Return and Volatility Connectedness in Electronic Warehouse Receipt Market of Turkey

Abstract

Over the course of the last century, globalization and integration have increased significantly around the world. The rise in economic and financial globalization and integration has increased the connectedness between national economies and financial markets and secured an important place in the systemic risk spillover. It is important to analyze the issue in terms of different markets. Food prices around the world have increased significantly over the last 20 years. The price and volatility increase associated with food products lead to important socioeconomic and social problems. In this context, it will be important for decision-makers to assess the issue from the perspective of financial markets and to understand and reveal the dynamic structure of food commodity markets. This study aims to examine the connectedness of return and volatility in the Electronic Warehouse Receipt (EWR) market, where agricultural commodities are traded in Turkey, and to analyze its dynamic structure that changes over time. In this study, the Diebold-Yilmaz connectedness measurement method based on the forecast error variance decomposition after the VAR (p) model was used to analyze the connectedness between financial assets. According to the results of the static analysis performed, it was observed that while the return connectedness in the EWR market is very low, the volatility connectedness is at a higher level than the return connectedness. Based on the results of the dynamic analysis, no trend was observed in return connectedness; however, rapid increases and decreases were observed for certain periods. On the other hand, while an increasing trend was observed in the dynamic analysis of volatility connectedness, sudden increases and decreases were observed during periods of crisis. Of all agricultural commodities, it was observed that barley was the asset that sent the most net shock into the system. The EWR market in Turkey has come up recently. The market's structure, dynamics, and synchronization with other markets are still at a low level. The spillover effect of return and volatility shocks in the market are also low. The findings of this study can be used by producers, financial market participants and various decision makers for risk management, hedging and profit maximization purposes.

Keywords

• Financial connectedness
• Electronic warehouse receipt
• Agricultural commodity market
• Agricultural finance
• Food prices volatility

Türkiye’nin Elektronik Ürün Senedi Piyasasında Getiri ve Volatilite Bağlantılılığı

Abstract

Dünyada son yüzyıldan bu yana küreselleşme ve entegrasyon ciddi oranda yükselmiştir. Ekonomik ve finansal küreselleşme ve entegrasyondaki artış ülke ekonomileri ve finansal piyasalar arasındaki bağlantılılığı artırmakta ve sistematik riskin yayılmasında önemli bir yer tutmaktadır. Konunun farklı piyasalar özelinde incelenmesi önem arz etmektedir. Dünyada gıda fiyatları son 20 yılda önemli oranda artmıştır. Gıda ürünlerinde yaşanan fiyat ve volatilite artışları önemli sosyoekonomik ve toplumsal sorunları da beraberinde getirmektedir. Bu bağlamda konuya finansal piyasalar açısından bakmak ve gıda emtia piyasalarının dinamik yapısını anlamak ve ortaya koymak karar alıcılar açısından önemli olacaktır. Bu hedefle çalışmanın amacı Türkiye’de gıda emtialarının işlem gördüğü Elektronik Ürün Senedi (ELÜS) piyasasında getiri ve getiri volatilitesi bağlantılılığının incelenmesi ve zamanla değişen dinamik yapısının analizidir. Çalışmada finansal varlıklar arasındaki bağlantılılığın analizinde VAR (p) modeli sonrası tahmin hata varyans ayrıştırmasına dayalı Diebold-Yılmaz bağlantılılık ölçümü yöntemi kullanılmıştır. Gerçekleştirilen statik analiz sonuçlarına göre ELÜS piyasasında getiri bağlantılılığının oldukça düşük düzeyde iken volatilite bağlantılılığının getiri bağlantılılığına nazaran daha yüksek düzeyde var olduğu görülmüştür. Dinamik analiz sonucunda getiri bağlantılılığında herhangi bir trend görülmemiş, fakat belirli dönemlerde hızlı yükselişler ve düşüşler görülmektedir. Volatilite bağlantılılığının dinamik analizinde ise yükselen bir trend görülmekle birlikte kriz dönemlerinde ani yükseliş ve düşüşler görülmüştür. Tüm gıda emtiaları içerisinde sisteme en çok net şok yayan varlığın arpa olduğu görülmüştür. Türkiye’de ELÜS piyasası oldukça yakın bir geçmişe sahiptir. Piyasanın yapısı, dinamikleri ve diğer piyasalarla senkronizasyonu henüz düşük seviyededir. Piyasadaki getiri ve getiri volatilitesi şoklarının yayılma etkisi henüz düşük düzeydedir. Bu çalışmanın bulguları, üreticiler, finansal piyasa katılımcıları ve çeşitli karar alıcılar tarafından, risk yönetimi, hedge ve kar maksimizasyonu amaçlarıyla kullanılabilir.

Keywords

• Finansal bağlantılılık
• Elektronik ürün senedi
• Gıda emtia piyasaları
• Tarım finansmanı
• Gıda fiyatları volatilitesi

References

1. Agizan S. and Bayramoglu, Z. (2021). Comparative Investment Analysis of Agricultural Irrigation Systems. Journal of Tekirdag Agricultural Faculty, 18(2): 222-233.
2. Alizadeh, S., Brandt, M. W. and Diebold, F. X. (2002). Range‐based estimation of stochastic volatility models. The Journal of Finance, 57(3): 1047-1091.
3. Alter, A. and Beyer, A. (2014). The dynamics of spillover effects during the European sovereign debt turmoil. Journal of Banking & Finance, 42: 134-153.
4. Antonakakis, N. and Kizys, R. (2015). Dynamic spillovers between commodity and currency markets. International Review of Financial Analysis, 41: 303-319.
5. Antonakakis, N., Chatziantoniou, I. and Gabauer, D. (2020). Refined measures of dynamic connectedness based on time-varying parameter vector autoregressions. Journal of Risk and Financial Management, 13(4): 84.
6. Aydın, A. (2021). The electronic warehouse receipt (EWR) and analysis in terms of islamic law. Journal of Commercial and Intellectual Property Law, 7(1): 21-36.
7. Balcilar, M. and Usman, O. (2021). Exchange rate and oil price pass-through in the BRICS countries: Evidence from the spillover index and rolling-sample analysis. Energy, 229: 120666.
8. Balli, F., de Bruin, A., Chowdhury, M. I. H. and Naeem, M. A. (2020). Connectedness of cryptocurrencies and prevailing uncertainties. Applied Economics Letters, 27(16): 1316-1322.

9. Balli, F., Naeem, M. A., Shahzad, S. J. H. and de Bruin, A. (2019). Spillover network of commodity uncertainties. Energy Economics, 81: 914-927.
10. Baruník, J. and Kočenda, E. (2019). Total, asymmetric and frequency connectedness between oil and forex markets. The Energy Journal, 40(Special Issue): 157-174.
11. Baruník, J., Kočenda, E. and Vácha, L. (2016). Asymmetric connectedness on the US stock market: Bad and good volatility spillovers. Journal of Financial Markets, 27: 55-78.
12. Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3): 307-327.
13. Bouri, E., Lucey, B., Saeed, T. and Vo, X. V. (2020). Extreme spillovers across Asian-Pacific currencies: A quantile-based analysis. International Review of Financial Analysis, 72: 101605.
14. Brooks, C. (2019). Introductory Econometrics for Finance (4th ed.). Cambridge University Press: Cambridge. Cayir, C. (2019). The effect of electronic warehouse receipts on agricultural prices: the case of Turkey. (Master’s Thesis) Istanbul University Institute of Social Sciences, Ankara.
15. Chen, S. T., Kuo, H. I. and Chen, C. C. (2010). Modeling the relationship between the oil price and global food prices. Applied Energy, 87(8): 2517-2525.
16. Corbet, S., Meegan, A., Larkin, C., Lucey, B. and Yarovaya, L. (2018). Exploring the dynamic relationships between cryptocurrencies and other financial assets. Economics Letters, 165: 28-34.
17. Demirer, M., Diebold, F. X., Liu, L. and Yilmaz, K. (2018). Estimating global bank network connectedness. Journal of Applied Econometrics, 33(1): 1-15.
18. Diebold, F. X. and Yilmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. The Economic Journal, 119(534): 158-171.
19. Diebold, F. X. and Yilmaz, K. (2011). Equity market spillovers in the Americas. Financial Stability, Monetary Policy, and Central banking, 15: 199-214.
20. Diebold, F. X. and Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of forecasting, 28(1): 57-66.
21. Diebold, F. X. and Yilmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182(1): 119-134.
22. Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50(4): 987-1007.
23. Ferrer, R., Shahzad, S. J. H. and Soriano, P. (2021). Are green bonds a different asset class? Evidence from time-frequency connectedness analysis. Journal of Cleaner Production, 292: 125988.
24. Ferrer, R., Shahzad, S. J. H., López, R., Jareño, F. (2018). Time and frequency dynamics of connectedness between renewable energy stocks and crude oil prices. Energy Economics, 76: 1-20.
25. Food and Agricultural Organization of the United Nations (FAO), (2021). Food Prices Index, http://www.fao.org/policy-support/tools-and-publications/resources-details/en/c/449297/ ,( Access Date: 20.08.2021).
26. Garman, M. B. and Klass, M. J. (1980). On the estimation of security price volatilities from historical data. Journal of Business, 53(1): 67-78.
27. Gillaizeau, M., Jayasekera, R., Maaitah, A., Mishra, T., Parhi, M. and Volokitina, E. (2019). Giver and the receiver: Understanding spillover effects and predictive power in cross-market Bitcoin prices. International Review of Financial Analysis, 63: 86-104.
28. Giudici, P. and Pagnottoni, P. (2019). High frequency price change spillovers in bitcoin markets. Risks, 7(4): 1-18. Hansen, P. R. and Lunde, A. (2005). A forecast comparison of volatility models: does anything beat a GARCH (1,1)? Journal of applied Econometrics, 20(7): 873-889.
29. Hasan, M., Arif, M., Naeem, M. A., Ngo, Q. T. and Taghizadeh–Hesary, F. (2021). Time-frequency connectedness between Asian electricity sectors. Economic Analysis and Policy, 69: 208-224.
30. Hussain Shahzad, S. J., Bouri, E., Arreola-Hernandez, J., Roubaud, D. and Bekiros, S. (2019). Spillover across Eurozone credit market sectors and determinants. Applied Economics, 51(59): 6333-6349.
31. Ji, Q., Bouri, E., Lau, C. K. M. and Roubaud, D. (2019). Dynamic connectedness and integration in cryptocurrency markets. International Review of Financial Analysis, 63: 257-272.
32. Kang, S. H., Tiwari, A. K., Albulescu, C. T. and Yoon, S. M. (2019). Exploring the time-frequency connectedness and network among crude oil and agriculture commodities V1. Energy Economics, 84: 104543.
33. Kliber, A. and Włosik, K. (2019). Isolated ıslands or communicating vessels?–Bitcoin price and volume spillovers across cryptocurrency platforms. Finance a Uver, 69(4): 324-341.
34. Koop, G., Pesaran, M.H. and Potter, S.M. (1996). Impulse response analysis in non-linear multivariate models. Journal of Econometrics, 74: 119–147.
35. Laborde, D., Martin, W., Swinnen, J. and Vos, R. (2020). COVID-19 risks to global food security. Science, 369(6503): 500-502.
36. Li, X., Zhang, R. and Wang, J. (2019). The casual relationship between China’s financial stress and economic policy uncertainty: a bootstrap rolling-window approach. American Journal of Industrial and Business Management, 9(6), 1395-1408.
37. Li, Z., Wang, Y. and Huang, Z. (2020). Risk connectedness heterogeneity in the cryptocurrency markets. Frontiers in Physics, 243.
38. Liu, T. and Hamori, S. (2020). Spillovers to renewable energy stocks in the US and Europe: are they different? Energies, 13(12): 3162.
39. Liu, T., He, X., Nakajima, T. and Hamori, S. (2020). Influence of fluctuations in fossil fuel commodities on electricity markets: evidence from spot and futures markets in Europe. Energies, 13(8): 1900.
40. Lovcha, Y. and Perez-Laborda, A. (2020). Dynamic frequency connectedness between oil and natural gas volatilities. Economic Modelling, 84: 181-189.
41. Naeem, M. A., Peng, Z., Suleman, M. T., Nepal, R. and Shahzad, S. J. H. (2020). Time and frequency connectedness among oil shocks, electricity and clean energy markets. Energy Economics, 91: 104914.
42. Negis, H., Gumus, I. and Seker, C. (2017). Effects of four different crops harvest processes on soils compaction. Journal of Tekirdag Agricultural Faculty, 14(Special Issue): 25-29.
43. Parkinson, M. (1980). The extreme value method for estimating the variance of the rate of return. Journal of Business, 53: 61-65.
44. Pesaran, M.H. and Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters, 58: 17-29.
45. Polat, O. (2020). Frequency connectedness and network analysis in equity markets: evidence from G-7 countries. Akdeniz IIBF Journal, 20(2): 221-226.
46. Qarni, M. O., Gulzar, S., Fatima, S. T., Khan, M. J. and Shafi, K. (2019). Inter-markets volatility spillover in US bitcoin and financial markets. Journal of Business Economics and Management, 20(4): 694-714.
47. Reboredo, J. C., Ugolini, A. and Aiube, F. A. L. (2020). Network connectedness of green bonds and asset classes. Energy Economics, 86: 104629.
48. Su, X. (2020). Dynamic behaviors and contributing factors of volatility spillovers across G7 stock markets. The North American Journal of Economics and Finance, 53: 101218.
49. Taylor, S. J. (1986). Modelling Financial Time Series. John Wiley and Sons, Ltd.: Chichester. Toyoshima, Y. and Hamori, S. (2018). Measuring the time-frequency dynamics of return and volatility connectedness in global crude oil markets. Energies, 11(11): 2893.
50. Trabelsi, N. (2018). Are there any volatility spill-over effects among cryptocurrencies and widely traded asset classes? Journal of Risk and Financial Management, 11(4): 66.
51. Turkstat (2021). Plant Production Statistics of Turkey, https://data.tuik.gov.tr/Kategori/GetKategori?p=tarim-111 . (Access Date: 03.03.2022).
52. Uddin, G. S., Shahzad, S. J. H., Boako, G., Hernandez, J. A. and Lucey, B. M. (2019). Heterogeneous interconnections between precious metals: Evidence from asymmetric and frequency-domain spillover analysis. Resources Policy, 64: 101509.
53. Worldometer, (2021). World Population Measurement, https://www.worldometers.info/world-population/world-population-by-year/ (Access Date: 20.08.2021).
54. Yi, S., Xu, Z. and Wang, G. J. (2018). Volatility connectedness in the cryptocurrency market: Is Bitcoin a dominant cryptocurrency? International Review of Financial Analysis, 60: 98-114.
55. Yilmaz, K. (2010). Return and volatility spillovers among the East Asian equity markets. Journal of Asian Economics, 21(3): 304-313.
56. Zhang, D. (2017). Oil shocks and stock markets revisited: Measuring connectedness from a global perspective. Energy Economics, 62: 323-333.
57. Zhang, W., He, X., Nakajima, T. and Hamori, S. (2020). How does the spillover among natural gas, crude oil, and electricity utility stocks change over time? Evidence from North America and Europe. Energies, 13(3): 727.