CFR CFR-Working Paper NO. 07 Working Paper NO. 07 Working Paper NO. 07-161616 The
WORKING PAPER CMVM
Transcript of WORKING PAPER CMVM
WORKING
PAPER
CMVM C O M I S S Ã O D O M E R C A D O D E V A L O R E S M O B I L I Á R I O S * N º 0 2 / 2 0 1 3
THE MARKET
OF STRUCTURED
RETAIL PRODUCTS
EVIDENCE FOR PORTRUGAL
WORKING PAPER
CMVM
The Market of Structured Retail Products
Evidence for Portugal
Paulo Pereira da Silva*
CMVM-Portuguese Securities Commission
Rua Laura Alves nº 4
Apartado 14258
1064-003 LISBOA
Email: [email protected]
Fernando Silva*
CMVM-Portuguese Securities Commission
Rua Laura Alves nº 4
Apartado 14258
1064-003 LISBOA
Email: [email protected]
* The views stated herein are those of the authors and not those of the Portuguese Securities Commission.
ABSTRACT
We analyze 108 SRPs issued in the Portuguese market between September 2009
and June 2011 and provide measures to determine their intrinsic value. On
average, SRPs are issued at an intrinsic value far below the initial subscription price
paid by investors and hidden costs amount to 4.9% yearly. The global intrinsic
value of these products is, on average, 85.6% of the issuance price and even when
counterparty risk is neglected (non-global intrinsic price), the respective intrinsic
price is inferior to the issuance price in more than 20% of the SRPs. Our
results reveal that the intrinsic value as a percentage of the issuance price of SRPs
is positively influenced by the non-existence of conflicts of interests between the
issuer and the seller of the product; equity and commodity SRPs, and protected
capital SRPs also exhibit higher intrinsic value. On the contrary, the intrinsic value
decreases with the number of the SRP’s underlying assets (more complexity means
more unfair pricing), the counterparty risk of the issuer and the existence of a
secondary market for the product. In addition, the results suggest that the yearly
implied hidden costs decrease with the length of the investment. We do not find
evidence that the possible conflicts of interests between the issuer and the
reference proprietary index affects either the overpricing of the SRPs or the yearly
hidden costs. As for the presence of an “issuer effect” in SRPs’ overpricing or yearly
hidden costs, our results indicate that this effect is not redundant, since some
issuers embed higher hidden costs than others even after removing the effect of
other explanatory variables.
KEYWORDS: Structured retail products, financial innovation, intrinsic value, pricing,
hidden costs, conflicts of interests and derivatives
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1. INTRODUCTION
In Portugal, the term “structured retail products” (SRPs) is used to subsume all
financial products issued to the public, normally by banks and insurance companies,
that combine at least two types of components (a primary and a contingent invest-
ment). Usually, one of its fundamental components is called underlying asset and is
a derivative by its very nature embedded in a wrapper that is the primary invest-
ment (e.g. bonds, notes or certificates). SRPs became very popular in the U. S. in
the 1980s and found their way through Europe in the mid-1990s during years of
low interest rates. Such products have experienced a massive boom in Europe and
Asia in recent years. By then, the rising importance of these products resulted
partially from a response of financial intermediaries to a shift in investors’ demand
towards more complex payoff structures, in a context where interest rates in
traditional deposit accounts were low. Meanwhile, the area of financial innovation
has become an essential function in large investment firms and commercial banks,
and, more recently, the search for liquidity that most banks are faced with due to
market restrictions may have shifted this paradigm in the sense that demand is
probably being conditioned by the supply side.
SRPs offer investors a certain pre-defined exposure to a wide range of underlying
assets, allowing them to access a wide set of desired risk exposure as well. This
exposure can include commodities, individual or baskets of equities, indexes or
ETF’s, credit instruments, currencies, sovereign or corporate interest rates, and
indexes or other measures of inflation rates. A typical SRP consists of a
zero-coupon or interest-bearing note combined with a derivative whose value is
typically realized at the maturity of the SRP. A very recent and outstanding
example is a note that makes a periodical interest payment of a fixed amount and
that pays at maturity the face value of the bond times the return of gold (that has
recorded a significant appreciation of its price in the last years) over the life of the
note.
These financial instruments differ in significant ways from other direct or indirect
investment alternatives, like mutual funds. A mutual fund investor benefits from a
direct financial claim over an underlying pool of assets’ price fluctuation; SRPs’
investors, on the other hand, usually enjoy a general claim against the institution
that has issued the SRP. The amount to be delivered based on this claim can be
contingent to the performance of an underlying asset. In the event of a default by
the issuer, the investor will recover value alongside other creditors.
T H E M A R K E T O F S T R U C T U R E D R E T A I L P R O D U C T S . . .
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In general, SRPs grant retail investors access to alternative market investments,
which they would be otherwise unable to reach and expose themselves to its poten-
tial gains and risks. For instance, it is very complex for a retail investor to create a
downside-limited position in an underlying equity, to dynamically hedge his expo-
sure to a company’s stock, or to trade in options markets, since the minimum mar-
ket size and transaction costs can make these strategies very difficult to be under-
taken directly by investors. SRPs can also be used to transfer underlying assets’ re-
turns across tax frameworks, and can be useful to deliver risk exposure that would
be otherwise difficult to achieve due to portfolio investment restrictions.
As with other financial instruments, the question of valuation is of particular rele-
vance for the case of SRPs. Therefore, it is important to ascertain whether issue
prices of SRPs can be deemed as being fair. All SRPs face the risk of being issued at
prices that differ substantially from the products' intrinsic values. Additionally, most
SRPs have no liquidity in secondary markets, although issuers, in general, but not
always, act as market makers for their own products until the SRPs’ maturity.
Alternatively, SRPs can be bought/sold over-the-counter from/to the issuing firm.
As a consequence, almost any transaction involves the product's issuer as one
partner of the trade. This type of trading, together with opacity in price discovery
mechanisms due to the rather complex valuation methodologies needed to access
SRPs’ intrinsic value, creates the possibility of market makers to quote unfavoura-
ble prices to investors. As a rule of thumb, the higher the complexity of the
products, the higher the margins incorporated in the quote, and, therefore, the
higher the hidden costs that investors have to bear.
In Portugal, SRPs worth € 13.4 billion were issued between 2008 and 2011 and the
outstanding amount of SRPs placed was around € 12.3 billion.1 The sale of
structured products to a wide range of investors, including retail investors, raises
investor protection concerns along several dimensions. At the most basic level, do
investors have even a rudimentary understanding of the complexity of the products
they are purchasing? Do investors realize the various fees, both explicit and
embedded in SRPs, they are paying for the product? The answer to these questions
has raised the concern of regulators in what respects the quality of information
disclosed to SRPs’ investors, the ability of investors to understand and analyse that
information, and the responsibility of those facilitating the sale of structured
products to ensure the suitability of SRPs’ to any given investor.
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1- Data available in structuredretailproduct.com.
In this study we try to provide an answer to those questions by carrying out a
detailed analysis and evaluation of 108 Portuguese SRPs’ intrinsic value. The paper
is organized as follows: Section 2 provides a literature survey of recent studies on
the worldwide market for SRPs, mainly focusing on overvaluation issues. Section 3
describes the methodologies used for the evaluation of SRPs. The sample descrip-
tion and the empirical analysis are presented in sections 4 and 5, respectively.
Finally, Section 6 summarizes the main results and considers future developments
in the market for structured products, as well as further research opportunities in
this field.
2. LITERATURE REVIEW
The financial literature about SRPs can be separated between two different fields.
One focuses on the overvaluation SRPs’ issue price and the other studies demand
factors that explain the success of these financial instruments. Wilkens, Erner and
Roder (2003), Grünbichler and Wohlwend (2005), Bergstresser (2008), Wallmeier
and Diethelm (2008), Szymanowska, Horst and Veld (2009), Jørgensen, Nørholm
and Skovmand (2011), and Henderson and Pearson (2011), for example, provide
empirical evidence on overvaluation.
Based on data sets (30 DAX companies and 8 NEMAX companies) obtained in the
German market for November 2001 (22 trading days), Wilkens, Erner and Roder
(2003) study the issuers’ pricing of reverse convertibles and discount certificates.
They aimed to understand the issuers’ pricing strategies and in particular to test
the proposed “order flow hypothesis” (that is, when pricing those products issuers
put emphasis on the expected volume of purchases and sales). The authors
conclude that there was an overpricing of the reverse convertibles and discount
certificates.
As for Jørgensen, Nørholm and Skovmand (2011), they analyse the cost structure
and pricing efficiency of principal-protected notes2 (PPNs) from the Danish retail
market issued in the period ranging from 1998 to 2009. For almost 400 PPNs
issued, the authors find an average 6% gap between the offer prices and the
theoretical intrinsic values. Only half of such figure can be explained by the costs
disclosed to investors at the time of issuance. Using regression analysis, the
authors also conclude that time to maturity, product complexity and issuer size3 are
important determinants of the degree of overpricing. Lastly, the overall degree of
overpricing has declined over time, but the unexplained cost component (costs not
disclosed by the issuer) has not.
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T H E M A R K E T O F S T R U C T U R E D R E T A I L P R O D U C T S . . .
2- PPNs usually comprise a simple (coupon or zero-coupon) bond and a European option.
3- PPNs issued by medium size arrangers are significantly more costly that comparable products issued by large issuers.
Grünbichler and Wohlwend (2005) examine the valuation of 192 non-capital
protected structured products in both the primary and the secondary markets in
Switzerland. Their analysis compares the implied volatilities of the options embed-
ded in the structured products with those of comparable EUREX options, and the
authors show that structured products are, on average, overpriced when they are
first issued. Although market misevaluations are detected in the secondary market
too, their magnitudes are considerably inferior. Plain vanilla products and exotic
products without coupons show a similar degree of overpricing, but exotic products
with periodic coupons show almost no overpricing. Their conclusions support the
hypothesis that there are certain inefficiencies in the Swiss market for structured
products and that the lead managers make rational use of their quasi-monopolistic
position to charge higher prices. Also for the primary Swiss market, Wallmeier and
Diethelm (2008) conduct a numerical evaluation of 468 multi-asset barrier reverse
convertibles (MBRCs) outstanding in April 2007. They use a multinomial tree-based
valuation method to determine the products’ theoretical intrinsic values. Comparing
these results to their actual issuing prices, the authors’ find an average premium of
3.4% to 6% paid by investors for triple barrier reverse convertibles. Their empirical
results suggest that MBRC are, on average, priced above their theoretical values,
but the overpricing was less pronounced for those having stocks as underlying
assets and higher for products with high coupons.
Szymanowska, Horst and Veld (2009) analyse the pricing of reverse convertible
bonds (RCs) in the primary market in Holland. Their sample consists of 108 RCs.
The authors find that, on average, plain vanilla RCs are overpriced by 5.92% of the
price of issuance and that knock-in RCs are overpriced by 5.50%. Transaction costs
and taxes are only able to explain 23% of that difference.
Henderson and Pearson (2007) conduct an analysis of structured equity products in
the U.S. markets during the period 1992 - 2005. In particular, the authors perform
a pricing analysis of a once equity-linked structured product, Morgan Stanley's
SPARQS, and conclude that investors pay a premium at the time of the initial public
offering of approximately 7.71% on a value-weighted basis and of 8.77% on an
equal-weighted basis. Henderson and Pearson (2011) address the subject of the
pricing of SRPs by studying 64 issues of a popular SRP in the US market and find
that those SRPs issuing price is, on average, almost 8% higher than the products’
intrinsic market values obtained using option pricing methods. The authors state
that “financial institutions can exploit the investors mistakes by creating financial
instruments that pay off in the states that investors overweight and pay off less in
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the states that investors underweight, leading the investors to value the new
instruments more highly than they would if they understood financial markets and
correctly evaluated information about probabilities of future events.”
Finally, Bergstresser (2008) claim that SRPs have been issued in the US by highly-
rated issuers, generally investment banks and commercial banks, because investors
in structured retail notes are generally searching for an exposure on the underlying
asset rather than the issuer’s credit risk. Patterns of issuance suggest that inves-
tors chase performance, and issuers prefer to issue notes whose underlying risks
are easier for them to hedge. The estimated performance of the notes suggests
that they are sold at a significant premium. The investors in this market over the
period since 2000 were getting a negative alpha, in aggregate, of approximately
100 basis points per month. However, after 2005 the underperformance is not
statistically significant.
The other investigation area of SRPs focuses on demand factors that explain the
success of these financial instruments. Fischer (2007) analyses whether investors
in SRPs act rationally and how investment in SRPs fits their overall investment
strategies. Among the reasons pointed by investors to invest in SRPs the following
were emphasised: i) risk diversification; ii) hedging against certain risks; iii)
reduced costs (versus e.g. mutual fund products) and iv) access to asset classes
and market segments otherwise not available. Reasons i) to iv) are taken as
proxies of rational investment behaviour. A fifth reason is taken as a proxy of
irrational behaviour and consists in betting on a certain feature of SRPs’ underlying
(e.g. leveraged SRPs that expose investors to twice the variation of a certain
index).4
As for Bernad, Boyle and Gornall (2009), they study locally-capped investment
products listed on the American Stock Exchange and its relationship with the retail
investor. The authors find that locally-capped products are popular, with a volume
of $2.39 billion outstanding, and issued primarily by large investment banks. The
authors further assert that the optimistic projections included in the sales material
of locally-capped products might contribute to their popularity. This sales practice
is misleading, and might influence investors to overweight the probability of
getting the maximum payoff. They conclude that locally-capped products tend to
be sold at an average of 6.5% above their intrinsic value, ignoring credit risk.
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T H E M A R K E T O F S T R U C T U R E D R E T A I L P R O D U C T S . . .
4- Notwithstanding the reasons leading to investing in SRPs, surveyed investors show a degree of irrationality insofar as that a large percentage of respondents denotes inconsistent investment strategies. Higher risk attitude and investment activity seem to be associated with this irrational behavior. Only a minority of respondents (other private investors) pursues betting strategies. Men act less rationally than women as far as the tradeoff between risk-taking and expected return is concerned. According to this study, investors who seek financial advice seem to act more rationally than those who do not. Higher education levels are not linked to more rational investment decisions.
Hens and Rieger (2008) show that structured products can, in theory, arise as a
solution to enhancing the performance of a portfolio. According to the authors,
most popular structured products follow behavioural factors, like loss-aversion or
probability misestimation to be attractive to the eyes of potential investors. The
currently most popular products clearly cannot be explained even within the frame-
work of prospect theory, but only when taking into account probability misestima-
tion. The market for structured products offers a utility gain for investor which
is most likely only an illusion. Rieger (2008) finds evidence that the attractiveness
of some of the most popular types of structured products drives from systematic
probability misestimation. For instance, in the case of barrier products, he shows
that a relative underestimation of the probability that the barrier is reached leads to
a positive investment decision.
According to Branger and Breuer (2008) an investor with constant relative risk
aversion (CRRA) utility benefits from having access to discount certificates and, to a
slightly lower degree, sprint certificates. Retail derivatives with a more sophisticat-
ed payoff structure are much less attractive. They conclude that standard CRRA
preferences cannot explain the high demand for SRPs. Breuer and Perst (2007) find
that Discount Reverse Convertibles (DRCs) and Reverse Convertible Bonds (RCBs)
are of interest to investors who moderately estimate the expected return of the
underlying stock, and who underestimate the corresponding return volatility. While
this result holds true for both fully rational individuals and bounded rational ones
as well, the possible demand for DRCs seems to be significantly overestimated, if
full rationality is used as an approximation of bounded rational investors.
Finally, Ofir and Wiener (2010) outline several key features embedded in various
structured products and associate each one of them with specific behavioural biases
identified in the decision theory literature. These include loss aversion, the
disposition effect, herd behaviour, probabilities distortion, the ostrich effect,
and hindsight bias. They perform an experiment to test the possible impact of
each behavioural bias on decisions pertaining to investments in structured
products. Their findings reveal that, to varying degrees, behavioural biases affect
investor decisions favouring the investment in structured products.
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3. METHODOLOGY
3.1 Market Risk
The main objective of this paper is to evaluate SRPs whose underlying assets are
equities, commodities, currencies and/or indexes composed by the former assets.
The estimation of the SRPs’ intrinsic values relies on three different methodologies.
The first is the standard approach based upon the Black and Scholes (1973) and
Merton (1973) framework in which under the risk-neutral probability the underlying
stock follows the geometric Brownian motion (GBM). Two other models are also
used (Variance Gamma Model and Heston Model) to assess the robustness of the
results. The GBM process can be described as follows:
(1)
where dz is a Wiener process, µ (t) denotes the expected yield rate at t and σ (t)
is the expected volatility at t.
For some SRPs that combine zero coupon bonds with plain vanilla options, the
valuation process is quite easy and consists in a closed formula solution (the
Black-Scholes-Merton Formula), while for some others (the majority) it entails the
use of numerical methods. Two numerical methods are used: Monte Carlo simula-
tion and binomial trees. The Monte Carlo simulation consists on the simulation of
the payoffs’ path for the SRPs’ underlying assets in a risk neutral environment. The
expected payoff is subsequently discounted at the risk-free interest rate.5
We assume that the interest rate and the dividend yield for each security are
constant, so that the path of each asset would be expressed as follows:
(2)
where ϵ is the random shock on the security’s variation, and r and d are the risk-
free interest rate and the dividend yield of the security, respectively.
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T H E M A R K E T O F S T R U C T U R E D R E T A I L P R O D U C T S . . .
5- The use of a risk-free interest rate in these models is due to the obligatory absence of arbitrage opportunities so that the pricing is fair.
Some structured retail products are designed in a way that the investors’ remuner-
ation is contingent to the performance of several assets. This means that the
Wiener process random shocks for several securities are also correlated, and should
be adjusted in order to reflect that fact. In those cases we model the correlation of
the returns of the various underlying assets. Financial literature presents several
methods to model correlation between financial securities, most notably, the
Cholesky decomposition and the principal components method. In computational
terms, the Cholesky decomposition seems to be the most appealing and is thus
used to incorporate the correlation structure between the various underlying assets
used in simulations.
As for the binomial trees methodology, it is an approximation of the asset price
behaviour. The Geometric Brownian Motion process is consistent with the following
binomial tree process - general additive binomial tree - (see Cox, Ross and
Rubinstein, 1979):
(3)
Where µ refers to a up-movement , d refers to a down-movement, r is the risk free
interest rate, g is the dividend yield and σ denotes the expected volatility at t.
Trigeorgis (1992) proposes a slight change in the CRR model to produce better
accuracy in the derivative evaluation:
(4)
The underlying asset price behaviour is defined by:
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Where Pu is the probability of an up-movement and Pd is the probability of a down
-movement. The Trigeorgis’ (1992) model is used to evaluate SRPs with American
and barrier features.
In this study, the choice of the valuation method depends primarily on the type of
derivative embedded in the SRP. Financial instruments with plain vanilla European
options embedded are evaluated using the Black-Scholes-Merton Formula.
Similarly, we use other closed-form solution formulas to evaluate SRPs, when
possible. For SRPs with American options or barrier options embedded over one or
two underlying assets binomial trees are used. Finally, we evaluate Bermudian
options and options with two or more underlying assets through Monte Carlo
simulation.
Despite its popularity, the Black-Scholes model has several limitations. On the one
hand, some of the model’s assumptions are not realistic. On the other hand, empir-
ical evidence shows that traders and investors use ‘valuation models’ which differ
from Black-Scholes’ model. A trader wanting to make the best possible decisions
should not disregard the limitations of this theoretical model. Therefore, despite the
importance of the Black-Scholes option pricing model, investors seek ways to
reduce potential errors due to the model’s drawbacks. Said evidence is shown by
several studies that reveal the so called «volatility smile».
Some researchers show that the implied volatility of plain vanilla options is a func-
tion of the strike price. A possible explanation for volatility smiles lies in investors’
perception of potential errors due to the use of the log-normal distribution function
used to model the path of the underlying asset price. The high kurtosis and
the skewness in the distribution function of the underlying asset’s returns help
explaining volatility smiles6. A high kurtosis empirical distribution of a security’s
returns means that the likelihood of an event occurring near the average is higher
than in the case of the normal distribution.
Madan, Carr and Chang (1998) suggest an alternative valuation model (a jump
diffusion model) based on the Variance-Gamma process. Jump diffusion models
allow us to accommodate return distributions (of underlying assets) with kurtosis
higher than 3. According to the authors, one may value an European call option
with the following semi-closed formula solution:
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T H E M A R K E T O F S T R U C T U R E D R E T A I L P R O D U C T S . . .
6- This method was applied to the products where in which analysis delivered high kurtosis and high skewness occurred (higher than normal).
(5)
where
(6)
r(y) is the gamma function; S0 is the price of the underlying asset; K is the strike
price; t represents the time (in years) to the maturity; r is the risk-free interest
rate; θ denotes the kurtosis and v represents the skewness.
ψ (a, b, y) is obtained through numerical integration using the trapezius rule.
To compute the value of European put option, one could use the Call-Put parity.
As for other types of derivatives, we assess the intrinsic value using Monte Carlo
Simulation as described in Hull (2009):
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(7)
Where v and θ denote the kurtosis and skewness of the distribution function of the
underlying asset’s return, σ is the underlying asset’s volatility, d is the dividend
yield, r is the risk-free interest rate, SiT represents the underlying asset price at t,
T is the remaining time-period until the product’s maturity and ϵ is the random
shock. The Cholesky decomposition is also used to model the returns’ correlation
structure when the SRP includes various underlying assets.
Finally, we confront the results obtained from the Black-Scholes-Merton framework
with the ones obtained from the Heston model (Appendix B). This model has in
consideration the relationship between returns and past volatility. The literature
shows that there is a negative relationship between market returns and volatility
and that this fact explains the so-called volatility smile in option markets. In certain
type of derivatives that may underlie some SRPs, the short term volatility may be
rather different from the long term volatility. In this sense, one of the main
features of the Heston model is that it assumes that volatility is mean reverted (CIR
model). According to the Heston model, the underlying price is modelled by the
following stochastic process:
(8)
where σ is the underlying asset’s volatility, d the dividend yield, r the risk-free
interest rate, SiT the underlying asset price at t, T the remaining time-period until
the product’s maturity, VL and ζ are the long term variance and the volatility of the
variance of the underlying asset return, α measures the speed of convergence to
the long term variance and dzσ is a Wiener process associated to the variance
process.
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T H E M A R K E T O F S T R U C T U R E D R E T A I L P R O D U C T S . . .
We calculate the Heston model intrinsic value using Monte Carlo simulation.
Input parameters
The three methodologies are very sensitive to the inputs (parameters) that are
used and the final outcome of any evaluation exercise could differ substantially
depending on the parameter estimation errors. Where available, it would be more
appropriate to use implied than historical volatilities. However, there are a few
cases where these forward looking (implied) volatilities are available. In fact,
implied volatilities are often only available for short maturities (e.g. 1 year) and
sometimes the illiquidity of the options from which one extracts such parameter
could make it not representative. When this type of data is not available,
underlying assets’ historical prices are used to calculate the expected volatility.
In what respects to kurtosis, skewness and correlations we also use historical data.
As a rule thumb the estimation time frame horizon used is equal to the SRPs’ maxi-
mum maturity (because some of them may knock-out earlier). In the particular
case of the Heston Model as initial parameters, we use long term volatility equal to
3 times the SRPs’ maximum maturity and a short term maturity equal to 1/3 of the
SRPs’ maximum maturity.
As for the expected dividend yield, we also use the historical data averaging the
last 10 years of the stock dividend yield (when such is not possible, namely in the
cases where the asset maybe recently listed, we use the maximum available histor-
ical data). Finally, we use implied swap rates or short term rates such as money
market rates as a proxy for the risk free rate.
3.2 Counterparty risk
The performance of SRPs depends not only on the performance of the underlying
asset or index, but also on the issuer’s capacity to honour his obligations to pay
back the SRPs’ face value and coupons. In the event of a default by the issuer, the
investor will recover value alongside other creditors. Thus, to assess the intrinsic
value of the SRP we need to consider the implied debt financing cost of the SRP’s
issuer (assumed equal to the implied yield of bonds with the same maturity of the
SRP). Nonetheless, this implied cost is obtained indirectly through the CDS market
because it exhibits a higher level of liquidity in comparison with the secondary
markets for bonds. To assess the price of CDS we use Bloomberg terminal, CMAN
contributor. To determine the probability of default of the issuer the ISDA Standard
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Upfront Model is used (available in Bloomberg). Summing up, we have
Intrinsic Value with Credit Risk
= P x Intrinsic Value with no Credit Risk + (1—P) x Recovery Value
(9)
where (1-P) is the probability of default considering a recovery value of 40%. The
probability of default is obtained for the issue date of the structured retail product.
4. SAMPLE DESCRIPTION
The data used in this paper is collected from the CMVM website (SRP prospectus)
and from Bloomberg. The CMVM website contains the SRP prospectuses placed as
public offerings. The prospectuses have information on the SRP’s payoff structure,
identifiers for the issuer, underlying assets, issuance date and redemption date, as
well as possible coupon payment dates.
The data available from Bloomberg includes the price quotes and dividends
(effectively paid and expected) for the underlying assets, the risk free interest rates
and the prices of credit default swaps for the issuers.
The issued dates of the analysed structured retail products are comprised between
22-09-2009 and 30-06-2011; 19 were issued during 2009, 57 during 2010 and the
remaining in 2011. They were issued by 14 different banks, but commercialized by
only 8 (Table 1).
In 88% of the cases, the entity that issued the SRP belongs to the same (or is
within the same financial conglomerate) group of the seller; in all the cases, the
issuer and the calculating agent are the same and in 9.3% of the cases the issuer
has property rights over the reference/underlying asset.
As for SRP’s characteristics, 35.2% have one underlying asset, 6.5% have two
underlying assets and 58.3% have three or more underlying assets. On the other
hand, 70.4% of the SRPs’ are capital protected. The median maximum maturity is
three years, and only 10% of the SRP’s have a maximum maturity less than two
years or higher than 5 years. In terms of the type of the underlying assets, in
33.3% of the SRP it is a basket of equity shares; in 12.0% of the cases it is a
basket of indexes and in 11.1% it is a structured index.
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T H E M A R K E T O F S T R U C T U R E D R E T A I L P R O D U C T S . . .
5. RESULTS
We compute three different variables: global intrinsic value, intrinsic value in the
absence of counterparty risk and hidden costs. The intrinsic value of the SRP in the
absence of counterparty risk is computed as the present value of the expected cash
flows to be received by the holder until the maturity of the SRP. In the computation
of the expected cash flows no counterparty risk is assumed. Differently, in the
global intrinsic value we take into account the probability of default of the SRP’s
issuer. The hidden cost is based on the difference between the issuance price and
the intrinsic value (that reflects market and counterparty risk), and is a proxy
for the annualized cost that SRP holders will have to support until the products’
maturity:
(10)
On average, the hidden cost of SRPs is 4.9% yearly. The median is 3.5%. The
global intrinsic value of these products is, on average, 85.6% of the issuance price.
Even when counterparty risk is neglected, the respective intrinsic value would be
inferior to the issuance price in more than 80% of the SRPs (Table 2).
Indeed, the counterparty risk represents an important component of SRPs’ value,
and it has been often neglected in similar studies. This component is worth almost
10% of the expected present value of the product (Table 3).
Table 4 exhibits the correlation between SRPs’ intrinsic value (with and in the
absence of counterparty risk), the hidden costs, SRPs maturity and counterparty
risk. The data shows that the link between intrinsic value and maturity is very
tenuous, but the association between maturity and hidden costs is negative and
statistically significant. This means that the longer the length of the investment, the
lower will be the yearly hidden costs.
Capital protected SRPs exhibit, on average, higher intrinsic values and substantially
lower hidden costs, as SRPs non-negotiable in the secondary markets and SRPs
with no early reimbursement options embedded. Another interesting result arises
from the comparison between SRPs issued and sold to the public by affiliated
entities and SRPs where the issuer and the seller are not related: hidden costs
are higher when the SRP is issued and sold by entities of the same financial
conglomerate.
W O R K I N G P A P E R N º 2 / 2 0 1 3
17
As for the association between counterparty risk and hidden costs/intrinsic value,
the results show that intrinsic values are lower when counterparty risk increases,
and that hidden costs increase with counterparty risk. In this sense, one can say
that hidden costs of SRPs are, on average, higher for low grade credit issuers. This
means that low grade credit issuers are taking advantage of investors’ mispercep-
tion of counterparty risk, financing themselves at lower rates than they would if
they issued plain vanilla bonds (Table 3).
According to the previously examined literature, the cross-sectional characteristics
of SRPs could help to explain the difference between the issuance price and the in-
trinsic value of the SRP. In this context, we test if the following factors could help
explaining the cross-sectional differences of intrinsic value and hidden costs:
Maturity: maximum length of the SRPs’ investment period;
CDS price implicit default probability: counterparty risk of the issuer measured
by the CDS price implicit default probability at the date of issuance;
Type of reference assets (1 - equity stocks or equity indexes; 2 – commodi-
ties; 3 – currency; 4 – complex indexes);
Sec. Market: dummy variable that equals one if there is a secondary market
for the SRP;
Early Reimbursement: equals one if there is the possibility of a knock-out with
early reimbursement;
Ln (Number of underlying’s): Log of the number of underlying assets;
Issuer/Seller: possible existence of conflicts of interest between the issuer and
the seller; 1 if the seller is a separated entity from the issuer and 0 otherwise;
Protected Capital: dummy variable that equals 1 if the SRP offers capital
protection.
A linear regression model is estimated to measure the effects of the above
mentioned variables on the intrinsic value (with and in the absence of counterparty
risk) and on hidden costs. A first estimation of the regression models followed by
the Cook’s distance test exposed two potential outliers, which are subsequently
removed from the analysis.
The intrinsic value (with and in the absence of counterparty risk) decreases with
the number of underlyings embedded on the payoff structure of the SRP. On
average, the hidden costs are higher in 0.89 p.p. if the number of reference assets
increases by 100%. Usually, the complexity of the SRP is positively related with the
T H E M A R K E T O F S T R U C T U R E D R E T A I L P R O D U C T S . . .
18
number of underlying assets, since the investors tend to disregard correlation
effects over the intrinsic value of the SRP. In this sense, our results show that
complexity is positively associated with hidden costs.
Apparently, the intrinsic value is not related with the maturity of the SRP, but hid-
den costs are. As for counterparty risk, one can say that riskier firms issue (and
market) SRPs with higher costs. This conclusion is supported by the negative rela-
tionship between the CDS price of the issuer and the intrinsic value. However, if
one disregards counterparty risk the results show a positive association between
the intrinsic value and the credit risk of the issuer. Nevertheless, the yearly hidden
costs are also positively related with counterparty risk, which means that riskier
issuers take advantage of investors due to their misperception of counterparty risk,
and obtain cheaper funding than they would if they have issued more conventional
or classic bonds.
The existence of a secondary market for SRPs decreases the intrinsic value and
raises hidden costs. In theory, the existence of a secondary market should force
the issuer to align the SRPs’ intrinsic value with the issue price, because the listing
of the SRP on the market would expose the hidden costs ‘charged’ by the issuer,
once the SRP is admitted to trading. Despite that, one should note that the sec-
ondary market for these financial instruments often exhibits low levels of liquidity
and the issuer acts in these markets as a market-maker.
On the contrary, protected capital SRPs exhibit lower hidden costs and higher
intrinsic values. The possibility of early reimbursement influences the SRP’s intrinsic
value or the yearly hidden costs. One possible explanation relies on the fact that
the early reimbursement clauses usually cap the performance of the product when
the reference assets have a favourable evolution, lowering its intrinsic value and
increasing the hidden costs.
In what concerns the type of underlying assets, the empirical evidence shows that
hidden costs are lower (and intrinsic values are higher) for SRPs’ that have equity
and commodities as reference assets. Our results also show that structured and
complex proprietary indexes, in general, are associated with higher hidden costs,
and this could be explained by the fact that it is more difficult to understand the
behaviour of these proprietary indexes.
Finally, our results show that the intrinsic value is usually higher when the issuer is
W O R K I N G P A P E R N º 2 / 2 0 1 3
19
not affiliated with the seller. Additionally, yearly hidden costs are, on average,
substantially reduced (-1.55 p.p.) in these cases (Table 5).
One other hypothesis is also tested: the possible effects arising from potential
conflicts of interests resulting from an affiliation relationship between the issuer and
the agent responsible for determining the performance of proprietary indexes. To
test this hypothesis, we add a dummy variable to the above mentioned regression.
C1 is a dummy variable that equals 1 if the issuer is the same entity or is affiliated
to the calculation agent but is not affiliated to the reference index proprietary. The
results suggest that C1 does not influence the intrinsic value of the SRP or the
hidden costs (Table 6).
As for the issuer impact (possible heterogeneity in SRP’s intrinsic value due to dif-
ferent issuers) on the yearly hidden costs and the intrinsic value, we test this
hypothesis in two stages. Firstly, we add a set of 12 dummy variables (one per
issuer) to the initial model. Afterwards, we re-estimate the model and test the null
hypothesis that those dummy variables are altogether not statistically different
from zero. Since we detect heteroskedasticity in our regression model, we perform
a heteroskedasticity-robust LM test. The results indicate that the set of dummy
variables related to the issuers of the SRPs does indeed influence the intrinsic value
and hidden costs (Table 7 – Panel A). Alternatively, for every issuer, we add one of
the “issuer” dummy variables to the initial regression model and test its individual
significance. The results reveal that the null hypothesis is rejected in 5 (4) cases in
the intrinsic value (hidden cost) equation. Combining these results, one could say
that the “issuer” variable plays an important role on the magnitude of hidden costs,
and that some issuers exhibit higher mark-up power than others.
Lastly, we examine the impact of the recent financial crises on the intrinsic values
and yearly hidden costs via the introduction of two time-dummies. The first one
(Dummy Year 2009) is equal to zero for SRPs issued in 2010 and 2011 and 1 for
SRPs issued in 2009. The second (Dummy Year 2010) is equal to zero for SRPs is-
sued in 2009 and 2011 and 1 for SRPs issued in 2010. Two different kind of statisti-
cal tests are used: a factor breakpoint test on the slope coefficients of the regres-
sion and a factor breakpoint test on the intercept. Both tests suggest that the “year
effect” plays no part in the SRPs’ intrinsic value and in the hidden costs, which
means that the costs ‘charged’ to investors did not change over the analysed period
(Table 8).
T H E M A R K E T O F S T R U C T U R E D R E T A I L P R O D U C T S . . .
20
Although the previous results are obtained with an evaluation model based on the
geometric Brownian motion stochastic process, our robustness check results, shown
in Appendix A, point to similar conclusions, if we use instead the variance–gamma
model or the Heston Model.
6. CONCLUSIONS
In conclusion we show that, on average, the 108 SRPs analysed were issued at an
intrinsic value far below the initial subscription price paid by investors and that the
hidden costs amount to 4.9% yearly. The global intrinsic value of these products is,
on average, 85.6% of the issuance price. Even when counterparty risk is ignored,
the respective intrinsic value would be inferior to the issuance price in more than
20% of the SRPs analysed. Our results reveal that the intrinsic value as a percent-
age of the issuance price of SRPs is positively influenced by the non-existence of
possible conflicts of interests between the issuer and the seller of the product; equi-
ty and commodity SRP’s, and protected capital SRPs also exhibit higher intrinsic
values. On the contrary, the intrinsic value decreases with the number of underly-
ing assets, the counterparty risk of the issuer and the existence of a secondary
market for the product. In addition, our results suggest that the yearly hidden costs
decrease with the length of the investment. We did not find evidence that the
possible conflicts of interests between the issuer and the reference proprietary
index impact either the overpricing of the SRP or the yearly hidden costs. As for the
presence of an “issuer effect” in SRPs’ overpricing or yearly hidden costs, our
results indicate that this effect is not redundant even after controlling for other
characteristics of the SRP.
In the light of this evidence, we conclude that SRPs’ hidden costs increase with the
complexity of the financial product since the intrinsic value (as a percentage of the
issued price) decreases with the number of underlying assets and SRPs with
complex indexes as references exhibit lower intrinsic values than SRPs with other
reference assets like equity or commodities. On the other hand, we show that the
possible existence of conflicts of interest between the issuer and the seller (that is,
the issuer and the seller belong to the same holding company) helps explaining the
hidden costs.
W O R K I N G P A P E R N º 2 / 2 0 1 3
21
Hereupon, for future research we suggest the study of the other determinants that
explain the huge success of SRPs in the Portuguese market. Another topic that may
be worth further researching is the case where alternative investment funds invest
directly in baskets of SRPs. In these situations, investors are faced with several
layers or cascades of fees and commissions, both at the fund and at the SRP level.
The potential for conflicts of interest is also higher since the SRPs in the portfolio of
those alternative investment funds are normally issued by the financial institution
that dominates the asset management company. We believe that this topic
warrants further research, which would contribute to enhance transparency.
T H E M A R K E T O F S T R U C T U R E D R E T A I L P R O D U C T S . . .
22
REFERENCES
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Bethel, J. and A. Ferrel (2007). “Policy Issues Raised by Structured Products”.
Harvard Center for Law, Economics and Business.
Döbeli, B. and P. Vanini (2010). "Stated and revealed investment decisions
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a case study of the pricing of a retail financial product”. Journal of Financial
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Fischer, R. (2007). “Do investors in structured products act rationally?”. European
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Jørgensen, P.L., H. Nørholm and Skovmand, D. (2011). “Overpricing and Hidden
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Wilkens, S., C. Erner and K. Roder (2003). “The Pricing of Structured Products -
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Products”. Available at SSRN: http://ssrn.com/abstract=1787216.
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REFERENCES
Szymanowska, M, J. R. Ter Horst, and C. H Veld (2009). “Reverse Convertible
Bonds Analysed”. Journal of Futures Markets, Vol. 29, No. 10, 895-919.
The Asian Banker (2009). “Building Sustainable Sales Capability: Lessons learnt
from Recent Episodes of Mis-selling”. Working Paper.
Yotsuzuka, T. (2010). “Complex Financial Products in Japan: Evolution of
Structured Products and Regulatory Responses”. Working Paper.
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Journal of Derivatives 17/2 (2009), 59-72
T H E M A R K E T O F S T R U C T U R E D R E T A I L P R O D U C T S . . .
24
TABLES
Table 1
Percentage of SRPs (by issuer and seller)
Table 2
Intrinsic value and hidden costs
Issuer % of Total Seller % of Total
Banco BPI 22.2% Banco BPI 22.2%
Barclays Bank Plc 6.5% Barclays Bank Plc 17.6% BES 14.8% BES 14.8%
BIG 3.7% BIG 3.7%
BNP Paribas 0.9% Deutsche Bank AG 23.1% Citigroup Funding Inc 3.7% Millenium BCP 8.3%
Credit Suisse 3.7% Montepio 5.6% Deutsche Bank AG 22.2% Banco Santander 4.6%
Millenium BCP 8.3%
Montepio 5.6%
Morgan Stanley 1.9%
Rabobank 0.9%
Royal Bank of Scotland 0.9% Banco Santander 4.6%
Intrinsic Value
(% of issuance price)
Intrinsic Value
in the absence of
counterparty risk
(% of issuance price)
Hidden
Costs
Mean 85.6% 95.1% 4.9% Std. Deviation 8.3% 8.0% 3.3%
Percentiles 10 75.9% 87.2% 1.5% 20 80.6% 90.2% 2.7% 30 84.0% 93.8% 3.0% 40 85.4% 95.3% 3.2% 50 86.1% 96.3% 3.5% 60 87.7% 97.4% 4.8% 70 89.7% 98.8% 6.3% 80 91.8% 100.0% 7.5%
90 95.4% 101.9% 9.5%
25
W O R K I N G P A P E R N º 2 / 2 0 1 3
Table 3
Intrinsic value and hidden costs, breakdown
Panel A - Intrinsic Value
Mean Median Std. Deviation Maximum Minimum
Year 2009 87.0% 89.7% 10.2% 96.9% 53.8% 2010 85.1% 85.6% 7.6% 96.9% 62.4% 2011 85.7% 86.0% 8.4% 107.7% 64.6%
Underlying
asset
Equity 84.7% 85.8% 8.4% 96.9% 53.8% Currency 87.2% 90.4% 8.3% 96.9% 76.0% Commodities 90.3% 92.6% 9.8% 107.7% 68.3% Complex Proprietary Indexes 85.1% 86.0% 3.0% 87.8% 77.7%
Capital
Protection No 83.3% 85.8% 10.4% 96.9% 53.8%
Yes 86.6% 86.7% 7.1% 107.7% 64.6%
Maturity
(in years)
1 93.3% 95.5% 5.3% 96.9% 85.5% 2 86.9% 85.9% 8.4% 107.7% 76.6% 3 84.2% 85.0% 7.9% 96.0% 63.1% 4 86.4% 87.7% 9.7% 97.7% 53.8% 5 84.9% 85.6% 6.4% 95.8% 68.3%
Secondary
Market No 87.6% 88.0% 6.8% 96.9% 77.7%
Yes 85.2% 86.1% 8.5% 107.7% 53.8%
Early
Reimbursement No 86.6% 86.4% 7.1% 107.7% 62.4%
Yes 83.4% 86.0% 10.4% 96.8% 53.8%
Issuer = Seller No 89.2% 89.8% 6.1% 97.7% 75.0%
Yes 85.1% 86.0% 8.5% 107.7% 53.8%
N.º of
Underlying
Assets
1 87.6% 86.7% 6.2% 107.7% 76.0% 2 86.6% 88.2% 11.6% 96.8% 62.4% 3 81.6% 85.5% 10.9% 96.8% 53.8% 4 86.1% 85.5% 7.5% 97.7% 64.6% 5 85.2% 84.9% 6.8% 96.9% 68.3%
Total 85.6% 86.1% 8.3% 107.7% 53.8%
T H E M A R K E T O F S T R U C T U R E D R E T A I L P R O D U C T S . . .
26
Panel B - Intrinsic value in the absence of counterparty risk
Panel C - Hidden Cost
27
Mean Median Std. Deviation Maximum Minimum
Year 2009 90.8% 95.3% 10.1% 100.7% 58.2%
2010 94.9% 96.0% 6.6% 105.9% 65.5% 2011 98.2% 98.0% 7.9% 126.4% 83.3%
Underlying asset
Equity 93.9% 96.1% 7.9% 104.2% 58.2%
Currency 98.9% 97.7% 3.5% 105.9% 95.7% Commodities 102.6% 100.4% 8.3% 126.4% 94.2%
Complex Proprietary Indexes 92.7% 92.6% 4.4% 102.7% 86.3%
Capital Protection No 88.8% 90.2% 9.6% 102.2% 58.2%
Yes 97.8% 97.5% 5.4% 126.4% 83.3%
Maturity (in years)
1 95.1% 97.4% 5.3% 98.4% 87.2%
2 100.4% 99.4% 9.8% 126.4% 88.4%
3 95.7% 96.5% 6.0% 105.9% 76.5% 4 94.0% 95.8% 10.2% 105.9% 58.2% 5 92.2% 93.4% 5.3% 98.8% 74.4%
Secondary Market No 98.2% 97.5% 2.6% 104.1% 93.9%
Yes 94.5% 95.5% 8.5% 126.4% 58.2%
Early Reimbursement No 96.9% 96.9% 6.7% 126.4% 65.5%
Yes 91.1% 93.3% 9.3% 104.1% 58.2%
Issuer = Seller No 93.7% 94.1% 7.5% 105.9% 76.7%
Yes 95.3% 96.4% 8.1% 126.4% 58.2%
N.º of Underlying Assets
1 96.1% 95.2% 7.3% 126.4% 86.3%
2 92.1% 93.3% 13.1% 102.6% 65.5% 3 90.7% 94.2% 10.5% 101.7% 58.2%
4 97.9% 97.9% 4.6% 105.9% 83.3% 5 96.7% 96.9% 2.7% 100.5% 90.2%
Total 95.1% 96.3% 8.0% 126.4% 58.2%
Mean Median Std. Deviation Maximum Minimum
Year 2009 5.0% 3.1% 3.6% 14.4% 1.3% 2010 4.8% 3.5% 2.9% 14.2% 0.8% 2011 5.1% 4.0% 3.9% 13.6% -3.9%
Underlying asset
Equity 5.2% 4.2% 3.3% 14.4% 0.9% Currency 5.1% 3.0% 4.1% 12.4% 1.6% Commodities 4.0% 2.9% 4.2% 11.2% -3.9% Complex Proprietary Indexes 4.0% 3.4% 1.5% 8.3% 2.9%
Capital Protection No 6.1% 4.9% 3.7% 14.4% 0.9%
Yes 4.4% 3.3% 3.0% 13.6% -3.9%
Maturity (in years)
1 6.4% 4.5% 4.7% 13.4% 3.1% 2 7.1% 7.6% 4.5% 12.4% -3.9% 3 5.7% 5.3% 3.1% 14.2% 1.4% 4 3.7% 3.0% 3.0% 14.4% 0.6% 5 3.3% 3.1% 1.5% 7.4% 0.9%
Secondary Market No 5.6% 4.2% 3.2% 11.2% 1.4%
Yes 4.8% 3.5% 3.4% 14.4% -3.9%
Early Reimbursement No 4.6% 3.4% 3.0% 13.4% -3.9%
Yes 5.6% 3.9% 4.0% 14.4% 0.8%
Issuer = Seller No 3.2% 3.1% 1.8% 6.9% 0.6%
Yes 5.1% 4.0% 3.4% 14.4% -3.9%
N.º of Underlying Assets
1 4.7% 3.5% 3.3% 13.4% -3.9% 2 3.9% 3.0% 3.5% 11.1% 0.8% 3 5.7% 4.2% 3.9% 14.4% 0.8% 4 5.2% 4.0% 3.5% 13.6% 0.6% 5 4.5% 4.3% 1.8% 7.4% 2.4%
Total 4.9% 3.5% 3.3% 14.4% -3.9%
W O R K I N G P A P E R N º 2 / 2 0 1 3
Table 4
Pearson’s correlation
Table 5
Regression model
Table 6
The influence of conflicts of interests
28
T H E M A R K E T O F S T R U C T U R E D R E T A I L P R O D U C T S . . .
Maturity Counterparty Risk
Intrinsic value -0.084 -0.411** Intrinsic value in the absence
of counterparty risk -0.246* 0.365**
Hidden cost -0.384** 0.401**
** Correlation is significant at the 0.01 level (2-tailed).
* Correlation is significant at the 0.05 level (2-tailed).
*** Significant at the 0.01 level (2-tailed).
** Significant at the 0.05 level (2-tailed).
* Significant at the 0.1 level (2-tailed).
Intrinsic
Value
Intrinsic Value
in the absence of
counterparty risk Hidden
Cost
Intercept 0.9307 *** 0.9309 *** 0.0688 ***
Issuer/Seller 0.0379 * 0.0215 -0.0155 **
Capital Protected 0.0709 *** 0.066 *** -0.0234 ***
Maturity -0.0029 -0.0093 -0.0131 ***
ln(Number of underlyings) -0.0338 *** -0.0227 ** 0.0089 *
CDS Price -0.733 *** 0.1776 ** 0.2423 ***
Secondary Market -0.0374 ** -0.0176 0.0127
Early Reimbursement -0.0272 * -0.0244 * 0.0102 *
Equity 0.0708 *** 0.0556 *** -0.0204 ***
Currency 0.0499 ** 0.0449 ** -0.0155
Commodities 0.0953 *** 0.0943 *** -0.0293 ***
Adjusted R-squared 0.47 0.46 0.46
F-statistic 10.46 *** 9.93 *** 9.76 ***
n.º obs. 106 106 106
Intrinsic value
Intrinsic value in the
absence of counterparty risk
Hidden cost
t-statistic [c1=0] -0.421 -1.056 0.399
White heteroskedasticity-consistent standard errors & covariance
*** Significant at the 0.01 level (2-tailed).
** Significant at the 0.05 level (2-tailed).
* Significant at the 0.1 level (2-tailed).
Table 7
The “issuer effect”
Panel A - Overall significance test
Panel B - Individual test of the “issuer effect”
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Intrinsic value
Intrinsic value in the absence of counterparty risk
Hidden cost
H0: Issuer’s impact on the dependent variable is negligible
26.12 (**) 22.47 (**) 22.94 (**)
Wooldridge heteroskedasticity-robust LM statistic
*** Significant at the 0.01 level (2-tailed).
** Significant at the 0.05 level (2-tailed).
* Significant at the 0.1 level (2-tailed).
Intrinsic value
Intrinsic value
in the absence of
counterparty risk Hidden cost
t-stat t-stat t-stat IF1 2.9 (***) 1.892 (*) -3.742 (***) IF2 4.301 (***) 4.249 (***) -2.918 (***) IF3 -0.332 0.53 0.447 IF4 -1.469 -2.157 (**) 1.588 IF5 1.306 1.513 -1.12 IF6 -2.983 (***) -1.898 (*) 4.096 (***) IF7 0.932 1.024 -1.139 IF8 -0.885 -0.507 0.452 IF9 -0.503 -0.115 1.371 IF10 1.828 (*) 0.907 -2.61 (**) IF11 -0.674 -1.006 -0.075 IF12 2.702 (***) 3.609 (***) -1.198
IF13 0.547 -0.23 -0.745
Table 8
The influence of the “year effect”
Panel A - Factor breakpoint test on the slope coefficients
Panel B - Factor breakpoint test on the intercept
***. Significant at the 0.01 level (2-tailed).
Intrinsic
value
Intrinsic value
in the absence of
counterparty risk Hidden
cost
Factor breakpoint test d2010 (slope) 8.89 7.93 9.98
Factor breakpoint test d2011 (slope) 9.8 9.01 16.02
Wooldridge heteroskedasticity-robust LM statistic
**. Significant at the 0.05 level (2-tailed).
*. Significant at the 0.1 level (2-tailed).
*** Significant at the 0.01 level (2-tailed).
Ho Intrinsic
value
Intrinsic value
in the absence of
counterparty risk Hidden
cost d2010 = 0 -0.043 1.029 0.009
d2011 = 0 0.193 1.294 0.262
Heteroskedasticity-robust t-statistic
** Significant at the 0.05 level (2-tailed). * Significant at the 0.1 level (2-tailed).
T H E M A R K E T O F S T R U C T U R E D R E T A I L P R O D U C T S . . .
30
APPENDIX A - ROBUSTNESS CHECK RESULTS
The reported results are obtained with an evaluation model based on the geometric
Brownian motion (GBM) stochastic process. Our robustness check results show that
the conclusions would not change substantially if we use instead the variance–
gamma model (VGM) or the Heston Model7. The results obtained through VGM are
quite similar to those obtained through GBM. In fact the interquartile range of the
percentage difference between the results of the two types of evaluation models is
less than 0.3 p.p.. As for the Heston Model, the intrinsic values exhibit higher
differences when compared to GBM (or even VGM). The percentage difference of
the intrinsic values computed through GBM and Heston Model is quite small
(-0.2%), and the interquartile range is equal to 1%.
Table A1
We also re-estimate the equation that explains the intrinsic value in the absence of
counterparty risk. The estimated coefficients are slightly different, but the results
are quite similar in terms of the signs and statistical significance of the explanatory
variables.
Difference between
VGM and GBM intrinsic value Difference between Heston Model
and GBM intrinsic value
Mean 0.0% -0.2% Std. Deviation 0.4% 4.9%
Percentiles 10 -0.3% -1.5% 20 -0.1% -0.8% 30 0.0% -0.3% 40 0.0% -0.1% 50 0.0% 0.0% 60 0.0% 0.1% 70 0.0% 0.1% 80 0.1% 1.0%
90 0.2% 3.9%
7- The number of observations in A1 and A2 do not exactly match the number of observations on Table 5 regression due to the impossibility of implementing VGM and Heston model to some of SRP’s.
W O R K I N G P A P E R N º 2 / 2 0 1 3
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Table A2
Panel A – GBM vs. VGM (in the absence of credit risk)
Panel B – GBM vs. Heston Model (in the absence of credit risk)
Intrinsic value
GBM Intrinsic value
VGM
Intercept 0.8855 *** 0.8835 ***
Issuer/Seller -0.0206 -0.0219 Protected Capital 0.0731 *** 0.0745 ***
Maturity -0.0055 -0.0054 ln(Number of underlyings) -0.0306 *** -0.0294 ***
CDS Price 0.2998 *** 0.3011 ***
Secondary Market -0.0056 -0.0039 Early Reimbursement -0.0181 -0.0173
Equity 0.0627 *** 0.0609 *** Currency 0.0788 *** 0.0784 *** Commodities 0.1168 *** 0.1153 ***
Adjusted R-squared 0.53 0.53
F-Stat 11.605 11.580
n.º obs. 95 95
*** Significant at the 0.01 level (2-tailed).
** Significant at the 0.05 level (2-tailed).
* Significant at the 0.1 level (2-tailed).
Intrinsic Value
GBM Intrinsic Value
Heston Model
Intercept 0.8986 *** 0.9207 ***
Issuer/Seller -0.0238 -0.0381
Protected Capital 0.079 *** 0.0899 ***
Maturity -0.0081 -0.0154 ln(Number of underlyings) -0.0328 *** -0.0349 ***
CDS Price 0.2578 *** 0.2184 * Secondary Market -0.0043 0.0019
Early Reimbursement -0.0183 -0.0135 Equity 0.0633 *** 0.0726 ***
Currency 0.0744 *** 0.0737 *** Commodities 0.1048 *** 0.1043 ***
Adjusted R-squared 0.52 0.35
F-Stat 11.315 6.124 n.º obs. 97 97
*** Significant at the 0.01 level (2-tailed).
** Significant at the 0.05 level (2-tailed).
* Significant at the 0.1 level (2-tailed).
T H E M A R K E T O F S T R U C T U R E D R E T A I L P R O D U C T S . . .
32
APPENDIX B - TOPICS FOR FURTHER RESEARCH
The SRP market has grown substantially in recent years. Concurrently, the mutual
fund market has experienced a strong decline since 2007. In fact, between 2007
and 2010 the assets under management by mutual funds fell by 51%, contrasting
with the value of new SRPs sold that increased 3% in the same period. Further-
more, between 2006 and 2009, the growth in the volume issued was huge (134%).
These figures suggest that investors might be shifting their preferences away from
mutual funds and towards SRPs. Comparing the SRP’s implicit cost that derives
from the difference between the issue price and the intrinsic value with the explicit
costs of mutual funds (subscription, redemption and administration fees) for the
year 2010 suggests that the shifting preferences are not related to a significant
competitive mutual fund cost disadvantage vis-a-vis SRPs.
Table B1
Average fees in the mutual fund industry
As for the redemption fees, they vary over the investment period. Table B2 displays
the average redemption fee for five different investment horizons (0.5, 1, 2, 4 and
5 years). Concurrently, we calculate the annual explicit cost of mutual funds
according to the following expression:
Exp. Costt = 1— [1— subs.fee—t * adm.fee—redemp.fee]1/t
In the table below, we compare the costs of investing in mutual funds and in SRPs.
It clearly shows that mutual funds costs are substantially lower than SRP’s hidden
costs in any investment period.
Table B2
Mutual fund and SRP Fees
Subscription fees Administration fees
Equity Mutual
Funds 0.0% 1.90%
Alternative
Investment Funds 0.0% 1.03%
Equity Mutual Funds Alternative Investment
Funds SRP’s
hidden
costs Years
Redemption
fees Explicit
Cost Redemption
fees Explicit
Cost
0.5 1.01% 4.22% 0.96% 2.96%
1 0.35% 2.59% 0.79% 1.86% 6.4%
2 0.32% 2.43% 0.73% 1.45% 5.7%
3 0.31% 2.40% 0.69% 1.32% 3.7% 5 0.28% 2.41% 0.51% 1.20% 3.3%
W O R K I N G P A P E R N º 2 / 2 0 1 3
33
WORKING PAPER
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Apartado 14258
1064-003 Lisboa . Portugal
Telefone 21 317 70 00 . Fax 21 353 70 77/ 78
Site: www.cmvm.pt
E-mail: [email protected]
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