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Exogeneous shocks, risk, and market convergence of real alternative and financial assets: evidence from nonlinear dynamics

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Abstract

This article investigates the dynamic process of convergence among a set of real alternative (art, fine wine, gold, residential real estate) and financial assets in the US context over the period 2003–2019. The objective is to explore the time-varying behavior of their links considering structural breaks coming from exogenous economic and financial shocks that prevent market convergence from being a linear process. Using a procedure that determines endogenously multiple structural breaks and a rolling co-integration framework, we show that price co-movements depend on the global financial and economic environments. Our results confirm the existence of a long-term co-integration relationship among price series but with structural breaks. We find that exogenous shocks lead to a lower degree of convergence of real alternative and conventional assets, and expansion phases promote market convergence between them. Our results contribute to guiding investors in their efforts to diversify their wealth and portfolio.

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Notes

  1. An article in The Wall Street Journal (December 31, 2018) reports that art and wine outperformed stocks and bonds in 2018 (https://www.wsj.com/articles/the-best-investments-of-2018-art-wine-and-cars-11546232460).

  2. UHNWI: Ultra-high-net-worth individuals are those whose net worth exceeds $30 million excluding their primary residence. Almost 31% of UHNWI are located in the United States (Knight Franck 2019).

  3. The availability of price series for residential real estate began in 2003. The beginning of the 2000s also coincides with the acceleration of the process of financialization and globalization from which alternative real assets such as art and wine became investable assets. This was not the case during prior periods.

  4. Financial integration has received considerable attention in the literature (e.g., Hamao et al., 1991; Giovannini & Mayer, 1992; Lemmen & Eijffinger, 1996; Bayoumi, 1997; Story & Walter, 1997; Yuhn, 1997; Korajczyk, 1999; Adam et al., 2002; Hartmann et al., 2003; Baele et al., 2004; Gropp & Kashyap, 2010; Bekaert et al., 2011; Ftiti et al., 2015; Zhu et al., 2018; Hoffmann et al., 2020; Miled et al., 2021).

  5. See Le Fur (2021b) for details of the contagion effect between financial markets and collectibles markets.

  6. According to the authors, the second test seems to be the most useful to determine structural breaks.

  7. Bordeaux 500 (33%—the ten most recently physical vintages for 50 top Bordeaux chateaux); Burgundy 150 (28%—the ten most recently physical vintages for 15 red and white Burgundy); Rest of the World 60 (12%—the ten most recently physical vintages for six wines from Australia, Portugal, Spain and the USA); Bordeaux Legends 40 (10%—40 Bordeaux wines from exceptional older vintages from 1989); Italy 100 (9%—the ten most recently physical vintages for the five Super Tuscan and five other leading Italian wines); Rhone 100 (4%—the ten most recently physical vintages for five Northern and five Southern Rhone wines); Champagne 50 (3%—the most recently physical vintages for 12 champagnes).

  8. Other econometric techniques could have complemented the unit root tests with structural breaks, in particular those concerning long memory process tests. In particular, the Geweke and Porter-Hudak (1983) (GPH) test, the Phillips’ Modified Log Periodogram Regression estimator or the ARFIMA models are particularly suitable in this case. We leave this alternative for future research.

  9. We also run the Gregory-Hansen (1996) co-integration tests with ART, LIV-EX, APART, or MSCI as the dependent variables. The results confirm the long-term co-integration links of the variables. Results are available on request from the authors.

  10. The Matlab codes for the testing procedure can be found on Pierre Perron’s website at http://blogs.bu.edu/perron/codes/.

  11. The long-run causality tests are performed on the basis of the first co-integrating vector.

  12. We report the trace test statistics on the last day of the subsample period from which they are computed.

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  1. INSEEC Grande Ecole, LAREFI University of Bordeaux, Bordeaux, France

    Benoît Faye, Eric Le Fur & Stéphanie Prat

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Appendix: rolling adjustment coefficients

Appendix: rolling adjustment coefficients

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Fig. 5
figure 5

a ΔArt α2 with its confidence interval, b ΔLivex α2 with its confidence interval, c ΔGold α2 with its confidence interval, d ΔAppart α2 with its confidence interval, e ΔMsci α2 with its confidence interval

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Faye, B., Le Fur, E. & Prat, S. Exogeneous shocks, risk, and market convergence of real alternative and financial assets: evidence from nonlinear dynamics. Ann Oper Res 334, 497–520 (2024). https://doi.org/10.1007/s10479-021-04510-5

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