CoChat
Sign Up
Cited by 15Market Dynamics and VolatilityFinancial Risk and Volatility ModelingEconomics of Agriculture and Food Markets

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

On the Volatility Spillover between Agricultural Commodities and Latin American Stock Markets

Open access
Candila, Vincenzo, Farace, Salvatore·2018·Risks
DOI 10.3390/risks6040116
Open this paper in CoChat — free accountOpen PDF

Abstract

Addressing the volatility spillovers of agricultural commodities is important for at least two reasons. First, for the last several years, the volatility of agricultural commodity prices seems to have increased. Second, according to the Food and Agriculture Organization, there is a strong need for understanding the potential (negative) impacts on food security caused by food commodity volatilities. This paper aims at investigating the presence, the size, and the persistence of volatility spillovers among five agricultural commodities (corn, sugar, wheat, soybean, and bioethanol) and five Latin American (Argentina, Brazil, Chile, Colombia, Peru) stock market indexes. Overall, when a negative shock hits the commodity market, Latin American stock market volatility tends to increase. This happens, for instance, for the relationships from corn to Chile and Colombia and from wheat to Peru and Chile.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/risks6040116

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Research works printed but not linked: 35

  1. Allen, 2017, Volatility spillover and multivariate volatility impulse response analysis of GFC news events, Applied Economics, 49, 3246, 10.1080/00036846.2016.1257210 · DOI
  2. Amendola, 2017, On the influence of US monetary policy on crude oil price volatility, Empirical Economics, 52, 155, 10.1007/s00181-016-1069-5 · DOI
  3. Amendola, Alessandra, Candila, Vincenzo, and Gallo, Giampiero M. (2018). On the asymmetric impact of macro–variables on volatility. Economic Modelling. · DOI
  4. Arouri, 2012, On the impacts of oil price fluctuations on European equity markets: Volatility spillover and hedging effectiveness, Energy Economics, 34, 611, 10.1016/j.eneco.2011.08.009 · DOI
  5. Bauwens, 2006, Multivariate GARCH models: A survey, Journal of Applied Econometrics, 21, 79, 10.1002/jae.842 · DOI
  6. Bollerslev, 1986, Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics, 31, 307, 10.1016/0304-4076(86)90063-1 · DOI
  7. Bouri, 2017, Volatility transmission from commodity markets to sovereign CDS spreads in emerging and frontier countries, International Review of Financial Analysis, 49, 155, 10.1016/j.irfa.2016.11.001 · DOI
  8. Casanova, 2016, Measuring Latin America’s export dependency on China, Journal of Chinese Economic and Foreign Trade Studies, 9, 213, 10.1108/JCEFTS-08-2016-0022 · DOI
  9. Cashin, 2002, The Long-Run Behavior of Commodity Prices: Small Trends and Big Variability, IMF Staff Papers, 49, 175, 10.2307/3872481 · DOI
  10. Chang, Chia-Lin, and McAleer, Michael (2017). A Simple Test for Causality in Volatility. Econometrics, 5. · DOI
  11. Chevallier, 2013, Cross-market linkages between commodities, stocks and bonds, Applied Economics Letters, 20, 1008, 10.1080/13504851.2013.772286 · DOI
  12. Conover, 1981, A comparative study of tests for homogeneity of variances, with applications to the outer continental shelf bidding data, Technometrics, 23, 351, 10.1080/00401706.1981.10487680 · DOI
  13. Delbianco, 2018, External cycles and commodities in Latin America and the Caribbean: A cointegration analysis with breaks, Lecturas de Economía, 88, 51
  14. Diebold, 2009, Measuring financial asset return and volatility spillovers, with application to global equity markets, The Economic Journal, 119, 158, 10.1111/j.1468-0297.2008.02208.x · DOI
  15. Diebold, 2012, Better to give than to receive: Predictive directional measurement of volatility spillovers, International Journal of Forecasting, 28, 57, 10.1016/j.ijforecast.2011.02.006 · DOI
  16. Engle, 1995, Multivariate simultaneous generalized ARCH, Econometric Theory, 11, 122, 10.1017/S0266466600009063 · DOI
  17. Engle, 2013, Stock Market Volatility and Macroeconomic Fundamentals, Review of Economics and Statistics, 95, 776, 10.1162/REST_a_00300 · DOI
  18. Engle, 1990, Meteor showers or heat waves? Heteroskedastic intra-day volatility in the foreign exchange market, Econometrica, 58, 525, 10.2307/2938189 · DOI
  19. Gilbert, 2010, Food price volatility, Philosophical Transactions of the Royal Society of London B: Biological Sciences, 365, 3023, 10.1098/rstb.2010.0139 · DOI
  20. Granger, 1969, Investigating causal relations by econometric models and cross-spectral methods, Econometrica, 37, 424, 10.2307/1912791 · DOI · Paper page
  21. Gruss, 2014, After the boom–commodity prices and economic growth in Latin America and the Caribbean, International Monetary Fund, 154, 1
  22. Hafner, 2006, Volatility impulse responses for multivariate GARCH models: An exchange rate illustration, Journal of International Money and Finance, 25, 719, 10.1016/j.jimonfin.2006.04.006 · DOI
  23. Hamadi, 2017, News surprises and volatility spillover among agricultural commodities: The case of corn, wheat, soybean and soybean oil, Research in International Business and Finance, 41, 148, 10.1016/j.ribaf.2017.04.006 · DOI
  24. Jin, 2012, Volatility transmission and volatility impulse response functions in crude oil markets, Energy Economics, 34, 2125, 10.1016/j.eneco.2012.03.003 · DOI
  25. Kang, 2017, Contagion Effects and Volatility Impulse Responses between US and Asian Stock Markets, Korea and the World Economy, 18, 111
  26. Kang, 2015, The impact of oil price shocks on the stock market return and volatility relationship, Journal of International Financial Markets, Institutions and Money, 34, 41, 10.1016/j.intfin.2014.11.002 · DOI
  27. Karali, 2014, Macro determinants of volatility and volatility spillover in energy markets, Energy Economics, 46, 413, 10.1016/j.eneco.2014.06.004 · DOI
  28. Lin, 1994, Do bulls and bears move across borders? International transmission of stock returns and volatility, Review of Financial Studies, 7, 507, 10.1093/rfs/7.3.507 · DOI
  29. Malik, 2009, Volatility transmission between oil prices and equity sector returns, International Review of Financial Analysis, 18, 95, 10.1016/j.irfa.2009.03.003 · DOI
  30. Mohammadi, 2015, Return and Volatility Spillovers across Equity Markets in Mainland China, Hong Kong and the United States, Econometrics, 3, 215, 10.3390/econometrics3020215 · DOI
  31. Nazlioglu, 2013, Volatility spillover between oil and agricultural commodity markets, Energy Economics, 36, 658, 10.1016/j.eneco.2012.11.009 · DOI
  32. Nazlioglu, 2015, Volatility transmission between Islamic and conventional equity markets: Evidence from causality-in-variance test, Applied Economics, 47, 4996
  33. Śmiech, Sławomir, Papież, Monika, Dąbrowski, Marek A., and Fijorek, Kamil (2018). What drives food price volatility? Evidence based on a generalized VAR approach applied to the food, financial and energy markets. Economics Discussion Papers, Kiel Institute for the World Economy. No 2018-55. · DOI
  34. Tyner, 2008, The US Ethanol and Biofuels Boom: Its Origins, Current Status, and Future Prospects, BioScience, 58, 646, 10.1641/B580718 · DOI
  35. Vardar, 2018, Shock transmission and volatility spillover in stock and commodity markets: Evidence from advanced and emerging markets, Eurasian Economic Review, 8, 231, 10.1007/s40822-018-0095-3 · DOI

Cited by

Sorted by citations

See all 15

Metadata

Authors
Candila, V., & Farace, S.
Affiliations
University of Salerno (IT)
Corresponding author
Candila, Vincenzo
ORCID
Venue
Risks
Publisher
MDPI AG
Published
2018-10-10
Volume
6
Issue
4
Pages
116
ISSN
2227-9091 (ISSN-L)
DOI
10.3390/risks6040116
OpenAlex
W2896647250
Cited by
15
References
1
Citations by year
2026: 1 · 2024: 2 · 2023: 3 · 2022: 3 · 2021: 4 · 2020: 2
FWCI
2.28
Type
journal article
Language
en
Open access
Licence
Locations
  • Risks (publishedVersion, cc-by)
  • IRIS Research product catalog (Sapienza University of Rome) (submittedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Topics
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Market Dynamics and Volatility
  • Social Sciences › Economics, Econometrics and Finance › Finance › Financial Risk and Volatility Modeling
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Economics of Agriculture and Food Markets
Keywords
volatility spillovers, agricultural commodities, Latin American stock markets, maize, sugar, wheat, soybean, bioethanol, food security, commodity price volatility, stock market volatility, Argentina, Brazil, Chile, Colombia, Peru

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review