=Paper=
{{Paper
|id=Vol-2713/paper17
|storemode=property
|title=The impact of COVID-induced shock on the risk-return correspondence of agricultural ETFs
|pdfUrl=https://ceur-ws.org/Vol-2713/paper17.pdf
|volume=Vol-2713
|authors=Andrii Kaminskyi,Maryna Nehrey,Nina Rizun
|dblpUrl=https://dblp.org/rec/conf/m3e2/KaminskyiNR20
}}
==The impact of COVID-induced shock on the risk-return correspondence of agricultural ETFs==
https://ceur-ws.org/Vol-2713/paper17.pdf
204
The impact of COVID-induced shock on the risk-return
correspondence of agricultural ETFs
Andrii Kaminskyi1[0000-0002-6574-8138], Maryna Nehrey2[0000-0001-9243-1534]
and Nina Rizun3[0000-0002-4343-9713]
1 Taras Shevchenko National University of Kyiv, 64 Volodymyrska Str., Kyiv, 01026, Ukraine
kaminskyi.andrey@gmail.com
2 National University of Life and Environment Science of Ukraine, 15 Heroyiv Oborony Str.,
Kyiv, 03041, Ukraine
marina.nehrey@gmail.com
3 Gdańsk University of Technology, 11/12 Gabriela Narutowicza Str., Gdańsk, 80-233, Poland
nina.rizun@pg.edu.pl
Abstract. Risk-return correspondence for different investment asset classes
forms one of the pillars of modern portfolio management. This correspondence
together with interdependency analysis allows us to create portfolios that are
adequate to given goals and constraints. COVID-induced shock unexpectedly
generated high uncertainty and turmoil. Our paper is devoted to the investigation
path through shock by agricultural assets (presented by ETFs) in comparison with
traditional assets. There were identified three time periods: before the shock,
explicitly shock, and post-shock. At the explicit shock period was suggested
estimation risk frameworks on the pair indicators: falling depth and recovery
ratio. Basic attention focuses on comparison risk-return estimations prior to
shock and post-shock. To this end was considered four approaches to risk
measurement and were applied to the sample of agricultural ETFs. The results
indicated differences in risk changing by the path from before shock to post-
shock. Differences arise from choosing the approach of risk measuring. The
variability approach reveals much growth of risk of traditional assets, but the
Value-at-Risk approach indicates higher risk growth for agricultural ETFs.
Combine together with relatively low correlation these estimations provide a
clear vision of risk-return frameworks.
Keywords: exchange traded funds, risk measurement, COVID, shock, portfolio
management, agriculture, investment.
1 Introduction
The COVID-19 pandemic has a strong influence on the prices of all financial
instruments [29]. Financial markets had shivered at the end of January 2020 and crashed
in the middle of March 2020. The shock was extremely forceful. COVID-induced shock
hit almost all assets: as traditional assets as alternative assets (including
cryptocurrencies). Correspondingly, the shock had an effect on investment portfolio
___________________
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
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management which led to decreasing portfolio value. Meanwhile, different assets have
different dynamics of passing such a turbulent period. Does it necessary to change the
asset allocation design of investment portfolios? This question became an actual one
for individual and institutional investors. The aim of this paper is to investigate risk-
return correspondence “transmission” through the COVID-induced shock for
agricultural Exchange Trade Funds (ETFs) and ETN.
Two hypotheses were put forward in our research. First hypothesis conjectures
differences of shock parameters for agricultural ETFs and two traditional asset classes
such as stocks and bonds (presented in our research by key stock indices). Especially,
it was supposed differences in the renewal level. Second, our hypothesis focuses on
verification of the assumption that risk is higher aftershock then before the shock. In
general, this is a typical effect and we have tried to estimate the level of such risk
increasing.
Class of agricultural ETFs one of the significant parts of the commodities ETFs and
has its own distinctive features. The first distinctive aspect is that the prices of
agricultural production are determined both by market factors (demand in the first
place) and the crop yield (production) of a particular agricultural product. The
dependence on the yield generates an additional level of lack of correlation of such
ETFs with other investment assets, which can be used in investment portfolio forming
procedures. The second distinctive feature of the agricultural ETFs is their structuring
into ETFs associated with one agricultural product (for example, wheat, rice, livestock,
sugar, and others), and associated with a specific fund diversified through different
agricultural products. One of the interesting points for analysis concerns the
meaningfulness of such features at the time of shock and renewal. Understanding the
difference in “risk-return correspondence” in this context will allow a better
justification for their using in the portfolio structure.
Our approach involves ETF using. The emergence of ETFs in the early 1990s and
their intensive development expanded the portfolio management tools in two ways.
First, the essence of the ETF design has allowed expanding the asset classes that can be
used in the portfolios. In this regard, it is possible to use ETF connected with non-
traditional investments (commodities, gold, private equity, and many others). Such
possibilities essentially expand the diversification effect through portfolio construction.
As a rule, alternative investments indicate a lower correlation level with others.
Secondly, ETFs make it easy to assess the risk and return of the entire portfolio based
on their characteristics. In addition, to some extent, with this approach, the task of
filling the class with assets can be removed, because ETF diversified funds can be used.
The task of portfolio investment, in fact, is more reduced to a strategic allocation. So,
we used ETFs for analysis risk-return correspondence for agricultural assets.
It should be noted that we applied a complex view of the notion of “risk
measurement”. Modern financial risk theory considers different approaches to measure
risk. Each approach reflects one or another property of the many-sided notion of “risk”.
We used three approaches to risk measurement. A first approach based on the classical
view for risk measurement at the frameworks of variability. The second approach
considers risk from point of view losses in a negative situation. The importance of such
an approach is explained by using the regulative risk measure Value-at-Risk (VaR) and
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coherent risk measure Conditional Value-at-Risk (CVaR). The third approach is based
on conception sensitivity. It is logically to use sensitivity analysis in concern both types
of traditional assets – stocks and bonds. The results of using such a complex approach
are a generalized estimation of risk characteristics changing. Such an approach provides
a deeper understanding of investment risk frameworks.
2 Materials and methods
2.1 Risk measurement conception
Risk measurement in the frameworks of portfolio investment can be structured into two
blocks. The first block is a risk assessment of an investment asset, considered
separately. The second block focuses on assessing the relationship between asset
returns and risk through diversification.
The first block of risk assessment supposes to introduce mapping μ which each
return of investment asset R (interpreting as random variable) correspond some non-
negative number µ(R) ⸦ [0;+∞]. The return of investment asset (in this paper – ETF)
over a period of time [t; t+1] will be expressed through the formula:
Rt, t+1 = (Pt+1 – Pt) / Pt (1)
where Pt and Pt+1 prices of ETF in USD at times t and t+1 correspondingly. Rt, t+1 will
be a random variable, because the future price Pt+1 is unknown. Thereafter R which
reflect return through the time is also random variable. Mapping which corresponds
to some rules interpret as risk measuring.
2.2 Investment risk measures approaches
There are many measures of investment risk present which formalise in mapping µ
different logic of risk interpreting [33]. In our research, we have divided risk measuring
into three conceptual approaches:
─ Variability approach. Such an approach is based on the measurement of return`s
variability (volatility). This approach goes back to the papers of H. Markowitz [21]
and underlies the models of modern portfolio theory. Critiques of it using in the non-
transparency connection between variability indicators and real losses.
─ Losses in a negative situation. This more practical and regulative approach. It
focuses on measuring possible losses and fulfill capital requirements.
─ Sensitivity approach. According to such an approach, the risk is measured as the rate
of response for occurring some factors.
Each of the abovementioned approaches had their pros and cons. Our point of that
investment risk should be estimated by all these conceptual approaches. It provides
multifaceted understanding of investment risk.
The logic of risk measuring leads to properties which reflect “natural properties” of
risk. Trying to understand the essence of properties which should be represented in risk
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measure was formulated in [3]. Authors created the notion of coherent risk measure.
Risk measure is coherent if satisfying following properties (axioms):
Axiom 1. Sub-additivity. For all random values presenting asset`s returns R1 and R2
we have
µ (R1 + R2) ≤ µ(R1) + µ(R2) (2)
Axiom 2. Positive Homogeneity. For all R and for all λ ≥ 0, we have
µ(λR) = λµ(R) (3)
Axiom 3. Monotonicity: If R1 ≥ R2 for all possible cases then
µ(R1) ≤ µ(R2). (4)
Axiom 4. Translation Invariance. For all R and for all α ≥ 0 which interpret as risk-free
asset, we have
µ(R+α) = µ(R)α. (5)
Examples of coherent risk measures are Conditional Value-at-Risk (considered
introduced below) [28] and T. Fischer measure [8]. It is necessary to note, that presented
approach for coherency is not unique. Other approaches of coherency are considered in
[18].
The second block of risk measurement in the portfolio aspect corresponds to estimate
interrelations of returns of different asset classes. It can be estimated as average
correlation, reducing the value of chosen risk measure for a naïve diversified portfolio
or risk value for the portfolio with minimum risk.
Below we try to realize these ideas for agricultural ETFs.
2.3 Risk measurement throughout the period of shock
A financial shock is an exceptionally extraordinary event that affects the entire market.
Therefore, the classical approaches to measuring risk may be ineffective and we used
the following approach. Based on the analysis of the manifestation of COVID-induced
shock, we divided the time interval into three periods. The first period is the “calm”
period before the onset of the shock. The shock-related asset price changes began to
show in the second half of January 2020. Therefore, we had to take 08/28/2019 to
01/15/2020 as the first period. The role of measuring risk in a given period serves as a
benchmark for further changes.
As the second period, we have identified the period 01/16/2020 – 03/31/2020 – the
direct manifestation of shock. The manifestation of COVID-induced shock was, in a
sense, a classic manifestation of shock. Namely, it had the form Sign of “tick”. At first,
the onset of a shock is a gradual fall in asset prices, and then a sharp and deep fall. The
shock drop was on 03/17/2020 for the studied assets. After that, a gradual slow price
recovery begins. Moreover, at first, after the maximum fall, there is a “rollback”, and
then the dynamics stabilize. Thus, as the post-shock period, we have defined the period
04/01/2020 to 08/14/2020.
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The use of classical risk measures is not correct due to a sharp fall in a short period.
To display risk during a shock period, we have proposed an approach based on two
parameters. The first parameter characterizes the depth of the fall, and the second – the
level of recovery over a certain period. The parameter that characterizes the depth of
the fall is calculated by us as the ratio of the lowest price to the average price for 1,5
months before the start of the shock period. And the second parameter is calculated
based on the average stabilization price after the maximum decline. In our case, for
calculating average prices, we took the periods 12/01/2019–01/15/2020 and
05/01/2020–06/15/2020.
The logic for calculating the parameters is shown in the fig. 1 for SPY (ETF which
correspond to leading stock index S&P500).
Shock assessment: 1) maximum fall 2) recovery
400
350
300
250
200
150
100
50
0
Mar 05, 2020
Mar 11, 2020
Mar 17, 2020
Mar 23, 2020
Mar 27, 2020
Dec 02, 2019
Dec 06, 2019
Dec 12, 2019
Dec 18, 2019
Dec 24, 2019
Dec 31, 2019
Jan 07, 2020
Jan 13, 2020
Jan 17, 2020
Jan 24, 2020
Jan 30, 2020
Feb 05, 2020
Feb 11, 2020
Feb 18, 2020
Feb 24, 2020
Feb 28, 2020
Apr 02, 2020
Apr 08, 2020
Apr 15, 2020
Apr 21, 2020
Apr 27, 2020
May 01, 2020
May 07, 2020
May 13, 2020
May 19, 2020
May 26, 2020
Jun 01, 2020
Jun 05, 2020
Jun 12, 2020
Fig. 1. Parameters of risk during shock period for SPY.
As the third, for this period, we have applied standard approaches to measuring risk.
They are compared with the values of these parameters in the first period. The economic
sense of the study is in assessing the risk changes as a result of shock.
3 Results and discussion
3.1 Literature review
There has been a lot of academic studies that have addressed agricultural investment
and agriculture assets. The last of them are [2; 5; 6; 7; 15; 16; 27; 36].
Martin and Clapp [22] investigated the relationship between agriculture, finance, and
the state. In [10] the authors analyzed the relation between the notional value of
commodity futures contracts and expected returns on futures contracts.
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ETFs as financial instruments investigated in [14] and [32]. Petajisto proposed a
method for ETFs mispricings detection [25].
The global challenges caused by COVID have updated crisis and shock research.
The analysis of the impact of macroeconomic changes on the financial market was
conducted in [1; 11; 17; 19; 24; 26; 30]. Financial security level analysis in order to
timely detect and neutralize possible crisis phenomena presents in [9; 13; 20].
Forecasting the dynamics of financial markets during the crisis is studied in [23; 31;
34; 35].
In spite of shortness time after COVID-induced shock, there are a lot of papers
described this phenomenon. The uncertainty which have raised from this shock is
analyzed in [4].
In new European Banking Study 2020, was quantified COVID induced effects on
balance sheets and P&Ls of Europe’s 50 largest banks and set out the implications for
bank management, governments, and regulators [12].
3.2 Sample of agricultural ETFs
Our sample of agricultural ETFs was created on the base of capitalization level of such
financial instrument which traded in the USA which are currently tagged by ETF
Database. It is necessary to note that we use term ETF in extend sense which include
both instruments which tracking indices: ETF and ETN. Of course, we pay attention
for the differences between these instruments, but our main focus for the conceptual
essence of tracking indices, after that we did not differentiate ETF and ETN in our paper
and use one term ETF.
Agriculture ETFs invest in agriculture commodities including sugar, corn, soybeans,
coffee, wheat and other. It can be single commodity fund or diversified fund. We have
formed sample (11 components) based on total assets volume by following ETFs
(ETN).
CORN. This ETF corresponds to Teucrium Corn Fund which tracks an index of corn
futures contracts.
COW. This ETN offers an opportunity for investors to gain exposure to hogs and
cattle iShares Global Agriculture Index ETF.
DBA. This ETF corresponds to diversified basket of various agricultural natural
resources.
FUD. This is ETN, associated with futures-based index that measures the
collateralized returns from a basket of 11 futures contracts from the agricultural and
livestock sectors.
JJSF. This is ETN which connected with sugar futures.
NIB. This ETN offers exposure to cocoa futures.
RJA. RJA ETN tracks Rogers International Commodity Index-Agriculture which is
consumption-based index of agricultural commodities.
UAG. Exchange-traded note which offers exposure to a number of agricultural
commodities, including corn, soybeans, wheat, coffee, cocoa, and other natural
resources.
CANE. This ETF offering exposure to the commodity of sugar.
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SOYB. This ETF invests in soybean futures contracts.
WEAT. This ETF offers exposure to wheat futures contracts.
The following ETFs were chosen for comparison agricultural ETFs with traditional
assets ETFs.
SPDR’s SPY to model the large-cap public equities, it tracks the Standard & Poor’s
500 and is the oldest and largest of all ETFs.
SPDR’s MDY that tracks the Standard & Poor’s 400 to model the mid-cap equities,
while being smaller than iShares IJH it has about the same turnover but offers a longer
time series.
iShares IJR to model the small-cap companies, it tracks the Standard & Poor’s 600
index and is much larger and liquid than the corresponding SPDR fund SLY.
iShares IEF to model a balanced portfolio of Treasury bonds, the choice of this
particular government bond fund is motivated by its duration 7,6 years that is
comparable to the duration of other bond funds analyzed in this paper.
iShares LQD to model a balanced portfolio of investment-grade corporate bonds,
it’s one of the oldest bond ETFs and its duration (8,5 years) is approximately the same
as for the IEF fund mentioned above, so we can contrast government and corporate
bonds.
iShares TIP to model inflation-linked bonds, an asset class that should have quite a
distinct characteristic, however its duration (7,6 years) aligned to LQD and IEF.
3.3 Measurement of shock characteristics
The measurement of the characteristics of the shock was carried out, as noted above,
within the framework of 01/16/2020–03/31/2020 based on two indicators. The first
indicator is the depth of the fall (fig. 2). In the context of our work, it can be interpreted
as a “measure of risk in shock conditions”. The second indicator, the percentage of
recovery after a fall, can be interpreted as “profitability in a shock”. The economic
meaning of this parameter can be interpreted in two directions. First, this is a formal
interpretation of the situation to buy assets at a low point and receive income in the
recovery process. The second direction concerns the comparison of the falling
percentage and the recovery percentage.
Two observations are interesting. The first is that ETFs that match stock indices
(especially MDY and IJR) have a deeper fall than most agricultural ETFs. However,
the recovery rate is higher. The second observation is that ETFs of bonds did not have
a great dip and a recovery rate of about 100%, or even more. The first indicates a high
sensitivity of stocks to shock, while bonds are in high demand. Agricultural ETFs are
in the middle.
3.4 The variability approach to risk measurement
Table 1 present the comparative analysis which was realized twofold. One side
characterizes differences in risk measures prior to and post-shock. The other side
characterizes differences of risk measures for alternative and traditional assets. Prior to
the shock agricultural ETFs indicate higher values of range than traditional assets (on
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average close to two times more). After the shock, the widening of the range had
concerned both types of assets, but growth of range for traditional assets was essentially
more. So, post-shock average ranges for traditional and agricultural ETFs
approximately equal. The average growth of ranges in returns was 4% for agricultural
ETFs and 5,8% for traditional assets.
1.200
IEF
LQP 1.000 TIP
SPY UAG NIB
RJA WEAT
MDY SOYB
0.800
Renewal level
IJR CANE FUD
JJSF COW CORNDBA
0.600
0.400
0.200
-
- 0.800 - 0.700 - 0.600 - 0.500 - 0.400 - 0.300 - 0.200 - 0.100 - 0.100
Depth of fall
Fig. 2. Depth of fall via renewal level.
Table 1. Statistical analysis for risk measures. Agricultural ETF.
min max mean std skewness kurtosis
Befo- Befo-
ETF Before Post- Post- Post- Before Post- Before Post- Before Post-
re re
shock shock shock shock shock shock shock shock shock shock
shock shock
Agricultural ETF
CORN -0,025 -0,031 0,031 0,037 0,000 -0,001 0,009 0,012 0,650 0,142 1,942 0,948
COW -0,030 -0,062 0,040 0,065 0,001 0,000 0,010 0,021 0,444 0,289 1,973 2,048
DBA -0,022 -0,027 0,026 0,024 0,001 0,000 0,006 0,009 0,382 0,053 2,925 0,531
FUD -0,020 -0,051 0,017 0,044 0,001 0,000 0,006 0,014 0,006 -0,185 0,691 1,729
JJSF -0,051 -0,073 0,018 0,085 0,000 0,001 0,010 0,032 -1,260 0,154 4,694 0,342
NIB -0,030 -0,058 0,051 0,065 0,002 0,001 0,016 0,020 0,262 0,185 -0,124 0,642
RJA -0,009 -0,028 0,021 0,025 0,001 0,001 0,006 0,009 0,882 -0,424 1,525 0,961
UAG -0,012 -0,052 0,022 0,056 0,001 0,000 0,006 0,013 0,561 0,065 0,589 5,571
CANE -0,015 -0,052 0,021 0,041 0,001 0,001 0,008 0,019 0,151 -0,172 -0,314 0,401
SOYB -0,014 -0,025 0,031 0,022 0,000 0,000 0,007 0,008 0,826 -0,069 3,581 0,765
WEAT -0,020 -0,031 0,030 0,044 0,002 -0,001 0,011 0,014 0,412 0,498 -0,365 0,363
ETF of traditional assets
SPY -0,018 -0,046 0,014 0,067 0,001 0,003 0,006 0,016 -0,482 0,034 0,840 2,870
MDY -0,019 -0,060 0,017 0,081 0,001 0,003 0,007 0,023 -0,188 0,127 0,581 1,219
IJR -0,020 -0,070 0,025 0,082 0,001 0,003 0,008 0,027 0,282 0,092 0,500 0,367
IEF -0,009 -0,006 0,009 0,009 0,000 0,000 0,004 0,003 -0,268 -0,236 -0,113 0,707
LQD -0,009 -0,017 0,008 0,047 0,000 0,001 0,004 0,007 -0,384 -0,085 -0,013 0,244
TIP -0,006 -0,009 0,006 0,015 0,000 0,001 0,003 0,003 -0,086 0,434 -0,263 0,198
212
The situation with standard deviation (std) is similar by essence. Growth of std was for
both types of assets, but std for traditional assets demonstrated a faster pace. Average
growth of std in returns was 0,68% for agricultural ETFs and 8,1% for traditional assets.
A very interesting difference between agricultural ETFs and traditional assets for
average return before and post-shock. They have equal average returns before shock
but traditional assets post-shock demonstrated triple higher average returns. At the same
time agricultural ETFs shown changing positive returns for negative.
The changing of risk-return correspondence prior to and post-shock is illustrated by
Fig. 3. It is very interesting that post-shock traditional assets form exactly efficient
frontier at the Markowitz sense.
0.0025
NIB MDY IJR
0.003
0.0020
SPY
WEAT
0.002
0.0015
SPY IJR
MDY
CANE factor(ETF) factor(ETF)
UAG
FUD CANE
NIB JJSF
mean
mean
RJA a agro a agro
0.0010 0.001 LQD
DBA RJA
a traditional a traditional
TIP
COW UAG
0.0005 SOYB
DBA FUD COW
SOYB 0.000 IEF
CORN
0.0000 TIP
CORN
LQD
-0.001
IEF JJSF WEAT
0.004 0.008 0.012 0.016 0.01 0.02 0.03
sigma sigma
a) 08/28/2019 – 01/15/2020 b) 04/01/2020 – 08/14/2020
Fig. 3. ETFs risk-return correspondence.
It is interesting results we can identify by analysis of skewness, which indicates
divergence from symmetry. Negative skewness indicates a long-left tail of the
distribution or the possibility of larger losses than profits. Positive skewness is a
desirable characteristic for risk-averse investors. The motivation of that is based on the
expected utility theory.
From this point of view, agricultural ETFs have demonstrated higher positive
skewness before shock than after. Traditional assets quite the contrary was
demonstrated better skewness post-shock. Kurtosis indicators were growth post-shock
for traditional assets and were multidirectional for agricultural ETFs.
3.5 Risk measurement as losses in a negative situation
This conceptual approach is based on considering measures relating to the
interpretation of “negative situation” for the investor. The most popular in this group is
Value-at-Risk (VaR), which presents a quantile of the probability distribution function.
This quantile corresponding to some level of safety (it maybe 95%, 99%, or 99,9%).
The logic of VaR is based on risk covering. If, for example, VaR orients for 95%, then
5% biggest losses will throw off. VaR will cover maximum losses at the framework of
95% possibilities. Risk measure Conditional Value-at-Risk (CVaR) is based on a
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generalization of VaR. This is the conditional mathematical expectation of losses which
higher than VaR (table 2).
Table 2. Risk measurement of ETFs by VaR and CVaR.
VaR CVaR
ETFs
Before shock Post-shock Before shock Post-shock
Agricultural ETFs
CORN -0,012 -0,020 -0,017 -0,028
COW -0,015 -0,032 -0,020 -0,049
DBA -0,008 -0,014 -0,012 -0,019
FUD -0,009 -0,023 -0,012 -0,033
JJSF -0,020 -0,049 -0,026 -0,065
NIB -0,022 -0,031 -0,027 -0,039
RJA -0,006 -0,015 -0,008 -0,022
UAG -0,008 -0,019 -0,010 -0,028
CANE -0,012 -0,030 -0,014 -0,043
SOYB -0,009 -0,013 -0,013 -0,018
WEAT -0,015 -0,022 -0,018 -0,024
ETFs of traditional assets
SPY -0,009 -0,022 -0,013 -0,035
MDY -0,010 -0,033 -0,015 -0,045
IJR -0,011 -0,040 -0,016 -0,051
IEF -0,006 -0,004 -0,008 -0,005
LQD -0,006 -0,002 -0,008 -0,011
TIP -0,005 -0,004 -0,006 -0,007
Considering risk measuring for agricultural ETFs we have found that Value-at-Risk and
Conditional Value-at-Risk is higher than similar values for traditional assets but not so
much. This fact true for both periods prior to and post-shock. Fig. 4 demonstrates the
risk-return correspondence between VaR and average returns.
0.0025
NIB IJR MDY
0.003
0.0020
SPY
WEAT
0.002
0.0015
IJR SPY
CANE
MDY factor(ETF) factor(ETF)
UAG
FUD JJSF CANE
NIB
mean
mean
RJA a agro a agro
0.0010 0.001 LQD
DBA RJA
a traditional a traditional
TIP
COW UAG
0.0005
SOYB COW FUD SOYB
DBA
0.000 IEF
CORN
0.0000
CORN
LQDTIP
-0.001
JJSF IEF WEAT
-0.020 -0.015 -0.010 -0.005 -0.05 -0.04 -0.03 -0.02 -0.01 0.00
VaR VaR
a) 08/28/2019 – 01/15/2020 b) 04/01/2020 – 08/14/2020
Fig. 4. ETFs Value-at-Risk.
214
Not less excitingly the comparison of changing risk measures values for an approach
based on losses in negative situations. In contrast to the results for variability risk
measuring here agricultural ETFs indicated higher growth.
It is an interesting conclusion that ratio CVaR/VaR is a good indicator of the
distinction of risk. The ratio CVaR/VaR characterizes correspondence between
“catastrophic” losses and maximal losses at the frameworks of 95% safety level. This
ration became extremely higher for traditional assets than for agricultural ETFs. The
changes of CVaR/VaR for agricultural ETFs are negligible in comparison with
traditional assets. These values for traditional values had grown 1,6 times on average.
3.6 Risk measurement based on sensitivity approach
Risk measurement at the frameworks of sensitivity analysis provides an opportunity to
understand the role of systematic and non-systematic risks. We have chosen for
sensitivity analysis SPY as systematic factors. The logic of this choice lies in
interpreting the S&P 500 as a leading factor in the stock market. And analysis should
provide an answer to the question: How the stock market as a whole affect the return
of ETFs? (table 3)
Table 3. Regression analysis.
SPY beta coefficient Intercept R2 p-value
Before Before Before
Before shock Post-shock Post-shock Post-shock Post-shock
shock shock shock
Agricultural ETFs
CORN 0,0214 0,2134 0,0014 0,0030 0,0010 0,0266 0,7534 0,1107
COW 0,0817 0,0728 0,0014 0,0029 0,0217 0,0092 0,1498 0,3487
DBA 0,0650 0,6853 0,0013 0,0028 0,0050 0,1438 0,4907 0,0001
FUD 0,0718 0,0045 0,0013 0,0029 0,0061 0,0000 0,4469 0,9687
JJSF 0,0402 0,3432 0,0014 0,0024 0,0052 0,4612 0,4829 0,0000
*NIB -0,0097 0,2578 0,0014 0,0025 0,0007 0,1094 0,7993 0,0009
RJA 0,2028 0,6773 0,0012 0,0023 0,0384 0,1465 0,0545 0,0001
UAG 0,0138 0,3302 0,0014 0,0028 0,0002 0,0682 0,8839 0,0098
CANE -0,0695 0,2528 0,0015 0,0025 0,0100 0,0879 0,3295 0,0032
SOYB 0,0632 0,8562 0,0014 0,0027 0,0056 0,1883 0,4663 0,0000
WEAT 0,0866 0,1820 0,0013 0,0031 0,0270 0,0247 0,1075 0,1242
ETFs of traditional assets
MDY 0,7303 0,6355 0,0005 0,0007 0,7545 0,8264 0,0000 0,0000
IJR 0,5297 0,5050 0,0006 0,0012 0,5524 0,7376 0,0000 0,0000
IEF -0,6213 -2,802 0,0013 0,0029 0,1538 0,2170 0,0001 0,0000
LQD -0,2184 0,9809 0,0014 0,0019 0,0176 0,1990 0,1951 0,0000
TIP -0,4011 0,0241 0,0014 0,0029 0,0348 0,0000 0,0672 0,9606
The main result is very low R-squared indicators. The economic consequence of this
is the domination of nonsystematic risks in returns of agro ETFs.
215
3.7 Correlation analysis
Correlation analysis was provided as inside the sample of agriculture ETF as between
traditional assets. It is interesting that agriculture ETFs indicate a very low correlation
not only with traditional assets but inside the sample group (table 4). This leads to
consideration of portfolio construction directly through agricultural ETFs and through
all types of ETFs.
Table 4. Correlation analysis
Average correlation
Average Average
between sample
correlation correlation
agriculture ETFs
between sample between sample
and sample of ETFs
agriculture ETFs traditional asset
of traditional assets
Before shock 0,31 0,02 0,30
Post-shock 0,33 0,11 0,35
We think that so low correlation can be explained by affecting these ETFs real prices
of agricultural products. Not by supply and demand as it appears at the stock market.
4 Conclusion
Risk-return correspondence for different asset classes one of the cornerstones of
modern portfolio management. This correspondence together with interdependency
analysis allows us to form a portfolio structure that is adequate to given goals and
constraints. But “pandemic risk” broke into the investment world and created
uncertainty and turmoil. This is a real “black swan” event in terms of Nassim Nicolas
Taleb. How much risk investments will involve post-shock? What returns can investors
expect? We believe strongly that search answers for these questions will be an actual
topic for active research in the nearest future.
Our paper is concentrated on one of such questions. How agricultural commodities
expressed by agricultural ETFs pass through COVID-induced shock? How to transform
their risk-return correspondence in comparison with traditional assets? The search for
the answer was realized through different approaches to risk measurement. First of all
was highlighted three time periods: specifically shock period, the quiet period before
the shock, and post-shock. It was considered three basic approaches for risk
measurement: variability, losses in negative situations, and sensitivity. Correlation
analysis also was realized.
Conclusions are the following. Traditional assets (stock indices) demonstrated a
higher depth of falling but at the same time higher level of recovery. Indices of bonds
not so much falling and then increased in price higher previous level. Agricultural ETFs
demonstrated an average level of falling and moderate recovery. The general
conclusion lies in increasing risk after shock as for agricultural ETFs as for traditional.
It is interesting that risk changing for the first two approaches provides us a discrepancy
that is presenting in fig. 5. The variability approach indicated that ranges and standard
216
deviations of traditional asset returns are increased higher. In the meantime, returns of
agricultural ETFs demonstrated higher increments in VaR and CVaR. Average returns
of agricultural ETFs moved down at the post-shock time but average returns of
traditional assets moved up. So, the reaction for shock is different at the frameworks of
approaches of risk measuring.
7.00%
5.80%
6.00%
5.00%
Increments of returns
4.00%
4.00%
3.00%
2.00% 1.73% 1.49%
0.81% 1.19% 0.96%
1.00% 0.68%
0.12%
-0.06%
0.00%
Range Std Mean VaR CVaR
-1.00%
Fig. 5. Growth/falling of risk measures values in absolute increments of returns.
The results of applying sensitivity risk measuring illustrate increasing beta-values to
returns of SPY, but R-squared is essentially low as before as after crises. These are
confirmed by correlation analysis which shows low correlations. These estimations
confirm facts effective diversification between traditional asset classes and alternatives
which involved agricultural ETFs.
Summarizing results, it is possible to note differences path of shock and post-shock
period for agricultural ETFs and traditional assets.
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