Research Article
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Year 2022, , 113 - 129, 30.09.2022
https://doi.org/10.17261/Pressacademia.2022.1620

Abstract

References

  • Avdjiev, S., Bruno, V., Koch, C. and Shin, H.S, (2019). The dollar exchange rate as a global risk factor: evidence from investment. IMF Economic Review, 67(1), 151-173.
  • Azar, S. (2013). Oil prices, US inflation, US money supply and the US dollar OPEC Energy Review, Organization of the Petroleum Exporting Countries, 37(4), 387-415.
  • Azar, S. (2015). The relation of the US Dollar with Oil Prices, gold prices, and the US stock market. Research in World Economy, Research in World Economy, Sciedu Press, 6(1), 159-171.
  • Bernardi, M., Casarin, R., Maillet, B., & Petrella, L. (2016). Dynamic Model Averaging for Bayesian Quantile Regression. arXiv: Statistics Theory. February, 1-45. DOI: 10.48550/arxiv.1602.00856
  • Black, K.(2012). The role of credit default swaps and other alternative betas in hedge fund factor analysis. Journal of Derivatives & Hedge Funds. 18(3), 201–222. DOI: 10.1057/jdhf.2012.9
  • Chaudhry, N. Asad, H.; Abdulghaffar, M.; Amir, M. (2021). Contagion effect of COVID-19 on stock market returns: Role of gold prices, real estate prices, and US dollar exchange rate. Pakistan Journal of Commerce and Social Sciences,15(3),614-635
  • Conyon, M.J., & He, L. (2017). Firm performance and boardroom gender diversity: A quantile regression approach, Journal of Business Research, 79(C), 198-211, https://EconPapers.repec.org/RePEc:eee:jbrese:v:79:y:2017:i:c:p:198-211.
  • Curcuru, S. & Kamin, S. & Li, C. & Rodriguez, M. (2018). International Spillovers of Monetary Policy: Conventional Policy vs. Quantitative Easing, No 1234, International Finance Discussion Papers, Board of Governors of the Federal Reserve System (U.S.), https://EconPapers.repec.org/RePEc:fip:fedgif:1234.
  • Georgiadis, G.& Müller, G.J. & Schumann, B, (2021). Global risk and the dollar. Working Paper Series 2628, European Central Bank.
  • Gurrib, I. & Elshareif, E. (2016). Optimizing the Performance of the Fractal Adaptive Moving Average Strategy: The Case of EUR/USD. International Journal of Economics and Finance, 8(2) 171-178.
  • Han, L. & Wan, L.& Xu, Y.. (2020). Can the Baltic Dry Index predict foreign exchange rates?. Finance Research Letters. 32(1), 101-157. DOI: 10.1016/j.frl.2019.04.014.
  • Harvey, A.C. (1990), "Forecasting, Structural Time Series and the Kalman Filter", Cambridge University Press.
  • https://finanswebde.com/dolar-endeksi/b/5fdf7d6d0f40270039fedcdc, date of Access, 10.01.2022
  • https://tr.investing.com/commodities/gold, date of Access, 01.03.2022
  • https://tr.investing.com/crypto/bitcoin, date of Access, 01.03.2022
  • https://tr.investing.com/currencies/us-dollar-index, date of Access, 01.03.2022
  • https://tr.investing.com/economic-calendar/cpi-733, date of Access, 01.03.2022
  • https://tr.investing.com/indices/baltic-dry, date of Access, 01.03.2022
  • https://tr.investing.com/indices/investing.com-eur-index, date of Access, 01.03.2022
  • https://tr.investing.com/indices/nq-100, date of Access, 01.03.2022
  • https://tr.investing.com/indices/volatility-s-p-500, date of Access, 01.03.2022
  • https://tr.investing.com/rates-bonds/u.s.-10-year-bond-yield, date of Access, 01.03.2022
  • https://tr.investing.com/rates-bonds/united-states-cds-10-years-eur, date of Access, 01.03.2022
  • https://www.policyuncertainty.com/gpr.html, date of Access, 01.03.2022
  • Ilalan, D. and Pirgaip, B. (2019). The Impact of US Dollar Index on Emerging Stock Markets: A Simultaneous Granger Causality and Rolling Correlation Analysis, Biswas, R. and Michaelides, M. (Ed.) Essays in Financial Economics (Research in Finance, Vol. 35), Emerald Publishing
  • Limited, Bingley, pp. 145-154. https://doi.org/10.1108/S0196-382120190000035007
  • Koenker, R. (1984). A Note on L-estimators for Linear Models. Statistics & Probability Letters, 2, 323-325.
  • Kumar, J., & Robiyanto, R. (2021). The Impact of Gold Price and Us Dollar Index: The Volatile Case of Shanghai Stock Exchange and Bombay Stock Exchange During the Crisis of Covid-19. Jurnal Keuangan dan Perbankan, 25(3), 508-531. doi:https://doi.org/10.26905/jkdp.v25i3.5142
  • Lee, D.& Liu. (2010). An Analysis of Several Factors Affecting the U.S. Dollar Index. DOI: 10.17863/CAM.1346
  • Long, H.& Demir, E. & Bedowska-S., Barbara & Zaremba, A.& Shahzad, S. (2022). Is Geopolitical Risk Priced in the Cross-Section of Cryptocurrency Returns? (May 13, 2022). Available at SSRN: https://ssrn.com/abstract=4109293, DOI: 10.2139/ssrn.4109293
  • Martin F.E, Mukhopadhyay M. and Hombeeck C, (2017). The Global role of the US dollar and its consequences. Bank of England Quarterly Bulletin 2017(4), 1-11, https://www.bankofengland.co.uk/-/media/boe/files/quarterly-bulletin/2017/the-global-role-of-the-us-dollar-andits-consequences.pdf, Date of Access, 10.03.2022
  • Mokni, K.,& Ajmi, Ahdi N.. (2020). Cryptocurrencies vs. US dollar: Evidence from causality in quantiles analysis. Economic Analysis and Policy, 69, 238-252. DOI: 10.1016/j.eap.2020.12.011.
  • Öner, H. (2018). Uluslararası Finansal Endekslerin Döviz Kurları Üzerine Etkileri: Ampirik Bir Analiz. Selçuk Üniversitesi Sosyal Bilimler Meslek Yüksekokulu Dergisi, 21(2), 173 - 185.
  • Plagborg-Moller M.& Gopinath G.& Boz E.(2017). Global trade and the dollar. National Bureau of Economic Research, Inc.,NBER Working Papers 23988, 1-66,
  • Seneviratne D. & Xu Y.& Raddatz C.& Xie P.& Deghi A.& Barajas A., (2020). Global Banks’ Dollar Funding: A Source of Financial Vulnerability, International Monetary Fund IMF Working Papers 2020(113),1-50.
  • Su, J. B. (2016).How the Quantitative Easing Affect the Spillover Effects between the Metal Market and United States Dollar Index?. Journal of Reviews on Global Economics, 2016(5), 254-272.
  • Sun, X., Lu, X., Yue, G., & Li, J. (2017). Cross-Correlations Between the US Monetary Policy, US Dollar Index and Crude Oil Market. Physica A, 467, 326–344. DOI: 10.1016/j.physa.2016.10.029
  • Ünvan, Y. A. & Demirel, O. (2020). Ekonomik Büyüme Oranını Etkileyen Faktörlerin Kantil Regresyon ile İncelenmesi: Türkiye Örneği. Anemon Muş Alparslan Üniversitesi Sosyal Bilimler Dergisi, 8, 175-188. DOI: 10.18506/anemon.712323
  • Wu, W.and Zhou Z. (2014).Structural Change Detectıon For Regressıon Quantiles Under Tıme Serıes Non-Stationarity University of Toronto Works, September 15, 2014,1-39.
  • Yavuz, A.A., & Aşık, E.G. (2017). Quantile Regression. Uluslararası Mühendislik Araştırma ve Geliştirme Dergisi, 9(2), 138-146.
  • Yildirim, H.. (2019). The Long-Term Relationship of Fear Index With Dollar Index, Dax Volatility Index and Crude Oil Prices: Ardl Bound Test. Editör. Çürük,T.Economics and Administrative Publisher: Akademisyen Kitabevi A.Ş. 47-58

A TIME-VARYING DYNAMIC ANALYSIS OF FACTORS AFFECTING THE LEVELS OF UNDERPRICING, AVERAGE PRICING, AND OVERPRICING OF THE US DOLLAR IN GLOBAL DERIVATIVES MARKETS

Year 2022, , 113 - 129, 30.09.2022
https://doi.org/10.17261/Pressacademia.2022.1620

Abstract

Purpose- The aim of this study is to calculate the coefficient parameters of the factors affecting the pricing in the low, average, and
overpricing intervals and points of the US Global dollar index and then to investigate the dynamic historical effects of these parameters.
Methodology in the study, "Quantile Regression" to calculate parameter differences in pricing intervals and "Kalman Filtering" methods to
calculate their historical dynamic effects.
Findings- In the design of the study, The intervals are 0.5-0.95 incremental overpricing, 0.5 (Median) average, and 0.05-0.5 (Median)
underpricing (low-pricing). The study's results show that model 0.4. quantile has respectively the highest value R2 and adjusted R2 values,
approximately 53.2 percent and 49.1 percent. Additionally, the probability value of all 19 estimated models is statistically significant at the
0.05 level. While the coefficients of the Baltic Dry Index (at 5%), the Global gold prices (at 5%), and the US 10-year bond yields (10%) are
negative, the coefficients of the Nasdaq (10%) and Vix (5% and 10%) have positive signs. These variables are significant in the underpricing
quantiles that conducted interval of the US Dollar index (0.05-0.5) in the research design. The price point that represents the median value
yields the same results. From that point of view, only the Vix index is significant and only at a 10% level. The Baltic Dry Index (5%), Bitcoin
and Gold prices (5% and 10%), US 10-year interest rates-yields (5%), and CDS premium are among the factors that are relevant in the highquantile overpricing range of the US Dollar index (0.05-0.5). (5 percent and 10 percent ), Although the variables' coefficients are negative,
the coefficients for inflation (at 5% and 10%), Nasdaq (at 5%), and the VIX index (10%) are positive. The dynamic coefficients determined
historically and dynamically using the Kalman filtering technique in all quantiles have had the same values.
Conclusion- Since Kalman analysis and quantile regression analysis have different theoretical background, parameter differences in
underpricing and overpricing periods may be eliminated when historical dynamics are examined. As a result, even though the findings of
quantile regression and the results of Kalman analysis were roughly parallel, the predicted parameters for some variables did not closely
match the effects of either technique. The literature has noted that research utilizing both methodologies might run into such statements
that can be encountered in the study’s findings under comparable circumstances (Bernardi v., 2016: 34). Additionally, as the geopolitical risk
index conveys countercyclical hazards, the rising geopolitical risks in the historical coefficients raised the US dollar index, according to
Kalman's study.

References

  • Avdjiev, S., Bruno, V., Koch, C. and Shin, H.S, (2019). The dollar exchange rate as a global risk factor: evidence from investment. IMF Economic Review, 67(1), 151-173.
  • Azar, S. (2013). Oil prices, US inflation, US money supply and the US dollar OPEC Energy Review, Organization of the Petroleum Exporting Countries, 37(4), 387-415.
  • Azar, S. (2015). The relation of the US Dollar with Oil Prices, gold prices, and the US stock market. Research in World Economy, Research in World Economy, Sciedu Press, 6(1), 159-171.
  • Bernardi, M., Casarin, R., Maillet, B., & Petrella, L. (2016). Dynamic Model Averaging for Bayesian Quantile Regression. arXiv: Statistics Theory. February, 1-45. DOI: 10.48550/arxiv.1602.00856
  • Black, K.(2012). The role of credit default swaps and other alternative betas in hedge fund factor analysis. Journal of Derivatives & Hedge Funds. 18(3), 201–222. DOI: 10.1057/jdhf.2012.9
  • Chaudhry, N. Asad, H.; Abdulghaffar, M.; Amir, M. (2021). Contagion effect of COVID-19 on stock market returns: Role of gold prices, real estate prices, and US dollar exchange rate. Pakistan Journal of Commerce and Social Sciences,15(3),614-635
  • Conyon, M.J., & He, L. (2017). Firm performance and boardroom gender diversity: A quantile regression approach, Journal of Business Research, 79(C), 198-211, https://EconPapers.repec.org/RePEc:eee:jbrese:v:79:y:2017:i:c:p:198-211.
  • Curcuru, S. & Kamin, S. & Li, C. & Rodriguez, M. (2018). International Spillovers of Monetary Policy: Conventional Policy vs. Quantitative Easing, No 1234, International Finance Discussion Papers, Board of Governors of the Federal Reserve System (U.S.), https://EconPapers.repec.org/RePEc:fip:fedgif:1234.
  • Georgiadis, G.& Müller, G.J. & Schumann, B, (2021). Global risk and the dollar. Working Paper Series 2628, European Central Bank.
  • Gurrib, I. & Elshareif, E. (2016). Optimizing the Performance of the Fractal Adaptive Moving Average Strategy: The Case of EUR/USD. International Journal of Economics and Finance, 8(2) 171-178.
  • Han, L. & Wan, L.& Xu, Y.. (2020). Can the Baltic Dry Index predict foreign exchange rates?. Finance Research Letters. 32(1), 101-157. DOI: 10.1016/j.frl.2019.04.014.
  • Harvey, A.C. (1990), "Forecasting, Structural Time Series and the Kalman Filter", Cambridge University Press.
  • https://finanswebde.com/dolar-endeksi/b/5fdf7d6d0f40270039fedcdc, date of Access, 10.01.2022
  • https://tr.investing.com/commodities/gold, date of Access, 01.03.2022
  • https://tr.investing.com/crypto/bitcoin, date of Access, 01.03.2022
  • https://tr.investing.com/currencies/us-dollar-index, date of Access, 01.03.2022
  • https://tr.investing.com/economic-calendar/cpi-733, date of Access, 01.03.2022
  • https://tr.investing.com/indices/baltic-dry, date of Access, 01.03.2022
  • https://tr.investing.com/indices/investing.com-eur-index, date of Access, 01.03.2022
  • https://tr.investing.com/indices/nq-100, date of Access, 01.03.2022
  • https://tr.investing.com/indices/volatility-s-p-500, date of Access, 01.03.2022
  • https://tr.investing.com/rates-bonds/u.s.-10-year-bond-yield, date of Access, 01.03.2022
  • https://tr.investing.com/rates-bonds/united-states-cds-10-years-eur, date of Access, 01.03.2022
  • https://www.policyuncertainty.com/gpr.html, date of Access, 01.03.2022
  • Ilalan, D. and Pirgaip, B. (2019). The Impact of US Dollar Index on Emerging Stock Markets: A Simultaneous Granger Causality and Rolling Correlation Analysis, Biswas, R. and Michaelides, M. (Ed.) Essays in Financial Economics (Research in Finance, Vol. 35), Emerald Publishing
  • Limited, Bingley, pp. 145-154. https://doi.org/10.1108/S0196-382120190000035007
  • Koenker, R. (1984). A Note on L-estimators for Linear Models. Statistics & Probability Letters, 2, 323-325.
  • Kumar, J., & Robiyanto, R. (2021). The Impact of Gold Price and Us Dollar Index: The Volatile Case of Shanghai Stock Exchange and Bombay Stock Exchange During the Crisis of Covid-19. Jurnal Keuangan dan Perbankan, 25(3), 508-531. doi:https://doi.org/10.26905/jkdp.v25i3.5142
  • Lee, D.& Liu. (2010). An Analysis of Several Factors Affecting the U.S. Dollar Index. DOI: 10.17863/CAM.1346
  • Long, H.& Demir, E. & Bedowska-S., Barbara & Zaremba, A.& Shahzad, S. (2022). Is Geopolitical Risk Priced in the Cross-Section of Cryptocurrency Returns? (May 13, 2022). Available at SSRN: https://ssrn.com/abstract=4109293, DOI: 10.2139/ssrn.4109293
  • Martin F.E, Mukhopadhyay M. and Hombeeck C, (2017). The Global role of the US dollar and its consequences. Bank of England Quarterly Bulletin 2017(4), 1-11, https://www.bankofengland.co.uk/-/media/boe/files/quarterly-bulletin/2017/the-global-role-of-the-us-dollar-andits-consequences.pdf, Date of Access, 10.03.2022
  • Mokni, K.,& Ajmi, Ahdi N.. (2020). Cryptocurrencies vs. US dollar: Evidence from causality in quantiles analysis. Economic Analysis and Policy, 69, 238-252. DOI: 10.1016/j.eap.2020.12.011.
  • Öner, H. (2018). Uluslararası Finansal Endekslerin Döviz Kurları Üzerine Etkileri: Ampirik Bir Analiz. Selçuk Üniversitesi Sosyal Bilimler Meslek Yüksekokulu Dergisi, 21(2), 173 - 185.
  • Plagborg-Moller M.& Gopinath G.& Boz E.(2017). Global trade and the dollar. National Bureau of Economic Research, Inc.,NBER Working Papers 23988, 1-66,
  • Seneviratne D. & Xu Y.& Raddatz C.& Xie P.& Deghi A.& Barajas A., (2020). Global Banks’ Dollar Funding: A Source of Financial Vulnerability, International Monetary Fund IMF Working Papers 2020(113),1-50.
  • Su, J. B. (2016).How the Quantitative Easing Affect the Spillover Effects between the Metal Market and United States Dollar Index?. Journal of Reviews on Global Economics, 2016(5), 254-272.
  • Sun, X., Lu, X., Yue, G., & Li, J. (2017). Cross-Correlations Between the US Monetary Policy, US Dollar Index and Crude Oil Market. Physica A, 467, 326–344. DOI: 10.1016/j.physa.2016.10.029
  • Ünvan, Y. A. & Demirel, O. (2020). Ekonomik Büyüme Oranını Etkileyen Faktörlerin Kantil Regresyon ile İncelenmesi: Türkiye Örneği. Anemon Muş Alparslan Üniversitesi Sosyal Bilimler Dergisi, 8, 175-188. DOI: 10.18506/anemon.712323
  • Wu, W.and Zhou Z. (2014).Structural Change Detectıon For Regressıon Quantiles Under Tıme Serıes Non-Stationarity University of Toronto Works, September 15, 2014,1-39.
  • Yavuz, A.A., & Aşık, E.G. (2017). Quantile Regression. Uluslararası Mühendislik Araştırma ve Geliştirme Dergisi, 9(2), 138-146.
  • Yildirim, H.. (2019). The Long-Term Relationship of Fear Index With Dollar Index, Dax Volatility Index and Crude Oil Prices: Ardl Bound Test. Editör. Çürük,T.Economics and Administrative Publisher: Akademisyen Kitabevi A.Ş. 47-58
There are 41 citations in total.

Details

Primary Language English
Subjects Economics, Finance, Business Administration
Journal Section Articles
Authors

Mehmet Kuzu This is me 0000-0001-5354-4368

Publication Date September 30, 2022
Published in Issue Year 2022

Cite

APA Kuzu, M. (2022). A TIME-VARYING DYNAMIC ANALYSIS OF FACTORS AFFECTING THE LEVELS OF UNDERPRICING, AVERAGE PRICING, AND OVERPRICING OF THE US DOLLAR IN GLOBAL DERIVATIVES MARKETS. Journal of Economics Finance and Accounting, 9(3), 113-129. https://doi.org/10.17261/Pressacademia.2022.1620

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