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4 Steps 4-6: Sampling, Generating Cash-Flow Distributions, and Calculating CFaR

4 Steps 4-6: Sampling, Generating Cash-Flow Distributions, and Calculating CFaR

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N. Andre´n et al.



Table 3 ExposureÀbased CFaR estimates for 2004:I (Mn NOK)

Expected

5th percentile

CFaR

cash flow (A)

cash flow (B)

(C ¼ A – B)

HOE

9,706

8,105

1,601

HAL

2,167

1,498

669

HA

2,061

1,572

489

HG

13,814

11,811

2,002



CFaR in percent

(D ¼ C//A)

16.5%

30.9%

23.7%

14.6%



Relative

frequency

0.16

0.14

0.12

0.10

0.08

0.06

0.04

0.02

0



9



X=11,811

5%



10



11



12



X=15,812

95%



Mean= 13,814



13



14

Bn NOK



15



16



17



18



19



Fig. 1 Simulated distribution for HG’s cash flow, 2004:1



covariances of those risks. The resulting cash-flow distributions in turn enabled us to

estimate the CFaRs for the next quarter (Q1 2004) for each of the three business

areas. These are summarized in Table 3 and depicted graphically for the company as

a whole in Fig. 1.

How do we interpret the information in Table 3? As an example, given our

selected confidence level of 95%, we interpret the CFaR estimate for HG as

follows: we are 95% certain that the company’s cash flow (EBITDA) will not fall

short of the expected amount of NOK 13,814 million by more than NOK 2,002

million. In other words, we expect cash flow to fall below NOK 11,812 million

(13,814–2,002) in only one quarter out of 20. Table 3 also shows that of the three

main businesses, HAL’s cash flow is associated with the largest risk (31%).



6 Analyzing the Corporate Risk Portfolio

Exposure-based CFaR opens up rich possibilities for decomposing the CFaR

estimate into individual risk exposures, thereby providing insights into the cash

flow dynamics of the company and the key drivers of risk. In particular, the method

allows for a clearer view of the portfolio aspects of corporate risk.

Portfolio considerations exist on three levels. First, there may be offsetting

exposures, or what amount to natural hedges, in Hydro’s portfolio of exposures.

For example, HOE had a long position in Brent, as indicated by the 219 million



Exposure-ased Cash-Flow-at-Risk for Value-Creating Risk Management



101



exposure coefficient (see exposure models in Table 1), whereas HA had an

offsetting short position of NOK À26 million. While the NOK/USD exchange

rate is significant for each of the three business areas when viewed separately,

there is no significant exposure to the company as a whole (HG) (p-value ¼ 0.25 if

it was included in the model). Thus, the long positions of HOE and HA in USD

appear to be cancelled out by HAL’s short position.

Second, the error terms in the regressions, which reflect cash flow changes

independent of the macroeconomic and market risk factors, could be correlated

across business areas. A correlation between the error terms would indicate that

there is a tendency for macro-independent changes to be systematic across business

areas. An analysis of the error terms from the models in Table 1 indicates that the

correlations are generally insignificant, which suggests that the macro-independent

changes in cash flows are diversified in the HG portfolio.

Third, there could be a portfolio effect from exposures to correlated risk factors.

A high correlation between two risk factors will have an impact on estimated CFaR,

and the sign of the exposure coefficients determines whether the overall net impact

is positive or negative. If two risk factors are positively correlated, but the firm is

negatively exposed to one and positively to the other, there is a dampening effect on

cash flow risk. Looking at Table 2, we see that the correlations among risk factors

are generally low, implying that there is a clear diversification effect. But some of

Hydro’s product prices do appear somewhat correlated. For example, the correlation coefficient between the prices of the company’s two main commodities, oil and

aluminum, is 0.39. Of all the correlations, this one is likely to have the largest

bearing on overall risk. Furthermore, Urea and NH3 have a correlation of 0.48.

Another insight that comes from taking a portfolio view of risk is that, in some

cases, not all product prices need be included in the exposure models. In the HG

model, for example, the inclusion of NH3 alone seems sufficient to capture the

entire commodity price exposure of the fertilizer business. In such a case, managing

exposure to a single price that, because of high correlations, represents exposures to

a whole category of risks could mean major savings in terms of transaction costs.

All in all, then, the effects of less-than-perfect correlations and natural hedges

add up to lower risk at the Hydro group level as compared to the sum of the risks in

the three main business areas. As a measure of this diversification benefit, the CFaR

for Hydro Group reported in Table 3 is NOK 2,002 million, considerably lower

than the sum of the CFaRs for the three business areas (NOK 2,759Mn). The

difference of NOK 757 million can be attributed to the natural hedges provided

by the less-than-perfect correlations between the risk factors and the error terms.



7 Exposure-Based CFaR and Hedging

Another benefit of exposure-based CFaR is its ability to inform hedging decisions.

Using the CFaR methodology, management can readily assess the impact on cash

flow variability of different hedging strategies. Indeed, much of the information



102

Table 4 Hydro Group’s CFaR estimates under different hedging strategies

Base case CFaR

Hedged CFaR (100%

(no hedge)

hedge of each risk factor)

Brent crude

2,002

1,727

Aluminum

2,002

1,829

2,002

1,777

NH3



N. Andre´n et al.



Risk reduction in %

13.7%

8.6%

11.2%



necessary for deciding the size of the hedge position is contained in the coefficients

in the exposure model. For example, in the HA model the indicated exposure to

NOK/USD is 240 million for each NOK depreciation to the dollar (as shown in

Table 1). This means that if management wishes to neutralize its exposure to this

exchange rate for the next quarter, it would sell forward exactly this number of

dollars. The forward position would then have the same exposure as HA’s cash

flow, but with opposite sign, and they would cancel out, leaving HA’s cash flow

unexposed. For example, if the NOK were to depreciate by 0.10 NOK to the dollar,

cash flow would increase by 24 million. But the forward position would fall by the

same amount, neutralizing the effect on Hydro’s cash flow.

The effectiveness of such partial hedges in terms of reducing cash flow risk

depends on three factors: (1) the size of the exposure; (2) the volatility of the risk

factor being hedged; and (3) the correlation between the risk factor being hedged

and other risk factors in the model. The effects of 1 and 2 are likely to be the most

important ones. Generally speaking, the combined effect of exposure and volatility

will determine a risk factor’s contribution to cash flow volatility. We have compared the effects of hedging 100% of the exposure for all variables in the Hydro

Group model (in reality, there is no forward market for NH3, but we assume the risk

can be hedged). The base case CFaR is the number reported for HG in Table 3. As

indicated by Table 4, hedging the exposure to Brent is the most effective way of

reducing risk (provided this is management’s goal). While NH3 has a higher

volatility than Brent, Hydro has a much larger exposure to Brent, which is the

dominating effect in this case. Exposure to the aluminum price is also relatively

large, but the effect of an aluminum hedge on risk is limited by the relative stability

of the aluminum price.



8 Separating Between Value-Adding and Non-Value-Adding

Risks

A further decomposition of exposures can be made by distinguishing between the

effects of macroeconomic risk and cash-flow changes independent of macroeconomics. To the extent the exposure models capture the impact of non-value-adding

risks, the independent component will capture the influence of value-adding risks.

For Hydro Group, macroeconomic and market risks account for about 69% of the



Exposure-ased Cash-Flow-at-Risk for Value-Creating Risk Management



103



variability in cash flow as measured by R2. The CFaR conditional on these macroeconomic and market risk factors is estimated to be NOK 1,385 million, as

compared to the CFaR estimated from macro-independent changes of NOK 1,444

million (to see how these respective numbers are estimated, see steps 4 and 5 in the

six-step process described earlier). The two risk components are not additive since

the error term is defined to be the cash flow volatility independent of macroeconomic and market risk (additivity would only come about in the case of perfect

correlation.) We also observe that while over two thirds of cash flow volatility is

explained by the exposure model, this doesn’t necessarily mean that the conditional

CFaR is higher than the CFaR due to value-adding risks. This will depend on the

degree of volatility and correlation among the explanatory variables in the model

relative to the volatility of error terms.

As stated earlier, we argue that an exclusive focus on either conditional CFaR or

total CFaR is likely to be a mistake. Only by examining both of these distributions

can corporate managers get a meaningful indicator of uncertainty about future cash

flow. An exclusive focus on the distribution of macro-independent changes could

lead to a minimum-variance strategy, one in which all hedgeable exposures are

reduced to zero. By hedging all its macroeconomic and market risk, Hydro could

reduce the CFaR to NOK 1,444 million (the CFaR from macro-independent

changes alone).



9 Concluding Remarks on the Exposure Based CFaR

for Value Adding Risk Management

Cash Flow at Risk is the cash flow equivalent of Value at Risk, which is widely

used as the basis for the risk management systems within financial institutions.

CFaR promises the same potential among industrial companies for much the same

reasons as VaR has succeeded with financial firms: it sums up all the company’s risk

exposures into a single number that can be used to guide corporate risk management

and performance management.

Competitive advantage derives from having the ability to identify and exploit

inefficiencies in markets for real production factors (Barney 1986). A gold miner

owning a mine with richer ores than competitors would, if the access to the unique

production factor is managed correctly, be able to generate residual incomes in

excess of the cost of capital. The same could be said for a consumer company with

unique abilities in designing products or services that are perceived by consumers

as more attractive than competing offers. Executives and academics tend to frame

argumentations on competitive advantage in terms of assets and capabilities and

overlook that the argumentation could be applied to risk taking as well. The gold

miner might have an advantage in bearing the risk of exploration or extraction,

while the consumer company’s comparative advantage in bearing risk may apply to

human resource management or research and development. The gold miner may

have no comparative advantage in bearing the risk of the price of gold, implying



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N. Andre´n et al.



that the firm may be better off hedging the gold price risk. The consumer company

may instead hold no advantage in manufacturing and may benefit from outsourcing

its production to a firm specializing in manufacturing consumer products.

Why is the distinction between value-adding and non-value-adding risk relevant? Because there is a limit to corporate risk taking. Corporate stakeholders,

not least owners and creditors, but also, for example, suppliers, customers, and

employees may not accept unlimited risk taking. This is apparent in the banking

industry, where the Basel III framework specifies minimum capital requirements

based on the bank’s risk taking; the greater the risks taken by the bank, the greater

the required equity cushion in the form of tier I and II capital. The same logic

applies to non-banking busineses. All risk taking requires an equity cushion,

either explicitly in the form of on-balance-sheet equity, or indirectly in the form

of investment in risk prevention, owner guarantees, or by facing a greater credit risk

premium on credit financing (Merton and Perold 1993). Being exposed to nonvalue-adding risk will thereby limit the firm’s ability to add exposure to valueadding risk. By reducing the exposure to non-value-adding risk, management may

accordingly increase its investments in value-adding risks.

Our approach to CFaR, which we call exposure-based CFaR, provides the

strategic CFO with a comprehensive framework for handling non-value adding

risks. The framework involves the estimation of a set of exposure coefficients that

provide information about how various macroeconomic and market variables are

expected to influence the company’s cash flow, and it also takes account of

interdependencies and correlations among such effects. The resulting exposure

model gives the strategic CFO a set of exposure coefficients that is capable of

explaining the variability in cash flow as a function of various risks; and for this

reason, it can also be used to predict how a hedging contract or change in financial

structure will affect the company’s risk profile. At the same time, our framework

also provides information about that part of the firm’s cash flow variability that is

not attributable to macroeconomic and market risks, but is necessary in calculating

the firm’s overall variability and CFaR.



References

Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate

bankruptcy. Journal of Finance, 4(4), 589–609.

Altman, E. I., & Saunders, A. (1998). Credit risk measurement developments over the last

20 years. Journal of Banking and Finance, 21(11–12), 1721–1742.

Andre´n, N., Jankensga˚rd, H., & Oxelheim, L. (2005). Exposure-based cash-flow-at-risk: An

alternative to VaR for industrial companies. Journal of Applied Corporate Finance, 17(3),

76–86.

Barney, J. B. (1986). Types of competition and the theory of strategy: Toward an integrative

framework. Academy of Management Review, 11(4), 791–800.

Bartram, S. M. (2000). Corporate risk management as a lever for shareholder value creation.

Financial Markets, Institutions & Instruments, 9(5), 279–324.

Borch, K. (1967). The theory of risk. Journal of the Royal Statistical Society, 29(3), 432–467.



Exposure-ased Cash-Flow-at-Risk for Value-Creating Risk Management



105



Culp, C., Miller, M., & Neves, A. (1998). Value at risk: Uses and abuses. Journal of Applied

Corporate Finance, 10(4), 26–38.

Froot, K., Scharfstein, D., & Stein, J. (1993). Risk management: Coordinating corporate investment and financing policies. Journal of Finance, 48(5), 1629–1658.

Froot, K., Scharfstein, D., & Stein, J. (1994). A framework for risk management. Harvard

Business Review, 72(6), 91–102.

Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk.

Econometrica, 47(2), 263–291.

Koller, T., Goedhart, M., & Wessels, D. (2005). Valuation. Measuring and managing the value of

companies. Chichester: Wiley.

Libby, R., & Fishburn, P. C. (1977). Behavioral models of risk taking in business decisions: A

survey and evaluation. Journal of Accounting Research, 15(2), 272–292.

Mayers, D., & Smith, C. W., Jr. (1982). On the corporate demand for insurance. Journal of

Business, 55(2), 281–296.

Merton, R. C. (2005). You have more capital than you think. Harvard Business Review, 83(11),

84–94.

Merton, R. C., & Perold, A. (1993). Theory of risk capital in financial firms. Journal of Applied

Corporate Finance, 6(3), 16–32.

Miller, K., & Leiblein, M. (1996). Corporate risk-return relations: Returns variability versus

downside risk. Academy of Management Journal, 39(1), 91–122.

Oxelheim, L., & Wihlborg, C. (1987). Macroeconomic uncertainty. International risks and

opportunities for the corporation. Chichester: Wiley.

Oxelheim, L., & Wihlborg, C. (1997). Managing in a turbulent world economy: Corporate

performance and risk exposure. Chichester: Wiley.

Oxelheim, L., & Wihlborg, C. (2008). Corporate decision-making with macroeconomic uncertainty. New York: Oxford University Press.

RiskMetrics. (1999). CorporateMetricsTM technical document. New York: RiskMetrics Group.

Schrand, C., & Unal, H. (1998). Hedging and coordinated risk management: Evidence from thrift

conversions. Journal of Finance, 53(3), 979–1013.

Scott, J. (1982). The probability of bankruptcy. A comparison of empirical predictions and

theoretical models. Journal of Banking and Finance, 5(3), 317–344.

Smith, C. W., & Stulz, R. M. (1985). The determinants of firms’ hedging policies. Journal of

Financial and Quantitative Analysis, 20(4), 391–405.

Stein, J., Usher, S., LaGatutta, D., & Youngen, J. (2001). A comparables approach to measuring

cashflow-at-risk for non-financial firms. Journal of Applied Corporate Finance, 13(4),

100–109.

Stulz, R. M. (1996). Rethinking risk management. Journal of Applied Corporate Finance, 9(3),

8–24.



Part II



Coping and Benefiting from the Dynamics

of Financial Markets



Capital Markets 2.0 – New Requirements

for the Financial Manager?

Holger Wohlenberg and Jan-Carl Plagge



Contents

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2 Opportunities from Networked Capital Markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2.1 Globalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2.2 Liberalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2.3 Innovation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3 Challenges from Fragmentation, Intransparency and Regulation . . . . . . . . . . . . . . . . . . . . . . . . .

3.1 Fragmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3.2 Intransparency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3.3 Frequency of Regulatory Interventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4 Summary and Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .



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Abstract Capital markets have evolved from geographically dispersed asset specific

investor- and trading communities to international, partially integrated networks of

transaction partners across multiple asset classes (Capital Markets 2.0). This new

environment is characterized by a set of changes, which creates new opportunities as

well as challenges for the financial manager. The chapter selects three of the typical

dynamics in Capital Markets 2.0 and discusses their implications on typical decisions

to be made by financial managers.



H. Wohlenberg (*)

Market Data & Analytics, Deutsche B€

orse AG, Frankfurt, Germany

e-mail: Holger.Wohlenberg@deutsche-boerse.com

J.-C. Plagge

Product Development, STOXX Ltd, Z€

urich, Switzerland

e-mail: Jan-Carl.Plagge@stoxx.com

U. Hommel et al. (eds.), The Strategic CFO,

DOI 10.1007/978-3-642-04349-9_7, # Springer-Verlag Berlin Heidelberg 2012



109



110



H. Wohlenberg and J.-C. Plagge



1 Introduction

A capital market is defined as a market for securities where corporations and

governments can raise and invest long-term capital. The capital market can be

divided into the primary and the secondary market. The primary market resembles

the market for emissions. Here the issuer (seller) faces the investor (buyer) of

securities, often intermediated by financial institutions such as investment banks.

On the secondary market, already issued securities are traded among investors.1

Capital markets nowadays are the subjects of numerous changes. In the last

decades, markets have evolved from the state of segmentation to more integration.

This process has been pressed ahead by issuers cross-listing their shares in foreign

capital markets. In addition, governmental institutions have started to cooperate on

regulations and market connectivity.

Significant efforts have been undertaken to open up and harmonize stock

exchanges within as well as across national borders. For example, investors should

be guaranteed the best execution among a set of possible trading places as it is

intended by the “Markets in Financial Instruments Directive” (MiFID) introduced

by the European Commission in 2007. The EU passport, to name another example,

grants financial institutions domiciled in the European Union the right to offer services

such as order collection and execution of orders in any other member state without the

need to obtain a special local permission.

On the other hand, the trend towards general harmonization provokes certain

antagonistic developments: (1) fragmentation of liquidity arising from an increasing

number of new trading venues, (2) decreased transparency, and (3) increased

regulatory controls.

In this chapter, we will focus on the coexistence of these antagonistic developments

and examine how they affect primary tasks of the financial manager, such as:

• Provision of funding for projects or transactions

• Optimization of the capital structure with regards to total cost and risk

• Management of investor relations

Section 2 summarizes opportunities arising for financial managers from increased

globalization, liberalization and innovation. Section 3 discusses the challenges

produced by the concurrently emerging antagonistic factors fragmentation,

intransparency, and regulatory control with regards to financial decision-making.

In Section 4, opportunities and challenges are subsumed and conclusions drawn as to

how the financial manager has to change decision-making in the new capital market

environment.



1



Perridon et al. (2009), pp. 161–162.



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