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Marian Risse

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Rangan Gupta & Jun Ma & Marian Risse & Mark E. Wohar, 2017. "Common Business Cycles and Volatilities in US States and MSAs: The Role of Economic Uncertainty," Working Papers 201766, University of Pretoria, Department of Economics.

    Cited by:

    1. Oğuzhan Çepni & Rangan Gupta & Mark E. Wohar, 2020. "The role of real estate uncertainty in predicting US home sales growth: evidence from a quantiles-based Bayesian model averaging approach," Applied Economics, Taylor & Francis Journals, vol. 52(5), pages 528-536, January.
    2. Afees A. Salisu & Rangan Gupta & Sayar Karmakar & Sonali Das, 2021. "Forecasting Output Growth of Advanced Economies Over Eight Centuries: The Role of Gold Market Volatility as a Proxy of Global Uncertainty," Working Papers 202133, University of Pretoria, Department of Economics.
    3. Wu, Bi-Bo, 2021. "The dynamics of oil on China’s commodity sectors: What can we learn from a quantile perspective?," Journal of Commodity Markets, Elsevier, vol. 23(C).
    4. Rangan Gupta & Hardik A. Marfatia & Christian Pierdzioch & Afees A. Salisu, 2020. "Machine Learning Predictions of Housing Market Synchronization across US States: The Role of Uncertainty," Working Papers 202077, University of Pretoria, Department of Economics.
    5. Antonakakis, Nikolaos & Gabauer, David & Gupta, Rangan, 2019. "Greek economic policy uncertainty: Does it matter for Europe? Evidence from a dynamic connectedness decomposition approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    6. Aviral Kumar Tiwari & Micheal Kofi Boachie & Rangan Gupta, 2019. "Network Analysis of Economic and Financial Uncertainties in Advanced Economies: Evidence from Graph-Theory," Working Papers 201982, University of Pretoria, Department of Economics.
    7. Gabauer, David & Gupta, Rangan, 2020. "Spillovers across macroeconomic, financial and real estate uncertainties: A time-varying approach," Structural Change and Economic Dynamics, Elsevier, vol. 52(C), pages 167-173.
    8. Afees A. Salisu & Wenting Liao & Rangan Gupta & Oguzhan Cepni, 2023. "Economic Conditions and Predictability of US Stock Returns Volatility: Local Factor versus National Factor in a GARCH-MIDAS Model," Working Papers 202323, University of Pretoria, Department of Economics.
    9. Oguzhan Cepni & David Gabauer & Rangan Gupta & Khuliso Ramabulana, 2020. "Time-Varying Spillover of US Trade War on the Growth of Emerging Economies," Working Papers 202002, University of Pretoria, Department of Economics.
    10. Selçuk Gül & Rangan Gupta, 2021. "Time‐varying impact of global, region‐, and country‐specific uncertainties on the volatility of international trade," Contemporary Economic Policy, Western Economic Association International, vol. 39(4), pages 691-700, October.
    11. Siphumlile Mangisa & Sonali Das & Rangan Gupta, 2022. "Analyzing The Impact Of Brexit On Global Uncertainty Using Functional Linear Regression With Point Of Impact: The Role Of Currency And Equity Markets," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 67(04), pages 1377-1388, June.
    12. Matthew W. Clance & Riza Demirer & Rangan Gupta & Clement Kweku Kyei, 2020. "Predicting Firm-Level Volatility in the United States: The Role of Monetary Policy Uncertainty," Working Papers 202007, University of Pretoria, Department of Economics.
    13. Afees A. Salisu & Rangan Gupta & Riza Demirer, 2021. "The Financial US Uncertainty Spillover Multiplier: Evidence from a GVAR Model," Working Papers 202145, University of Pretoria, Department of Economics.
    14. Nikolaos Antonakakis & David Gabauer & Rangan Gupta & Vasilios Plakandaras, 2018. "Dynamic Connectedness of Uncertainty across Developed Economies: A Time-Varying Approach," Working Papers 201802, University of Pretoria, Department of Economics.
    15. Semei Coronado & Rangan Gupta & Besma Hkiri & Omar Rojas, 2020. "Time-Varying Spillovers between Currency and Stock Markets in the USA: Historical Evidence From More than Two Centuries," Advances in Decision Sciences, Asia University, Taiwan, vol. 24(4), pages 44-76, December.
    16. Christou Christina & Naraidoo Ruthira & Gupta Rangan, 2020. "Conventional and unconventional monetary policy reaction to uncertainty in advanced economies: evidence from quantile regressions," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(3), pages 1-17, June.
    17. Aamir Aijaz Syed & Muhammad Abdul Kamal & Assad Ullah & Simon Grima, 2022. "An Asymmetric Analysis of the Influence That Economic Policy Uncertainty, Institutional Quality, and Corruption Level Have on India’s Digital Banking Services and Banking Stability," Sustainability, MDPI, vol. 14(6), pages 1-21, March.
    18. Aharon, David Y. & Umar, Zaghum & Aziz, Mukhriz Izraf Azman & Vo, Xuan vinh, 2022. "COVID-19 related media sentiment and the yield curve of G-7 economies," The North American Journal of Economics and Finance, Elsevier, vol. 61(C).
    19. Christos Bouras & Christina Christou & Rangan Gupta & Keagile Lesame, 2020. "Forecasting State- and MSA-Level Housing Returns of the US: The Role of Mortgage Default Risks," Working Papers 202037, University of Pretoria, Department of Economics.
    20. Rangan Gupta & Jun Ma & Konstantinos Theodoridis & Mark E. Wohar, 2020. "Is there a National Housing Market Bubble Brewing in the United States?," Working Papers 202023, University of Pretoria, Department of Economics.
    21. Hossein Hassani & Mohammad Reza Yeganegi & Rangan Gupta & Riza Demirer, 2018. "Forecasting Stock Market (Realized) Volatility in the United Kingdom: Is There a Role for Economic Inequality?," Working Papers 201880, University of Pretoria, Department of Economics.
    22. Samuel F. Onipede & Nafiu A. Bashir & Jamaladeen Abubakar, 2023. "Small open economies and external shocks: an application of Bayesian global vector autoregression model," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(2), pages 1673-1699, April.
    23. Golitsis, Petros & Gkasis, Pavlos & Bellos, Sotirios K., 2022. "Dynamic spillovers and linkages between gold, crude oil, S&P 500, and other economic and financial variables. Evidence from the USA," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    24. Xin Sheng & Rangan Gupta & Oguzhan Cepni, 2022. "Persistence of State-Level Uncertainty of the United States: The Role of Climate Risks," Working Papers 202208, University of Pretoria, Department of Economics.
    25. Woraphon Yamaka & Rangan Gupta & Sukrit Thongkairat & Paravee Maneejuk, 2021. "Structural and Predictive Analyses with a Mixed Copula-Based Vector Autoregression Model," Working Papers 202108, University of Pretoria, Department of Economics.
    26. Christina Christou & David Gabauer & Rangan Gupta, 2019. "Time-Varying Impact of Uncertainty Shocks on Macroeconomic Variables of the United Kingdom: Evidence from Over 150 Years of Monthly Data," Working Papers 201962, University of Pretoria, Department of Economics.
    27. Vasilios Plakandaras & Rangan Gupta & Mehmet Balcilar & Qiang Ji, 2021. "Evolving United States Stock Market Volatility: The Role of Conventional and Unconventional Monetary Policies," Working Papers 202113, University of Pretoria, Department of Economics.
    28. Mehmet Balcilar & David Gabauer & Rangan Gupta & Christian Pierdzioch, 2021. "Uncertainty and Forecastability of Regional Output Growth in the United Kingdom: Evidence from Machine Learning," Working Papers 202111, University of Pretoria, Department of Economics.
    29. Canh Phuc Nguyen & Su Dinh Thanh & Bach Nguyen, 2022. "Economic uncertainty and tourism consumption," Tourism Economics, , vol. 28(4), pages 920-941, June.
    30. Yang, Dong-Xiao & Wu, Bi-Bo & Tong, Jing-Yang, 2021. "Dynamics and causality of oil price shocks on commodities: Quantile-on-quantile and causality-in-quantiles methods," Resources Policy, Elsevier, vol. 74(C).
    31. Huynh, Toan Luu Duc & Nasir, Muhammad Ali & Nguyen, Duc Khuong, 2023. "Spillovers and connectedness in foreign exchange markets: The role of trade policy uncertainty," The Quarterly Review of Economics and Finance, Elsevier, vol. 87(C), pages 191-199.
    32. Nikolaos Antonakakis & David Gabauer & Rangan Gupta, 2018. "Greek Economic Policy Uncertainty: Does it Matter for the European Union?," Working Papers 201840, University of Pretoria, Department of Economics.
    33. Elie Bouri & Rangan Gupta & Clement Kweku Kyei & Rinsuna Shivambu, 2020. "Uncertainty and Daily Predictability of Housing Returns and Volatility of the United States: Evidence from a Higher-Order Nonparametric Causality-in-Quantiles Test," Working Papers 202071, University of Pretoria, Department of Economics.
    34. Ahmed Ali & Granberg Mark & Uddin Gazi Salah & Troster Victor, 2022. "Asymmetric dynamics between uncertainty and unemployment flows in the United States," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 26(1), pages 155-172, February.
    35. Semei Coronado & Rangan Gupta & Besma Hkiri & Omar Rojas, 2020. "Time-Varying Spillover between Currency and Stock Markets in the United States: More than Two Centuries of Historical Evidence," Working Papers 202060, University of Pretoria, Department of Economics.
    36. Cepni, Oguzhan & Dul, Wiehan & Gupta, Rangan & Wohar, Mark E., 2021. "The dynamics of U.S. REITs returns to uncertainty shocks: A proxy SVAR approach," Research in International Business and Finance, Elsevier, vol. 58(C).
    37. Afees A. Salisu & Rangan Gupta & Ahamuefula E. Ogbonna, 2020. "Point and Density Forecasting of Macroeconomic and Financial Uncertainties of the United States," Working Papers 202058, University of Pretoria, Department of Economics.
    38. Renee van Eyden & Rangan Gupta & Christophe Andre & Xin Sheng, 2021. "The Effect of Macroeconomic Uncertainty on Housing Returns and Volatility: Evidence from US State-Level Data," Working Papers 202131, University of Pretoria, Department of Economics.
    39. Mehmet Balcilar & David Gabauer & Rangan Gupta & Christian Pierdzioch, 2022. "Uncertainty and forecastability of regional output growth in the UK: Evidence from machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1049-1064, September.
    40. Christina Christou & Giray Gozgor & Rangan Gupta & Chi keung Marco Lau, 2020. "Are Uncertainties across the World Convergent?," Economics Bulletin, AccessEcon, vol. 40(1), pages 855-862.
    41. Syed Jawad Hussain Shahzad & Rangan Gupta & Riza Demirer & Christian Pierdzioch, 2022. "Oil shocks and directional predictability of macroeconomic uncertainties of developed economies: Evidence from high‐frequency data†," Scottish Journal of Political Economy, Scottish Economic Society, vol. 69(2), pages 169-185, May.
    42. David Gabauer & Rangan Gupta, 2018. "On the Transmission Mechanism of Country-Specific and International Economic Uncertainty Spillovers: Evidence from a TVP-VAR Connectedness Decomposition Approach," Working Papers 201829, University of Pretoria, Department of Economics.
    43. Pierdzioch Christian & Gupta Rangan, 2020. "Uncertainty and Forecasts of U.S. Recessions," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(4), pages 1-20, September.
    44. Alan Tidwell & Yan (Olivia) Lu & Junsoo Lee & Piyali Banerjee, 2023. "Nature of comovements in US state and MSA housing prices," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 51(4), pages 959-989, July.
    45. Demirer, Riza & Gupta, Rangan & Salisu, Afees A. & van Eyden, Reneé, 2023. "Firm-level business uncertainty and the predictability of the aggregate U.S. stock market volatility during the COVID-19 pandemic," The Quarterly Review of Economics and Finance, Elsevier, vol. 88(C), pages 295-302.
    46. Canh Phuc Nguyen & Thanh Dinh Su, 2022. "When ‘uncertainty’ becomes ‘unknown’: Influences of economic uncertainty on the shadow economy," Annals of Public and Cooperative Economics, Wiley Blackwell, vol. 93(3), pages 677-716, September.
    47. Oguzhan Cepni & Hardik A. Marfatia & Rangan Gupta, 2021. "The Time-Varying Impact of Uncertainty Shocks on the Comovement of Regional Housing Prices of the United Kingdom," Working Papers 202168, University of Pretoria, Department of Economics.
    48. Afees A. Salisu & Rangan Gupta & Ahamuefula E. Ogbonna, 2021. "Point and density forecasting of macroeconomic and financial uncertainties of the USA," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(4), pages 700-707, July.
    49. Ji, Qiang & Gupta, Rangan & Bekun, Festus Victor & Balcilar, Mehmet, 2019. "Spillover of mortgage default risks in the United States: Evidence from metropolitan statistical areas and states," The Journal of Economic Asymmetries, Elsevier, vol. 19(C), pages 1-1.
    50. Oguzhan Cepni & Rangan Gupta & Wenting Liao & Jun Ma, 2022. "Climate Risks and Forecastability of the Weekly State-Level Economic Conditions of the United States," Working Papers 202251, University of Pretoria, Department of Economics.
    51. Rangan Gupta & Godwin Olasehinde-Williams & Mark E. Wohar, 2018. "The Impact of US Uncertainty Shocks on a Panel of Advanced and Emerging Market Economies: The Role of Exchange Rate, Trade and Financial Channels," Working Papers 201857, University of Pretoria, Department of Economics.
    52. Khoo, Joye & Cheung, Adrian (Wai Kong), 2021. "Does geopolitical uncertainty affect corporate financing? Evidence from MIDAS regression," Global Finance Journal, Elsevier, vol. 47(C).
    53. Selçuk Gul & Rangan Gupta, 2020. "A Note on the Time-Varying Impact of Global, Region- and Country-Specific Uncertainties on the Volatility of International Trade," Working Papers 202025, University of Pretoria, Department of Economics.
    54. Solarin, Sakiru Adebola & Gil-Alana, Luis A., 2021. "The persistence of economic policy uncertainty: Evidence of long range dependence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 568(C).
    55. Rangan Gupta & Christian Pierdzioch, 2021. "Forecasting the Volatility of Crude Oil: The Role of Uncertainty and Spillovers," Energies, MDPI, vol. 14(14), pages 1-15, July.
    56. Canh Phuc Nguyen & Christophe Schinckus & Thanh Dinh Su, 2020. "Economic policy uncertainty and demand for international tourism: An empirical study," Tourism Economics, , vol. 26(8), pages 1415-1430, December.
    57. Śmiech, Sławomir & Papież, Monika & Dąbrowski, Marek A., 2019. "How important are different aspects of uncertainty in driving industrial production in the CEE countries?," Research in International Business and Finance, Elsevier, vol. 50(C), pages 252-266.
    58. Christou, Christina & Gupta, Rangan & Nyakabawo, Wendy, 2019. "Time-varying impact of uncertainty shocks on the US housing market," Economics Letters, Elsevier, vol. 180(C), pages 15-20.
    59. Gupta, Rangan & Sun, Xiaojin, 2020. "Forecasting economic policy uncertainty of BRIC countries using Bayesian VARs," Economics Letters, Elsevier, vol. 186(C).
    60. Hossein Hassani & Mohammad Reza Yeganegi & Rangan Gupta & Riza Demirer, 2022. "Forecasting stock market (realized) volatility in the United Kingdom: Is there a role of inequality?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 2146-2152, April.
    61. Ruipeng Liu & Rangan Gupta, 2020. "Investors' Uncertainty and Forecasting Stock Market Volatility," Working Papers 202090, University of Pretoria, Department of Economics.
    62. Linyan Dai & Xin Sheng, 2021. "The Impact of Uncertainty on State-Level Housing Markets of the United States: The Role of Social Cohesion," Sustainability, MDPI, vol. 13(6), pages 1-9, March.
    63. Rangan Gupta & Chi Keung Marco Lau & Jacobus A Nel & Xin Sheng, 2020. "Monetary Policy Uncertainty Spillovers in Time- and Frequency-Domains," Working Papers 202005, University of Pretoria, Department of Economics.
    64. Christian Pierdzioch & Rangan Gupta & Hossein Hassani & Emmanuel Silva, 2018. "Forecasting Changes of Economic Inequality: A Boosting Approach," Working Papers 201868, University of Pretoria, Department of Economics.
    65. Jacobus Nel & Rangan Gupta & Mark E. Wohar & Christian Pierdzioch, 2022. "Climate Risks and Predictability of Commodity Returns and Volatility: Evidence from Over 750 Years of Data," Working Papers 202242, University of Pretoria, Department of Economics.

  2. Christian Pierdzioch & Marian Risse & Rangan Gupta & Wendy Nyakabawo, 2016. "On REIT Returns and (Un-) Expected Inflation: Empirical Evidence Based on Bayesian Additive Regression Trees," Working Papers 201677, University of Pretoria, Department of Economics.

    Cited by:

    1. Das, Mahamitra & Sarkar, Nityananda, 2017. "Re-investigating the anomalous relationship between inflation and equity REIT returns: A regime-switching approach," MPRA Paper 95135, University Library of Munich, Germany, revised 05 Nov 2018.
    2. Christou, Christina & Gupta, Rangan & Nyakabawo, Wendy & Wohar, Mark E., 2018. "Do house prices hedge inflation in the US? A quantile cointegration approach," International Review of Economics & Finance, Elsevier, vol. 54(C), pages 15-26.
    3. Das, Mahamitra & Sarkar, Nityananda, 2019. "Revisiting the Anomalous Relationship between Inflation and REIT Returns in Presence of Structural Breaks: Empirical Evidence from the USA and the UK," MPRA Paper 95130, University Library of Munich, Germany, revised 05 Nov 2019.
    4. Shi, Qi, 2023. "The RP-PCA factors and stock return predictability: An aligned approach," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    5. Bouri, Elie & Nekhili, Ramzi & Kinateder, Harald & Choudhury, Tonmoy, 2023. "Expected inflation and U.S. stock sector indices: A dynamic time-scale tale from inflationary and deflationary crisis periods," Finance Research Letters, Elsevier, vol. 55(PA).

  3. Rangan Gupta & Christian Pierdzioch & Marian Risse, 2015. "On International Uncertainty Links: BART-Based Empirical Evidence for Canada," Working Papers 201594, University of Pretoria, Department of Economics.

    Cited by:

    1. Jaewon Jung, 2023. "Multinational Firms and Economic Integration: The Role of Global Uncertainty," Sustainability, MDPI, vol. 15(3), pages 1-18, February.
    2. Mehmet Balcilar & Rangan Gupta & Clement Kyei & Mark Wohar, 2015. "Does Economic Policy Uncertainty Predict Exchange Rate Returns and Volatility? Evidence from a Nonparametric Causality-in-Quantiles Test," Working Papers 201599, University of Pretoria, Department of Economics.
    3. Gabauer, David & Gupta, Rangan, 2020. "Spillovers across macroeconomic, financial and real estate uncertainties: A time-varying approach," Structural Change and Economic Dynamics, Elsevier, vol. 52(C), pages 167-173.
    4. Siphumlile Mangisa & Sonali Das & Rangan Gupta, 2022. "Analyzing The Impact Of Brexit On Global Uncertainty Using Functional Linear Regression With Point Of Impact: The Role Of Currency And Equity Markets," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 67(04), pages 1377-1388, June.
    5. Nikolaos Antonakakis & David Gabauer & Rangan Gupta & Vasilios Plakandaras, 2018. "Dynamic Connectedness of Uncertainty across Developed Economies: A Time-Varying Approach," Working Papers 201802, University of Pretoria, Department of Economics.
    6. Trung, Nguyen Ba, 2019. "The spillover effects of US economic policy uncertainty on the global economy: A global VAR approach," The North American Journal of Economics and Finance, Elsevier, vol. 48(C), pages 90-110.
    7. Christou, Christina & Gupta, Rangan & Hassapis, Christis, 2017. "Does economic policy uncertainty forecast real housing returns in a panel of OECD countries? A Bayesian approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 65(C), pages 50-60.
    8. Helena Chuliá & Rangan Gupta & Jorge M. Uribe & Mark E. Wohar, 2016. "Impact of US Uncertainties on Emerging and Mature Markets: Evidence from a Quantile-Vector Autoregressive Approach," Working Papers 201656, University of Pretoria, Department of Economics.
    9. Mehmet Balcilar & David Gabauer & Rangan Gupta & Christian Pierdzioch, 2021. "Uncertainty and Forecastability of Regional Output Growth in the United Kingdom: Evidence from Machine Learning," Working Papers 202111, University of Pretoria, Department of Economics.
    10. Gupta, Rangan & Lau, Chi-Keung (Marco) & Sheng, Xin, 2020. "Graph theory-based network analysis of regional uncertainties of the US Economy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    11. Rangan Gupta & Chi Keung Marco Lau & Mark E. Wohar, 2016. "The Impact of US Uncertainty on the Euro Area in Good and Bad Times: Evidence from a Quantile Structural Vector Autoregressive Model," Working Papers 201681, University of Pretoria, Department of Economics.
    12. Christina Christou & Giray Gozgor & Rangan Gupta & Chi keung Marco Lau, 2020. "Are Uncertainties across the World Convergent?," Economics Bulletin, AccessEcon, vol. 40(1), pages 855-862.
    13. David Gabauer & Rangan Gupta, 2018. "On the Transmission Mechanism of Country-Specific and International Economic Uncertainty Spillovers: Evidence from a TVP-VAR Connectedness Decomposition Approach," Working Papers 201829, University of Pretoria, Department of Economics.
    14. Christian Pierdzioch & Marian Risse & Rangan Gupta & Wendy Nyakabawo, 2016. "On REIT Returns and (Un-) Expected Inflation: Empirical Evidence Based on Bayesian Additive Regression Trees," Working Papers 201677, University of Pretoria, Department of Economics.
    15. Śmiech, Sławomir & Papież, Monika & Shahzad, Syed Jawad Hussain, 2020. "Spillover among financial, industrial and consumer uncertainties. The case of EU member states," International Review of Financial Analysis, Elsevier, vol. 70(C).
    16. Gupta, Rangan & Sun, Xiaojin, 2020. "Forecasting economic policy uncertainty of BRIC countries using Bayesian VARs," Economics Letters, Elsevier, vol. 186(C).
    17. Sun, Xiaolei & Yao, Xiaoyang & Wang, Jun, 2017. "Dynamic interaction between economic policy uncertainty and financial stress: A multi-scale correlation framework," Finance Research Letters, Elsevier, vol. 21(C), pages 214-221.
    18. Rangan Gupta & Chi Keung Marco Lau & Jacobus A Nel & Xin Sheng, 2020. "Monetary Policy Uncertainty Spillovers in Time- and Frequency-Domains," Working Papers 202005, University of Pretoria, Department of Economics.
    19. Rangan Gupta & Chi-Keung (Marco) Lau & Xin Sheng, 2019. "Macroeconomic Uncertainty Connections across the US States: Evidence from a Bayesian Graphical Structural VAR (BGSVAR) Model," Working Papers 201910, University of Pretoria, Department of Economics.
    20. Śmiech, Sławomir & Papież, Monika, 2018. "Volatility spillovers among uncertainty measures. The case of EU member states," MPRA Paper 90319, University Library of Munich, Germany.
    21. Genc, Ismail H., 2022. "Are Indian Subcontinent remittance markets connected to each other?," Journal of Asian Economics, Elsevier, vol. 80(C).
    22. Massaporn Cheuathonghua & Chaiyuth Padungsaksawasdi & Pattana Boonchoo & Jittima Tongurai, 2019. "Extreme spillovers of VIX fear index to international equity markets," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 33(1), pages 1-38, March.

  4. Rohloff, Sebastian & Pierdzioch, Christian & Risse, Marian, 2014. "Fluctuations of the Real Exchange Rate, Real Interest Rates, and the Dynamics of the Price of Gold in a Small Open Economy," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100429, Verein für Socialpolitik / German Economic Association.

    Cited by:

    1. Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2019. "Forecasting Realized Oil-Price Volatility: The Role of Financial Stress and Asymmetric Loss," Working Papers 201903, University of Pretoria, Department of Economics.
    2. Demirer, Riza & Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2019. "Time-varying risk aversion and realized gold volatility," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    3. Mehmet Balcilar & Elie Bouri & Rangan Gupta & Christian Pierdzioch, 2021. "El Nino, La Nina, and the Forecastability of the Realized Variance of Heating Oil Price Movements," Working Papers 202138, University of Pretoria, Department of Economics.
    4. Gupta, Rangan & Majumdar, Anandamayee & Pierdzioch, Christian & Wohar, Mark E., 2017. "Do terror attacks predict gold returns? Evidence from a quantile-predictive-regression approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 65(C), pages 276-284.
    5. Mehmet Balcilar & Rangan Gupta & Christian Pierdzioch, 2022. "Oil-Price Uncertainty and International Stock Returns: Dissecting Quantile-Based Predictability and Spillover Effects Using More than a Century of Data," Energies, MDPI, vol. 15(22), pages 1-26, November.
    6. Kucher, Oleg & McCoskey, Suzanne, 2017. "The long-run relationship between precious metal prices and the business cycle," The Quarterly Review of Economics and Finance, Elsevier, vol. 65(C), pages 263-275.
    7. Le, Thai-Ha & Chang, Youngho, 2016. "Dynamics between strategic commodities and financial variables: Evidence from Japan," Resources Policy, Elsevier, vol. 50(C), pages 1-9.
    8. Salisu, Afees A. & Pierdzioch, Christian & Gupta, Rangan & Gabauer, David, 2022. "Forecasting stock-market tail risk and connectedness in advanced economies over a century: The role of gold-to-silver and gold-to-platinum price ratios," International Review of Financial Analysis, Elsevier, vol. 83(C).
    9. Rangan Gupta & Christian Pierdzioch, 2023. "Do U.S. economic conditions at the state level predict the realized volatility of oil-price returns? A quantile machine-learning approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-22, December.
    10. Shahbaz, Muhammad & Balcilar, Mehmet & Abidin Ozdemir, Zeynel, 2017. "Does oil predict gold? A nonparametric causality-in-quantiles approach," Resources Policy, Elsevier, vol. 52(C), pages 257-265.
    11. Zhou, Ying-Zhe & Huang, Jian-Bai & Chen, Jin-Yu, 2019. "Time-varying effect of the financialization of nonferrous metals markets on China's industrial sector," Resources Policy, Elsevier, vol. 64(C).
    12. Chen, Jinyu & Zhu, Xuehong & Zhong, Meirui, 2019. "Nonlinear effects of financial factors on fluctuations in nonferrous metals prices: A Markov-switching VAR analysis," Resources Policy, Elsevier, vol. 61(C), pages 489-500.

Articles

  1. Christian Pierdzioch & Marian Risse, 2020. "Forecasting precious metal returns with multivariate random forests," Empirical Economics, Springer, vol. 58(3), pages 1167-1184, March.

    Cited by:

    1. Rangan Gupta & Sayar Karmakar & Christian Pierdzioch, 2022. "Safe Havens, Machine Learning, and the Sources of Geopolitical Risk: A Forecasting Analysis Using Over a Century of Data," Working Papers 202201, University of Pretoria, Department of Economics.
    2. David Gabauer & Rangan Gupta & Sayar Karmakar & Joshua Nielsen, 2022. "Stock Market Bubbles and the Forecastability of Gold Returns (and Volatility)," Working Papers 202228, University of Pretoria, Department of Economics.
    3. Du, Pei & Guo, Ju’e & Sun, Shaolong & Wang, Shouyang & Wu, Jing, 2021. "Multi-step metal prices forecasting based on a data preprocessing method and an optimized extreme learning machine by marine predators algorithm," Resources Policy, Elsevier, vol. 74(C).
    4. Syed Abul, Basher & Perry, Sadorsky, 2022. "Forecasting Bitcoin price direction with random forests: How important are interest rates, inflation, and market volatility?," MPRA Paper 113293, University Library of Munich, Germany.
    5. Salisu, Afees A. & Gupta, Rangan & Nel, Jacobus & Bouri, Elie, 2022. "The (Asymmetric) effect of El Niño and La Niña on gold and silver prices in a GVAR model," Resources Policy, Elsevier, vol. 78(C).
    6. Dylan Norbert Gono & Herlina Napitupulu & Firdaniza, 2023. "Silver Price Forecasting Using Extreme Gradient Boosting (XGBoost) Method," Mathematics, MDPI, vol. 11(18), pages 1-15, September.
    7. Perry Sadorsky, 2021. "Predicting Gold and Silver Price Direction Using Tree-Based Classifiers," JRFM, MDPI, vol. 14(5), pages 1-21, April.
    8. Díaz, Juan D. & Hansen, Erwin & Cabrera, Gabriel, 2023. "Gold risk premium estimation with machine learning methods," Journal of Commodity Markets, Elsevier, vol. 31(C).
    9. Martha Carpinteyro & Francisco Venegas-Martínez & Alí Aali-Bujari, 2021. "Modeling Precious Metal Returns through Fractional Jump-Diffusion Processes Combined with Markov Regime-Switching Stochastic Volatility," Mathematics, MDPI, vol. 9(4), pages 1-17, February.

  2. Christoph Behrens & Christian Pierdzioch & Marian Risse, 2020. "Do German economic research institutes publish efficient growth and inflation forecasts? A Bayesian analysis," Journal of Applied Statistics, Taylor & Francis Journals, vol. 47(4), pages 698-723, March.

    Cited by:

    1. Heinisch, Katja & Behrens, Christoph & Döpke, Jörg & Foltas, Alexander & Fritsche, Ulrich & Köhler, Tim & Müller, Karsten & Puckelwald, Johannes & Reichmayr, Hannes, 2023. "The IWH Forecasting Dashboard: From forecasts to evaluation and comparison," IWH Technical Reports 1/2023, Halle Institute for Economic Research (IWH).

  3. Risse, Marian, 2019. "Combining wavelet decomposition with machine learning to forecast gold returns," International Journal of Forecasting, Elsevier, vol. 35(2), pages 601-615.

    Cited by:

    1. Zhang, Yaojie & Wang, Yudong, 2023. "Forecasting crude oil futures market returns: A principal component analysis combination approach," International Journal of Forecasting, Elsevier, vol. 39(2), pages 659-673.
    2. Yishun Liu & Chunhua Yang & Keke Huang & Weiping Liu, 2023. "A Multi-Factor Selection and Fusion Method through the CNN-LSTM Network for Dynamic Price Forecasting," Mathematics, MDPI, vol. 11(5), pages 1-20, February.
    3. Nakagawa, Kei & Sakemoto, Ryuta, 2022. "Cryptocurrency network factors and gold," Finance Research Letters, Elsevier, vol. 46(PB).
    4. Esparcia, Carlos & Jareño, Francisco & Umar, Zaghum, 2022. "Revisiting the safe haven role of Gold across time and frequencies during the COVID-19 pandemic," The North American Journal of Economics and Finance, Elsevier, vol. 61(C).
    5. Du, Pei & Guo, Ju’e & Sun, Shaolong & Wang, Shouyang & Wu, Jing, 2021. "Multi-step metal prices forecasting based on a data preprocessing method and an optimized extreme learning machine by marine predators algorithm," Resources Policy, Elsevier, vol. 74(C).
    6. Yelin Wang & Ping Yang & Zan Song & Julien Chevallier & Qingtai Xiao, 2024. "Intelligent Prediction of Annual CO2 Emissions Under Data Decomposition Mode," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 711-740, February.
    7. Syed Abul, Basher & Perry, Sadorsky, 2022. "Forecasting Bitcoin price direction with random forests: How important are interest rates, inflation, and market volatility?," MPRA Paper 113293, University Library of Munich, Germany.
    8. Shang, Yue & Wei, Yu & Chen, Yongfei, 2022. "Cryptocurrency policy uncertainty and gold return forecasting: A dynamic Occam's window approach," Finance Research Letters, Elsevier, vol. 50(C).
    9. Zhifeng Dai & Jie Kang & Hua Yin, 2023. "Forecasting equity risk premium: A new method based on wavelet de‐noising," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 4331-4352, October.
    10. Kim C. Raath & Katherine B. Ensor, 2023. "Wavelet-L2E Stochastic Volatility Models: an Application to the Water-Energy Nexus," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 150-176, May.
    11. Robert Czudaj, 2019. "Crude oil futures trading and uncertainty," Chemnitz Economic Papers 027, Department of Economics, Chemnitz University of Technology, revised Jan 2019.
    12. Liu, Qing & Liu, Min & Zhou, Hanlu & Yan, Feng, 2022. "A multi-model fusion based non-ferrous metal price forecasting," Resources Policy, Elsevier, vol. 77(C).
    13. Xu, Kunliang & Wang, Weiqing, 2023. "Limited information limits accuracy: Whether ensemble empirical mode decomposition improves crude oil spot price prediction?," International Review of Financial Analysis, Elsevier, vol. 87(C).
    14. Perry Sadorsky, 2021. "Predicting Gold and Silver Price Direction Using Tree-Based Classifiers," JRFM, MDPI, vol. 14(5), pages 1-21, April.
    15. Lu, Xinjie & Ma, Feng & Xu, Jin & Zhang, Zehui, 2022. "Oil futures volatility predictability: New evidence based on machine learning models11All the authors contribute to the paper equally," International Review of Financial Analysis, Elsevier, vol. 83(C).
    16. Berger, Theo & Czudaj, Robert L., 2020. "Commodity futures and a wavelet-based risk assessment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    17. Plakandaras, Vasilios & Ji, Qiang, 2022. "Intrinsic decompositions in gold forecasting," Journal of Commodity Markets, Elsevier, vol. 28(C).
    18. Li, Yuze & Jiang, Shangrong & Li, Xuerong & Wang, Shouyang, 2021. "The role of news sentiment in oil futures returns and volatility forecasting: Data-decomposition based deep learning approach," Energy Economics, Elsevier, vol. 95(C).
    19. Lin, Boqiang & Su, Tong, 2021. "Do China's macro-financial factors determine the Shanghai crude oil futures market?," International Review of Financial Analysis, Elsevier, vol. 78(C).
    20. Cervantes, Paula & Díaz, Antonio & Esparcia, Carlos & Huélamo, Diego, 2022. "The impact of COVID-19 induced panic on stock market returns: A two-year experience," Economic Analysis and Policy, Elsevier, vol. 76(C), pages 1075-1097.
    21. Sasan Barak & Navid Parvini, 2023. "Transfer‐entropy‐based dynamic feature selection for evaluating Bitcoin price drivers," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(12), pages 1695-1726, December.
    22. Julien Lachuer & Sami Ben Jabeur, 2022. "Explainable artificial intelligence modeling for corporate social responsibility and financial performance," Journal of Asset Management, Palgrave Macmillan, vol. 23(7), pages 619-630, December.
    23. Sakemoto, Ryuta, 2021. "Economic Evaluation of Cryptocurrency Investment," MPRA Paper 108283, University Library of Munich, Germany.
    24. Huang, Yu-ting & Bai, Yu-long & Yu, Qing-he & Ding, Lin & Ma, Yong-jie, 2022. "Application of a hybrid model based on the Prophet model, ICEEMDAN and multi-model optimization error correction in metal price prediction," Resources Policy, Elsevier, vol. 79(C).
    25. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou, 2021. "Gold Against the Machine," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 5-28, January.

  4. Pierdzioch, Christian & Risse, Marian & Gupta, Rangan & Nyakabawo, Wendy, 2019. "On REIT returns and (un-)expected inflation: Empirical evidence based on Bayesian additive regression trees," Finance Research Letters, Elsevier, vol. 30(C), pages 160-169.
    See citations under working paper version above.
  5. Gupta, Rangan & Ma, Jun & Risse, Marian & Wohar, Mark E., 2018. "Common business cycles and volatilities in US states and MSAs: The role of economic uncertainty," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 317-337.
    See citations under working paper version above.
  6. Behrens, Christoph & Pierdzioch, Christian & Risse, Marian, 2018. "Testing the optimality of inflation forecasts under flexible loss with random forests," Economic Modelling, Elsevier, vol. 72(C), pages 270-277.

    Cited by:

    1. Giovannelli, Alessandro & Pericoli, Filippo Maria, 2020. "Are GDP forecasts optimal? Evidence on European countries," International Journal of Forecasting, Elsevier, vol. 36(3), pages 963-973.
    2. Ming Meng & Chenge Song, 2020. "Daily Photovoltaic Power Generation Forecasting Model Based on Random Forest Algorithm for North China in Winter," Sustainability, MDPI, vol. 12(6), pages 1-17, March.

  7. Christian Pierdzioch & Marian Risse, 2018. "A machine‐learning analysis of the rationality of aggregate stock market forecasts," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 23(4), pages 642-654, October.

    Cited by:

    1. Joao Vitor Matos Goncalves & Michel Alexandre & Gilberto Tadeu Lima, 2023. "ARIMA and LSTM: A Comparative Analysis of Financial Time Series Forecasting," Working Papers, Department of Economics 2023_13, University of São Paulo (FEA-USP).
    2. Christian Pierdzioch & Rangan Gupta & Hossein Hassani & Emmanuel Silva, 2018. "Forecasting Changes of Economic Inequality: A Boosting Approach," Working Papers 201868, University of Pretoria, Department of Economics.

  8. Christoph Behrens & Christian Pierdzioch & Marian Risse, 2018. "A test of the joint efficiency of macroeconomic forecasts using multivariate random forests," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(5), pages 560-572, August.

    Cited by:

    1. Rangan Gupta & Hardik A. Marfatia & Christian Pierdzioch & Afees A. Salisu, 2020. "Machine Learning Predictions of Housing Market Synchronization across US States: The Role of Uncertainty," Working Papers 202077, University of Pretoria, Department of Economics.
    2. Gupta, Rangan & Pierdzioch, Christian & Vivian, Andrew J. & Wohar, Mark E., 2019. "The predictive value of inequality measures for stock returns: An analysis of long-span UK data using quantile random forests," Finance Research Letters, Elsevier, vol. 29(C), pages 315-322.
    3. Alexander Foltas & Christian Pierdzioch, 2022. "Business-cycle reports and the efficiency of macroeconomic forecasts for Germany," Applied Economics Letters, Taylor & Francis Journals, vol. 29(10), pages 867-872, June.
    4. Mengxi He & Xianfeng Hao & Yaojie Zhang & Fanyi Meng, 2021. "Forecasting stock return volatility using a robust regression model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1463-1478, December.
    5. Pierdzioch, Christian, 2023. "A bootstrap-based efficiency test of growth and inflation forecasts for Germany," Economics Letters, Elsevier, vol. 224(C).
    6. Behrens, Christoph, 2020. "German trade forecasts since 1970: An evaluation using the panel dimension," Working Papers 26, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    7. Christian Pierdzioch & Marian Risse, 2020. "Forecasting precious metal returns with multivariate random forests," Empirical Economics, Springer, vol. 58(3), pages 1167-1184, March.

  9. Risse, Marian & Ohl, Ludwig, 2017. "Using dynamic model averaging in state space representation with dynamic Occam’s window and applications to the stock and gold market," Journal of Empirical Finance, Elsevier, vol. 44(C), pages 158-176.

    Cited by:

    1. Nima Nonejad, 2021. "An Overview Of Dynamic Model Averaging Techniques In Time‐Series Econometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 566-614, April.
    2. Borup, Daniel, 2019. "Asset pricing model uncertainty," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 166-189.
    3. Risse, Marian, 2019. "Combining wavelet decomposition with machine learning to forecast gold returns," International Journal of Forecasting, Elsevier, vol. 35(2), pages 601-615.
    4. Markus Vogl, 2022. "Quantitative modelling frontiers: a literature review on the evolution in financial and risk modelling after the financial crisis (2008–2019)," SN Business & Economics, Springer, vol. 2(12), pages 1-69, December.
    5. Zhifeng Dai & Jie Kang & Hua Yin, 2023. "Forecasting equity risk premium: A new method based on wavelet de‐noising," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 4331-4352, October.
    6. Plakandaras, Vasilios & Ji, Qiang, 2022. "Intrinsic decompositions in gold forecasting," Journal of Commodity Markets, Elsevier, vol. 28(C).
    7. Bakerman, Jordan & Pazdernik, Karl & Korkmaz, Gizem & Wilson, Alyson G., 2022. "Dynamic logistic regression and variable selection: Forecasting and contextualizing civil unrest," International Journal of Forecasting, Elsevier, vol. 38(2), pages 648-661.
    8. Krzysztof Drachal, 2018. "Some Novel Bayesian Model Combination Schemes: An Application to Commodities Prices," Sustainability, MDPI, vol. 10(8), pages 1-27, August.

  10. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2016. "A boosting approach to forecasting the volatility of gold-price fluctuations under flexible loss," Resources Policy, Elsevier, vol. 47(C), pages 95-107.

    Cited by:

    1. Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2019. "Forecasting Realized Oil-Price Volatility: The Role of Financial Stress and Asymmetric Loss," Working Papers 201903, University of Pretoria, Department of Economics.
    2. Asai, Manabu & Gupta, Rangan & McAleer, Michael, 2020. "Forecasting volatility and co-volatility of crude oil and gold futures: Effects of leverage, jumps, spillovers, and geopolitical risks," International Journal of Forecasting, Elsevier, vol. 36(3), pages 933-948.
    3. Demirer, Riza & Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2019. "Time-varying risk aversion and realized gold volatility," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    4. Bouri, Elie & Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2021. "Forecasting power of infectious diseases-related uncertainty for gold realized variance," Finance Research Letters, Elsevier, vol. 42(C).
    5. Guo, Hongquan & Nguyen, Hoang & Vu, Diep-Anh & Bui, Xuan-Nam, 2021. "Forecasting mining capital cost for open-pit mining projects based on artificial neural network approach," Resources Policy, Elsevier, vol. 74(C).
    6. Rangan Gupta & Sayar Karmakar & Christian Pierdzioch, 2022. "Safe Havens, Machine Learning, and the Sources of Geopolitical Risk: A Forecasting Analysis Using Over a Century of Data," Working Papers 202201, University of Pretoria, Department of Economics.
    7. Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2019. "Forecasting Realized Gold Volatility: Is there a Role of Geopolitical Risks?," Working Papers 201943, University of Pretoria, Department of Economics.
    8. Troster, Victor & Bouri, Elie & Roubaud, David, 2019. "A quantile regression analysis of flights-to-safety with implied volatilities," Resources Policy, Elsevier, vol. 62(C), pages 482-495.
    9. Bonato, Matteo & Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2021. "A note on investor happiness and the predictability of realized volatility of gold," Finance Research Letters, Elsevier, vol. 39(C).
    10. Hollstein, Fabian & Prokopczuk, Marcel & Tharann, Björn & Wese Simen, Chardin, 2021. "Predictability in commodity markets: Evidence from more than a century," Journal of Commodity Markets, Elsevier, vol. 24(C).
    11. Syed Abul, Basher & Perry, Sadorsky, 2022. "Forecasting Bitcoin price direction with random forests: How important are interest rates, inflation, and market volatility?," MPRA Paper 113293, University Library of Munich, Germany.
    12. Văn, Lê & Bảo, Nguyễn Khắc Quốc, 2022. "The relationship between global stock and precious metals under Covid-19 and happiness perspectives," Resources Policy, Elsevier, vol. 77(C).
    13. Riza Demirer & Rangan Gupta & Christian Pierdzioch & Syed Jawad Hussain Shahzad, 2021. "A note on oil price shocks and the forecastability of gold realized volatility," Applied Economics Letters, Taylor & Francis Journals, vol. 28(21), pages 1889-1897, December.
    14. Afees A. Salisu & Rangan Gupta & Elie Bouri & Qiang Ji, 2020. "The Role of Global Economic Conditions in Forecasting Gold Market Volatility: Evidence from a GARCH-MIDAS Approach," Working Papers 202043, University of Pretoria, Department of Economics.

  11. Gupta, Rangan & Pierdzioch, Christian & Risse, Marian, 2016. "On international uncertainty links: BART-based empirical evidence for Canada," Economics Letters, Elsevier, vol. 143(C), pages 24-27.
    See citations under working paper version above.
  12. Risse, Marian & Kern, Martin, 2016. "Forecasting house-price growth in the Euro area with dynamic model averaging," The North American Journal of Economics and Finance, Elsevier, vol. 38(C), pages 70-85.

    Cited by:

    1. Jan Prüser, 2019. "Adaptive learning from model space," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(1), pages 29-38, January.
    2. Nima Nonejad, 2021. "An Overview Of Dynamic Model Averaging Techniques In Time‐Series Econometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 566-614, April.
    3. Hamid Norfiqiri & Razali Muhammad Najib & Azmi Fatin Afiqah & Daud Siti Zaleha & Yunus Nurhidayah Md., 2022. "Prospecting Housing Bubbles in Malaysia," Real Estate Management and Valuation, Sciendo, vol. 30(4), pages 74-88, December.
    4. Laurynas Narusevicius & Tomas Ramanauskas & Laura Gudauskaitė & Tomas Reichenbachas, 2019. "Lithuanian house price index: modelling and forecasting," Bank of Lithuania Occasional Paper Series 28, Bank of Lithuania.
    5. Hossein Hassani & Mohammad Reza Yeganegi & Rangan Gupta, 2018. "Does Inequality Really Matter in Forecasting Real Housing Returns of the United Kingdom?," Working Papers 201859, University of Pretoria, Department of Economics.
    6. Christou, Christina & Gupta, Rangan & Hassapis, Christis, 2017. "Does economic policy uncertainty forecast real housing returns in a panel of OECD countries? A Bayesian approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 65(C), pages 50-60.
    7. Nonejad, Nima, 2021. "Predicting equity premium using dynamic model averaging. Does the state–space representation matter?," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    8. Nuri Hacıevliyagil & Krzysztof Drachal & Ibrahim Halil Eksi, 2022. "Predicting House Prices Using DMA Method: Evidence from Turkey," Economies, MDPI, vol. 10(3), pages 1-27, March.
    9. Risse, Marian & Ohl, Ludwig, 2017. "Using dynamic model averaging in state space representation with dynamic Occam’s window and applications to the stock and gold market," Journal of Empirical Finance, Elsevier, vol. 44(C), pages 158-176.
    10. Risse, Marian, 2019. "Combining wavelet decomposition with machine learning to forecast gold returns," International Journal of Forecasting, Elsevier, vol. 35(2), pages 601-615.
    11. Robert A. Hill & Paulo M. M. Rodrigues, 2022. "Forgetting approaches to improve forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1356-1371, November.
    12. Dong, Xiyong & Yoon, Seong-Min, 2019. "What global economic factors drive emerging Asian stock market returns? Evidence from a dynamic model averaging approach," Economic Modelling, Elsevier, vol. 77(C), pages 204-215.
    13. Wang, Shengquan & Chen, Langnan, 2019. "Driving factors of equity bubbles," The North American Journal of Economics and Finance, Elsevier, vol. 49(C), pages 304-317.
    14. Linlin Zhao & Jasper Mbachu & Zhansheng Liu, 2019. "Exploring the Trend of New Zealand Housing Prices to Support Sustainable Development," Sustainability, MDPI, vol. 11(9), pages 1-18, April.
    15. Hanan Naser & Fatema Alaali, 2018. "Can oil prices help predict US stock market returns? Evidence using a dynamic model averaging (DMA) approach," Empirical Economics, Springer, vol. 55(4), pages 1757-1777, December.
    16. Tsai, I-Chun & Lin, Che-Chun, 2022. "A re-examination of housing bubbles: Evidence from European countries," Economic Systems, Elsevier, vol. 46(2).
    17. Krzysztof Drachal, 2018. "Some Novel Bayesian Model Combination Schemes: An Application to Commodities Prices," Sustainability, MDPI, vol. 10(8), pages 1-27, August.

  13. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2016. "Are precious metals a hedge against exchange-rate movements? An empirical exploration using bayesian additive regression trees," The North American Journal of Economics and Finance, Elsevier, vol. 38(C), pages 27-38.

    Cited by:

    1. Reboredo, Juan Carlos & Ugolini, Andrea & Hernandez, Jose Arreola, 2021. "Dynamic spillovers and network structure among commodity, currency, and stock markets," Resources Policy, Elsevier, vol. 74(C).
    2. Chi-Wei Su & Kai-Hua Wang & Oana-Ramona Lobonţ & Meng Qin, 2023. "Continuous Wavelet Transform of Time-Frequency Analysis Technique to Capture the Dynamic Hedging Ability of Precious Metals," Mathematics, MDPI, vol. 11(5), pages 1-18, February.
    3. Naeem, Muhammad & Tiwari, Aviral Kumar & Mubashra, Sana & Shahbaz, Muhammad, 2019. "Modeling volatility of precious metals markets by using regime-switching GARCH models," Resources Policy, Elsevier, vol. 64(C).
    4. Kucher, Oleg & McCoskey, Suzanne, 2017. "The long-run relationship between precious metal prices and the business cycle," The Quarterly Review of Economics and Finance, Elsevier, vol. 65(C), pages 263-275.
    5. Bhatia, Vaneet & Das, Debojyoti & Tiwari, Aviral Kumar & Shahbaz, Muhammad & Hasim, Haslifah M., 2018. "Do precious metal spot prices influence each other? Evidence from a nonparametric causality-in-quantiles approach," Resources Policy, Elsevier, vol. 55(C), pages 244-252.
    6. Vigne, Samuel A. & Lucey, Brian M. & O’Connor, Fergal A. & Yarovaya, Larisa, 2017. "The financial economics of white precious metals — A survey," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 292-308.
    7. Joscha Beckmann & Theo Berger & Robert Czudaj, 2017. "Gold Price Dynamics and the Role of Uncertainty," Chemnitz Economic Papers 006, Department of Economics, Chemnitz University of Technology, revised May 2017.
    8. Ioannis E. Tsolas, 2020. "Precious Metal Mutual Fund Performance Evaluation: A Series Two-Stage DEA Modeling Approach," JRFM, MDPI, vol. 13(5), pages 1-13, April.
    9. Tweneboah, George & Alagidede, Paul, 2018. "Interdependence structure of precious metal prices: A multi-scale perspective," Resources Policy, Elsevier, vol. 59(C), pages 427-434.
    10. Salisu, Afees A. & Ndako, Umar B. & Oloko, Tirimisiyu F., 2019. "Assessing the inflation hedging of gold and palladium in OECD countries," Resources Policy, Elsevier, vol. 62(C), pages 357-377.
    11. Hanif, Waqas & Mensi, Walid & Alomari, Mohammad & Andraz, Jorge Miguel, 2023. "Downside and upside risk spillovers between precious metals and currency markets: Evidence from before and during the COVID-19 crisis," Resources Policy, Elsevier, vol. 81(C).
    12. Christian Pierdzioch & Marian Risse & Rangan Gupta & Wendy Nyakabawo, 2016. "On REIT Returns and (Un-) Expected Inflation: Empirical Evidence Based on Bayesian Additive Regression Trees," Working Papers 201677, University of Pretoria, Department of Economics.
    13. Talbi, Marwa & de Peretti, Christian & Belkacem, Lotfi, 2020. "Dynamics and causality in distribution between spot and future precious metals: A copula approach," Resources Policy, Elsevier, vol. 66(C).
    14. Cheng, Sheng & Zhang, Zongyou & Cao, Yan, 2022. "Can precious metals hedge geopolitical risk? Fresh sight using wavelet coherence analysis," Resources Policy, Elsevier, vol. 79(C).
    15. Kunkler, Michael, 2022. "Hedging local currency risk with precious metals," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).
    16. Kliber, Agata, 2022. "Looking for a safe haven against American stocks during COVID-19 pandemic," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    17. Huifu Nong, 2024. "Connectedness and risk transmission of China’s stock and currency markets with global commodities," Economic Change and Restructuring, Springer, vol. 57(1), pages 1-24, February.
    18. Aktham Maghyereh & Hussein Abdoh, 2022. "Can news-based economic sentiment predict bubbles in precious metal markets?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-29, December.
    19. Jianhua Ding & Turen Guo & Bin Guo, 2018. "Fat Tails, Value at Risk, and the Palladium Returns," Journal of Applied Management and Investments, Department of Business Administration and Corporate Security, International Humanitarian University, vol. 7(2), pages 95-103, May.
    20. Wu, Wei & Tang, Xiaoping & Lv, Jiake & Yang, Chao & Liu, Hongbin, 2021. "Potential of Bayesian additive regression trees for predicting daily global and diffuse solar radiation in arid and humid areas," Renewable Energy, Elsevier, vol. 177(C), pages 148-163.
    21. Mensi, Walid & Hernandez, Jose Arroeola & Yoon, Seong-Min & Vo, Xuan Vinh & Kang, Sang Hoon, 2021. "Spillovers and connectedness between major precious metals and major currency markets: The role of frequency factor," International Review of Financial Analysis, Elsevier, vol. 74(C).

  14. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2016. "A quantile-boosting approach to forecasting gold returns," The North American Journal of Economics and Finance, Elsevier, vol. 35(C), pages 38-55.

    Cited by:

    1. Gupta, Rangan & Majumdar, Anandamayee & Pierdzioch, Christian & Wohar, Mark E., 2017. "Do terror attacks predict gold returns? Evidence from a quantile-predictive-regression approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 65(C), pages 276-284.
    2. Rangan Gupta & Sayar Karmakar & Christian Pierdzioch, 2022. "Safe Havens, Machine Learning, and the Sources of Geopolitical Risk: A Forecasting Analysis Using Over a Century of Data," Working Papers 202201, University of Pretoria, Department of Economics.
    3. David Gabauer & Rangan Gupta & Sayar Karmakar & Joshua Nielsen, 2022. "Stock Market Bubbles and the Forecastability of Gold Returns (and Volatility)," Working Papers 202228, University of Pretoria, Department of Economics.
    4. Gupta, Rangan & Pierdzioch, Christian & Vivian, Andrew J. & Wohar, Mark E., 2019. "The predictive value of inequality measures for stock returns: An analysis of long-span UK data using quantile random forests," Finance Research Letters, Elsevier, vol. 29(C), pages 315-322.
    5. Mehmet Balcilar & Rangan Gupta & Christian Pierdzioch, 2017. "On exchange-rate movements and gold-price fluctuations: evidence for gold-producing countries from a nonparametric causality-in-quantiles test," International Economics and Economic Policy, Springer, vol. 14(4), pages 691-700, October.
    6. Syed Abul, Basher & Perry, Sadorsky, 2022. "Forecasting Bitcoin price direction with random forests: How important are interest rates, inflation, and market volatility?," MPRA Paper 113293, University Library of Munich, Germany.
    7. Risse, Marian & Ohl, Ludwig, 2017. "Using dynamic model averaging in state space representation with dynamic Occam’s window and applications to the stock and gold market," Journal of Empirical Finance, Elsevier, vol. 44(C), pages 158-176.
    8. Risse, Marian, 2019. "Combining wavelet decomposition with machine learning to forecast gold returns," International Journal of Forecasting, Elsevier, vol. 35(2), pages 601-615.
    9. Salisu, Afees A. & Gupta, Rangan & Nel, Jacobus & Bouri, Elie, 2022. "The (Asymmetric) effect of El Niño and La Niña on gold and silver prices in a GVAR model," Resources Policy, Elsevier, vol. 78(C).
    10. Zhu, Huiming & Peng, Cheng & You, Wanhai, 2016. "Quantile behaviour of cointegration between silver and gold prices," Finance Research Letters, Elsevier, vol. 19(C), pages 119-125.
    11. Demirer, Riza & Pierdzioch, Christian & Zhang, Huacheng, 2017. "On the short-term predictability of stock returns: A quantile boosting approach," Finance Research Letters, Elsevier, vol. 22(C), pages 35-41.
    12. Shahzad, Syed Jawad Hussain & Rahman, Md Lutfur & Lucey, Brian M. & Uddin, Gazi Salah, 2021. "Re-examining the real option characteristics of gold for gold mining companies," Resources Policy, Elsevier, vol. 70(C).
    13. Díaz, Juan D. & Hansen, Erwin & Cabrera, Gabriel, 2023. "Gold risk premium estimation with machine learning methods," Journal of Commodity Markets, Elsevier, vol. 31(C).
    14. Dichtl, Hubert, 2020. "Forecasting excess returns of the gold market: Can we learn from stock market predictions?," Journal of Commodity Markets, Elsevier, vol. 19(C).
    15. Balcilar, Mehmet & Gupta, Rangan & Pierdzioch, Christian, 2016. "Does uncertainty move the gold price? New evidence from a nonparametric causality-in-quantiles test," Resources Policy, Elsevier, vol. 49(C), pages 74-80.
    16. Christian Pierdzioch & Marian Risse, 2020. "Forecasting precious metal returns with multivariate random forests," Empirical Economics, Springer, vol. 58(3), pages 1167-1184, March.

  15. Christian Pierdzioch & Marian Risse & Sebastian Rohloff, 2016. "Fluctuations of the real exchange rate, real interest rates, and the dynamics of the price of gold in a small open economy," Empirical Economics, Springer, vol. 51(4), pages 1481-1499, December.
    See citations under working paper version above.
  16. Christian Pierdzioch & Marian Risse & Sebastian Rohloff, 2016. "A boosting approach to forecasting gold and silver returns: economic and statistical forecast evaluation," Applied Economics Letters, Taylor & Francis Journals, vol. 23(5), pages 347-352, March.

    Cited by:

    1. Robert Lehmann & Klaus Wohlrabe, 2016. "Boosting and Regional Economic Forecasting: The Case of Germany," CESifo Working Paper Series 6157, CESifo.
    2. Liu, Guo-Dong & Su, Chi-Wei, 2019. "The dynamic causality between gold and silver prices in China market: A rolling window bootstrap approach," Finance Research Letters, Elsevier, vol. 28(C), pages 101-106.
    3. Wolfgang Nierhaus & Timo Wollmershäuser, 2016. "ifo Konjunkturumfragen und Konjunkturanalyse: Band II," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 72.
    4. Robert Lehmann & Klaus Wohlrabe, 2016. "Boosting and Forecasting German Industrial Output: What Does a Closer Look at the Details Tell Us?," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 69(03), pages 30-33, February.
    5. Vigne, Samuel A. & Lucey, Brian M. & O’Connor, Fergal A. & Yarovaya, Larisa, 2017. "The financial economics of white precious metals — A survey," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 292-308.
    6. Risse, Marian & Ohl, Ludwig, 2017. "Using dynamic model averaging in state space representation with dynamic Occam’s window and applications to the stock and gold market," Journal of Empirical Finance, Elsevier, vol. 44(C), pages 158-176.
    7. Risse, Marian, 2019. "Combining wavelet decomposition with machine learning to forecast gold returns," International Journal of Forecasting, Elsevier, vol. 35(2), pages 601-615.
    8. Salisu, Afees A. & Ndako, Umar B. & Oloko, Tirimisiyu F., 2019. "Assessing the inflation hedging of gold and palladium in OECD countries," Resources Policy, Elsevier, vol. 62(C), pages 357-377.
    9. Lehmann, Robert & Wohlrabe, Klaus, 2015. "The role of component-wise boosting for regional economic forecasting," MPRA Paper 68186, University Library of Munich, Germany, revised 03 Dec 2015.
    10. Neil A. Wilmot, 2019. "Heavy Metals: Might as Well Jump," IJFS, MDPI, vol. 7(2), pages 1-14, June.
    11. Yaya, OlaOluwa S. & Lukman, Adewale F. & Vo, Xuan Vinh, 2022. "Persistence and volatility spillovers of bitcoin price to gold and silver prices," Resources Policy, Elsevier, vol. 79(C).
    12. Díaz, Juan D. & Hansen, Erwin & Cabrera, Gabriel, 2023. "Gold risk premium estimation with machine learning methods," Journal of Commodity Markets, Elsevier, vol. 31(C).
    13. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou, 2021. "Gold Against the Machine," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 5-28, January.

  17. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2015. "A real-time quantile-regression approach to forecasting gold returns under asymmetric loss," Resources Policy, Elsevier, vol. 45(C), pages 299-306.

    Cited by:

    1. Gupta, Rangan & Majumdar, Anandamayee & Pierdzioch, Christian & Wohar, Mark E., 2017. "Do terror attacks predict gold returns? Evidence from a quantile-predictive-regression approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 65(C), pages 276-284.
    2. Rohloff, Sebastian & Pierdzioch, Christian & Risse, Marian, 2014. "Fluctuations of the Real Exchange Rate, Real Interest Rates, and the Dynamics of the Price of Gold in a Small Open Economy," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100429, Verein für Socialpolitik / German Economic Association.
    3. Rangan Gupta & Sayar Karmakar & Christian Pierdzioch, 2022. "Safe Havens, Machine Learning, and the Sources of Geopolitical Risk: A Forecasting Analysis Using Over a Century of Data," Working Papers 202201, University of Pretoria, Department of Economics.
    4. Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2019. "Forecasting Realized Gold Volatility: Is there a Role of Geopolitical Risks?," Working Papers 201943, University of Pretoria, Department of Economics.
    5. Mehmet Balcilar & Rangan Gupta & Christian Pierdzioch, 2017. "On exchange-rate movements and gold-price fluctuations: evidence for gold-producing countries from a nonparametric causality-in-quantiles test," International Economics and Economic Policy, Springer, vol. 14(4), pages 691-700, October.
    6. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2016. "A boosting approach to forecasting the volatility of gold-price fluctuations under flexible loss," Resources Policy, Elsevier, vol. 47(C), pages 95-107.
    7. Salisu, Afees A. & Gupta, Rangan & Nel, Jacobus & Bouri, Elie, 2022. "The (Asymmetric) effect of El Niño and La Niña on gold and silver prices in a GVAR model," Resources Policy, Elsevier, vol. 78(C).
    8. Wang, Ningli & You, Wanhai, 2023. "New insights into the role of global factors in BRICS stock markets: A quantile cointegration approach," Economic Systems, Elsevier, vol. 47(2).
    9. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2016. "A quantile-boosting approach to forecasting gold returns," The North American Journal of Economics and Finance, Elsevier, vol. 35(C), pages 38-55.
    10. Zhu, Huiming & Peng, Cheng & You, Wanhai, 2016. "Quantile behaviour of cointegration between silver and gold prices," Finance Research Letters, Elsevier, vol. 19(C), pages 119-125.
    11. Plakandaras, Vasilios & Ji, Qiang, 2022. "Intrinsic decompositions in gold forecasting," Journal of Commodity Markets, Elsevier, vol. 28(C).
    12. Dichtl, Hubert, 2020. "Forecasting excess returns of the gold market: Can we learn from stock market predictions?," Journal of Commodity Markets, Elsevier, vol. 19(C).

  18. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2015. "Cointegration of the prices of gold and silver: RALS-based evidence," Finance Research Letters, Elsevier, vol. 15(C), pages 133-137.

    Cited by:

    1. Mishra, Bibhuti Ranjan & Pradhan, Ashis Kumar & Tiwari, Aviral Kumar & Shahbaz, Muhammad, 2019. "The dynamic causality between gold and silver prices in India: Evidence using time-varying and non-linear approaches," Resources Policy, Elsevier, vol. 62(C), pages 66-76.
    2. Salisu, Afees A. & Adediran, Idris A., 2019. "Assessing the inflation hedging potential of coal and iron ore in Australia," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
    3. Liu, Guo-Dong & Su, Chi-Wei, 2019. "The dynamic causality between gold and silver prices in China market: A rolling window bootstrap approach," Finance Research Letters, Elsevier, vol. 28(C), pages 101-106.
    4. Berhan ÇOBAN & Esin FİRUZAN, 2019. "Convergence and Cointegration Analysis under Structural Breaks: Application of Turkey Tourism Markets," Sosyoekonomi Journal, Sosyoekonomi Society, issue 27(39).
    5. Andria C. Evripidou & David I. Harvey & Stephen J. Leybourne & Robert Sollis, 2022. "Testing for Co‐explosive Behaviour in Financial Time Series," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(3), pages 624-650, June.
    6. Kucher, Oleg & McCoskey, Suzanne, 2017. "The long-run relationship between precious metal prices and the business cycle," The Quarterly Review of Economics and Finance, Elsevier, vol. 65(C), pages 263-275.
    7. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2016. "Are precious metals a hedge against exchange-rate movements? An empirical exploration using bayesian additive regression trees," The North American Journal of Economics and Finance, Elsevier, vol. 38(C), pages 27-38.
    8. Vigne, Samuel A. & Lucey, Brian M. & O’Connor, Fergal A. & Yarovaya, Larisa, 2017. "The financial economics of white precious metals — A survey," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 292-308.
    9. Pradhan, Ashis Kumar & Mishra, Bibhuti Ranjan & Tiwari, Aviral Kumar & Hammoudeh, Shawkat, 2020. "Macroeconomic factors and frequency domain causality between Gold and Silver returns in India," Resources Policy, Elsevier, vol. 68(C).
    10. Salisu, Afees A. & Ndako, Umar B. & Oloko, Tirimisiyu F., 2019. "Assessing the inflation hedging of gold and palladium in OECD countries," Resources Policy, Elsevier, vol. 62(C), pages 357-377.
    11. Zhong, Wanxing & Kong, Rui & Chen, Guang, 2019. "Gold prices fluctuation of co-movement forecast between China and Russia," Resources Policy, Elsevier, vol. 62(C), pages 218-230.
    12. Zhu, Huiming & Peng, Cheng & You, Wanhai, 2016. "Quantile behaviour of cointegration between silver and gold prices," Finance Research Letters, Elsevier, vol. 19(C), pages 119-125.
    13. Yaya, OlaOluwa S. & Lukman, Adewale F. & Vo, Xuan Vinh, 2022. "Persistence and volatility spillovers of bitcoin price to gold and silver prices," Resources Policy, Elsevier, vol. 79(C).
    14. Sami, Janesh, 2021. "Has the long-run relationship between gold and silver prices really disappeared? Evidence from an emerging market," Resources Policy, Elsevier, vol. 74(C).
    15. Abdulrazak Nur Mohamed & Idiris Sid Ali Mohamed, 2023. "Precious Metals and Oil Price Dynamics," International Journal of Energy Economics and Policy, Econjournals, vol. 13(6), pages 119-128, November.
    16. Christian Pierdzioch & Marian Risse, 2020. "Forecasting precious metal returns with multivariate random forests," Empirical Economics, Springer, vol. 58(3), pages 1167-1184, March.

  19. Christian Pierdzioch & Marian Risse & Sebastian Rohloff, 2015. "Forecasting gold-price fluctuations: a real-time boosting approach," Applied Economics Letters, Taylor & Francis Journals, vol. 22(1), pages 46-50, January.

    Cited by:

    1. Robert Lehmann & Klaus Wohlrabe, 2016. "Boosting and Regional Economic Forecasting: The Case of Germany," CESifo Working Paper Series 6157, CESifo.
    2. Lehmann, Robert & Wohlrabe, Klaus, 2015. "Looking into the Black Box of Boosting: The Case of Germany," MPRA Paper 67608, University Library of Munich, Germany.
    3. Ruan, Qingsong & Huang, Ying & Jiang, Wei, 2016. "The exceedance and cross-correlations between the gold spot and futures markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 139-151.
    4. Rangan Gupta & Sayar Karmakar & Christian Pierdzioch, 2022. "Safe Havens, Machine Learning, and the Sources of Geopolitical Risk: A Forecasting Analysis Using Over a Century of Data," Working Papers 202201, University of Pretoria, Department of Economics.
    5. Wolfgang Nierhaus & Timo Wollmershäuser, 2016. "ifo Konjunkturumfragen und Konjunkturanalyse: Band II," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 72.
    6. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2015. "A real-time quantile-regression approach to forecasting gold returns under asymmetric loss," Resources Policy, Elsevier, vol. 45(C), pages 299-306.
    7. Robert Lehmann & Klaus Wohlrabe, 2016. "Boosting and Forecasting German Industrial Output: What Does a Closer Look at the Details Tell Us?," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 69(03), pages 30-33, February.
    8. Christian Pierdzioch & Marian Risse & Sebastian Rohloff, 2016. "A boosting approach to forecasting gold and silver returns: economic and statistical forecast evaluation," Applied Economics Letters, Taylor & Francis Journals, vol. 23(5), pages 347-352, March.
    9. Syed Abul, Basher & Perry, Sadorsky, 2022. "Forecasting Bitcoin price direction with random forests: How important are interest rates, inflation, and market volatility?," MPRA Paper 113293, University Library of Munich, Germany.
    10. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2016. "A boosting approach to forecasting the volatility of gold-price fluctuations under flexible loss," Resources Policy, Elsevier, vol. 47(C), pages 95-107.
    11. Salisu, Afees A. & Gupta, Rangan & Nel, Jacobus & Bouri, Elie, 2022. "The (Asymmetric) effect of El Niño and La Niña on gold and silver prices in a GVAR model," Resources Policy, Elsevier, vol. 78(C).
    12. Lehmann, Robert & Wohlrabe, Klaus, 2015. "The role of component-wise boosting for regional economic forecasting," MPRA Paper 68186, University Library of Munich, Germany, revised 03 Dec 2015.
    13. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2016. "A quantile-boosting approach to forecasting gold returns," The North American Journal of Economics and Finance, Elsevier, vol. 35(C), pages 38-55.
    14. Perry Sadorsky, 2021. "Predicting Gold and Silver Price Direction Using Tree-Based Classifiers," JRFM, MDPI, vol. 14(5), pages 1-21, April.
    15. Díaz, Juan D. & Hansen, Erwin & Cabrera, Gabriel, 2023. "Gold risk premium estimation with machine learning methods," Journal of Commodity Markets, Elsevier, vol. 31(C).
    16. Pattnaik, Debidutta & Hassan, M. Kabir & DSouza, Arun & Ashraf, Ali, 2023. "Investment in gold: A bibliometric review and agenda for future research," Research in International Business and Finance, Elsevier, vol. 64(C).
    17. Baur, Dirk G. & Dichtl, Hubert & Drobetz, Wolfgang & Wendt, Viktoria-Sophie, 2020. "Investing in gold – Market timing or buy-and-hold?," International Review of Financial Analysis, Elsevier, vol. 71(C).
    18. Dichtl, Hubert, 2020. "Forecasting excess returns of the gold market: Can we learn from stock market predictions?," Journal of Commodity Markets, Elsevier, vol. 19(C).

  20. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2014. "The international business cycle and gold-price fluctuations," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(2), pages 292-305.

    Cited by:

    1. Goodness C. Aye & Rangan Gupta & Shawkat Hammoudeh & Won Joong Kim, 2014. "Forecasting the Price of Gold Using Dynamic Model Averaging," Working Papers 201415, University of Pretoria, Department of Economics.
    2. Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2019. "Forecasting Realized Oil-Price Volatility: The Role of Financial Stress and Asymmetric Loss," Working Papers 201903, University of Pretoria, Department of Economics.
    3. Mehmet Balcilar & Elie Bouri & Rangan Gupta & Christian Pierdzioch, 2021. "El Nino, La Nina, and the Forecastability of the Realized Variance of Heating Oil Price Movements," Working Papers 202138, University of Pretoria, Department of Economics.
    4. Hossein Hassani & Emmanuel Sirimal Silva & Rangan Gupta & Mawuli K. Segnon, 2015. "Forecasting the price of gold," Applied Economics, Taylor & Francis Journals, vol. 47(39), pages 4141-4152, August.
    5. Rohloff, Sebastian & Pierdzioch, Christian & Risse, Marian, 2014. "Fluctuations of the Real Exchange Rate, Real Interest Rates, and the Dynamics of the Price of Gold in a Small Open Economy," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100429, Verein für Socialpolitik / German Economic Association.
    6. Rangan Gupta & Sayar Karmakar & Christian Pierdzioch, 2022. "Safe Havens, Machine Learning, and the Sources of Geopolitical Risk: A Forecasting Analysis Using Over a Century of Data," Working Papers 202201, University of Pretoria, Department of Economics.
    7. Mehmet Balcilar & Rangan Gupta & Christian Pierdzioch, 2022. "Oil-Price Uncertainty and International Stock Returns: Dissecting Quantile-Based Predictability and Spillover Effects Using More than a Century of Data," Energies, MDPI, vol. 15(22), pages 1-26, November.
    8. Kucher, Oleg & McCoskey, Suzanne, 2017. "The long-run relationship between precious metal prices and the business cycle," The Quarterly Review of Economics and Finance, Elsevier, vol. 65(C), pages 263-275.
    9. Sharma, Susan Sunila, 2016. "Can consumer price index predict gold price returns?," Economic Modelling, Elsevier, vol. 55(C), pages 269-278.
    10. Ayinde, Taofeek O. & Olaniran, Abeeb O. & Abolade, Onomeabure C. & Ogbonna, Ahamuefula Ephraim, 2023. "Technology shocks - Gold market connection: Is the effect episodic to business cycle behaviour?," Resources Policy, Elsevier, vol. 84(C).
    11. Qian, Yao & Ralescu, Dan A. & Zhang, Bo, 2019. "The analysis of factors affecting global gold price," Resources Policy, Elsevier, vol. 64(C).
    12. McCown, James Ross & Shaw, Ron, 2017. "Investment potential and risk hedging characteristics of platinum group metals," The Quarterly Review of Economics and Finance, Elsevier, vol. 63(C), pages 328-337.
    13. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2015. "A real-time quantile-regression approach to forecasting gold returns under asymmetric loss," Resources Policy, Elsevier, vol. 45(C), pages 299-306.
    14. Guo Jianhua & Xu Songjin, 2014. "The Relationship and Spillover Effects between Chinese and Foreign Gold Markets an Empirical Study based on Var-Mvgarch-Bekk Model," Journal of Empirical Economics, Research Academy of Social Sciences, vol. 3(1), pages 25-30.
    15. Christian Pierdzioch & Marian Risse & Sebastian Rohloff, 2016. "A boosting approach to forecasting gold and silver returns: economic and statistical forecast evaluation," Applied Economics Letters, Taylor & Francis Journals, vol. 23(5), pages 347-352, March.
    16. Zhang, Guangyong & Jiang, Le & Tian, Lixin & Fu, Min, 2021. "Analysis of the gold fixing price fluctuation in different times based on the directed weighted networks," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    17. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2016. "A boosting approach to forecasting the volatility of gold-price fluctuations under flexible loss," Resources Policy, Elsevier, vol. 47(C), pages 95-107.
    18. Salisu, Afees A. & Pierdzioch, Christian & Gupta, Rangan & Gabauer, David, 2022. "Forecasting stock-market tail risk and connectedness in advanced economies over a century: The role of gold-to-silver and gold-to-platinum price ratios," International Review of Financial Analysis, Elsevier, vol. 83(C).
    19. Salisu, Afees A. & Gupta, Rangan & Nel, Jacobus & Bouri, Elie, 2022. "The (Asymmetric) effect of El Niño and La Niña on gold and silver prices in a GVAR model," Resources Policy, Elsevier, vol. 78(C).
    20. Pradhan, Ashis Kumar & Mishra, Bibhuti Ranjan & Tiwari, Aviral Kumar & Hammoudeh, Shawkat, 2020. "Macroeconomic factors and frequency domain causality between Gold and Silver returns in India," Resources Policy, Elsevier, vol. 68(C).
    21. Rangan Gupta & Christian Pierdzioch, 2023. "Do U.S. economic conditions at the state level predict the realized volatility of oil-price returns? A quantile machine-learning approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-22, December.
    22. Aviral K. Tiwari & Claudiu T. Albulescu & Rangan Gupta, 2016. "Time-frequency relationship between US output with commodity and asset prices," Applied Economics, Taylor & Francis Journals, vol. 48(3), pages 227-242, January.
    23. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2016. "A quantile-boosting approach to forecasting gold returns," The North American Journal of Economics and Finance, Elsevier, vol. 35(C), pages 38-55.
    24. Malliaris, A.G. & Malliaris, Mary, 2015. "What drives gold returns? A decision tree analysis," Finance Research Letters, Elsevier, vol. 13(C), pages 45-53.
    25. Apergis, Nicholas & Eleftheriou, Sofia, 2016. "Gold returns: Do business cycle asymmetries matter? Evidence from an international country sample," Economic Modelling, Elsevier, vol. 57(C), pages 164-170.
    26. Plakandaras, Vasilios & Ji, Qiang, 2022. "Intrinsic decompositions in gold forecasting," Journal of Commodity Markets, Elsevier, vol. 28(C).
    27. Dichtl, Hubert, 2020. "Forecasting excess returns of the gold market: Can we learn from stock market predictions?," Journal of Commodity Markets, Elsevier, vol. 19(C).
    28. Yan Wang & Tong Lin, 2023. "A Novel Deterministic Probabilistic Forecasting Framework for Gold Price with a New Pandemic Index Based on Quantile Regression Deep Learning and Multi-Objective Optimization," Mathematics, MDPI, vol. 12(1), pages 1-21, December.

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