1. Machine Learning and the Forecastability of Cross-Sectional Realized Variance: The Role of Realized Moments. (2025). GUPTA, RANGAN ; Cepni, Oguzhan ; Plakandaras, Vasilios ; Bonato, Matteo.
   In: Working Papers.
   RePEc:pre:wpaper:202518.

   Full description at Econpapers || Download paper

2. Forecasting Spot and Futures Price Volatility of Agricultural Commodities: The Role of Climate-Related Migration Uncertainty. (2025). Salisu, Afees ; Ogbonna, Ahamuefula ; GUPTA, RANGAN ; Bouri, Elie.
   In: Working Papers.
   RePEc:pre:wpaper:202516.

   Full description at Econpapers || Download paper

3. Multi-Task Forecasting of the Realized Volatilities of Agricultural Commodity Prices. (2024). Pierdzioch, Christian ; GUPTA, RANGAN.
   In: Working Papers.
   RePEc:pre:wpaper:202423.

   Full description at Econpapers || Download paper

4. Forecasting Realized US Stock Market Volatility: Is there a Role for Economic Policy Uncertainty?. (2024). Pierdzioch, Christian ; GUPTA, RANGAN ; Cepni, Oguzhan ; Bonato, Matteo.
   In: Working Papers.
   RePEc:pre:wpaper:202408.

   Full description at Econpapers || Download paper

5. Multi-Task Forecasting of the Realized Volatilities of Agricultural Commodity Prices. (2024). Pierdzioch, Christian ; GUPTA, RANGAN.
   In: Mathematics.
   RePEc:gam:jmathe:v:12:y:2024:i:18:p:2952-:d:1483479.

   Full description at Econpapers || Download paper

6. Financial stress and realized volatility: The case of agricultural commodities. (2024). Pierdzioch, Christian ; GUPTA, RANGAN ; Cepni, Oguzhan ; Bonato, Matteo.
   In: Research in International Business and Finance.
   RePEc:eee:riibaf:v:71:y:2024:i:c:s0275531924002356.

   Full description at Econpapers || Download paper

1. Aït‐Youcef, C. (2019). How index investment impacts commodities: A story about the financialization of agricultural commodities. Economic Modelling, 80, 23–33.
   Paper not yet in RePEc: Add citation now
   

2. Akyildirim, E., Çepni, O., Pham, L., & Uddin, G. S. (2022). How connected is the agricultural commodity market to the news‐based investor sentiment? Energy Economics, 113(C), 106174. https://doi.org/10.1016/j.eneco.2022.106174.

3. Amaya, D., Christoffersen, P., Jacobs, K., & Vasquez, A. (2015). Does realized skewness predict the cross‐section of equity returns? Journal of Financial Economics, 118, 135–167. https://doi.org/10.1016/j.jfineco.2015.02.009.
   Paper not yet in RePEc: Add citation now
   

4. Andersen, T. G., & Bollerslev, T. (1998). Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International Economic Review, 39(4), 885–905. https://doi.org/10.2307/2527343.

5. Bahloul, W. (2018). Short‐term contrarian and sentiment by traders' types on futures markets: Evidence from the DCOT traders' positions. Review of Behavioral Finance, 10(4), 298–319. https://doi.org/10.1108/RBF-07-2017-0063.
   Paper not yet in RePEc: Add citation now
   

6. Bahloul, W., & Bouri, A. (2016). The impact of investor sentiment on returns and conditional volatility in US futures markets. Journal of Multinational Financial Management, 36(C), 89–102. https://doi.org/10.1016/j.mulfin.2016.07.003.

7. Bahloul, W., Balcilar, M., Cunado, J., & Gupta, R. (2018). The role of economic and financial uncertainties in predicting commodity futures returns and volatility: Evidence from a nonparametric causality‐in‐quantiles test. Journal of Multinational Financial Management, 45(C), 52–71. https://doi.org/10.1016/j.mulfin.2018.04.002.

8. Balcilar, M., Sertoglu, K., & Agan, B. (2022). The COVID‐19 effects on agricultural commodity markets. Agrekon, 61(3), 239–265. https://doi.org/10.1080/03031853.2022.2078381.
   Paper not yet in RePEc: Add citation now
   
9. Barndorff‐Nielsen, O. E., & Shephard, N. (2004). Power and bipower variation with stochastic volatility and jumps. Journal of Financial Econometrics, 2, 1–37. https://doi.org/10.1093/jjfinec/nbh001.
   Paper not yet in RePEc: Add citation now
   

10. Barndorff‐Nielsen, O. E., & Shephard, N. (2006). Econometrics of testing for jumps in financial economics using bipower variation. Journal of Financial Econometrics, 4, 1–30. https://doi.org/10.1093/jjfinec/nbi022.

11. Barndorff‐Nielsen, O. E., Kinnebrouk, S., & Shephard, N. (2010). Measuring downside risk: realised semivariance. In T. Bollerslev, J. Russell, & M. Watson (Eds.), Volatility and time series econometrics: Essays in honor of Robert F. Engle (pp. 117–136). Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199549498.003.0007.
    Paper not yet in RePEc: Add citation now
    

12. Barro, R. J. (2006). Rare disasters and asset markets in the twentieth century. Quarterly Journal of Economics, 121(3), 823–866. https://doi.org/10.1162/qjec.121.3.823.

13. Barro, R. J. (2009). Rare disasters, asset prices, and welfare costs. American Economic Review, 99(1), 243–264. https://doi.org/10.1257/aer.99.1.243.

14. Bonato, M. (2019). Realized correlations, betas and volatility spillover in the agricultural commodity market: What has changed? Journal of International Financial Markets Institutions and Money, 62(C), 184–202. https://doi.org/10.1016/j.intfin.2019.07.005.

15. Bonato, M., Çepni, O., Gupta, R., & Pierdzioch, C. (2022). El Niño, La Niña, and forecastability of the realized variance of agricultural commodity prices: Evidence from a machine learning approach. Journal of Forecasting, 42, 785–801. https://doi.org/10.1002/for.2914.
    Paper not yet in RePEc: Add citation now
    
16. Bonato, M., Cepni, O., Gupta, R., & Pierdzioch, C. (2023). Climate risks and state‐level stock‐market realized volatility. Journal of Financial Markets, 66, 100854.
    Paper not yet in RePEc: Add citation now
    
17. Borgards, O., & Czudaj, R. L. (2022). Long‐short speculator sentiment in agricultural commodity markets. International Journal of Finance and Economics, 28, 3511–3528. https://doi.org/10.1002/ijfe.2605.
    Paper not yet in RePEc: Add citation now
    
18. Bouri, E., Gupta, R., Pierdzioch, C., & Salisu, A. A. (2021). El Niño and forecastability of oil‐price realized volatility. Theoretical and Applied Climatology, 144, 1173–1180. https://doi.org/10.1007/s00704-021-03569-1.
    Paper not yet in RePEc: Add citation now
    

19. Campbell, J. Y. (2008). Viewpoint: Estimating the equity premium. Canadian Journal of Economics, 41(1), 1–21. https://doi.org/10.1111/j.1365-2966.2008.00453.x.

20. Chatziantoniou, I., Degiannakis, S., Filis, G., & Lloyd, T. (2021). Oil price volatility is effective in predicting food price volatility. Or is it? Energy Journal, 42, 2. https://doi.org/10.5547/01956574.42.6.icha.

21. Clark, T. D., & West, K. D. (2007). Approximately normal tests for equal predictive accuracy in nested models. Journal of Econometrics, 138, 291–311. https://doi.org/10.1016/j.jeconom.2006.05.023.

22. Corsi, F. (2009). A simple approximate long‐memory model of realized volatility. Journal of Financial Econometrics, 7, 174–196. https://doi.org/10.1093/jjfinec/nbp001.
    Paper not yet in RePEc: Add citation now
    

23. Degiannakis, S., Filis, G., Klein, T., & Walther, T. (2022). Forecasting realized volatility of agricultural commodities. International Journal of Forecasting, 38(1), 74–96. https://doi.org/10.1016/j.ijforecast.2019.08.011.

24. Demirer, R., Gupta, R., Nel, J., & Pierdzioch, C. (2022). Effect of rare disaster risks on crude oil: Evidence from El Niño from over 140 years of data. Theoretical and Applied Climatology, 147, 691–699. https://doi.org/10.1007/s00704-021-03856-x.
    Paper not yet in RePEc: Add citation now
    

25. Friedman, J., Tibshirani, R., & Hastie, T. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1), 1–22. https://doi.org/10.18637/jss.v033.i01.

26. Gil‐Alana, L. A., Cunado, J., & de Gracia, F. P. (2012). Persistence, long memory, and unit roots in commodity prices. Canadian Journal of Agricultural Economics, 60(4), 451–468. https://doi.org/10.1111/j.1744-7976.2012.01253.x.

27. Giot, P., & Laurent, S. (2003). Market risk in commodity markets: A VaR approach. Energy Economics, 25(5), 435–457. https://doi.org/10.1016/S0140-9883(03)00052-5.

28. Giot, P., Laurent, S., & Petitjean, M. (2010). Trading activity, realized volatility and jumps. Journal of Empirical Finance, 17(1), 168–175. https://doi.org/10.1016/j.jempfin.2009.07.001.

29. Granger, C. W., & Jeon, Y. (2004). Thick modeling. Economic Modelling, 21(2), 323–343. https://doi.org/10.1016/S0264-9993(03)00017-8.
    Paper not yet in RePEc: Add citation now
    
30. Greb, F., & Prakash, A. (2015). Has price volatility changed? Food outlook. Food and Agriculture Organization of the United Nations.
    Paper not yet in RePEc: Add citation now
    
31. Greb, F., & Prakash, A. (2017). Assessing volatility patterns in food crops. In FAO commodity and trade policy research working paper series. Available for download from: http://www.fao.org/3/a-i7066e.pdf.
    Paper not yet in RePEc: Add citation now
    
32. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning. Data mining, inference, and prediction (2nd ed.). Springer. https://doi.org/10.1007/978-0-387-84858-7.
    Paper not yet in RePEc: Add citation now
    
33. Hill, B. (1975). A simple general approach to inference about the tail of a distribution. Annals of Statistics, 3, 1163–1173. https://doi.org/10.1214/aos/1176343247.
    Paper not yet in RePEc: Add citation now
    
34. Ji, Q., Bahloul, W., Geng, J.‐B., & Gupta, R. (2020). Trading behaviour connectedness across commodity markets: Evidence from the hedgers' sentiment perspective. Research in International Business and Finance, 52(C), 101114. https://doi.org/10.1016/j.ribaf.2019.101114.
    Paper not yet in RePEc: Add citation now
    
35. Johnson, T. (2011). Food price volatility and insecurity. Council on Foreign Relations (CFR). Available for download from: https://www.cfr.org/backgrounder/foodprice-volatility-and-insecurity.
    Paper not yet in RePEc: Add citation now
    
36. Lumley, T. based on Fortran code by A. Miller (2020). leaps: Regression subset selection. R package version 3.1. Available for download from: https://CRAN.R-project.org/package=leaps.
    Paper not yet in RePEc: Add citation now
    

37. Luo, J., Klein, T., Ji, Q., & Hou, C. (2019). Forecasting realized volatility of agricultural commodity futures with infinite hidden Markov HAR models. International Journal of Forecasting, 38(1), 51–73. https://doi.org/10.1016/j.ijforecast.2019.08.007.

38. Manela, A., & Moreira, A. (2017). News implied volatility and disaster concerns. Journal of Financial Economics, 123(1), 137–162. https://doi.org/10.1016/j.jfineco.2016.01.032.

39. Marfatia, H. A., Luo, J., & Ji, Q. (2022). Forecasting the volatility of agricultural commodity futures: The role of co‐volatility and oil volatility. Journal of Forecasting, 41(2), 383–404. https://doi.org/10.1002/for.2811.

40. McAleer, M., & Medeiros, M. C. (2008). Realized volatility: A review. Econometric Reviews, 27, 10–45. https://doi.org/10.1080/07474930701853509.

41. Mišecka, T., Ciaian, P., Rajcaniova, M., & Pokrivcak, J. (2019). In search of attention in agricultural commodity markets. Economics Letters, 184(3), 108668. https://doi.org/10.1016/j.econlet.2019.108668.

42. Müller, U. A., Dacorogna, M. M., Davé, R. D., Olsen, R. B., & Pictet, O. V. (1997). Volatilities of different time resolutions—Analyzing the dynamics of market components. Journal of Empirical Finance, 4, 213–239. https://doi.org/10.1016/S0927-5398(97)00007-8.

43. Ordu, B. M., Oran, A., & Soytas, U. (2018). Is food financialized? Yes, but only when liquidity is abundant. Journal of Banking and Finance, 95, 82–96. https://doi.org/10.1016/j.jbankfin.2017.06.001.

44. R Core Team. (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL: https://www.R-project.org/.
    Paper not yet in RePEc: Add citation now
    

45. Roache, S. K. (2010). What explains the rise in food price volatility? International Monetary Fund Working Paper No. 2010/129.

46. Sakariyahu, R., Lawal, R., Oyekola, O., Dosumu, O. E., & Adigun, R. (2023). Natural disasters, investor sentiments and stock market reactions: Evidence from Turkey–Syria earthquakes. Economics Letters, 228, 111153. https://doi.org/10.1016/j.econlet.2023.111153.

47. Shan, L., & Gong, S. X. (2012). Investor sentiment and stock returns: Wenchuan earthquake. Finance Research Letters, 9(1), 36–47. https://doi.org/10.1016/j.frl.2011.07.002.

48. Shiba, S., Aye, G. C., Gupta, R., & Goswami, S. (2022). Forecastability of agricultural commodity futures realised volatility with daily infectious disease‐related uncertainty. Journal of Risk and Financial Management, 15(11), 525. https://doi.org/10.3390/jrfm15110525.

49. Sujithan, K. O., Avouyi‐Dovi, S., & Koliai, L. (2014). On the determinants of food price volatility. International Conference on Food Price Volatility: Causes & Challenges, 25–26 February, 2014, Rabat. Available for download from: https://www.imf.org/external/np/seminars/eng/2014/food/pdf/Avouyi-Dovi.pdf.
    Paper not yet in RePEc: Add citation now
    

50. Tian, F., Yang, K., & Chen, L. (2017a). Realized volatility forecasting of agricultural commodity futures using long memory and regime switching. Journal of Forecasting, 36(4), 421–430. https://doi.org/10.1002/for.2443.

51. Tian, F., Yang, K., & Chen, L. (2017b). Realized volatility forecasting of agricultural commodity futures using the HAR model with time‐varying sparsity. International Journal of Forecasting, 33(1), 132–152. https://doi.org/10.1016/j.ijforecast.2016.08.002.
    Paper not yet in RePEc: Add citation now
    

52. Xu, J.‐L., & Hsu, Y.‐L. (2022). The impact of news sentiment indicators on agricultural product prices. Computational Economics, 59(2), 1645–1657. https://doi.org/10.1007/s10614-021-10189-4.

53. Yang, K., Tian, F., Chen, L., & Li, S. (2017). Realized volatility forecast of agricultural futures using the HAR models with bagging and combination approaches. International Review of Economics and Finance, 49, 276–291. https://doi.org/10.1016/j.iref.2017.01.030.

54. Zhou, H., & Zhu, J. Q. (2012). An empirical examination of jump risk in asset pricing and volatility forecasting in China's equity and bond markets. Pacific‐Basin Finance Journal, 20(5), 857–880. https://doi.org/10.1016/j.pacfin.2009.05.005.
    Paper not yet in RePEc: Add citation now
    

55. Živkov, D., Njegić, J., & Pećanac, M. (2019). Multiscale interdependence between the major agricultural commodities. Agricultural Economics – Czech, 65(2), 82–92. https://doi.org/10.17221/147/2018-AGRICECON.

1. Joint multifractality in cross-correlations between grains & oilseeds indices and external uncertainties. (2025). Zhou, Wei-Xing ; Yang, Yan-Hong ; Gao, Xing-Lu ; Shao, Ying-Hui.
   In: Financial Innovation.
   RePEc:spr:fininn:v:11:y:2025:i:1:d:10.1186_s40854-024-00669-5.

   Full description at Econpapers || Download paper

2. Effects of the climate-related sentiment on agricultural spot prices: Insights from Wavelet Rényi Entropy analysis. (2025). Quaresima, Greta ; Mazzoccoli, Alessandro ; Mastroeni, Loretta.
   In: Energy Economics.
   RePEc:eee:eneeco:v:142:y:2025:i:c:s0140988324008557.

   Full description at Econpapers || Download paper

3. Evaluating the hedging potential of energy, metals, and agricultural commodities for U.S. stocks post-COVID-19. (2025). Han, Seungoh.
   In: The North American Journal of Economics and Finance.
   RePEc:eee:ecofin:v:77:y:2025:i:c:s1062940825000208.

   Full description at Econpapers || Download paper

4. Re-examining China and the u.s.’s respective green bond markets in extreme conditions: Evidence from quantile connectedness. (2025). Wang, Mei-Chih ; Chang, Tsangyao ; Jiang, Peiyun.
   In: The North American Journal of Economics and Finance.
   RePEc:eee:ecofin:v:75:y:2025:i:pa:s1062940824002110.

   Full description at Econpapers || Download paper

5. Forecasting the realized volatility of agricultural commodity prices: Does sentiment matter?. (2024). Pierdzioch, Christian ; GUPTA, RANGAN ; Cepni, Oguzhan ; Bonato, Matteo.
   In: Journal of Forecasting.
   RePEc:wly:jforec:v:43:y:2024:i:6:p:2088-2125.

   Full description at Econpapers || Download paper

6. Understanding Intraday Oil Price Dynamics during the COVID-19 Pandemic: New Evidence from Oil and Stock Investor Sentiments. (2024). Ftiti, Zied ; Madani, Mohamed Arbi.
   In: The Energy Journal.
   RePEc:sae:enejou:v:45:y:2024:i:3:p:57-86.

   Full description at Econpapers || Download paper

7. Time-Varying Multilayer Networks Analysis of Frequency Connectedness in Commodity Futures Markets. (2024). GUPTA, RANGAN ; Ouyang, Zisheng ; Zhou, Xuewei ; Ji, Qiang.
   In: Working Papers.
   RePEc:pre:wpaper:202422.

   Full description at Econpapers || Download paper

8. COVID-19, the Russia–Ukraine war and the connectedness between the U.S. and Chinese agricultural futures markets. (2024). Zhang, Yongmin ; Sun, Yiru ; Zhao, Yingxue ; Ding, Shusheng ; Shi, Haili.
   In: Palgrave Communications.
   RePEc:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-024-02852-6.

   Full description at Econpapers || Download paper

9. Do dirty and clean energy investments react to infectious disease-induced uncertainty?. (2024). Uddin, Gazi ; PARK, DONGHYUN ; Ghosh, Sajal ; Kanjilal, Kakali ; Dutta, Anupam.
   In: Technological Forecasting and Social Change.
   RePEc:eee:tefoso:v:205:y:2024:i:c:s0040162524003111.

   Full description at Econpapers || Download paper

10. Anatomy of sovereign yield behaviour using textual news. (2024). Sensoy, Ahmet ; Akhtaruzzaman, Md ; Dann, Susan ; Pradhan, H K ; Banerjee, Ameet Kumar.
    In: Research in International Business and Finance.
    RePEc:eee:riibaf:v:71:y:2024:i:c:s0275531924002514.

    Full description at Econpapers || Download paper

11. Financial stress and realized volatility: The case of agricultural commodities. (2024). Pierdzioch, Christian ; GUPTA, RANGAN ; Cepni, Oguzhan ; Bonato, Matteo.
    In: Research in International Business and Finance.
    RePEc:eee:riibaf:v:71:y:2024:i:c:s0275531924002356.

    Full description at Econpapers || Download paper

12. Blessings or curse: How do media climate change concerns affect commodity tail risk spillovers?. (2024). Pham, Linh ; Kamal, Javed Bin.
    In: Journal of Commodity Markets.
    RePEc:eee:jocoma:v:34:y:2024:i:c:s2405851324000266.

    Full description at Econpapers || Download paper

13. Risk spillovers and optimal hedging in commodity ETFs: A TVP-VAR Approach. (2024). Vasileiou, Evangelos ; Malhotra, Davinder ; Hadad, Elroi.
    In: Finance Research Letters.
    RePEc:eee:finlet:v:70:y:2024:i:c:s1544612324014016.

    Full description at Econpapers || Download paper

14. Impact of media hype and fake news on commodity futures prices: A deep learning approach over the COVID-19 period. (2024). Sensoy, Ahmet ; Goodell, John W ; Mahapatra, Biplab ; Banerjee, Ameet Kumar.
    In: Finance Research Letters.
    RePEc:eee:finlet:v:59:y:2024:i:c:s1544612323010309.

    Full description at Econpapers || Download paper

15. Market responses to spillovers in the energy commodity markets: Evaluating short-term vs. long-term effects and business-as-usual vs. distressed phases. (2024). Chiappari, Mattia ; Flori, Andrea ; Scotti, Francesco.
    In: International Review of Financial Analysis.
    RePEc:eee:finana:v:96:y:2024:i:pb:s1057521924005970.

    Full description at Econpapers || Download paper

16. Time-frequency extreme risk spillovers between COVID-19 news-based panic sentiment and stock market volatility in the multi-layer network: Evidence from the RCEP countries. (2024). Shi, Yujie ; Li, Yanshuang ; Xiong, Xiong ; Yi, Shangkun.
    In: International Review of Financial Analysis.
    RePEc:eee:finana:v:94:y:2024:i:c:s1057521924002710.

    Full description at Econpapers || Download paper

17. Volatility spillovers and hedging strategies between impact investing and agricultural commodities. (2024). Sensoy, Ahmet ; Akhtaruzzaman, Md ; Goodell, John W ; Banerjee, Ameet Kumar.
    In: International Review of Financial Analysis.
    RePEc:eee:finana:v:94:y:2024:i:c:s1057521924001698.

    Full description at Econpapers || Download paper

18. Novel and old news sentiment in commodity futures markets. (2024). El-Jahel, Lina ; Chi, Yeguang ; Vu, Thanh.
    In: Energy Economics.
    RePEc:eee:eneeco:v:140:y:2024:i:c:s014098832400714x.

    Full description at Econpapers || Download paper

19. Does M&A activity spin the cycle of energy prices?. (2024). Kizys, Renatas ; Enilov, Martin ; Wang, Jianuo.
    In: Energy Economics.
    RePEc:eee:eneeco:v:137:y:2024:i:c:s0140988324004894.

    Full description at Econpapers || Download paper

20. Joint multifractality in the cross-correlations between grains \& oilseeds indices and external uncertainties. (2024). Zhou, Wei-Xing ; Yang, Yan-Hong ; Gao, Xing-Lu ; Shao, Ying-Hui.
    In: Papers.
    RePEc:arx:papers:2410.02798.

    Full description at Econpapers || Download paper

21. Financial Stress and Realized Volatility: The Case of Agricultural Commodities. (2023). Pierdzioch, Christian ; GUPTA, RANGAN ; Cepni, Oguzhan ; Bonato, Matteo.
    In: Working Papers.
    RePEc:pre:wpaper:202320.

    Full description at Econpapers || Download paper

22. Negative Media Sentiment about the Pig Epidemic and Pork Price Fluctuations: A Study on Spatial Spillover Effect and Mechanism. (2023). Li, Xia ; Liu, Wei ; Tao, Jianping ; Ma, Chi ; Tan, Caifeng.
    In: Agriculture.
    RePEc:gam:jagris:v:13:y:2023:i:3:p:658-:d:1094452.

    Full description at Econpapers || Download paper

23. Energy transition metals and global sentiment: Evidence from extreme quantiles. (2023). Teplova, Tamara ; Gubareva, Mariya ; Pham, Linh ; Ghosh, Bikramaditya.
    In: Resources Policy.
    RePEc:eee:jrpoli:v:86:y:2023:i:pa:s0301420723008814.

    Full description at Econpapers || Download paper

24. Dynamic connectedness in commodity futures markets during Covid-19 in India: New evidence from a TVP-VAR extended joint connectedness approach. (2023). Olson, Dennis ; Arunachalam, Vairam ; Patnaik, Debasis ; Mishra, Aswini Kumar.
    In: Resources Policy.
    RePEc:eee:jrpoli:v:82:y:2023:i:c:s0301420723001988.

    Full description at Econpapers || Download paper

25. Climate uncertainty and information transmissions across the conventional and ESG assets. (2023). Pham, Linh ; Demirer, Riza ; Cepni, Oguzhan ; Rognone, Lavinia.
    In: Journal of International Financial Markets, Institutions and Money.
    RePEc:eee:intfin:v:83:y:2023:i:c:s1042443122002025.

    Full description at Econpapers || Download paper

26. Analysis of the Impact of COVID-19 Pandemic on the Intraday Efficiency of Agricultural Futures Markets. (2022). Ferreira, Paulo ; Ali, Haider ; Aslam, Faheem.
    In: JRFM.
    RePEc:gam:jjrfmx:v:15:y:2022:i:12:p:607-:d:1004060.

    Full description at Econpapers || Download paper

Report date: 2025-10-01 17:22:16 || Missing content? Let us know