Finance Resea ch Le e s 53 (2023) 103692
A ailable online 10 Feb ua y 2023
1544-6123/© 2023 The Au ho (s). Published by Else ie Inc. This is an open access a icle unde he CC BY-NC-ND license
(h p://c ea i ecommons.o g/licenses/by-nc-nd/4.0/).
Connec edness be ween DeFi, c yp ocu ency, s ock, and
sa e-ha en asse s
And ea Ugolini
a
, Juan C. Rebo edo
b
,
c
,
*
, Walid Mensi
d
,
e
a
Depa men o Economics, Managemen and S a is ics. Uni e si y o Milan-Bicocca, I aly
b
Depa men o Economics, Uni e sidade de San iago de Compos ela, Spain
c
ECOBAS Resea ch Cen e, Spain
d
Depa men o Finance and Accoun ing, Uni e si y o Tunis El Mana and IFGT, Tunisia
e
Depa men o Economics and Finance, College o Economics and Poli ical Science, Sul an Qaboos Uni e si y, Musca , Oman
ARTICLE INFO
JEL classi ica ion:
C32
G15
R30
Keywo ds:
DeFi
C yp ocu ency
Sa e-ha en asse
Financial condi ions
Spillo e s
ABSTRACT
This pape examines e u n spillo e s wi hin and be ween di e en DeFi, c yp ocu ency, s ock,
and sa e-ha en asse s. Fo he pe iod Janua y 2019 o Ma ch 2022, we ind ha DeFi and
c yp ocu ency asse ma ke s exhibi s ong wi hin-ma ke and be ween-ma ke e u n spillo e s,
ha s ock and sa e-ha en ma ke s show weak connec edness, and ha sa e-ha en asse s a e
mino ecei e s and ansmi e s o be ween-ma ke spillo e e ec s. The connec edness be ween
ma ke s is ime- a ying and e eals s uc u al changes in ea ly 2020. Fu he mo e, we documen
ha inancial condi ions shape he dynamics o e u n spillo e e ec s be ween ma ke s.
1. In oduc ion
In ecen yea s, digi al asse ma ke s ha e gained popula i y among in es o s; howe e , hose ma ke s a e ex emely ola ile, wi h
unexpec ed up u ns o down u ns ha ha e impo an size e ec s on hei ma ke capi aliza ion ha make hem pa icula ly agile
(Taleb, 2021). Along wi h c yp ocu encies, di e en kinds o in es able c yp o asse s ha e been in oduced in o he ma ke , ep e-
sen ing new oppo uni ies o in es men in as -g owing echnology-backed asse classes. One such asse class gaining p ominence – as
hey a e suppo ed by inancial se ices, including lending, bo owing, spo ading, online walle s, and de i a i es – is decen alized
inance (DeFi) asse s, which a e aded pee - o-pee on he basis o blockchain echnology wi h no cen al au ho i y (Guba e a, 2021;
Sch¨
a , 2021; Yousa e al., 2022). Such new asse s o e new oppo uni ies o in es men in e ms o bo h pe o mance and hedging
abili ies.
In his pape , we assess how DeFi asse s a e ela ed o o he asse classes, such as c yp ocu encies and he usual s ocks and sa e-
ha en asse s. This in o ma ion is c ucial o in es o po olio and isk managemen decisions, mainly unde ex eme ma ke ci -
cums ances when hedging and sa e-ha en ea u es o DeFi asse s could be pa icula ly use ul o p o ec ion agains downside isk.
Recen e en s such as he global pandemic (COVID-19) and geopoli ical con lic ( he Russian-Uk aine mili a y con lic ), as well as
soa ing ene gy cos s, deglobaliza ion, and s onge egula ion o c yp ocu encies ha e inc eased spillo e s among in e na ional
ma ke s, making hedging di icul and cos ly (Ha and Nham, 2022; Mai a e al., 2022; Bossman e al., 2023). The e o e, s udying
* Co esponding au ho .
E-mail add ess: [email p o ec ed] (J.C. Rebo edo).
Con en s lis s a ailable a ScienceDi ec
Finance Resea ch Le e s
jou nal homepage: www.else ie .com/loca e/ l
h ps://doi.o g/10.1016/j. l.2023.103692
Recei ed 22 No embe 2022; Recei ed in e ised o m 3 Feb ua y 2023; Accep ed 8 Feb ua y 2023
Finance Resea ch Le e s 53 (2023) 103692
2
whe he and how DeFi asse s a e connec ed wi h o he inancial asse s in he ligh o ecen e en s p o ides aluable in o ma ion on
he po en ial a ac i eness o DeFi asse s o hedge o sa e ha en in es men po olios.
P e ious esea ch shows ha digi al asse s a e weakly co ela ed wi h inancial and commodi y ma ke s, and he ac ha hey can
be used o hedge agains downside s ock ma ke mo emen s (see, e.g., Cao and Xie, 2022; Guesmi e al., 2019). Rega ding DeFi asse s,
Yousa e al. (2022) poin s o s ong connec edness be ween DeFi and con en ional cu ency ma ke s, mainly in ea ly 2020. Yousa and
Ya o aya (2022a) ind no e idence o he ding, excep o ime- a ying he ding o c yp ocu ency and DeFi asse s o e sho in-
es men ho izons and o low- ola ili y days. In con as , Co be e al. (2021) show ha DeFi asse alues luc ua e independen ly o
con en ional c yp ocu encies, p o iding hedging abili y o digi al in es o s. Simila ly, Yousa and Ya o aya (2022b) ind ha DeFi
asse s a e decoupled om adi ional asse classes. Pi˜
nei o-Chousa e al. (2022) show ha DeFi okens se e as sa e-ha en asse agains
s ock ma ke ola ili y. Ce ik e al. (2022) ind ha DeFis ha e he p ope y o sa e-ha en asse s o s a egic commodi y (c ude oil and
gold) ma ke s. Uma e al. (2022) show an inc easing in e dependence be ween DeFi, NFT and inancial ma ke s mainly du ing he
pandemic c isis. Finally, in analyzing p ice explosi eness in DeFi alues, Wang e al. (2022) epo ha DeFi p ices a e highly
co ela ed wi h c yp ocu ency ma ke unce ain y.
This pape con ibu es o exis ing esea ch by s udying spillo e s wi hin and be ween DeFí asse s (including BAT, Make , LINK, and
SNX), c yp ocu encies (including Bi coin, E he eum, Te he , and BNB), con en ional s ocks (including ma ke s in Japan, US, UK, and
Eu ope), and adi ional sa e-ha en asse s (including gold, he USD, and US T easu y bills). We use he block agg ega ion p ocedu e o
G eenwood-Nimo e al. (2015, 2016), based, in u n, on he connec edness app oach o Diebold and Yilmaz (2014). Fo he u bulen
2018–2022 pe iod, ma ked by he COVID-19 pandemic, he c yp ocu ency p ice c ash, and he mili a y con lic in Uk aine, we ind
ha all ma ke s a e mainly a ec ed by hei own shocks, and ha s ong spillo e s occu wi hin each asse class. We also ind s ong
e idence o spillo e s be ween DeFi and c yp ocu ency ma ke s, bu weak e idence o spillo e s be ween sa e-ha en ma ke s and he
emaining asse ma ke s. Finally, we documen ha inancial condi ions, as gi en by gold and s ock ma ke ola ili y, illiquidi y,
c yp ocu ency ma ke ola ili y, e m sp ead, and economic policy unce ain ies all play a ele an ole in shaping he dynamics o ne
spillo e s o he DeFi, c yp ocu ency, s ock, and sa e-ha en asse ma ke s.
The pape is o ganized as ollows: Sec ion 2 discusses he me hodology and da a, Sec ion 3 discusses he esul s, and Sec ion 4
concludes he pape .
2. Me hods and da a
2.1. Modelling connec edness
We s udy e u n spillo e connec edness be ween ou ma ke blocks: DeFi (d), c yp ocu ency (c), s ock (s), and sa e-ha en ( )
asse s, assuming ha asse e u ns in hose ma ke s a e endogenously de e mined by a ec o au o eg essi e (VAR) model wi h p lags:
y =
μ
+∑
p
k=1
Aky −k+
ε
,(1)
whe e y
=(y
d
,y
c
,y
s
,y
)′is a column ec o con aining j- ou column ec o s o e u ns in he ma ke j =d, c, s, ;
μ
is a column ec o o
cons an s; and A is a (4 ×4) coe icien block ma ix, whe e each block A
jj
accoun s o eedback e ec s be ween asse e u ns in ma ke
j, and A
ji
accoun s o eedback e ec s be ween asse e u ns in ma ke s j and i, wi h j, i =d, c, s, .
ε
is a s ochas ic column ec o wi h
ze o mean and a (4 ×4) block a iance-co a iance ma ix Σ, wi h blocks Σ
jj
accoun ing o a iance-co a iance be ween j-ma ke
e u ns, and Σ
ji
accoun ing o co a iances be ween e u ns in ma ke s j and i. F om he mo ing a e age (MA) ep esen a ion o p ice
dynamics as pe Eq. (1) we ha e:
y =∑
∞
w=1
Bw
μ
+∑
∞
w=1
Bw
ε
−w,(2)
wi h he MA coe icien s B
w
=A
1
B
w −1
+⋅ ⋅ ⋅ +A
p
B
w −p
, B
w
=0 o w <0, and B
0
equal o he iden i y ma ix. Re u n spillo e s be ween
asse e u ns a e ob ained on he basis o he h-s ep ahead o ecas e o a iance decomposi ion (Pesa an and Shin, 1998) as:
θh
i←j=
σ
−1
jj ∑H−1
h=0(e′
iBwΣej)2
∑H−1
h=0(e′
iBwΣB′
wei),(3)
whe e
σ
jj
is he j-diagonal elemen o Σ, and e
i
is a column ec o o ze os wi h 1 o i s i
h
componen . Hence, he alue o θh
i←j depends
on bo h he eedback e ec s be ween asse e u ns and he a iance-co a iance ma ix. Diebold and Yilmaz (2014) use he in o ma ion
in Eq. (3) o build a connec edness ma ix, wi h θh
i←j componen s a anged in ows epo ing in o ma ion on he impac o a shock om
ma ke j o ma ke i (in ows), o om ma ke i o ma ke j wi h θh
i←j componen s a anged in columns. By no malizing θh
i←jin ows o
sum 1 (
θh
i←j), o al spillo e s o ma ke i a e compu ed as 
θh
i←⋅ =∑d
j=1,j∕=i
θh
i←j, and o al spillo e s om ma ke i o di e en ma ke s a e
compu ed as 
θh
⋅←i=∑d
j=1,j∕=i
θh
j←i. Hence, 
θh
i←i+
θh
i←⋅ =100%.
By ex ending he Diebold-Yilmaz app oach as in G eenwood-Nimmo e al. (2015, 2016), we can assess connec edness wi hin and
A. Ugolini e al.
Finance Resea ch Le e s 53 (2023) 103692
3
be ween ma ke blocks, as gi en by he block o m o he connec edness ma ix:
⎡
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎣
Θh
d←dΘh
d←c
Θh
c←dΘh
c←c
Θh
d←sΘh
d←
Θh
c←sΘh
c←
Θh
s←dΘh
s←c
Θh
←dΘh
←c
Θh
s←sΘh
s←
Θh
h←sΘh
←
⎤
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎦
,(4)
whe e Θh
j←j and Θh
j←l include wi hin-block ma ke j spillo e s and spillo e s om ma ke block l o ma ke block j, espec i ely. Hence,
we can easily ob ain o al spillo e s om di e en ma ke blocks o ma ke block j and also he e e se spillo e e ec s.
F om es ima es o pa ame e ma ices o he VAR model, we can ob ain he bi a ia e and block ela ionship me ics as in Eq. (4),
and e alua e s a is ical signi icance by Mon e Ca lo simula ion o he VAR model so as o build 95% con idence in e als.
2.2. Da a
The da ase consis s o p ices o ou asse classes: (a) DeFi asse s (including BAT, Make , LINK, and SNX); (b) c yp ocu ency asse s
(including Bi coin, E he eum, Te he , and BNB); (c) s ocks ( ep esen ed by he Nikkei 225 o Japan, Eu o S oxx 50 o Eu ope, FTSE
100 o he UK, and S&P 500 o he USA); and (d) sa e-ha en asse s (including gold, a ade-weigh ed index o he USD, and 3-mon h
US T easu y bills). Da a we e sou ced om Bloombe g o daily pe iods om 1 Janua y 2018 (wi h he s a ing da e delimi ed by da a
a ailabili y) o 18 Ma ch 2022. The sample pe iod includes se e al key economic and poli ical e en s such as he Bi coin p ice c ash
(wi h a p ice all o 65% in Feb ua y 2018), he COVID-19 pandemic wi h i s di e en a ian s and wa es, he global all in oil demand,
and he mili a y con lic in Uk aine.
Table 1 summa izes he main s a is ical ea u es o he da a. A e age daily p ice e u ns a e nea ze o; DeFi and sa e-ha en asse s
exhibi he highes and lowes ola ili ies, espec i ely; asse e u ns a e asymme ic and lep oku ic; no mali y is ejec ed; and all
e u n se ies a e s a iona y a he 1% le el.
3. Empi ical e idence
3.1. Connec edness es ima es
We compu ed e u n spillo e s as pe Eq. (3) om he es ima ed VAR model in Eq. (1) wi h 1 lag, selec ed acco ding o he Bayesian
in o ma ion c i e ion (BIC). The e idence in Table 2 shows ha all ma ke s a e mainly in luenced by hei own shocks. Fo example,
31% o o al shocks o BAT come om hei own shocks, while BAT con ibu es 9.3%, 10.3%, and 8% o he o ecas ing a iance o
Make , LINK, and SNX, espec i ely. I also con ibu es 33.5% o he o ecas ing a iance o c yp ocu ency, 9.1% o s ocks, and unde
Table 1
Desc ip i e s a is ics.
Mean S d. de . Max Min Skewness Ku osis JB ADF PP KPSS
DeFi asse s
BAT 0.001 0.073 0.303 -0.595 -0.486 9.472 1868.408* -9.557*** -35.237*** 0.064
Make 0.001 0.073 0.442 -0.881 -1.338 27.767 27,071.808* -9.840*** -36.994*** 0.094
LINK 0.003 0.078 0.479 -0.662 -0.520 11.129 2929.634* -9.877*** -33.327*** 0.105
SNX 0.002 0.096 0.543 -0.515 0.265 6.778 634.815* -8.058*** -35.623*** 0.251
C yp o asse s
Bi coin 0.002 0.045 0.203 -0.465 -1.045 15.473 6977.370* -9.128*** -33.431*** 0.175
E he eum 0.001 0.060 0.354 -0.551 -0.829 12.761 4276.465* -9.238*** -34.273*** 0.450*
Te he 0.000 0.004 0.053 -0.053 0.311 49.598 94,743.029* -13.026*** -61.450*** 0.007
BNB 0.004 0.064 0.529 -0.543 -0.193 14.959 6245.657* -8.700*** -32.404*** 0.165
S ocks
Nikkei 225 0.000 0.012 0.077 -0.063 -0.089 7.534 898.057* -9.348*** -32.446*** 0.055
S&P 500 0.000 0.013 0.090 -0.128 -0.984 20.862 14,087.699* -8.935*** -39.974*** 0.071
FTSE 100 0.000 0.012 0.087 -0.115 -1.181 18.651 10,930.191* -9.188*** -33.628*** 0.071
Eu o S oxx 50 0.000 0.013 0.088 -0.132 -1.155 18.867 11,215.886* -9.273*** -33.715*** 0.045
Sa e-ha en asse s
Gold 0.000 0.010 0.058 -0.051 -0.305 8.705 1436.229* -10.453*** -32.848*** 0.086
USDX 0.000 0.004 0.020 -0.015 0.265 4.973 182.079* -10.958*** -30.563*** 0.137**
T-Bill 0.000 0.003 0.012 -0.015 -0.086 6.364 495.038* -9.286*** -33.449*** 0.628
No es: This able p esen s summa y s a is ics o he analyzed DeFi, c yp ocu ency, s ock, and sa e-ha en asse s. JB deno es he Ja que-Be a s a is ic,
wi h he as e isk deno ing ejec ion o he null o no mali y a he 5% le el. ADF, PP, and KPSS deno e he augmen ed Dickey-Fulle es , Phillips-
Pe on uni oo es , and he one-sided Kwia kowski-Phillips-Schmid -Shin s a iona i y es , espec i ely, wi h an as e isk indica ing ejec ion o
he null hypo hesis.
A. Ugolini e al.
Finance Resea ch Le e s 53 (2023) 103692
4
Table 2
Connec edness ma ix be ween a iables.
BAT Make LINK SNX Bi coin E he eum Te he BNB S&P 500 FTSE 100 Eu o
S oxx 50
Nikkei
225
Gold USDX T-Bill
BAT 31.06 9.36 9.91 5.75 11.82 14.03 0.54 11.07 1.95 1.48 2.10 0.50 0.10 0.24 0.10
[29.64,
31.50]
[8.79,
9.78]
[9.38,
10.40]
[5.40,
6.28]
[11.25,
12.15]
[13.25,
14.26]
[0.18,
1.72]
[10.40,
11.46]
[1.88,
2.84]
[1.38,
2.90]
[1.94,
3.48]
[0.47,
1.56]
[0.09,
0.70]
[0.21,
1.17]
[0.07,
1.10]
Make 9.32 30.06 9.07 5.80 11.51 15.84 1.15 10.05 1.90 1.57 2.35 0.24 0.35 0.70 0.10
[8.68,
10.72]
[27.16,
30.55]
[8.26,
9.72]
[5.34,
6.64]
[10.40,
11.96]
[14.57,
16.31]
[0.51,
3.33]
[9.14,
10.63]
[1.65,
2.86]
[1.24,
3.88]
[1.86,
4.66]
[0.12,
1.38]
[0.17,
1.11]
[0.32,
1.58]
[0.02,
0.97]
Link 10.30 9.66 32.00 7.05 10.91 14.33 0.53 9.71 1.42 1.44 2.06 0.11 0.11 0.36 0.02
[9.70,
10.99]
[8.89,
10.17]
[29.60,
32.43]
[6.39,
7.60]
[10.19,
11.56]
[13.27,
14.75]
[0.08,
1.75]
[9.01,
10.06]
[1.30,
2.81]
[1.20,
3.25]
[1.69,
4.16]
[0.10,
0.83]
[0.04,
0.80]
[0.19,
1.72]
[0.01,
0.90]
SNX 8.05 8.50 9.33 42.66 8.69 11.81 0.59 6.92 0.65 0.61 1.18 0.05 0.39 0.55 0.04
[7.61,
9.04]
[8.01,
9.16]
[8.89,
10.47]
[39.25,
43.14]
[8.16,
9.15]
[11.22,
12.37]
[0.04,
2.23]
[6.41,
7.60]
[0.57,
1.55]
[0.47,
1.58]
[0.95,
2.08]
[0.03,
0.84]
[0.33,
1.56]
[0.52,
2.03]
[0.02,
1.10]
Bi coin 10.55 10.54 9.52 5.66 27.74 18.43 0.34 12.09 1.21 1.19 1.67 0.12 0.36 0.53 0.05
[9.94,
10.83]
[9.87,
10.65]
[9.11,
10.36]
[5.32,
6.09]
[26.35,
28.03]
[17.50,
18.61]
[0.05,
1.56]
[11.44,
12.55]
[1.16,
2.02]
[1.04,
2.86]
[1.45,
3.44]
[0.10,
0.63]
[0.32,
0.78]
[0.41,
1.27]
[0.03,
0.79]
E he eum 11.05 12.85 10.97 6.81 16.30 24.79 0.50 11.71 1.29 1.15 1.64 0.21 0.26 0.43 0.03
[10.64,
11.89]
[12.17,
13.01]
[10.51,
11.52]
[6.45,
7.07]
[15.46,
16.67]
[23.61,
25.00]
[0.13,
1.46]
[11.12,
11.97]
[1.20,
2.14]
[1.03,
2.61]
[1.41,
2.87]
[0.17,
0.96]
[0.23,
0.77]
[0.31,
1.35]
[0.03,
0.97]
Te he 1.01 2.94 0.70 0.77 0.30 0.66 81.55 0.74 3.44 2.30 1.88 1.33 0.07 0.44 1.86
[0.38,
2.00]
[0.56,
4.10]
[0.13,
2.09]
[0.03,
3.34]
[0.06,
2.21]
[0.30,
2.77]
[72.22,
87.76]
[0.47,
2.12]
[1.68,
6.21]
[1.48,
4.76]
[1.29,
4.25]
[0.33,
3.03]
[0.02,
2.03]
[0.05,
3.30]
[1.22,
6.43]
BNB 10.87 10.04 9.26 4.89 13.42 14.53 0.41 30.62 1.48 1.38 1.78 0.29 0.41 0.54 0.09
[9.98,
11.29]
[9.10,
10.34]
[8.55,
9.83]
[4.41,
5.50]
[12.41,
14.59]
[13.29,
14.80]
[0.24,
1.99]
[27.31,
30.54]
[1.33,
2.83]
[1.27,
3.61]
[1.60,
3.77]
[0.17,
1.62]
[0.31,
1.07]
[0.25,
1.70]
[0.02,
1.54]
S&P 500 2.83 3.10 2.22 0.83 2.42 2.68 3.29 2.17 36.63 15.27 17.43 5.69 0.52 1.08 3.83
[2.36,
5.01]
[2.14,
4.85]
[1.63,
4.64]
[0.51,
3.15]
[1.64,
4.04]
[2.14,
4.97]
[1.28,
5.89]
[1.63,
4.02]
[31.46,
38.99]
[12.87,
16.51]
[14.68,
18.52]
[4.75,
6.81]
[0.03,
1.67]
[0.64,
2.60]
[3.04,
4.51]
FTSE 100 1.76 1.25 1.62 0.68 1.55 1.85 1.40 1.37 13.46 35.98 27.61 6.41 0.24 0.53 4.29
[1.58,
2.75]
[1.07,
2.09]
[1.35,
3.25]
[0.41,
1.80]
[1.29,
1.97]
[1.45,
2.81]
[0.69,
2.35]
[1.27,
2.06]
[12.75,
14.35]
[33.98,
36.78]
[26.13,
28.32]
[6.02,
7.28]
[0.04,
0.98]
[0.23,
2.17]
[4.00,
4.98]
Eu o S oxx
50
2.40 1.90 2.09 1.10 2.12 2.42 1.20 1.86 14.40 25.75 34.07 5.56 0.25 0.86 4.03
[2.14,
3.87]
[1.51,
2.51]
[1.73,
3.55]
[0.77,
2.49]
[1.66,
2.88]
[1.92,
3.48]
[0.57,
2.42]
[1.63,
2.79]
[13.62,
15.55]
[23.98,
26.43]
[31.79,
34.96]
[5.19,
6.17]
[0.01,
1.05]
[0.55,
1.85]
[3.73,
4.91]
Nikkei 225 2.11 1.81 1.47 0.90 1.98 1.72 0.49 1.15 16.60 11.67 15.09 40.90 0.24 0.71 3.15
[1.25,
5.00]
[1.16,
5.51]
[0.72,
4.86]
[0.10,
3.09]
[0.56,
4.44]
[0.82,
4.70]
[0.14,
1.45]
[0.49,
3.55]
[11.52,
17.79]
[8.90,
13.18]
[12.49,
17.83]
[31.33,
47.82]
[0.05,
1.24]
[0.18,
3.17]
[1.92,
4.41]
Gold 0.37 0.89 0.50 0.86 1.41 1.07 0.51 1.35 0.49 0.38 0.76 0.22 74.22 8.93 8.04
[0.22,
1.91]
[0.45,
3.08]
[0.11,
2.13]
[0.57,
3.19]
[0.98,
5.09]
[0.71,
2.75]
[0.04,
1.78]
[0.76,
3.67]
[0.10,
4.20]
[0.07,
1.71]
[0.02,
2.60]
[0.10,
3.61]
[62.74,
76.23]
[7.64,
10.68]
[6.53,
11.77]
USDX 1.31 2.47 0.93 1.39 2.29 2.15 0.47 2.04 4.68 2.44 3.86 0.93 8.66 64.92 1.48
[0.67,
3.75]
[1.22,
6.14]
[0.55,
3.42]
[0.90,
3.36]
[1.43,
5.06]
[1.37,
5.02]
[0.07,
2.77]
[1.28,
6.30]
[1.93,
6.58]
[0.76,
3.47]
[1.61,
5.39]
[0.22,
2.85]
[6.56,
10.34]
[50.41,
71.66]
[1.22,
5.24]
T-Bill 0.24 0.25 0.05 0.07 0.12 0.15 2.10 0.07 6.99 7.85 7.69 3.11 5.78 1.25 64.27
[0.17,
2.11]
[0.12,
3.43]
[0.03,
1.39]
[0.03,
1.56]
[0.10,
2.44]
[0.08,
2.61]
[0.91,
6.67]
[0.05,
1.40]
[6.11,
8.89]
[6.85,
8.54]
[6.76,
8.66]
[2.74,
4.26]
[4.97,
7.60]
[0.94,
3.25]
[54.82,
63.81]
No es: The able epo s esul s on spillo e s among he DeFi, c yp ocu ency, s ock, and sa e-ha en asse s indica ed in he i s ow and i s column using a o ecas ho izon o h =10 ading days o he
(no malized) spillo e me ic in Eq. (3). Repo ed o he ma ke in each ow is he o ecas e o a iance ha is explained by he ma ke s indica ed in he columns, wi h alues adding 100%. Repo ed o
he ma ke in each column is he con ibu ion o he a iance o ha ma ke o he ma ke s indica ed in he ows. 95% con idence in e als epo ed in squa ed b acke s a e compu ed using 10,000 Mon e
Ca lo simula ions o he educed- o m VAR model.
A. Ugolini e al.
Finance Resea ch Le e s 53 (2023) 103692
5
2% o sa e-ha en asse s. Two-way spillo e s o he o he asse classes also e eal ha own shocks a e mo e ele an han shocks om
o he asse s. Table 3 con i ms ha ma ke s a e la gely a ec ed by hei own shocks, e.g., s ock and sa e-ha en ma ke s. In con as ,
DeFi ma ke s show a lowe own in luence, ecei ing a g ea e impac om o he ma ke s (mainly om c yp ocu ency) and
con ibu ing mos shocks (29.6%) o he c yp ocu ency ma ke . O e all, DeFi and he c yp ocu ency ma ke s a e closely in e -
connec ed, and also ela i ely disconnec ed om bo h s ock ma ke s, as epo ed by Yousa and Ya o aya (2022b), and sa e-ha en
ma ke s; he la e , in u n, show weak connec edness, as would be expec ed om hei sa e-ha en na u e, a esul ha is consis-
en wi h Ce ik e al. (2022).
Fig. 1 plo s connec edness and he size and di ec ion o spillo e s wi hin and be ween DeFi, c yp ocu ency, s ock and sa e-ha en
asse ma ke s. DeFi, c yp ocu ency, and s ock ma ke s a e ne con ibu o s o spillo e s, whe eas sa e-ha en asse s a e a ne ecei e
o spillo e s. A s onge bidi ec ional spillo e is obse ed be ween DeFi and c yp ocu ency ma ke s, e lec ing g ea e in eg a ion
be ween hose ma ke s – consis en wi h he e idence epo ed by Yousa e al. (2022) bu a odds wi h ha epo ed by Co be e al.
(2021). Sa e-ha en asse s ecei e spillo e s om all ma ke s, ansmi ing some isk o s ock ma ke s and negligible isk o he DeFi
and c yp ocu ency ma ke s. S ock ma ke s show bidi ec ional connec edness wi h all ma ke s. Wi hin DeFi asse s, BAT, Make , and
LINK a e ne con ibu o o spillo e s, whe eas SNX is a ne ecei e . In he c yp ocu ency ma ke , he connec edness ne wo k shows
signi ican bidi ec ional linkages be ween Bi coin and E he eum, consis en wi h he indings o Beneki e al. (2019). BNB is connec ed
wi h bo h Bi coin and E he eum, while Te he , in e es ingly, is disconnec ed om he h ee emaining c yp ocu encies, poin ing o
po en ial implica ions o hedging agains downwa d c yp ocu ency p ice mo emen s. Fo s ock ma ke s, he e is high connec edness,
wi h he Japanese s ock ma ke a ne ecei e o spillo e s om o he s ock ma ke s. In con as , sa e-ha en asse s a e weakly con-
nec ed, sugges ing possible di e si ica ion e ec s be ween gold, T easu y bills, and he USD index. O e all, equi y in es o s may
conside DeFi asse s, c yp ocu encies, gold, T easu y bills, and he USD index o hedge hei posi ions agains s ock p ice downwa d
mo emen s.
3.2. Spillo e dynamics and inancial condi ions
We explo e whe he spillo e s change o e he sample pe iod by es ima ing hose spillo e s o a daily olling window o 220
ading days, which allows eedback e ec s and a iance-co a iance ma ix o swing o e he sample pe iod, and hus connec edness
alues as pe Eq (4). The g aphical e idence in Fig. 2 shows ha spillo e s ose du ing he i s COVID-19 wa e (Ma ch-Ap il 2020) and
om ea ly 2021: (a) spillo e s om DeFi asse s o c yp ocu encies and ice e sa exhibi ed he same pa e ns, anging om 22% in
Decembe 2020 o abo e 35% in Ma ch 2022, he la e e lec ing he mili a y con lic in Uk aine; (b) spillo e s be ween sa e-ha en
asse s and DeFi and c yp ocu ency asse s we e smoo he han hose be ween DeFi asse s and s ock ma ke s; (c) be ween Janua y 2020
and Janua y 2021, spillo e s be ween s ocks and c yp ocu ency and DeFi asse s inc eased, and dec eased o sa e-ha en asse s,
unde lying he impo ance o adding sa e-ha en asse s o equi y-DeFi o equi y-c yp ocu ency po olios; and inally, (d) spillo e s
om sa e-ha en asse s o he o he ma ke s we e lowe han hose om DeFi asse s, c yp ocu encies, and s ocks o sa e-ha en asse s.
We examine whe he ime- a ying ne spillo e s a e shaped by inancial ma ke condi ions. Pa icula ly, we conside : (a) un-
ce ain y as gi en by ola ili y in he gold and s ock ma ke s (CBOE gold and VIX indices); (b) illiquidi y in he in e bank ma ke as
gi en by he TED sp ead (3-mon h LIBOR based on he USD minus he 3-mon h T easu y yield); (c) T easu y sp ead (US go e nmen
10-yea yield minus US go e nmen 3-mon h yield); (d) c yp ocu ency ma ke ola ili y (see Wang e al., 2022) as gi en by he VCRIX
index (Royal on VCRIX C yp o Index; see Kim e al., 2021); and (e) he Economic Policy Unce ain y (EPU) index.
Table 4 epo s es ima ed impac s o six con ol a iables on ne spillo e s o each o he ou asse classes. Gold ola ili y has a
nega i e and signi ican impac on ne spillo e s o DeFi, c yp ocu ency, and s ock ma ke s, implying ha an inc ease in gold un-
ce ain y educes ne spillo e s in hose ma ke s. In con as , e ec s a e posi i e o sa e-ha en asse s. VIX has no impac on ne
spillo e s o c yp ocu ency and sa e-ha en asse s, bu does posi i ely in luence ne spillo e s in DeFi ma ke s. Likewise, he impac o
VIX on ne spillo e s in he s ock ma ke s is nega i e, implying ha a ise in VIX educes ne spillo e s in s ock ma ke s. As o he
illiquidi y impac , TED posi i ely a ec s ne spillo e s in all ma ke s, wi h he excep ion o sa e-ha en asse s whe e he sign is
Table 3
Connec edness ma ix o DeFi, c yp ocu ency, s ock, and sa e-ha en asse ma ke s.
DeFi C yp o S ocks Sa e-ha en
DeFi 59.47 34.88 4.90 0.77
(55.25, 62.14) (32.02, 37.82) (4.21, 10.16) (0.50, 3.68)
C yp o 29.61 63.53 5.59 1.27
(26.78, 32.48) (57.99, 68.16) (4.17, 11.90) (0.79, 5.50)
S ocks 7.02 7.42 80.63 4.93
(5.11, 14.60) (4.79, 13.46) (70.36, 86.82) (3.60, 8.39)
Sa e-ha en 3.11 4.58 13.13 79.18
(1.67, 11.82) (2.59, 15.18) (9.08, 20.26) (65.27, 86.86)
No es. This able p esen s e idence o ma ke connec edness among DeFi, c yp ocu ency, s ock, and sa e-ha en ma ke s using a o ecas ho izon o h
=10 ading days and he block agg ega ion p ocedu e o G eenwood-Nimmo e al. (2015). Repo ed o he ma ke in each ow is he ac ion o he
o ecas e o a iance ha is explained by he ma ke s indica ed in he columns, wi h alues adding 100%. Repo ed o he ma ke in each column is
he con ibu ion o each ma ke indica ed in he ows. 95% con idence in e als epo ed in ound b acke s a e compu ed using 10,000 Mon e Ca lo
simula ions o he educed- o m VAR model.
A. Ugolini e al.

Finance Resea ch Le e s 53 (2023) 103692
6
nega i e. The T easu y sp ead impac s nega i ely on spillo e s o bo h he DeFi and sa e-ha en asse ma ke s, and posi i ely o
c yp ocu ency and s ock ma ke s. The VCRIX con ibu es posi i ely o ne spillo e s o DeFi, s ock, and c yp ocu ency ma ke s, and
nega i ely o ne spillo e s o sa e-ha en asse ma ke s. Finally, a ise in EPU educes spillo e s in all ma ke s, excep o sa e-ha en
asse s. O e all, hose esul s highligh he ele ance o inancial ma ke condi ions in shaping spillo e s ac oss ma ke s, a inding ha
is consis en wi h Rebo edo e al. (2021) o commodi y ma ke s.
4. Conclusions
This s udy examines spillo e s be ween ou ma ke blocks, namely DeFi (BAT, Make , LINK, and SNX), c yp ocu encies (Bi coin,
E he eum, Te he , and BNB), s ock ma ke s (Japan, US, UK, and Eu ope), and sa e-ha en asse s (gold, USD index, and US T easu y
bills) using he Diebold and Yilmaz (2014) spillo e index and he G eenwood-Nimmo e al. (2015, 2016) me hodology. Addi ionally
in es iga ed is he impac o ce ain inancial condi ions on he size o spillo e s, including implied gold ola ili y, equi y ma ke
unce ain y (VIX), TED sp ead, T easu y sp ead, he Royal on VCRIX C yp o Index, and he EPU index.
Ou esul s e eal ha all ma ke s a e mainly a ec ed by hei own shocks. O all he asse classes, DeFi asse s and c yp ocu encies
exhibi he highes spillo e s, while he sa e-ha en asse s a e hose leas connec ed wi h o he asse s. Spillo e s among ma ke s a e
dynamic and each hei highes le el be ween ea ly 2020 and ea ly 2021. Finally, he gold ola ili y index, VIX, TED, T easu y sp ead,
Royal on VCRIX C yp o Index, and EPU index all impac on ne spillo e size wi hin each asse class. These indings a e help ul o
in es o s and po olio manage s and ha e implica ions o he design o policies.
CRediT au ho ship con ibu ion s a emen
And ea Ugolini: Me hodology, So wa e, Da a cu a ion, W i ing – o iginal d a , W i ing – e iew & edi ing. Juan C. Rebo edo:
Concep ualiza ion, Me hodology, W i ing – o iginal d a , Fo mal analysis, W i ing – e iew & edi ing, Funding acquisi ion. Walid
Mensi: Concep ualiza ion, W i ing – o iginal d a , W i ing – e iew & edi ing.
Fig. 1. Connec edness wi hin and be ween DeFi, c yp ocu ency, s ock, and sa e-ha en asse ma ke s.
A. Ugolini e al.
Finance Resea ch Le e s 53 (2023) 103692
7
Decla a ion o Compe ing In e es
The e is no con lic o in e es among au ho s.
Da a a ailabili y
Da a will be made a ailable on eques .
Fig. 2. Connec edness dynamics o DeFi, c yp ocu ency, s ock, and sa e-ha en asse ma ke s.
Table 4
Financial ma ke condi ions and ne spillo e s in he DeFi, c yp ocu ency, s ock, and sa e-ha en asse ma ke s.
DeFi C yp o S ocks Sa e-ha en
β
0
4.553*** -8.059*** 4.245*** -0.739
(0.615) (0.691) (0.502) (1.353)
Gold Vol -0.495*** -0.628*** -0.415*** 1.538***
(0.054) (0.06) (0.044) (0.118)
VIX 0.114*** 0.051 -0.138*** -0.027
(0.921) (1.035) (0.752) (2.025)
TED 14.101*** 10.084*** 1.294* -25.479***
(0.921) (1.035) (0.752) (2.025)
TS -4.248*** 3.809*** 2.840*** -2.400***
(0.244) (0.275) (0.199) (0.538)
VCRIX 0.003*** 0.008*** 0.003*** -0.013***
(0.001) (0.001) (0.001) (0.001)
EPU -0.027*** -0.031*** -0.012*** 0.069***
(0.002) (0.002) (0.001) (0.004)
Adj. R
2
0.647 0.622 0.656 0.694
No es. This able p esen s e idence on he e ec s o inancial ma ke condi ions as gi en by he ola ili y o gold, s ocks, c yp ocu encies, illiquidi y,
T easu y sp ead, and economic policy unce ain y on ne spillo e s in he DeFi, c yp ocu ency, s ocks, and sa e-ha en asse ma ke s. T-s a is ics a e
epo ed in ound b acke s. *, ** and *** indica e signi icance a he 10%, 5%, and 1% le els, espec i ely.
A. Ugolini e al.
Finance Resea ch Le e s 53 (2023) 103692
8
Acknowledgemen s
We would like o hank wo anonymous e iewe s o cons uc i e and insigh ul commen s. Juan C. Rebo edo acknowledges
inancial suppo p o ided by he Agencia Es a al de In es igaci´
on (Minis e io de Ciencia, Inno aci´
on y Uni e sidades) unde esea ch
p ojec wi h e e ence PID2021–124336OB-I00 co- unded by he Eu opean Regional De elopmen Fund (ERDF/FEDER).
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