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Measuring Business Risk through Cash Flow at Risk: Modeling and Hedging Choices in a Multinational Company based in an

Emerging Country

Jorge Arturo Martínez-González, CFA∗ Roberto Joaquín Santillán-Salgado∗∗

X Congreso Anual de la Academia de Ciencias

Administrativas AC (ACACIA) San Luis Potosí

May 3-5, 2006

Area del conocimiento: Economía y Finanzas

Contact autor: Roberto Santillan Roberto.santillan@itesm.mx

∗ Jorge Martinez is an Associate Professor of Economics and Finance, ITESM-EGADE, México, and Head of Risk Management, Banorte-Generali. ∗∗ Roberto Santillan is a Full Professor of Finance, ITESM-EGADE, México. Dr. Santillan wishes to acknowledge the research support received from ESC-Lille, where he finished this work while spending a sabbatical period.

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Measuring Business Risk through Cash Flow at Risk: Modeling and Hedging Choices in a Multinational Company based in an

Emerging Country

Jorge Arturo Martínez-González Roberto Joaquín Santillán-Salgado

Abstract

This paper describes a real life exercise of financial statements modeling in the context of business risk management. From the origins of risk management to the generalized use of Enterprise Risk Management (ERM) as a corporate tool, increasing evidence supports the adoption of a disciplined approach to anticipate “value-driving” risk factors that affect the firm’s economic value by using simulation techniques. After establishing a conceptual framework that justifies the utilization of integrated firm-wide ERM and describing the steps required for setting the stage for simulation, we proceed to the presentation of a Cash Flow at Risk Model expressly designed for the measurement and risk/investment calibration choices in a multinational cement company, Cemex, based in Monterrey, Mexico. Introduction

Since the mid-1990s, the discipline of focused risk management has rapidly moved toward a more holistic view called Enterprise Risk Management, ERM. The central motivation behind ERM in the private firm is to smooth earnings volatility by applying a rigorous and coordinated method to assess and respond to risks that affect the achievement of a business entity’s strategic and financial objectives. ERM is different from traditional risk management in that it recognizes that risk has two faces. Besides hedging against possible losses associated with the realization of unfavorable events, ERM also implies the development of information systems and response mechanisms that allow management to take hold of environmental opportunities, i.e.,

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companies can achieve competitive advantages by correctly identifying which risks they can handle better than their competitors.

ERM is designed to perform the more traditional function of preventing downside risks as well as to take advantage of upside risk potential. In brief, the ERM approach implies looking at the entire organization as subject to various kinds of risks, and continuously putting in place procedures to identify, measure, and manage them. Does Risk Management increase Firm Value?

While the ERM proposition is conceptually very attractive, the question of whether there is awakens the interest of investors remains open. To answer that question, Merkley (2001) made a careful review of several empirical works that reported relevant evidence on that issue, and concluded that enterprise risk management is worth the investment made in it. Furthermore, evidence supports the idea that investors will pay a premium when they see risk is managed in a disciplined way.

Moreover Behavioral Finance, a relatively new filed of study, holds that very few professional investors make decisions strictly on the basis of calculating the standard deviation of returns. Instead, Behavioral Finance authors believe suggest that “investors make judgments from within their own frame of reference, using contextual and situational information that the purely mathematical model does not take into account” (Merkley 2001).

Another well-known theoretical model that could be used to challenge the adequacy of the adoption of ERM as a fundamental building block of the financial strategy of a firm is the famous Miller and Modigliani (1958) Proposition I, which states that in a world with perfect information, no taxes, no transaction costs, no bankruptcy costs and a given investment decision, the relative proportions of debt and equity used by the firm should not affect its value. This truism is validated by demonstrating that investors can “undo” firm’s capital structure decisions. The ulterior logical extrapolation of that idea leads to the realization that no financial policy decisions affect the valuation of the firm.

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Myers and Majluf (1984) questioned the perfect information assumption implicit in the competitive financial markets that Miller and Modigliani used to derive their 1958 theorem and proposed an asymmetric information argument that shows that internally generated resources can be less costly than external resources and, subsequently, Froot et al. (1993), used the difference in the cost of internal versus external resources to demonstrate an optimal hedging result. They proposed that if internal financing is less costly than external financing, then risk management can add value because it allows the firm to manage its cash flows make sure that the less costly internal finance is available on an “as needed basis”.

Miller and Modigliani’s original contribution was very useful to help us understand the nature of financial decision making. However, in the real world, taxes are unavoidable, transaction costs are present for most market participants (with the possible exception of very large investment bankers), bankruptcy costs do have an impact on the cost of capital, and the resource allocation decisions are, indeed, influenced by the financial health of the company, making the “irrelevance of capital structure” theorem no longer valid; i.e., the value of the firm ends up being affected by financial decisions.

From a shareholder’s perspective, the reduction of financial distress costs or the elimination of certain tax liabilities by hedging are most welcome, since individual investors are unlikely to be in a position to achieve the same hedging results. Business Risk Management versus Financial Risk Management: Cash Flow Simulation at Cemex

As referenced above, several studies have analyzed strategies that firms have taken to manage and reduce their exposures to different kinds of risks. Nevertheless the literature on risk management by multinational corporations lacks a sound and detailed case study about an exercise with a particular measurement tool (Cash Flow at Risk Modeling) such as the one presented in this paper.

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Cemex is company in the cement and ready mix business that operates on a global scale, present in more than 20 countries around the world, and with annual sales over 7 billion USD. It is the world’s third largest cement company and one of the top Latin-American multinationals. It’s growth strategy has been twofold: first, it has followed an intense expansion into emerging markets, whose construction industry growth rate is well above the average OECD countries; and, second, Cemex developed the concept of “branding” a commoditized product, such as cement and ready mix.

The international expansion of Cemex has been carried out mostly through mergers and acquisitions in all continents, and it has propagated its success in the application of cost-cutting and productivity enhancing tactics in each purchase. More recently, its strategy shifted a little towards mature cash-cows, such as the purchase of the USA’s Southdown and the UK’s RMC.

Business risk management at Cemex is nested within the planning areas, versus traditional organizations’ financial risk management organizational function which is typically carried out by the Finance department. The main differences among the two endeavors are threefold: First, the timing of risks is much more frequent when located in the Finance area (daily or weekly in financial affairs vs. quarterly in planning); second, the business risk scope is much more long term and its focus is more strategic than the kind of risks dealt with by financial managers (financial risk management usually requires the monitoring of more yes/no binary risks such as adverse regulatory events, legal decisions or structural changes in price levels). Thirdly, the hedging and managing of business risks, in light of the diversity and (sometimes) obscurity of their nature, is not as straightforward as in the case of financial risk (it is much harder to find derivatives for risks related to sales cycles than for interest rates, for example).

By adopting the more encompassing definition of “business risk” we can account, for example, for “the risk of a destructive shift in assumptions, parameters and targets that underpin a business initiative”. As can be seen from this definition, managing and

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measuring business risk has to do with stressing the business plan through a set of comprehensive scenarios for interrelated and dynamic risk factors in order to obtain an outcome in the form of a bottom line figure such as EBITDA, Free Cash Flow or Total Return.

Most companies begin their risk management effort hedging the most easy-to-monitor and apparently more dangerous exposures, such as interest rates for debt obligations or exchange rates and equity for employee stock options plans, all this carried out from the finance and treasury departments. The planning department monitors the business plan assumptions and targets, and the insurance unit negotiates the more traditional property, casualty and liability risks through captives or third party insurers.

This functional division of tasks gives rise to a syndrome known as “managing risk by silos”, which is typical of a big firm with complex financial positions and numerous business units, but the main consequence of this syndrome is that each family of risks (financial, business and property/casualty) are managed separately without regard to the cross effects and correlations among them, creating a double-counting of certain exposures (and sometimes an over or under-hedging of them).

By contrast, integrating all risks effects through a dynamic risk model, that incorporates the financial risks in the P&L statement line of “net financing cost” and the property/casualty effects into “other income” and/or “other costs”, one can actually implement an integrated risk-measurement and risk-management tool. This was the underlying motivation for the development of the Cash Flow at Risk Model at the core of this case study. Setting the stage for cash flow simulation

The average manager’s need for cash flow projections that permit the anticipation of funding requirements or liquidity surpluses was the motivation for the development of financial models. Eventually, cash flow projections have become, in a way, a

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substitute for earnings as a measure of financial performance. Any model centered on cash flow projections begins by defining the financial statement framework to use in order to get to the “bottom line”. In the case of Cemex, that framework was the earnings statement adjusted to reflect a cash flow statement.

In what follows, we present a description of a model (subsequently called “the model”) that was built based on the historical financial statements of Cemex, in which each line (account) was first estimated as an econometric equation, either in the form of a behavioral relationship or in a time series pure form (known as an ARIMA-type equation). Once the functional relations parameters were calculated for all the variables (sales and costs of goods sold, for example) these were, in turn, used to perform Montecarlo simulations. The output variables of interest were EBITDA, Free Cash Flow and Total Return. In the description that follows, all efforts directed at producing this model will be labeled throughout the paper as “the project”.

Before a model is chosen, one needs to address five very important decisions:

1.- the choice of a financial statement structure that would go hand-in-hand with the corporate structure;

2.- the period of study; 3.- whether to model real or nominal, local currency or dollar

variables; 4.- to determine the final set of global risk factors that would

be simulated; and, 5.- to choose a simulation technique (and software) to apply to

the model. The first thing after the choice of a financial statement

framework was to define the actual corporate structure of the financial statement. This meant that the inputs of the accounting puzzle needed to be isolated in business units, divisions, countries or subsidiaries.

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Each financial statement had to be consistent and self-standing in terms of risk factors or, as we shall call them from now on, “key value drivers”.

This meant that the variables we wanted to trace and simulate had to be present in some form (or, at least, embedded) in the individual financial statement, according to the structure of the multinational.

When modeling Cemex’s cash flows, that structure was divided by business units, where each business unit was a country or a region1. So if, for example, Panama was to be included in the simulation process, we had to verify Panama’s key value drivers were present in the general/corporate model (which was, by definition, the fundamental observational unit of analysis), so that the assumptions we made regarding the economic and financial risk factors affecting the firm’s global operations would include Cemex’s operations in Panama and would also be captured by simulating the global (versus local) key value drivers2.

There were important considerations to make in order to have all the variables related to one another: first, and dwelling still upon the Panama example for descriptive purposes, if local interest rates were a source of risk for the firm since any rise in rates would bring less construction business, then local interest rates in Panama should be considered and modeled as a (local) key value driver, but in such a way that a link to the general model –we shall call it the “Corporate Model”- would be established and maintained throughout the project, for example, by making Panama’s local interest rates a direct function of global interest rates, such as LIBOR rates or US Treasuries and in doing so, local interest rates

1 Individual models were constructed in the first stage of the project for the USA, Mexico, Spain, Egypt, Dominican Republic, Panama, Costa Rica, Colombia, Venezuela, The Philippines, Thailand and Indonesia. 2 If two countries had perfectly negative correlation between certain variables, say interest rates, the final effect on the consolidated/corporate model would be captured through the correlation matrix used for simulating the aggregate financial statements.

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would receive a shock every time the corporate model received a shock in global rates.

This was the approach with each local key value driver -and their correspondent correlations with other business unit’s key value drivers-, say exchange rates, costs, revenues or prices, although some times it was difficult to find a direct (or even indirect) link to the corporate model because some variables had a “pure” domestic nature, which set them apart from global economic and financial forces.

For example unemployment, labor and electricity prices, to name a few, tended to be classified as pure local key value drivers, and as such, had to be captured in a separate section in the corporate model and simulated at the outset, in conjunction with the global variables, so that their effect on cash flow would be depicted in each simulation exercise.

The second decision had to do with the choice of a period of study and the time aggregation that would be applied to high frequency data (such as prices), in order to be consistent across equations and financial statements. Here, the main concern was that the database for, both, internal and external variables had to be constructed in such a way that the base year -if in constant terms-, and frequency was coherent and consistent within the model and with related corporate planning and accounting models that the organization uses for various purposes and that would be eventually linked.

In the first stage of the experiment that we present, all variables in the database were collected in a quarterly fashion for five years 1995-2000 (individual vectors of 20 data points). That decision was influenced by natural and financial constraints, for example, the length of time that certain business units had been in the books of the company (since acquired). A special effort was devoted to the decision of either averaging higher than quarterly frequency variables or the use of end of period data because this, in fact, is not a trivial matter, for certain external variables such as exchange rates, would loose their “shocking power” (precisely the

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very character one looks for in a key value driver) by smoothing it out when averaged across quarters, and yet other variables such as indirect costs (with a less volatile nature) would benefit from averaging, by representing sudden peaks and valleys in the quarterly figure. In conclusion, this was a worthy effort, because the value that a risk factor could bring to the whole exercise outweighed the resources committed to settle, one by one, each vector in the data set. The final decision was taken on a case by case basis, with the considerations mentioned above. 3.- A third decision had to do with the comparability of the data used, both among variables and through time. The decision was to model all the variables in real terms and in the currency used for financial statements consolidation purposes, -dollars in this case-, then simulate shocks in all the key value drivers and just before the risk was measured at the cash flow and return levels, re-inflate all variables to leave them in nominal terms, so managers felt comfortable with the final reports and analyzed familiar figures.

The importance of modeling in real terms is stressed by two facts: on the one hand, econometric modeling of the financial statements will find the true underlying relations and parameters with the key risk factors when both are stripped from the effects of inflation and, second, inflation prints certain upward trend in most economic and financial variables, and a golden rule of simulation is to never simulate a series with a distinguishable pattern or trend embedded in the data. So, for the modeling of Cemex’s financial statements and cash flow, all the variables were treated in real terms, and then filtered for seasonality or cyclicality, leaving only the noise/error components of each variable, getting them ready for processing with the simulation engine. Roughly around 60 to 70%, of the filtered variables (this is, only the noise component without trend, season effects or cycles) were found to be very close to normal distributions according to standard tests (like Jarque-Bera). Thus, means, standard deviations and correlations were calculated for each of them, and simulation tests were conducted with the aim of double-checking the consistency of the database and the

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economic sense of the ranges found for each re-leveled variable (remember variables used for simulation were filtered two or three times, so they were analyzed back in level terms including trend, cycle and seasonality with the only aim of checking economic sense and consistency after the simulation exercise).

At that point there were still a couple of additional technical problems, in terms of “simulation-readiness” for each variable. One of them was the time-scaling of volatility for higher than quarterly frequencies. Among the most important input parameters for the simulation engine is the volatility –or standard deviation- of percentage changes on the levels of each risk factor (or internal variable). But when initial observations of the variable were made on a monthly or daily basis, volatility would also correspond to monthly or daily periodicity, so a re-scaling to quarterly and then annual terms was carried out for each variable in this situation. This implied strong assumptions about independence in the series, but the decision was to go ahead with it, for the absence of this assumption would bring much higher costs in terms of the time invested in calculations and the use of proper heteroscedastic models. Summing up, there was an array of previous steps required to set the stage for the actual modeling of the financial statements and ultimately the cash flow of the firm, and their relevance was so fully recognized that more time and computational resources were devoted to them than to the final modeling of the business risk factors.

These steps, which were presented in this section, are summarized in Box # 4 in the Annex and as can be seen from that list, a lot of work was invested in this groundwork effort, but once it was done, we were ready for hands-on modeling of the cash flows of the firm with the peace of mind that the inputs were of good quality and consistency. The following section is devoted to describe in depth decisions 4 and 5, and to depict the model’s critical building blocks, as well as the final structure used to measure the cash flow at risk for Cemex.

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Cash-Flow-at-Risk Model main building blocks 4.- A core initial objective of a “cash-flow-at-risk” model is to identify a set of key value and risk drivers for the firm. We numbered this phase as decision # 4 in the last section. The exercise began by making a list of possible risk factors affecting the financial statements through a brainstorming session with the project team. The financial statements composition was then analyzed line by line and each concept (account) was modeled individually, keeping in mind as an important principle to make sure the highest percentage of variability in that row (like sales or cost of goods sold) was explained by one (or several) of the variables in the set of key value drivers previously obtained, ensuring that all shocks applied would be absorbed at the level of each argument of the financial statements. In Box #1 and Box # 2 in the Annex, we present a list of these “Key Value Drivers” depicted as the explanatory variables of the regression equations for the Mexico business unit. For example, the equations for sales (called Cement and Ready-Mix Revenues in the model) had to include exchange rates or interest rates as arguments, so the simulation of exchange rates or interest rates was absorbed by the sales figure, making it vary with each iteration. Thus, a model was fitted for each account row in the earnings and cash flow statements, looking for the conditions previously mentioned to be met; that is, to find the best model and to include at least one key value driver in each of the equations.

Finding the best fit for an equation is a task that demands patience. Most equations in Cemex’s model were econometric or behavioral in nature, using other variables as arguments –key value drivers preferably; others were time series or autoregressive equations, using lagged observations or errors as their arguments. In those cases where there was no obvious direct “connection” with the simulation engine, an indirect linkage was laid down, sometimes through the error term (relating it to other equation’s errors) but most of the time through inflation.

General prices are, by definition, a local variable. In simulation models, they bear a very powerful influence in most other variables

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for two reasons: first, because prices are a “translation” variable (one that turns nominal into real figures and real into nominal); second, because they can also be thought of as a core variable, i.e., as one of the key value drivers in the list of risky factors. This dual character of inflation allows it to be present in each equation as a translation vehicle; but also, it can be modeled individually and be used as the vital link between autoregressive equations and the rest of the key value drivers affecting the behavioral equations.

Typically, inflation was modeled in terms of lagged prices, exchange rates, wages and productivity or GDP gaps and, likewise, became an argument in foreign exchange, cement and ready mix prices, as well as in the direct cost equations. For that reason, inflation was found to be a fundamental link among the equations used to simulate the cash flow of the firm. But this can also be a two-edged sword because, if inaccurately modeled, it may introduce significant noise into the whole process, leaving cash flow drifting away from its “real” distribution. 5.- In the final key decision according to our list, a simulation technique was chosen on the basis of its efficiency and precision, this was the Latin Hypercube Montecarlo Simulation option inside the software “@Risk” easily found in the market; and although the exercise consisted of a random simulation of economic and financial variables inside the income and cash flow statements, an important consideration in the modeling of cash flow was to distinguish those variables that are simulation-type variables and those that are not. What we mean by this is that some variables are so complex in their nature (GDP or unemployment, for example), that a simulation exercise applied to them would lack any economic sense. Such variables are often endogenous, meaning that other (known and unknown) variables explain their behavior, so in order to simulate and create alternative states of nature for them, one has to track their arguments and simulate those arguments and then build upon their influence on the original variable to get to its distribution. When variables of this kind (non-simulation) were included in the individual equations, a fix value was left (the last observation

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available) when the simulation was carried out, with the aim of being able to shock the system by isolated hits on that variable, and although the result was not a continuous distribution, a deterministic measure of impact could be obtained on cash flow or total business return by changes in those non simulation-type macro variables. Macro endogenous variables were often an important argument in the equations, and whereas not subjected to a simulation exercise in the first phase of the project, they where crucial to the best fitted equation for the particular variable at hand. In a second phase of the project, we did model macroeconomic risk factors as can be seen in Box # 3, which depicts the case of Mexico business unit equations. Cash-Flow-at-Risk Model main assumptions In Box #1, in the Annex, we present the central axis described above in the form of modified Financial Statement made of the Income Statement and the Cash Flow Statement. In it, one can see the modeling assumptions made for each variable, both internal and external “key value drivers”, the operations sequence among them and the final outcome, which takes the form of a probabilistic distribution, presented in a gray rectangle: EBITDA and Free Cash Flow3. Several assumptions taken are worth mentioning beyond what is obvious from the information in Box #1: 1.- Ready Mix Volume was represented as a ratio that consists in the penetration that Ready-Mix has on the cement market. The ratio differences were simulated with a normal distribution. 2.- Working Capital was treated as a simulation based on a normal distribution of the ratio of change in Working Capital to Sales. 3.- Special distributions were used mainly for variables that needed negotiation between the business unit and the corporate headquarters. These are discretionary variables that originate from managerial decisions and can be simulated using a very ingenious technique known as “triangular distribution”. In this technique, a 3 The structure of equations leading to Total Business Return, which was also a final outcome of the Model, and departs from Free Cash Flow, is omitted for being proprietary material of the firm’s Planning Department.

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range of values was defined setting a minimum, a maximum and a most likely outcome, thus imposing realistic limits to the simulation engine which in turn generated random numbers, correlated with other variables but observing these limits. In this way, a variable that happens to be hard to model (like market share) or a politically charged variable (such as the exchange rate) can be simulated in a controlled and administered way, giving the manager the power to decide as to how much of that uncertainty will be allowed to influence cash flow at risk. 4.- ARIMA models (auto-regressive and moving average components make up the explanatory variables in regressions) were used for Deferred Charges and Capex accounts mainly because the fit was good and there was a lack of economic or financial explanatory variables for them. 5.- Other lines in the financial statements were simply deterministic (a product of a simple sum, or a subtraction) or non subject to any explanation, either because they constitute a discretionary variable (a tool of management control) or because their derivation was so complex (taxes, for example) and dependent upon so many influences, that the best course of action was to leave them as scalars (a fixed number, not subject to the simulation). After each internal variable had been modeled in terms of other internal variables or external (key value drivers) variables in each business unit (or country) sub-model, an aggregated model had to be constructed in order to consolidate all economic effects on cash flow, which is by definition a corporate measure. What that meant in practical terms was that to finally get an estimation of cash flow distribution and total business return by simulation, by simulating line by line each row of the financial statements, which in turn was built by the corresponding row in all the components of the structure of the corporate financial statements (in Cemex’s case, each business unit). For example, Cemex’s corporate sales are made of sales in Panama, plus sales in the USA, plus sales in Mexico and so on; the same applies to the rest of the lines in the earnings and cash flow statements.

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6.- Some special accounts such as integral financing costs and dividend payments, depreciation and amortization of certain assets, were only present in the consolidated corporate financial statement. For these accounts, the staff team involved in the development of the model had to evaluate whether they would be estimated with an econometric behavioral equation, an autoregressive equation, a triangular distribution, or simply left alone as a scalar.

We have mentioned that the model took off from each country’s financial statements, and then aggregated all of them into a consolidated final observational unit of analysis, which took into account all possible cross-effects and correlations. Although it would be easier to work with bottom line results, such as individual Net Incomes and then build up towards consolidated Net Income, that approach would have lacked the richness obtained by the complete array of risk factor’s effects on each account of the firm, allowing a detailed description of exposures and their exact consequence on cash flow. Model Results and Interpretation In the Box #24 and Box #3 in the Annex, we can see the structure of the equations for the main lines in the P&L and cash flow statements for the Mexico business unit. The first box represents the first phase of the project; the second box belongs to a second phase, one in which some assumptions on macroeconomic value drivers were dropped, and new behavioral equations were plugged in instead. Hypotheses tests and signs for each key value-driver in each equation are shown in the third and middle columns. Most elasticities are hard to interpret on an individual basis, but the relevant result is the final distribution that the simulation tool 4 The final form of each variable -dependent and independent- in each equation showed for each country, was chosen on a case by case basis, under the best fit criteria. Most of them though, were calculated in percentage terms, first differences or natural logarithms.

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generated using these equations, inside the cash flow statement identities as their vertebrae. Figure # 1 represent one such final outcome of the model. It depicts the simulated distribution for EBITDA in the Mexico business unit for fiscal year 2001. The most candid interpretation for this outcome would be as follows: The expected figure for 2001 operating cash flow is USD 307 million and the target set by the business unit managers was USD 272 million, which had a probability of 61.3% of being achieved. A sensible range around that target would be USD 231 and 299 million dollars but a really hard target would be 436 million, which had a probability of only 20% or less of being reached within the assumptions of the model. So, if a capex figure or a performance threshold should be negotiated, the probabilities of those indicators would be prove key to reach an agreement among the business unit managers and corporate planning headquarters, and the model would then meet its end. In the following section we will discuss these and other applications of the final outputs.

Distribution for EBITDA

Mean = 0.307

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

EBITDA (Billion)

ACC

. PRO

BA

ILIT

Y (%

)

Max 0.299

Target 0.272

Min 0.231

Project conclusions and main applications for business risk management Although a Cash Flow at Risk Model can serve multiple purposes, we mention here the two chief tasks the model built in Cemex was aimed to answer. 1.- The first one was to calibrate variable

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compensation for managers. The way this can be implemented, is by setting a target probability associated with the EBITDA outcome that will trigger a variable compensation bond for the managers in each business unit. Once the analyst calibrates a sufficiently-hard-to-get EBITDA level, say, one with a 30% or less probability of occurrence, then a policy can be implemented in the human resources and compensation department in such a way that only on those really good performance years (those in which EBITDA surpassed the 30% probability mark) will the managers of each business unit receive an extra bonus. In fact, several progressive EBITDA targets with probabilities assigned could be calibrated in order to differentiate various levels of achievement. Used in this fashion, the model becomes a forecasting tool, offering the analyst a complete distribution of outcomes for EBITDA and their associated probabilities; when shocks are continuous, a simulation is used based on the statistical parameters found for each series, so each iteration –some 5,000 were carried out in each exercise- represents a different shock for the model, inside the boundaries previously set by the parameters (mean, max, min, volatilities and correlations). The model is maintained at least annually, allowing the team to imprint the latest elasticity values for key value drivers; it allows the re-estimation of complete equations and parameters such as the correlation matrix, the finding of new risk factors, and revamping data sets. 2.- Operating the model with such discipline, brought in another feature for the tool – and quite a powerful one- that consisted of a kind of scrutiny and inspection device for budgeting purposes. In this feature of the model, each business unit package of forecasts for the annual budget was double-checked against the corporate model forecasts, giving the team strong arguments for the negotiation of capital expenditures and operating costs and sales figures. It should be stressed out though, that corporate negotiations are very hard to carry out solely on the basis of probabilistic and economic assumptions, so the budgeting process, as in any other firm, entitles

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a wide range of decisions and considerations before a final figure is reached. 3.- A third task the model can perform, has to do with the annual budgeting process, particularly to one of its main undertakings: the corporate capital expenditure figure. Despite being predominantly a negotiating activity between the corporate officials, the business unit managers and the planning and comptrollership departments, the capex definition could be elevated to the status of an educated guess, through the utilization of the cash flow at risk model. The main question here is: What is the probability of achieving certain EBITDA level, associated with the capex (capital expenditure) figure negotiated at the outset of the budgeting process? If the probabilities of reaching that level of operating cash flow are slim, then a new, more realistic figure would have to be matured and evaluated again in the model until an attainable capital expenditure figure is found. Another shortcut for the same task is finding out through the model what is that capital expenditure figure associated with the level of operating cash flow that offers a maximum probability of occurrence, say an 85%, so that the target for investment is realistic from the start. Besides the main applications that were discussed in the paragraphs above, there are other, subsidiary purposes for a cash flow at risk model, specially if it is shared with other areas in the corporate headquarters such as treasury, planning and comptrollership departments. 4.- One of these endeavors is to use it as a simulation and scenario testing tool, stressing the key value drivers with a predetermined scenario of future economic and financial performance which should have been agreed upon by the main planning officials and economists of the firm. Used in this fashion, one is able to test the main shocks represented in future scenarios for the firm, and thus capable to recommend a change of strategy or a different approach to corporate investment. Summing up, and beyond the elegant framework of Miller and Modigliani’s Proposition I and the underlying assumptions of the ERM Model, when real world conditions are given full account,

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theory predicts risk management could increase value if: a) it reduces tax liabilities; b) it reduces potential financial distress costs; c) it optimizes the firm’s investment decisions. A non-tested hypothesis (the post-model period is still short) would be that after risk is managed in a disciplined way, the difference in actual free cash flow and analyst’s expectations of it, would diminish significantly. With this analysis of Cemex’s Cash-Flow-at-Risk model, we have illustrated the usefulness of the approach and possibly motivate reflection on the fact that this technique still offers many potential applications that can improve the quality of decisions in a global corporation. References Beaver, William, Paul Kettler, and Myron Scholes (1970). The Association Between Market Determined and Accounting Determined Risk Measures. The Accounting Review, October. Culp, Christopher L. (2001).The Revolution in Corporate Risk Management: A Decade of Innovations in Process and Products. Journal of Applied Corporate Finance. Froot, K.A., Scharfstein, D.S. et al. (1993) Risk Management: Coordinating Corporate Investment and Financing Policies. Journal of Finance 48 (5). Harrington, Scott E., Greg Niehaus, Kenneth J. Risko. (2002). Enterprise Risk Management: The Case of United Grain Growers. Journal of Applied Corporate Finance. Merkley, Bryan W. (2001) Does Enterprise Risk Management Count? Risk Management, April.

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Meulbroek, Lisa K., (2002). A Seniors Manager’s Guide to Integrated Risk Management. Journal of Applied Corporate Finance. Modigliani, F., Miller, M.H. (1958) The Cost of Capital, Corporation Finance and the Theory of Investment. American Economic Review 48 (2). Myers, S.C., Majluf, N.S. (1984), Corporate Financing and Investment Decisions When Firms Have Information That investors do not Have. Journal of Financial Economics 13. Olsen, Robert A., and George H. Troughton (2000). Are Risk Premium Anomalies Caused by Ambiguity? Financial Analysts Journal, March/April. Smithson, Charles W. (1996). Managing Risk in the Industrial Company. Risk Management: Principles and Practices. AIMR. Statistical Annex:

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Step Initial Variable Treament Operation Transition Variable Treatment Equals =

1 % Change in Cement Demand

Triangular Simulation Multiplied By Demand t-1 Behavioral

Regression Demand t

2 Demand t By-Product Multiplied By Market Share Triangular Simulation

Cement Volume t

3 % Change in Cement Price

Normal Simulation Multiplied By Cement Price t-1 Behavioral

RegressionCement Price t

4 Cement Price t By-Product Multiplied By Cement Volume t

Behavioral Regression

Cement Revenues

5 Cement Volume t

By-Product & Regression Multiplied By Ratio of Ready

Mix to CementNormal Simulation

Ready Mix Volume t

6 % Change in Ready-Mix Price

Normal Simulation Multiplied By Ready Mix

Volume t By-Product Ready Mix Revenue

7 Ready Mix Revenue By-Product Plus Cement

Revenues By-Product Revenues

8 Revenues By-Product Plus Other Revenues Regression

9 Minus Eliminations Scalar Net Revenues

10 Net Revenues By-Product Minus Cement COGS Regression

11 Minus Ready Mix COGS Regression

12 Minus Other COGSNormal Sim. on Ratio to Total COGS

Gross Profit

13 Gross Profit By-Product Minus SG&A Regression EBIT

14 Minus Depreciation Amortization Scalars EBITDA

15 EBITDA By-Product Minus Operating Leases Scalars Operating Cash Flow

16 Operating Cash Flow By-Product Minus Taxes Fix % of EBIT

17 Minus Increase in Working Capital

Normal Simulation

18 Minus Deferred Charges ARIMA

19 Minus CAPEX ARIMA20 Minus Acquisitions Scalar

21 Plus Liquidations Scalar Free Cash Flow

Box # 1: The Central Vertebrae of the Model (an Integrated Income and Cash Flow Statements)

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Dependent Variable

Independent Arguments (sign) Statistical Significance

(+) National Cement Consumption Significant at 98%(-) Installed Capacity Significant at 70%(+) Market Share Significant at 90%(+) Cement Price Significant at 97%(+) Cement Volume Significant at 97%(+) Ready-Mix Volume Significant at 97%(+) Ready-Mix Price Significant at 84%(+) Cement Volume Significant at 97%(+) Ready-Mix Volume Significant at 88%(+) Revenue Significant at 98%(-) Cement Price Significant at 98%(-) Exchange Rate Significant at 98%(+) Ready-Mix Volume Significant at 98%(-) Ready-Mix Price Significant at 95%(-) Exchange Rate Significant at 92%(+) Cement Volume Significant at 99%(+) Revenue Significant at 99%(-) Cement Revenue Significant at 95%(+) Ready-Mix Revenue Significant at 96%

Other Sales

Revenues

Cement COGS

Ready-Mix COGS

SG&A

Box # 2 Basic Equations from Model’s First Stage

Cement Volume

Dependent Variable

Independent Arguments (sign) Statistical Significance

(+) Log of Ready-Mix Prices Significant at 85%(-) Log of Exchange Rates Significant at 92% (+) Inflation Significant at 98%(-) Log of Construction of New Homes Significant at 94%(-) Real Interest Rates Significant at 90%(+) Log of Industrial Production Index Significant at 98% (-) Real Cement Prices Significant at 88%(-) Real Exchange Rates Significant at 92% (+) Log of Housing Production Significant at 98%(-) Real Interest Rates Significant at 86%(-) Real Exchange Rates Significant at 93% (+) Log of Housing Production Significant at 98%(-) Real Interest Rates Significant at 84%(+) Log of Employment Significant at 94% (+-) Log of Housing Prices Significant at 91%(+) Real Cement Prices Significant at 89%(+) Real Exchange Rates Significant at 92% (+) Log of Housing Production Significant at 98%

Log of Cement Volume

Log of Ready-Mix Volume

Log of Housing

Log of Cement Imports

Box # 3 Macro Equations from Model’s Second Stage

Log of Cement Prices

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1 A financial statement framework is chosen (BS, P&L,..)

2 A modeling strategy is chosen given the structure of the financial statements in the firm(ex., ordered by business units, countries in Cemex case)

3 A set of internal variables is outlined (individual and corporate), coming from thefinancial statements.

4 A set of external risk factors or key value drivers is determined for the global modeland for each individual component (a country or business unit).

5 A period of study is chosen and also a frequency of data points in each variable isestablished.

6g p

techniques.

7 In the case of variables with higher than quarterly frequencies, tests are made todetermine if average or end of period figures will be used.

8 Each variable is graphed and analyzed for visual cycles, trends and seasonality.

9 Each variable is stripped from trend by deflating it and leaving it in real terms. If sometrend persists, percentage changes or first differences are calculated for the series.

10 Each de-trended variable is stripped from seasonality and cyclicality using the standardtechniques (X-11 and other filters).

11 Once all variables (internal or external) are stationary by all visual arguments, tests areconducted to prove randomness.

12 True random variables are tested for normality or some other catalog distribution.

13 True random normally distributed variables are then analyzed for means, standarddeviations and correlations.

14 For higher than quarterly frequency variables, standard deviations are re-scaled toquarterly or annual fashion.

15 Simulation tests are conducted to evaluate if the ranges obtained for each variablemake economic sense.

16 Simulations are carried out for the percentage change (or any intermediate mode) ofeach variable.

17Once simulated, each variable is converted into levels again and then is added inflationto get to the nominal final figure.

Box # 4 Previous Steps Required to set the Stage for the Actual Modeling of Cash F