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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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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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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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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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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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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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.

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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


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).


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.


22

REFERENCES

Bergstresser, D. (2008). “The retail market for structured notes: Issuance patterns

and performance, 1995-2008”. Harvard Business School Working Paper.

Bernard, C., P. Boyle, W. Tian (2008). “Optimal Design of Structured Products and

the Role of Capital Protection” (available at SSRN).

Bernard, C. and P. Boyle (2009). “Locally-Capped Investment Products and

the Retail Investor”. Conference proceedings European Financial Management

Association June 2009 (EFMA).

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

concerning retail structured products". Journal of Banking & Finance, Elsevier,

vol. 34(6), pages 1400-1411, June.

Grünbichler, A and H. Wohlwend (2005). “The Valuation of Structured Products:

Empirical Findings for the Swiss Market”. Financial Markets and Portfolio

Management Volume 19, Number 4, 361-380.

Henderson, B. J. and N. D. Pearson (2007). “Patterns in the Payoffs of Structured

Equity Derivatives”. AFA 2008 New Orleans Meetings Paper.

Henderson, B. J. and N. D. Pearson (2011). “The dark side of financial innovation:

a case study of the pricing of a retail financial product”. Journal of Financial

Economics nº11, 227 -247.

Fischer, R. (2007). “Do investors in structured products act rationally?”. European

Business School Working Paper.

Jørgensen, P.L., H. Nørholm and Skovmand, D. (2011). “Overpricing and Hidden

Costs of Structured Bonds for Retail Investors: Evidence from the Danish Market for

Principal Protected Notes”. Working Paper.

Madan, B., P. P. Carr and E. C. Chang (1998). “The Variance-Gamma Process and

Option Pricing”. European Finance Review.

Wilkens, S., C. Erner and K. Roder (2003). “The Pricing of Structured Products -

An Empirical Investigation of the German Market”. Working Paper.

Ruf, T. (2011). “The Bank Always Wins: The Dynamics of Overpricing in Structured

Products”. Available at SSRN: http://ssrn.com/abstract=1787216.

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23

http://ssrn.com/abstract=1787216

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.

Wallmeier, M. and M. Diethelm (2009). “Market pricing of exotic structured

products: The case of multi-asset barrier reverse convertibles in Switzerland”.

Journal of Derivatives 17/2 (2009), 59-72


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%


26

Panel B - Intrinsic value in the absence of counterparty risk

Panel C - Hidden Cost

27


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%


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


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




Table 7

The “issuer effect”

Panel A - Overall significance test

Panel B - Individual test of the “issuer effect”

W O R K I N G P A P E R N º 2 / 2 0 1 3

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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




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


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).


Ho Intrinsic

value

Intrinsic value

in the absence of


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).


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

31

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




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





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

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