. (2017). Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions. Finance Research Letters, 23, 87-95.
  • Böhme, R., Christin, N., Edelman, B., Moore, T. (2015). Bitcoin: Economics, technology, and governance. Journal of Economic Perspectives, 29(2), 213-38.
  • Chaim, P., & Laurini, M. P. (2018). Volatility and return jumps in bitcoin. Economics Letters, 173, 158-163.
  • Cheah, E. T., Fry, J. (2015). Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin. Economics Letters, 130, 32-36.
  • Cherubini, U., Luciano, E., Vecchiato, W. (2004). Copula methods in finance. John Wiley & Sons.
  • Dastgir, S., Demir, E., Downing, G., Gozgor, G., & Lau, C. K. M. (2018). The causal relationship between Bitcoin attention and Bitcoin returns: Evidence from the Copula-based Granger causality test. Finance Research Letters.
  • Demir, E., Gozgor, G., Lau, C. K. M., Vigne, S. A. (2018). Does economic policy uncertainty predict the Bitcoin returns? An empirical investigation. Finance Research Letters.
  • Dyhrberg, A. H. (2016a). Bitcoin, gold and the dollar–A GARCH volatility analysis. Finance Research Letters, 16, 85-92.
  • Dyhrberg, A. H. (2016b). Hedging capabilities of bitcoin. Is it the virtual gold? Finance Research Letters, 16, 139-144.
  • Du, J., Lai, K. K. (2017). Modeling Dependence between European Electricity Markets with Constant and Time-varying Copulas. Procedia computer science, 122, 94-101.
  • Embrechts, P., McNeil, A., Straumann, D. (2002). Correlation and dependence in risk management: properties and pitfalls. Risk management: value at risk and beyond, 176223.
  • Engle, R. F., Bollerslev, T. (1986). Modelling the persistence of conditional variances. Econometric reviews, 5(1), 1-50.
  • Engle, R. F., Kroner, K. F. (1995). Multivariate simultaneous generalized ARCH. Econometric theory, 11(1), 122-150.
  • Eyal, I., Sirer, E. G. (2014, March). Majority is not enough: Bitcoin mining is vulnerable. In International conference on financial cryptography and data security (pp. 436-454). Springer, Berlin, Heidelberg.
  • Garay, J., Kiayias, A., Leonardos, N. (2015, April). The bitcoin backbone protocol: Analysis and applications. In Annual International Conference on the Theory and Applications of Cryptographic Techniques (pp. 281-310). Springer, Berlin, Heidelberg.
  • Georgoula, I., Pournarakis, D., Bilanakos, C., Sotiropoulos, D. N., Giaglis, G. M. (2015). Using time-series and sentiment analysis to detect the determinants of bitcoin prices.
  • Grinberg, R. (2012). Bitcoin: An innovative alternative digital currency. Hastings Sci. & Tech. LJ, 4, 159.
  • Gronwald, M. (2014). The Economics of Bitcoins--Market Characteristics and Price Jumps.
  • Hale, G., Krishnamurthy, A., Kudlyak, M., Shultz, P. (2018). How Futures Trading Changed Bitcoin Prices. FRBSF Economic Letter, 2018, 12.
  • Holub, M., & Johnson, J. (2018). The Impact of the Bitcoin Bubble of 2017 on Bitcoin's P2P Market. Finance Research Letters.
  • Hughes, L., Dwivedi, Y. K., Misra, S. K., Rana, N. P., Raghavan, V., & Akella, V. (2019). Blockchain research, practice and policy: Applications, benefits, limitations, emerging research themes and research agenda. International Journal of Information Management, 49, 114-129.
  • Jiang, Y., Nie, H., & Ruan, W. (2018). Time-varying long-term memory in Bitcoin market. Finance Research Letters, 25, 280-284.
  • Jondeau, E., Rockinger, M. (2006). The copula-garch model of conditional dependencies: An international stock market application. Journal of international money and finance, 25(5), 827-853.
  • Karame, G. O., Androulaki, E., Capkun, S. (2012, October). Double-spending fast payments in bitcoin. In Proceedings of the 2012 ACM conference on Computer and communications security (pp. 906-917). ACM.
  • Katsiampa, P. (2017). Volatility estimation for Bitcoin: A comparison of GARCH models. Economics Letters, 158, 3-6.
  • Katsiampa, P. (2018). Volatility co-movement between Bitcoin and Ether. Finance Research Letters.
  • Kroll, J. A., Davey, I. C., Felten, E. W. (2013, June). The economics of Bitcoin mining, or Bitcoin in the presence of adversaries. In Proceedings of WEIS (Vol. 2013, p. 11).
  • Lahmiri, S., Bekiros, S., & Salvi, A. (2018). Long-range memory, distributional variation and randomness of bitcoin volatility. Chaos, Solitons & Fractals, 107, 43-48.
  • McNally, S., Roche, J., & Caton, S. (2018, March). Predicting the price of Bitcoin using Machine Learning. In 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP) (pp. 339-343). IEEE.
  • Mitchell, H., McKenzie, M. D. (2003). GARCH model selection criteria. Quantitative Finance, 3(4), 262-284.
  • Munim, Z. H., Shakil, M. H., & Alon, I. (2019). Next-Day Bitcoin Price Forecast. Journal of Risk and Financial Management, 12(2), 103.
  • Nadarajah, S., Chu, J. (2017). On the inefficiency of Bitcoin. Economics Letters, 150, 6-9.
  • Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system.
  • Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the Econometric Society, 347-370.
  • Nelson, R. B. (1999). An Introduction to Copulas, Lectures Notes in Statistics Vol. 39.
  • O'Dwyer, K. J., Malone, D. (2014). Bitcoin mining and its energy footprint.
  • Pal, D., & Mitra, S. K. (2019). Hedging bitcoin with other financial assets. Finance Research Letters, 30, 30-36.
  • Pant, D. R., Neupane, P., Poudel, A., Pokhrel, A. K., & Lama, B. K. (2018, October). Recurrent Neural Network Based Bitcoin Price Prediction by Twitter Sentiment Analysis. In 2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS) (pp. 128-132). IEEE.
  • Patton, A. J. (2006). Modelling asymmetric exchange rate dependence. International economic review, 47(2), 527-556.
  • Poon, S. H., Granger, C. W. (2003). Forecasting volatility in financial markets: A review. Journal of economic literature, 41(2), 478-539.
  • Reid, F., Harrigan, M. (2013). An analysis of anonymity in the bitcoin system. In Security and privacy in social networks (pp. 197-223). Springer, New York, NY.
  • Ron, D., Shamir, A. (2013, April). Quantitative analysis of the full bitcoin transaction graph. In International Conference on Financial Cryptography and Data Security (pp. 6-24). Springer, Berlin, Heidelberg.
  • Sklar, M. (1959). Fonctions de repartition an dimensions et leurs marges. Publ. Inst. Statist. Univ. Paris, 8, 229-
  • Symitsi, E., & Chalvatzis, K. J. (2018). Return, volatility and shock spillovers of Bitcoin with energy and technology companies. Economics Letters, 170, 127-130.
  • Urquhart, A. (2016). The inefficiency of Bitcoin. Economics Letters, 148, 80-82.231.
  • Urquhart, A. (2017). Price clustering in Bitcoin. Economics Letters, 159, 145-148.
  • Urquhart, A., & Zhang, H. (2019). Is Bitcoin a hedge or safe haven for currencies? An intraday analysis. International Review of Financial Analysis, 63, 49-57.
  • Van Wijk, D. (2013). What can be expected from the BitCoin. Erasmus Universiteit Rotterdam.
  • Wu, S., Tong, M., Yang, Z., & Derbali, A. (2019). Does gold or Bitcoin hedge economic policy uncertainty?. Finance Research Letters, 31, 171-178.
  • Yermack, D. (2015). Is Bitcoin a real currency? An economic appraisal. In Handbook of digital currency (pp. 31-43).

Ayrıntılar

Birincil Dil İngilizce
Konular İşletme
Yayınlanma Tarihi Bahar
Bölüm Araştırma
Yazarlar

Binali Selman EREN
Bitlis Eren Üniversitesi
0000-0001-5136-6406
Türkiye


Mustafa EREK (Sorumlu Yazar)
Bitlis Eren Üniversitesi
0000-0001-9681-6351
Türkiye

Yayımlanma Tarihi 26 Haziran 2020
Başvuru Tarihi 20 Aralık 2019
Kabul Tarihi 29 Haziran 2020
Yayınlandığı Sayı Yıl 2020, Cilt 25, Sayı 98

Kaynak Göster

APA Eren, B. S. & Erek, M. (2020). The Interdependence of Bitcoin and Financial Markets: A Copula-Garch Approach . Liberal Düşünce Dergisi , 25 (98) , 35-63 . DOI: 10.36484/liberal.662625