Applying Scenario Optimization to Portfolio Credit Risk
In addition to measuring and monitoring risk, it is important for risk managers to understand the sources of portfolio risk, and how portfolios can be restructured effectively to reduce risk and maximize returns. However, while much academic and industrial effort has been devoted to develop methodologies to measure portfolio credit risk, the development of tools to manage and, more specifically, to optimize credit risk have lagged behind.
Risk management tools for market risk are largely based on modern portfolio theory (Markowitz1952; Sharpe1964) and assume that the profit and loss or the return distributions are normal. In a normal world, the standard deviation of the portfolio returns is a good measure of risk, and optimal portfolios are those that generate the best mean-variance profiles. However, distributions of credit losses are generally far from normal; they are heavily skewed with a long fat tail. Most of the time, an obligor neither defaults nor changes credit rating, but when default occurs losses are generally substantial. While standard risk management and optimization tools originally developed for market risk (see, for example, Litterman1996a and 1996b) cannot be effective in this setting, scenario-based tools are ideal for these problems (see Dembo1991, 1999; Dembo and
Rosen1998; Mausser and Rosen1998).
This paper develops scenario optimization tools that can restructure portfolios effectively to reduce credit risk and optimize the risk/return trade-off. The inputs to these models are the Mark-to-Future tables of portfolio credit risk simulations.
W e present three optimization models. The first one minimizes portfolio risk by modifying the position of a single asset or obligor. This is achieved by constructing the trade risk profile (TRP) of a single position (or basket) as shown in Mausser and Rosen(1998); the best hedge is the position that gives the minimum risk on this profile. The second optimization model minimizes the credit risk of a portfolio by simultaneously adjusting multiple positions
Applying Scenario Optimization to Portfolio Credit Risk
Helmut Mausser and Dan Rosen
Standard market risk optimization tools, based on assumptions of normality, are
ineffective for credit risk. In this paper, we develop three scenario optimization
models for portfolio credit risk. We first create the trade risk profile and find the
best hedge position for a single asset or obligor. The second model adjusts all
positions simultaneously to minimize the regret of the portfolio subject to general
linear restrictions. Finally, a credit risk/return efficient frontier is constructed
using parametric programming. While scenario optimization of quantile-based
credit risk measures leads to problems that are not generally tractable, regret is a
relevant and tractable measure that can be optimized using linear programming.
We demonstrate the models on a portfolio of emerging market bonds.
First published in the Algo Research Quarterly, Vol. 2, No. 2, June 1999, pp. 19–33. Also published in The Journal of Risk Finance, Vol. 2, No. 2, Winter 2001, pp. 36–48.
Enterprise credit risk using Mark-to-Future
subject to general (linear) restrictions. Finally, a credit risk/return efficient frontier is constructed using parametric linear programming. W e further demonstrate the application of these models by extending the portfolio credit risk case study by Bucay and Rosen(1999), in which the CreditMetrics methodology (J. P. Morgan1997) is used to calculate the credit risk of a portfolio of long-dated corporate and sovereign bonds issued in emerging markets.
Credit risk scenario optimization is difficult due to the sheer size of the problems; because credit events are relatively rare, it is generally necessary to sample a large number of scenarios on joint obligor credit migration and default in order to obtain accurate estimates of the loss distribution. For practical purposes, therefore, we limit our attention to linear programming models and demonstrate their utility.
It is important to note that there are very few papers in the risk management literature that deal with optimizing credit risk. One reason for this is the fact that the advances in credit risk measurement methodologies are quite recent. More importantly, the problem of minimizing credit risk is less tractable than that of minimizing market risk. Kealhofer(1995, 1998) applies standard mean-variance optimization to portfolio credit risk. More recently, Arvanitis et al.(1998) briefly discuss applying simulation-based tools to estimate the efficient frontier in the space of expected returns and unexpected percentile losses (CreditVaR). They solve the optimization problem by a brute force random search method, and only briefly mention the possibility of using more advanced numerical tools. The authors also demonstrate in a simple example how the mean-variance efficient portfolio might be far from the efficient frontier, which is expected given the non-normality of the problem.
This paper is organized as follows. First, we formally introduce the credit risk optimization problem and then describe the basic ideas behind the optimization tools; the full mathematical formulations are given in the appendix. The utility of these models is illustrated by applying them to a portfolio comprising long-dated corporate and sovereign bonds issued in emerging markets. Following a discussion of the case study, we present our conclusions and offer suggestions for further study.
Formulating portfolio credit risk optimization problems
In this section we present the basic principles behind the credit risk scenario optimization models. The mathematical details of the first model can be found in Mausser and
Rosen(1998) and the linear programming formulations of the second and third models are given in the appendix.
As in the CreditMetrics framework, we consider a one-step model. Credit losses are measured as the losses at a fixed time horizon, due exclusively to credit events, which include both default and credit migration of a set of obligors in a portfolio. It is straightforward to use the optimization models presented here in more general settings that include multi-period and joint market and credit events.
The credit risk optimization problem, namely, the rebalancing of a portfolio to achieve a better risk profile, can be formulated at various levels. At a strategic level, the risk manager may wish to restructure the concentrations in various credit products or sector classes. In this case, the optimization model solves for the weights in each class. At a second, or tactical level, the manager might be interested in risk capital allocations for each obligor or type of obligor. The portfolio weights associated with the obligors are then the unknowns of the corresponding model. Finally, at an operational level, the manager may choose which positions to take in a set of financial instruments in order to hedge portfolio risk optimally or obtain a better risk/return trade-off. In this case, the unknowns are the size of the positions to take in those tradeable instruments. These alternate formulations are summarized in Figure1.
For ease of exposition, we formulate the problem at the second level, in terms of obligor weights, but the models presented apply to all levels. Suppose that the portfolio is simulated over a
Optimizing credit risk
large set of scenarios (i.e., 10,000 to 100,000). Each scenario describes the joint credit states of all obligors at the specified time horizon. The result of this simulation is a Mark-to-Future table containing the scenario-dependent values of the holdings for each obligor. The optimization problem, then, is to choose the obligor weights that minimize the risk or maximize some trade-off between risk and return over the given scenarios, subject to several constraints on the weights. Models will differ with respect to the risk measure used in the objective function, and the limitations imposed by the constraints.
Figure 1: A hierarchy of credit risk
optimization problems In general, the obligor weights can be expressed in monetary terms, as changes in the current positions or as a fraction of the portfolio value or exposure. In this paper, the existing (i.e., non-optimized) portfolio is assumed to consist of one unit of each obligor. The obligor weights then represent multiples of the original positions. For example, a weight of zero implies liquidating a position, while a weight of two denotes doubling the position size.
In the following, we present three portfolio credit risk optimization models in progressive order of sophistication.
Optimizing the position of a single obligor
As a first step to re-balancing a portfolio, we consider trading the debt of only a single obligor in order to minimize a selected risk measure. This is achieved by constructing the simulation-based, or non-parametric, trade risk profile (nTRP) for the obligor and finding the best hedge position, as described in Mausser and Rosen (1998). The
nTRP plots the portfolio risk as a function of the size of the exposure in the selected obligor. The best hedge is simply the position that yields the minimum value on the nTRP .From the Mark-to-Future table, it is possible to construct an nTRP for any desired risk measure. Quantile-based measures, such as the maximum losses at the 99th percentile level, for example, result in a piecewise-linear nTRP . Fitting a
smooth approximation to the nTRP may provide more robust estimates of the relevant risk analytics, such as the best hedge position, in some cases.
Optimizing the positions of multiple obligors
A more general model simultaneously adjusts the position in each obligor to minimize the risk of the portfolio subject to a set of constraints, such as a fixed budget or limits on the obligor weights. The objective is typically to reshape the loss
distribution in order to balance the expected credit losses , which define the credit reserves , and the unexpected losses , which determine the amount of capital required to support the portfolio.
Unexpected losses are commonly measured by CreditVaR, the difference between the maximum percentile losses and the expected losses. In practice, the definition of the risk measures is the key to these optimization models. These measures must be chosen to meet two criteria: relevance and tractability . Relevant measures are directly linked to the management process and capture the main properties of the distribution. Expected losses, maximum percentile losses, CreditVaR and average
shortfall are some examples of relevant measures. Note that when distributions are not normal, minimizing variance does not guarantee an effective minimization of the capital required to cover unexpected credit losses. In such
circumstances, risk measures such as standard deviation or variance are not relevant.
T ractable measures can be optimized using methods that are computationally efficient. Though relevant, quantile measures such as
maximum percentile losses and CreditVaR lead to optimization problems that are conceptually
Optimize weights in a set of financial products
Optimize weights in debt of obligors or classes of obligors Optimize weights
in credit products or sector classes Strategic problem
Tactical problem
Operational problem
Enterprise credit risk using Mark-to-Future
simple, but very difficult to solve in practice. Simply stated, calculating any of these risk measures requires identifying the k-th largest loss, over all scenarios, for some k corresponding to the selected quantile. For example, in a set of 1,000 (equally likely) scenarios, the Maximum losses (99%) correspond to k = 10. However, in an optimization framework, identifying the k-th largest loss requires the use of a discrete, or integer variable (i.e., one that can assume only the value zero or one) for each scenario. Given the number of scenarios involved in a credit risk simulation, the optimization problem can potentially contain tens of thousands of integer variables. Methods for solving problems that include integer variables, collectively known as mixed integer programming, are far more computationally demanding than linear programming, which deals only with continuous variables.
Fortunately, risk measures exist that are both relevant and tractable. Optimizing such measures effectively reshapes the loss distribution and, therefore, also provides the means to improve quantile-based measures, albeit in a somewhat indirect manner. W e now present two risk minimization models based on a measure that is not only relevant, but that can be optimized using linear, rather than integer, programming. Minimizing expected regret
Regret (Dembo1991, 1999), which measures the difference between a scenario outcome and a benchmark, exhibits both tractability and relevance. W e define the expected regret as the expectation of losses that exceed some fixed threshold K. While this definition is similar to that of expected shortfall, the latter involves conditional probabilities and a quantile-based, rather than a pre-specified, threshold. It is important to note that, in general, the benchmark need not be a fixed amount; it can be a scenario-dependent value, such as an index. Regret is relevant since it is a general measure that can effectively capture both the mean and the tail of the distribution, without the need for any distributional assumptions. Furthermore, it leads to linear programming optimization models, which can be solved very quickly. Specifying an appropriate threshold in advance eliminates the need to solve an integer program, since it is not necessary to identify the particular scenario that corresponds to a given quantile.
One cannot use regret to minimize quantile-based measures directly, since there is no guarantee that the specified threshold will in fact correspond to the desired quantile of the optimized loss distribution. Nevertheless, it is possible to select a reasonable threshold based on the risk characteristics of an existing portfolio. For example, by setting the threshold K to one million USD, the optimization problem finds the weights that yield the smallest expected losses in excess of this amount. Thus K acts as a parameter of the model that can be chosen by the risk manager and can be used to shape the distribution. A small (or even negative) K leads to a reduction in the expected losses (reserves), perhaps at the expense of a longer distribution tail (capital). On the other hand, a large value of K reduces the unexpected losses in the tail (capital), at the expense of higher expected losses (reserves). Thus, the manager has the flexibility to create a portfolio that best fits the liquidity and capital structure of the firm at a given time. Minimizing maximum regret
Another relevant, solvable measure is MaxRegret, the largest loss in excess of a threshold, K. This is a more conservative measure than expected regret that also leads to linear programming problems. Again, K is a parameter of the optimization model, but the optimal solution is independent of values of K that are less than the smallest possible maximum loss.
Optimizing risk/return and the efficient frontier Investors generally take credit positions to obtain higher yields that compensate them for their extra risk. The achievement of any risk reduction from a risk minimization model likely comes at the expense of returns. Since the seminal work by Markowitz(1952), tools that perform mean-variance optimization and compute efficient frontiers have been well known to investment professionals. Portfolios on the frontier are called efficient since they provide the best returns for the level of risk they pose, or conversely, they pose the least risk for their level of returns.
Optimizing credit risk
Mean-variance optimization tools have also been applied recently to credit risk (Kealhofer1995, 1998). Although analytically and computationally tractable, it is quite clear that these tools are generally ineffective in credit risk applications, given the pronounced non-normality of the distributions. An example of this is given in Arvanitis et al.(1998).
Scenario risk minimization models can be naturally extended to construct risk/return efficient frontiers or to maximize risk/return utility functions (Dembo1999; Dembo and Rosen1998). The solution to the regret minimization problem represents the point on the efficient frontier having the lowest risk (and the lowest return). The entire efficient frontier is defined by the solutions of a linear parametric program that minimizes regret as a function of the expected excess returns over the risk-free rate or any other benchmark (equivalently, the problem can be formulated as a maximization of the expected excess returns subject to the portfolio regret not exceeding a given level). Case study
Bucay and Rosen(1999) apply the CreditMetrics methodology to calculate the credit risk of a portfolio of long-dated corporate and sovereign bonds issued in emerging markets. Credit events include both default and credit migration. W e apply the optimization models introduced in the previous section to this emerging market bond portfolio.
The date of the analysis is October 13,1998 and the time frame for estimating credit risk is one year. The portfolio consists of 197 emerging markets bonds, issued by 86 obligors in 29 countries. The mark-to-market value of the portfolio is 8.8 billion USD. Most instruments are denominated in USD, except for 11 fixed-rate bonds, which are denominated in seven other currencies. Bond maturities range from a few months to 98 years with a portfolio duration of approximately five years.
Credit migration probabilities are obtained from Standard & Poor’s transition matrix as of
July1998 (Standard & Poor’s1998). Recovery rates, in the event of default, are assumed to be constant and equal to 30% of the risk-free value for all obligors except two, which have lower rates. Asset correlations are driven by a multi-factor model through a set of country and industry indices chosen from the CreditMetrics dataset, and through a specific volatility component. The mappings of obligors to the indices and the assumptions are described in Bucay and
Rosen(1999). The portfolio’s loss distribution is created from a Monte Carlo simulation of 20,000 scenarios on joint credit states.
T able1 summarizes the relevant statistics from the one-year credit loss distribution of the portfolio. In addition to the expected losses and standard deviation, we also report maximum percentile losses, CreditVaR and expected shortfall, at the 99% and 99.9% percentiles.
Best hedges
As a first step, let us focus on the risk reductions that can be achieved by modifying the position of a single obligor. Figure2 shows the nTRP and its polynomial approximation for Brazil, currently the largest contributor to risk. The horizontal axis gives the weight (multiple of the current holdings) in Brazilian debt, while the vertical axis gives the corresponding Maximum losses (99%) of the portfolio (all other positions remain unchanged). The nTRP indicates that the Maximum losses (99%) can be reduced to
607million USD when Brazil is given a weight of –5.02 (i.e., a short position of five times the current holdings). For comparison, the polynomial approximation suggests that the Expected losses95
Standard deviation232
Maximum losses (99%)1,026
CreditVaR (99%)931
Expected shortfall (99%)1,320
Maximum losses (99.9%)1,782
CreditVaR (99.9%)1,687
Expected shortfall (99.9%)1,998
T a ble 1: Statistics for one-year loss distribution
in millions USD
Enterprise credit risk using Mark-to-Future
Maximum losses (99%) can be reduced to
606million USD when Brazil is weighted –4.56. Similarly, Figure3 shows the nTRP and a polynomial approximation for Colombia. In this case, a weight of –44.49 reduces the Maximum losses (99%) to 807 million USD, as determined by the nTRP (the respective values for the smooth approximation are –45.07 and
809million USD). Note that the large number of scenarios used in the simulation results in a good fit between the nTRP and the smooth approximation.
Figure 2: T rade risk profiles for Brazil
Figure 3: T rade risk profiles for Colombia
T able2 presents a “Hot Spots and Best Hedges” report of the 10 obligors making the largest contributions to portfolio risk. It shows first the percent contribution of each obligor to mean losses and Maximum losses (99%). In addition, it presents for each obligor the best hedge position, the Maximum losses (99%) at that position and the corresponding percent reductions in the Maximum losses (99%) as given by the nTRP (the results are virtually the same for the polynomial approximation). Note from T able 2 the substantial additional risk reductions that can be achieved by taking short positions in these obligors. For example, while roughly 12% of the risk can be eliminated if the positions on
V enezuelan debt are closed, the risk can be reduced by 34% by entering into a short position of about three times its current holdings.
It is important to understand the limitations of this best hedge analysis. First, the trade risk profile of a single obligor assumes that the remainder of the portfolio remains fixed; hence, multiple positions cannot be traded concurrently. Second, no consideration is given to factors that may limit the size of the trade, or of the best hedge position itself. For example, the best hedge position in Brazilian government bonds is obtained by taking a leveraged short position. In practice, it may not be feasible to enter directly into such a position. However, the hedge may be achieved by entering into a credit derivative contract, such as a total return swap. The best hedge position suggests the size of the optimal contract. Similarly, simulation can be used to construct the trade risk profile of the credit derivative contract.
Minimizing expected regret
W e turn our attention now to the linear programming formulations of the risk minimization problem. First, we minimize expected regret for a fixed threshold K, then we examine the impact of the choice of the threshold on the optimal regret. Finally, we investigate the impact of the alternate, MaxRegret, objective function. Minimizing regret has the effect of reducing the tail of the distribution and thus it also affects the quantile-based risk measures.
From T able1, the original portfolio has Maximum losses (99%) of 1,026 million USD and an expected shortfall (99%) of
1,320million USD. Thus, when setting the parameters of the optimization, it is reasonable for a risk manager to consider thresholds below these values. Let us first fix the threshold at
K=750 million
USD, so that losses less than
Optimizing credit risk
this amount are effectively ignored and the optimization focuses only on the tail of the loss distribution beyond 750 million USD.
W e impose the following constraints. First, the re-balanced portfolio maintains the same expected future value, in the absence of any credit migration, as the original portfolio; this is a simple normalization constraint. Second, to avoid unrealistically large long or short positions in any one counterparty, the size of each position is bounded. The bounds are expressed as a multiple of the current long position of one unit, for each obligor. T wo cases are considered:
?no short positions, and the existing long positions can be, at most, doubled in size ?positions, whether long or short, can be, at most, double the current size.
The optimal solution to the problem posed in the first case is identified as Regret(750,0,2) and as Regret(750,–2,2) in the second case. T able3 compares the original and optimized portfolios with respect to various risk measures. It is evident that the optimized portfolios significantly improve all of the risk measures. However, relaxing the trading limits to allow for short positions results only in a slightly better performance overall.
The largest reduction in Maximum losses (99%) achievable by trading in the debt of a single obligor is 41% (T able2). By trading simultaneously in the debt of various obligors, we can achieve a reduction of 50% in this measure, of 30–35% in expected losses and, more generally, 40–60% in standard deviation and all quantile-based risk measures.
T o understand the effect of the optimization model better, it is instructive to look at Figures4 and 5, which compare the frequency and cumulative distributions of credit losses, respectively, for the original portfolio and for the optimized portfolio with no short positions. The optimization effectively reduces the tails, resulting in a more peaked distribution. The shortening of the right tail indicates reductions of the loss measures; this is achieved through shortening the left tail, i.e., at the expense of some potential gains.
Percentage contribution Best hedge
Obligor Expected
losses
(%)
Maximum
losses (99%)
(%)
Position size
Maximum
losses (99%)
(millions USD)
Percentage
Reduction in
Maximum losses
(99%)
(%)
Brazil14.5520.27–5.0260741 Russia9.8114.31–8.7166635 V enezuela 6.1612.25–3.3267534 Argentina9.8710.47–7.8173728 Peru10.338.30–6.6573928 Colombia 2.30 3.26–44.4980721 Russia CCC 1.29 1.80–18.2975127 Mexico9.20 1.69–2.839943 Morocco 1.580.82–77.9278424 Philippines 6.670.26–4.671,0082
T a ble 2: “Hot Spots and Best Hedges” report
Enterprise credit risk using Mark-to-Future
Figure 4: Loss distributions
Figure 5: Cumulative loss distributions As a further comparison, Figures 6 and 7 plot the marginal risk (marginal standard deviation as a percent of mean exposure) versus mean exposure for the original and optimized portfolios when no short positions are allowed. These plots illustrate the differences in obligor weights between the two portfolios. For example, while the original portfolio has the largest positions in Brazil, Russia and Argentina, the optimal portfolio reduces
these holdings and roughly doubles the original positions in Poland, the Philippines and Israel, which become the largest positions in the optimal portfolio.
Some intuitively appealing properties of the
optimal portfolio are apparent from these figures. First, the optimal portfolio reduces substantially the marginal risk contributions. For example, the maximum risk contribution is reduced from 12% (Russia CCC) to about 8% (Vietnam); the rest of the contributions remain below 4%. Second, while the largest positions in the original portfolio (Brazil, Russia and Argentina) have marginal risks of about 4%, those in the optimal portfolio are much lower (Philippines about 2%, Poland and Israel less than 1%). Finally, the optimal portfolio eliminates outliers on the plot (i.e., obligors with a large position and large marginal risk). In the original portfolio we observe three groups of outliers: Russia CCC with a marginal risk of 12% and an exposure of 50million USD; Peru and V enezuela with marginal risk of about 8% and exposures of between 300 and 400million USD; and
Argentina, Russia and Brazil with marginal risk of about 4% and exposures of between 600 and 900million USD. In contrast, the main outlier, if any, from the optimal portfolio is Bulgaria with a marginal risk of about 4% and an exposure of 300million USD.
Next, we examine the effect of the threshold K on the optimization results. The trading limits prevent short positions and allow the current long positions for each obligor to be, at most, doubled. The optimal solutions to the problem described here are identified as Regret(K,0,2),
Case
Expected losses
Standard deviation
Maximum losses (99%)
Expected shortfall (99%)
Maximum losses (99.9%)
Expected shortfall (99.9%)
Original 952321,0261,3201,7821,998Regret(750,0,2)65(32)128(45)511(50)604(54)750(58)772(61)Regret(750, –2,2)
63(35)
128(45)
505(51)
601(54)
738(59)
759(62)
T a ble 3: Comparison of portfolio risk measures in millions USD (% reduction)
Optimizing credit risk
Figure 6: Marginal risk vs. exposure for original portfolio
Figure 7: Marginal risk vs. exposure for Regret(750,0,2)
Enterprise credit risk using Mark-to-Future
where K = 0, 250, 500, 750 and 1,000 (millions USD).
T able 4 presents the statistics of the various
optimal portfolios and compares them to those of the original portfolio. It is interesting to note that a significant improvement in all risk measures is obtained even with K =1,000million USD. Different thresholds generally yield more
favourable results for different risk measures. For
instance, expected losses and standard deviation benefit from a low K , the 99% level measures are lower for K =250 million and 500million USD, while the 99.9% level measures benefit from a higher K in the range 500 to 750million USD.T o explain these patterns further, we graph the loss distributions of four of these portfolios in Figure 8. This demonstrates how the various
Case Expected losses
Standard deviation
Maximum
losses
(99%)Expected
shortfall (99%)
Maximum losses (99.9%)
Expected shortfall (99.9%)
Original 952321,0261,3201,7821,998Regret(0,0,2)47(51)114(51)495(52)727(45)1,074(40)1,370(31)Regret(250,0,2)52(45)109(53)408(60)598(55)999(44)1,152(42)Regret(500,0,2)60(37)121(48)461(55)561(57)696(61)791(60)Regret(750,0,2)65(32)128(45)511(50)604(54)750(58)772(61)Regret(1000,0,2)
70(26)
142(39)
650(37)
735(44)
876(51)
931(53)
T a ble 4: Comparison of portfolio risk measures in millions USD (% reduction)
Figure 8: Loss distributions for different thresholds
Optimizing credit risk
choices can be used to “shape” the loss distribution to effect changes in various risk measures such as expected losses, maximum percentile losses and expected shortfall. While the objective functions do not explicitly optimize the quantile-based risk measures, the ability to solve the resulting linear programs quickly allows a risk manager to perform several analyses in order to obtain the desired distribution characteristics.
For example, the time required to solve the problem on a Sun Ultra1 workstation, using the CPLEX LP solver (CPLEX 1995), ranges from three seconds (K=1,000 million) to almost
40minutes (K=0). This pattern is expected, since, at lower values of K, more scenario losses exceed the threshold, and thus contribute to regret. This increases the computational effort required to solve the problem. Note that significant reductions in computation time can be achieved by using specialized techniques to solve multiple instances of the problem under various thresholds.
Minimizing maximum regret
Finally, we examine the effect that the objective function has on the loss distribution by solving the MaxRegret optimization problem. W e specify K=0 to minimize the maximum loss over all scenarios. Again, the trading limits prevent short positions and allow the current long positions for each obligor to be, at most, doubled.
T able5 presents the statistics of the MaxRegret optimal portfolio and compares them to those of the original portfolio. Again, there is a significant improvement in all risk measures. Comparing the results of T ables4and5, we note that the loss measures associated with MaxRegret lie between those of Regret(750,0,2) and Regret(1000,0,2) for each measure other than expected losses.
Risk/return analysis
The previous examples focus exclusively on credit risk reductions, without considering the expected portfolio returns. W e now compute the risk/return efficient frontier with the following specifications:
?the risk measure used is regret with a threshold K=250million USD
?the current mark-to-market value of the portfolio must be maintained
?no short positions are allowed
?the long position in the debt of an individual counterparty cannot exceed 20% of the
(current) portfolio value.
Note that the trading limits are not as restrictive as those imposed in the previous minimization models.
For simplicity, the expected returns for each obligor are given by the one-year forward returns of their holdings, assuming they do not change rating. The original portfolio has an expected one-year return of 7.26%, which exceeds the one-year risk-free rate (5.86%) by 1.40%. Figure9 shows the efficient frontier and the relative position of the original portfolio. The portfolio with the minimum regret (93,283) attains a return of 6.84%.
Note that the original portfolio is clearly inefficient in this case: Figure9 compares the original portfolio with its “radial projections,”portfolio A and portfolio B, on the efficient
Case Expected
losses
Standard
deviation
Maximum
losses
(99%)
Expected
shortfall (99%)
Maximum
losses
(99.9%)
Expected
shortfall (99.9%)
Original952321,0261,3201,7821,998
MaxRegret(0,0,2)63
(34)129
(44)
527
(49)
619
(53)
757
(58)
790
(40)
T a ble 5: Comparison of portfolio risk measures in millions USD (% reduction)
Enterprise credit risk using Mark-to-Future
Figure 9: Efficient frontier
frontier. At this level of return, the minimum regret incurred is substantially smaller than the regret in the current portfolio, though
portfolio A is not risk free. Alternatively, at this level of regret, the maximum return is
significantly higher (portfolio B). The various measures of portfolio risk for the portfolios indicated in Figure 9 are summarized in T able 6.Portfolio A achieves the same level of returns based on about one-fifth of the total capital. The quantile-based risk measures are reduced
significantly, by about 80%, and expected losses by over 90%. Note that these reductions are considerably larger than the 40–60% reductions achieved with regret minimization (T able 4) as a result of the relaxed trading limits. Figure 10 shows that the optimization not only reduces the right tail (extreme losses) of the distribution, but also offers greater potential gains (left tail) in this case.
Figure 10: Loss distributions of original portfolio
and of portfolio A On the other hand, portfolio B achieves an extra 240 basis points of return for the same regret. Furthermore, there are corresponding reductions in the other measures of portfolio risk. The
expected losses are negative, suggesting expected gains from credit events and the unexpected losses are reduced by approximately one-third. Note that once again standard deviation is a deceiving measure of risk, suggesting that the risk more than doubles for portfolio B, when, in fact, risk reductions are achieved.
Concluding remarks
Currently, the practice of managing, and particularly optimizing, credit risk presents a major challenge for risk managers. T echniques developed exclusively for market risk, based on assumptions of normality, are ineffective in this case. However, scenario-based techniques, such as constructing trade risk profiles, finding best hedges and scenario optimization, extend
Portfolio
Return (%)Regret (K =250)Expected
losses
Standard deviation
Maximum losses (99%)
Expected shortfall (99%)
Maximum losses (99.9%)
Expected shortfall (99.9%)
Original 7.2639.3952321,0261,3201,7821,998A 7.260.28(92)137(41)234(77)270(80)326(82)365(82)B
9.66
39.3
–73(–)
593(–156)
692(33)
868(34)
1,174(34)
1,330(33)
T a ble 6: Performance comparison of original and efficient portfolios in millions USD (% reduction)
Optimizing credit risk
naturally to credit risk. Scenario optimization of credit risk is complicated by the fact that, given the large number of scenarios required to model credit events, many relevant risk measures are not tractable. This paper demonstrates that regret is an attractive risk measure, exhibiting both relevance and tractability. The fact that optimizing regret involves only linear, rather than integer, programming allows regret-based models to be used to reshape the loss distributions of portfolios that are exposed to credit risk. In doing so, risk managers can also obtain substantial improvements in intractable (i.e., quantile-based) measures such as maximum percentile losses and CreditVaR.
W e have considered models for minimizing risk and optimally trading off risk and return. These results can be extended in several ways. For instance, it is straightforward to use multi-attribute optimization to implement a risk measure that is the weighted combination of expected regret and MaxRegret. Also, a more thorough investigation of the effects of the threshold K on reshaping the loss distribution may provide guidance for risk managers who need to balance the allocation of reserves and capital. While we have observed improvements in quantile-based risk measures as a result of optimizing regret, the development of heuristic or other specialized methods for explicitly optimizing such measures remains an interesting possibility. Using the solution found by the regret model as an initial starting point for such techniques may prove beneficial. Finally, a more sophisticated model that incorporates both market and credit factors over multiple horizons, while more demanding in terms of both scenario generation and optimization, will significantly enhance an organization’s ability to manage risk on an enterprise-wide basis. Acknowledgements
W e would like to thank Nisso Bucay for his valuable assistance and insights in modelling the portfolio used in this analysis.
References
Arvanitis A., C. Browne, J. Gregory and R. Martin, 1998, “Credit loss distribution and
economic capital,” Research Paper, Quantitative Credit & Risk Research, Paribas. Bucay, N. and D. Rosen, 1999, “Credit risk of an international bond portfolio: a case study,”Algo Research Quarterly 2(1): 9–29. CreditMetrics: The Benchmark for Understanding Credit Risk, T echnical Document, 1997, New Y ork, N.Y.: J.P. Morgan & Co. Inc.
CPLEX , 1995, “Using the CPLEX callable library,” CPLEX Optimization, Inc., Incline Village, NV.
Dembo, R., 1991, “Scenario optimization,”Annals of Operations Research 30: 63–80. Dembo, R. and D. Rosen, 1998, “The practice of portfolio replication,”Annals of Operations Research, 8: 267–284.
Dembo, R., 1999, “Optimal portfolio replication,” Research Paper Series 95–01, Algorithmics Inc.
Kealhofer, S., 1995, “Managing Default Risk in Portfolios of Derivatives,” in Derivative Credit Risk, London: Risk Publications.
Kealhofer S., 1998, “Portfolio management of default risk,”Net Exposure 1(2).
Litterman, R., 1996a, “Hot spots and hedges,”Risk Management Series, Goldman Sachs. Litterman, R., 1996b, “Hot spots and hedges”, Journal of Portfolio Management (Special Issue): 52–75.
Markowitz, H., 1952, “Portfolio selection,”Journal of Finance 7: 77–91.
Mausser, H. and D. Rosen, 1998, “Beyond VaR: from measuring risk to managing risk,”Algo Research Quarterly 1(2): 5–20.
Sharpe, W., 1964, “Capital asset prices: a theory of market equilibrium under conditions of risk,”Journal of Finance 19: 425–442. Standard & Poor’s Ratings, July 1998,
https://www.sodocs.net/doc/2c4330442.html,/ ratingsdirect/, Accessed January 1999. Appendix
W e present the formulation of the expected regret and MaxRegret minimization models, and their extension to a risk/return model. The
Enterprise credit risk using Mark-to-Future
formulations are based on the notation
summarized in T able A1. Suppose the problem involves n counterparties, indexed by i , and m scenarios, indexed by j.
Let V denote the (n × m ) matrix of losses due to credit migration, where
is the loss incurred by the existing holding (i.e., weight equal to one) of counterparty i in scenario j . Note that an upward migration (i.e., a move to a more favorable credit state) implies a negative loss. The loss of the entire portfolio in scenario j is
Expected regret minimization model
Let us restrict our attention to those losses that exceed some threshold K (i.e., we focus only on the tail of the loss distribution extending beyond the point K ). The following model adjusts the counterparty weights to minimize the expectation of all losses that exceed K:
The objective function, Equation A1.1,
minimizes the probability-weighted sum of the amounts by which losses exceed the threshold K , as defined in Equation A1.2. The normalization constraint, Equation A1.3, ensures that the future values of the original and the re-balanced portfolios are equal in the absence of any credit migration. Equation A1.4 specifies the upper and lower limits for each obligor weight, while Equation A1.5 stipulates that regret must be non-negative.
Note that, alternatively, one can choose to maintain the current value of the portfolio, in which case Equation A1.3 is replaced by
Definition
Elements
Dimension x counterparty weights n × 1y losses exceeding the threshold
m × 1q current mark-to-market counterparty values n × 1b future counterparty values with no credit migration n × 1D future (scenario-dependent) counterparty values with credit migration
n × m l lower trading limits, as multiple of current weighting n × 1u upper trading limits, as multiple of current weighting n × 1p scenario probabilities
m × 1V
matrix of losses due to credit migration
n × m
T a ble A1: Variables and parameters of the models
x i y j q i b i
d ij l i u i p j v ij
v ij b i d ij
–= v ij x i
i 1
=n
?
min p j y j
j 1
=m ?
A1.1()
s.t.
v ij x i i 1
=n
?
y
j
– K ≤
j 1, ..., m =A1.2()
b i x i
i 1
=n
?
b i
i 1
=n
?
=
A1.3()
l i ≤x i ≤u i i 1, ..., n =A1.4()y j ≥0
j 1, ..., m =A1.5()
Optimizing credit risk
MaxRegret minimization model
An alternative model, that minimizes
MaxRegret, the maximum loss exceeding K, is
The objective function, Equation A2.1,
minimizes the maximum amount by which losses exceed the threshold K , as defined in
Equation A2.2. The normalization and trading limit constraints, (Equations A2.3 and A2.4, respectively) are exactly as described in the
previous model. Finally, Equation A2.5 stipulates that the maximum excess loss, z , must be non-negative.
Note that, mathematically, while expected regret is given by the 1-Norm of the excess losses, MaxRegret is given by the infinity-Norm.
Risk/return model
Suppose that we know the expected return, r i , for counterparty i in the absence of credit migration. The expected portfolio return, , given counterparty weights x, is
Thus, the requirement that the portfolio achieves an expected return of at least R when there is no credit migration, can be modelled by the constraint
or, more simply,
(A3)
Equation A3 can be added to any of the above models. Varying the required return, R , produces an efficient frontier for trading off risk, as measured by the objective function in the respective model, and return.
q i x i i 1
=n ?
q i
i 1
=n
?
=
min z A2.1()
s.t.
v ij x i i 1
=n
?
z
– K ≤
j 1, ..., m =A2.2()
b i x i
i 1
=n
?
b i
i 1
=n
?
=
A2.3()
l i ≤x i ≤u i i 1, ..., n =A2.4()
z ≥0A2.5()
r P r P q i x i ()r i
i 1=n
?
q i x i
i 1
=n
?
---------------------------------= q i x i ()r i
i 1
=n
?
R q i x i
i 1
=n
?
≥ q i r i R –()x i i 1
=n
?
≥
Enterprise credit risk using Mark-to-Future
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改革开放以来我国的经济发展出现了很大的变化，市场经济时代的到来让企业发展速度不断加快。经营策略随着企业发展目标的变化也要逐渐改变，大部分企业在市场环境中应运而生，这也让市场中的企业竞争有了更大的突破，想要提升企业的市场竞争力，企业要注重营销策略的转变，还要积极从市场中树立属于自身的品牌形象。这样才能让企业的产业化发展逐步符合企业的规划目标，发展速度也得到进一步提升。 一、品牌概念以及具体的作用分析 （一）品牌的概念和特征分析 品牌主要指的就是用来区分售出的产品或者劳务，让其和同类型产品有一定的区别。这样的专业名词是企业在发展过程中需要有足够重视的内容，品牌为企业带来的力量是无穷的，其中文字、符号以及图案是品牌的主要构成形式，名称定义也是品牌的主要特征之一。品牌的特征和品牌的属性、价值以及文化含义有关[1]。成功的品牌需要集质量、性能以及服务为一体，成为可以代表企业的重要表现形式。其中的内涵则主要和产品价值以及企业发展相关，管理人员在设计的过程中要将企业内部的价值观融合到品牌中，体现出企业的经营理念以及价值观念，让消费者可以通过品牌更好的理解企业的产品和文化背景。 （二）品牌形象对企业的作用 通常来看，品牌和企业之间的联系比较密切，品牌可以体现出企业的基本素质、销售理念和市场营销特点，品牌形象和企业的市场竞争力也有着很大的联系，想要提升自身品牌形象，一定要通过品牌分析衡量出企业真正的经济实力。企业在设立品牌的过程中要对自身的发展速度以及形象提升需求进行分析，及时调整品牌形象才能大大提高企业的核心竞争力，让品牌成为提升企业发展速度的重要促进力量，成为企业产业化的重要路径[2]。
营销年度工作计划正式版
Making a comprehensive plan from the target requirements and content, and carrying out activities to complete a certain item, are the guarantee of smooth implementation.营销年度工作计划正式版
营销年度工作计划正式版 下载提示：此计划资料适用于对某个事项从目标要求、工作内容、方式方法及工作步骤等做出全面、 具体而又明确安排的计划类文书，目的为完成某事项而进行的活动而制定，是能否顺利和成功实施的 重要保障和依据。文档可以直接使用，也可根据实际需要修订后使用。 1. 长远目标：20xx年实现行业品牌效应 2. 1-2年目标：打好营销内部基础，抓好产品品质，树立好辉达品牌在消费者心中的形象，调研市场需求，开发设计新产品，不断的完善产品链，推动好企业正式运行品牌渠道销售进程。 3. 20xx年度目标： ①. 业绩目标 理想目标：0.7个亿基本目标：0.5亿 产品种类：电子人体秤电子厨房秤
电子厨房秤电子营养秤电子口袋秤机械人体秤机械厨房秤 ②. 营销团队：6-8人 ③. 市场覆盖面：国内范围发展、一线城市、二线城市为主拓展到地级市国外市场主要以欧美亚洲市场为主. ④. 市场占有率：3%-5% ⑤. 其他目标： 外贸销售目标：做电子衡器的专业贸易公司 渠道销售目标：做国内家用电子秤行业领军者 构建正常运转的营销管理体制; 组建优秀的营销团队; 一、市场调研与分析
(一)经营环境 1. 国际经营环境 整个国际环境经济萎靡，大部分垮地区、垮国界的投资在衰退。小型家用电子产品国际贸易有上升趋势 2. 国内经营环境 一线城市不断成熟，二线城市不断崛起，国家经济重点开发区快速发展 (二) 市场需求 1. 行业环境：随着人们生活的稳定，品味的提高，消费者对美的追求，对人体秤也越来越受女性朋友的追捧，随着人们健康的要求也越来越高，厨房秤也随之走进千家万户。市场需求在不断提高，行业处于快速发展期。
品牌营销策略分析研究报告
××品牌营销策略分析 研究报告 【摘要】金徽集团是我省白酒企业的龙头老大，从建国初期的“陇南春”到现在的“世纪金徽”、“金徽年代系列酒”，企业形象换然一新，企业竞争力日趋增强，这全归功于金徽的营销策略，本文从营销学的角度在探讨了金徽的营销策略的成功之处和不足之后，提出了金徽应在以后的营销中注意的地方和营销建议。 一、序言 白酒是我国独有的传统产品，历史悠久，源远流长。中国的酒文化作为一种特殊的文化形式，在传统的中国文化中有其独特的地位。在几千年的文明史中，酒几乎渗透到社会生活中的各个领域。 时下，放眼五花八门的白酒市场，近来由于不少品牌在原有品牌基础和价值提升方面都做出了良好的业绩表现。我们看到了国窖1573、洋河蓝色经典、杏花村汾酒、金剑南、等品牌在市场的成功崛起，也看到了茅台，五粮液、水井坊等老品牌的市场占有面，同时也看到了在华丽包装下的后面，是大阔步的产品价格提升和渠道加大的利润空间。但是，随之而来的是国内白酒品牌竞争的格局和形式发生变化；终端竞争的成本急剧上升，酒店的运营费用高涨，操作上稍有不慎，往往会导致企业巨额的亏损，而作为陇酒第一的金徽在经历了短暂的辉煌腾飞取得巨大的发展之后该何去何从呢？如何面对国内白酒业的残酷竞争呢？在甘肃“一年喝到一个牌子”的特有文化中如
何保持屹立不倒和市场份额呢？本文将从营销学、广告学、传播学、公关学的角度来论述金徽做低中高端市场、参与激烈的终端竞争怎样酝酿品牌提升工程，进行改良包装、提升价格及提高产品档次、建立强势品牌，将企业做强做大。 二、金辉集团简介 金徽酒业位于甘肃东南部徽成盆地徽县北部，这里背靠秦岭山麓，南依嘉陵江畔，气候温暖湿润，素有陇上江南之美誉。其和中国五粮液、泸洲老窖等名酒厂家同属长江流域、同一个地理板块，具有生产优质白酒得天独厚的自然条件。公司始建于1951年，由当时的康庆坊、永盛源等几个老作坊公私合营为甘肃徽县地方国营酒厂，并与五粮液等国家名酒一起注册，是我省建厂最早的中华老字号白酒酿造企业和中国白酒百强企业，后更名为陇南春酒厂、陇南春酒业集团公司。2006年，亚特投资集团投资数亿元，对企业进行进行重组经营，更名为甘肃金徽酒业集团公司。公司占地23万平方米，资产总额 2.5亿元，年生产白酒能力1万吨。公司主导产品“金徽”、“陇南春”、“金徽”三大系列产品，均系部、省优质产品、中华文化名酒和国家、省有关部门确认的“消费者满意产品”，2009年年底“金徽”荣获国家驰名商标，产品销量已连续几年保持甘肃省内第一。 三、金徽的市场现状分析 （一）省内市场概况 甘肃是白酒消费大省，年白酒消费量约10万吨，消费额达15亿元。甘肃占全国0.2％的人口却有着全国3％的酒民，白酒
营销年度工作计划表(新版)
营销年度工作计划表(新版) Frequent work plans can improve personal work ability, management level, find problems, analyze problems and solve problems more quickly. ( 工作计划) 部门：_______________________ 姓名：_______________________ 日期：_______________________ 本文档文字可以自由修改
营销年度工作计划表(新版) 导语：工作计划是我们完成工作任务的重要保障，制订工作计划不光是为了很 好地完成工作，其实经常制订工作计划可以更快地提高个人工作能力、管理水平、发现问题、分析问题与解决问题的能力。 频道。 一、市场分析。 年度销售计划制定的依据，便是过去一年市场形势及市场现状的分析，采用的工具是目前企业经常使用的swot分析法，即企业的优劣势分析以及竞争威胁和存在的机会，通过swot分析，从中了解市场竞争的格局及态势，并结合企业的缺陷和机会，整合和优化资源配置，使其利用化。 比如，通过市场分析，清晰地知道市场现状和未来趋势：产品(档次)向上走，渠道向下移(通路精耕和深度分销)，寡头竞争初露端倪，营销组合策略将成为下一轮竞争的热点等等。 二、营销思路。 营销思路是根据市场分析而做出的指导全年销售计划的"精
神"纲领，是营销工作的方向和"灵魂",也是销售部需要经常灌输和贯彻的营销操作理念。针对这一点，制定具体的营销思路，其中涵盖了如下几方面的内容： 1、树立全员营销观念，真正体现"营销生活化，生活营销化". 2、实施深度分销，树立决战在终端的思想，有计划、有重点地指导经销商直接运作末端市场。 3、综合利用产品、价格、通路、促销、传播、服务等营销组合策略，形成强大的营销合力。 4、在市场操作层面，体现"两高一差",即要坚持"运作差异化，高价位、高促销"的原则，扬长避短，体现独有的操作特色等等。营销思路的确定，充分结合了企业的实际，不仅翔实、有可操作性，而且还与时俱进，体现了创新的营销精神，因此，在以往的年度销售计划中，都曾发挥了很好的指引效果。 三、销售目标。 销售目标是一切营销工作的出发点和落脚点，因此，科学、
我国企业品牌营销的现状分析
我国企业品牌营销的现状分析 摘要：随着经济全球化与市场竞争的加剧，品牌营销的作用突显，但我国企业的品牌营销任处于较低水平。 中国企业的品牌营销起步于20世纪90年代，经过十多年的实践，各行各业的成熟程度有很大的差别。从整体上讲，本土品牌的综合实力，品牌价值，赢利能力与国际知名品牌相比差距还很悬殊，多数企业的市场营销还处于产品导向阶段，对品牌的认识，理解，对品牌的创立，传播，管理与维护，均缺乏力度，再品牌营销方面还存在诸多的问题。就现目前而言，主要表现在以下几个方面： （一）品牌国际化程度低，与国际知名品牌差距大 近年来，我国对外贸易持续增长。在每年2000多亿的出口商品中，品牌状况令人担忧，为外商贴牌生产和无品牌的居多，即使一部分以本土品牌出口的，也多以中低档商品为主，缺乏竞争力，与国外品牌的差距主要体现在以下两个方面： 1.运作模式方面的差距 世界著名品牌专家莱维特说过：对于公司来说，拥有市场比拥有工厂更重要，而拥有市场的唯一途径就是拥有一个优势品牌，可以形成一个超越空间的虚拟市场，实现高层次的品牌营销。近年来，国外企业的运作方式，既是先将其品牌塑造成强势的知名品牌，然后再用其带动一系列产品的市场推广，品牌与产品相互借力，相得益彰，最后实现虚拟的品牌运作。 例如，全球鞋业的第一大品牌“耐克”，全公司没有一个制鞋工人，主要资源投入再产品研发与市场网络的铺设上，靠国外的协作厂家为其加工，仅付廉价的加工费。但由于其品牌的魅力，产品的市场销售价格却非常高，赚取超额利润，属于典型的“哑铃式”运作模式。其特点是注重两头投入，即研究开发和渠道网络，能灵活适应市场的变化，这就是高层次的“品牌营销”。与此相反，本土企业多数为两头小中间大得“橄榄球式”运作，再生产设备和工艺上投入太大，市场一旦转型，往往缺乏调整余地，而其上游——研究开发，和下游——销售网络，却投入不足，又会出现一系列市场问题。 （二）品牌价值方面的差距 根据国外权威杂志评出2000年世界最有价值的品牌排名, 前? 位分别是可口可乐、微软、佰米、英特尔、诺基亚、通用电气、福特、迪斯尼、麦当劳、美国电报电话 公司。其第一位可口可乐品牌资产价值为725亿美元, 第十位美国电报电话公司的品牌价值为255 亿美元。根据最新品牌研究报告显示, 2002年中国最有价值品牌前十名中,
营销年度工作计划表
营销年度工作计划表 The work plan is a prerequisite for improving work efficiency. A complete work plan can make the work progress in an orderly manner, orderly, and more efficiently and quickly. ( 工作计划 ) 单位：______________________ 姓名：______________________ 日期：______________________ 编号：YB-JH-0668
营销年度工作计划表 频道。 一、市场分析。 年度销售计划制定的依据，便是过去一年市场形势及市场现状的分析，采用的工具是目前企业经常使用的swot分析法，即企业的优劣势分析以及竞争威胁和存在的机会，通过swot分析，从中了解市场竞争的格局及态势，并结合企业的缺陷和机会，整合和优化资源配置，使其利用化。 比如，通过市场分析，清晰地知道市场现状和未来趋势：产品(档次)向上走，渠道向下移(通路精耕和深度分销)，寡头竞争初露端倪，营销组合策略将成为下一轮竞争的热点等等。 二、营销思路。 营销思路是根据市场分析而做出的指导全年销售计划的"精神"
纲领，是营销工作的方向和"灵魂",也是销售部需要经常灌输和贯彻的营销操作理念。针对这一点，制定具体的营销思路，其中涵盖了如下几方面的内容： 1、树立全员营销观念，真正体现"营销生活化，生活营销化". 2、实施深度分销，树立决战在终端的思想，有计划、有重点地指导经销商直接运作末端市场。 3、综合利用产品、价格、通路、促销、传播、服务等营销组合策略，形成强大的营销合力。 4、在市场操作层面，体现"两高一差",即要坚持"运作差异化，高价位、高促销"的原则，扬长避短，体现独有的操作特色等等。营销思路的确定，充分结合了企业的实际，不仅翔实、有可操作性，而且还与时俱进，体现了创新的营销精神，因此，在以往的年度销售计划中，都曾发挥了很好的指引效果。 三、销售目标。 销售目标是一切营销工作的出发点和落脚点，因此，科学、合理的销售目标制定也是年度销售计划的最重要和最核心的部分。
年度营销工作计划书
工作计划：________ 年度营销工作计划书 单位：______________________ 部门：______________________ 日期：______年_____月_____日 第1 页共6 页
年度营销工作计划书 1.计划概要 xx本公司要继续保持销售和利润高速增长，销量目标为10万吨，合销售额10亿元，利润目标为1亿元，分别比去年增长50%。这一目标实现的途径是，继续扩大北方根据地市场家庭消费市场和注重营养保健人群的销量，以及对华东、西南三省市(上海、江苏、四川)新市场的开拓，同时用“热了更好喝”创造一个饮料消费的“冬季市场”(把淡季变成旺季)。因此，本年度的营销费用预算为1.5亿元，比去年增长60%(费用率比去年增长2%)，增长部分主要用于开拓新的地理市场和创造“冬季市场”的广告宣传。 2.营销状况 ①市场需求状况：营养型饮料(包括水果饮料、植物蛋白饮料、茶饮和液态奶)约占整体饮料市场的20%，即160亿元，其中植物蛋白饮料约占整体饮料市场的5%，即40亿元，预计未来几年营养型饮料(及植物蛋白饮料)均将以年30%左右的增长率增长。营养型饮料的主要消费者是大中型城市及沿海发达地区中小城镇的老人、少年、儿童、青年女性，因为人们收入的增长，消费场所已由餐饮娱乐场所发展到家庭小宗购买(成箱、大包装)，人们(及家长)希望饮料不仅能购解渴、好喝，而且要有营养，有保健功能更好，但不是特别在意、也不是购买植物蛋白饮料的主要诱因。对本公司来说，最大的问题是，杏仁露的口味南方人很难习惯，说服南方人接受此一口味是一个需要耐心的艰苦任务，而且不确定是否能够和值得去完成。 ②行业及本公司(品类)销售和利润状况：从饮料行业及本公司过去 第 2 页共 6 页
麦当劳品牌战略经营现状与对策分析
1 引言 品牌是企业和产品的象征和代表，品牌在企业的营销活动中发挥着重要作用。探讨名牌战略的构成具有重大的现实意义。当今世界有许多十分知名的品牌，我们能向他们学习些什么？麦当劳是世界知名企业的代表，它的品牌更是家喻户晓。那么它是如何做到的呢，在品牌战略运营上又有哪些过人之处呢？ 2 品牌战略经营概述 2.1 什么是品牌 品牌是一个复杂重要的概念，对这一概念的准确把握涉及到企业对待品牌的态度。很多人对于品牌的概念是模糊的。实际上，品牌是一个综合、复杂的概念，是商标、名称、包装、价格、历史、声誉、符号、广告风格的无形总和。美国市场营销协会曾为品牌做出这样的定义：品牌是一个名称、名词、标志、符号、或者设计或是它们的组合，其目的是识别某个销售者或某个群体销售者的产品或劳务，并使不同竞争对手的产品或劳务相区别开来。[1] 2.2 什么是品牌战略 什么是品牌战略？企业通过树立品牌形象和企业形象建立起自己产品在市
场上的地位，使消费者给予推崇认可，达到长期占领市场的战略。品牌就是一种价值，而企业就是要追求企业利润的最大化。[1] 2.3 什么是品牌战略经营 品牌战略经营说的就是：如何使企业很好的推广品牌战略，更好的运用品牌，为企业获得更长远的发展，奠定坚实的基础。如麦当劳企业的品牌战略经营，已经比较成功，在全世界大部分国家和地区已经认可了它的品牌。[2] 3 企业品牌战略的意义 3.1 品牌战略可以树立良好的企业形象 企业形象（ CI）是企业自身在消费者心目中的地位和价值的体现。良好的企业形象是企事业的一项重要无形资产，也是企业在市场竞争中取胜的有力武器。品牌战略和企业形象息息相关，知名品牌往往就是企业形象良好的具体证明。领先品牌战略而树立良好企业形象的企业数量众多，如生产“海尔冰箱”的海尔集团、生产“可口可乐”的可口可乐公司。品牌战略有助于企业形象的改善，良好的企业形象也有助于品牌战略的实施，二者相互促进，相互保障。[3] 3.2 品牌战略可以促进产品销售 营销是企业的先锋，也是企业运行的灵魂。品牌战略作为一种促销手段可以很好地实现企业预定的销售目标。消费者也日益认识到品牌的价值之所在，对品牌也越来越情有独钟。企业营销部门如不能抓住品牌战略这一有力武器，就很可能被成熟的消费者所抛弃。事实证明，品牌产品的市场占有率和销售额都高于非品牌的同类产品。[4，5] 3.3 品牌战略可以提高员工向心力 品牌战略是企业文化的一部分，也是增强企业凝聚力的粘合剂。一个具有知名品牌的企业在内部组织管理中更容易统一意志，协调行动。企业员工的团队精神和对企业的忠诚度也可通过品牌战备而培养提高。此种向心力是企业的宝贵财富，也是品牌对思想意识深刻影响的体现。品牌战略对内还可提高员工精神上的满足感和归属感，更能调动职工积极性，提高劳动生产率。同时，品牌战略也有
营销总监年度工作计划
营销总监年度工作计划 销售总监是公司上级领导与销售人员和具体工作之间的纽带，销售总监与销售团队代表着公司的形象与品牌，来看看销售总监的工作计划是怎样的吧!下面是收集整理的营销总监年度工作计划，欢迎阅读。 营销总监年度工作计划篇一其实我个人认为，每位销售人员都会有自己的一套销售理念。一开始，我是不能够即时知道每位销售人员的特色在哪里，需等完全了解的时候，就应该充分发挥其潜在的优势，如果某个别销售人员存在可挖掘的潜力，我会对其进行相应的督导，我们相互学习，帮助完成公司下达的销售指标，从而来弥补其不足之处。 作为销售负责人，新的一年需要做的工作很多： 1、分析市场状况，正确作出市场销售预测报批; 2、拟订年度销售计划，分解目标，报批并督导实施; 3、根据业务发展规划合理进行人员配备; 4、汇总市场信息，提报产品改善或产品开发建议; 5、洞察、预测危机，及时提出改善意见报批; 6、关注所辖人员的思想动态，及时沟通解决; 7、根据销售预算进行过程控制，降低销售费用; 8、参与重大销售谈判和签定合同; 9、组织建立、健全客户档案;
10、向直接下级授权，并布置工作; 11、定期向直接上级述职; 12、定期听取直接下级述职，并对其作出工作评定; 13、负责参与制定销售部门的工作程序和规章制度，报批后实行; 负责督促销售人员的工作： 1、销售部工作目标的完成; 2、销售指标制定和分解的合理性; 3、工作流程的正确执行; 4、开发客户的数量; 5、拜访客户的数量; 6、客户的跟进程度; 7、独立的销售渠道; 8、销售策略的运用; 9、销售指标的完成; 10、确保货款及时回笼; 11、预算开支的合理支配; 12、良好的市场拓展能力 13、纪律行为、工作秩序、整体精神面貌; 14、销售人员的计划及总结; 15、市场调查与新市场机会的发现; 16、成熟项目的营销组织、协调和销售绩效管理;
果汁饮料市场营销现状分析及规划
一、策划概述 1.公司简介 神内公司是一家位于新疆石河子市，以生产功能性果蔬汁饮料为主，以特色绿色食品为进展方向的现代股份有限公司。它诞生于1996年11月，最初隶属于石河子大学新疆食品研究开发中心，在公司经营之初就制定了以功能性的果蔬汁饮料为主，以特有绿色食品为进展方向，利用资源出精品，创品牌、争效益、走产业化进展的道路。通过原有三年自身的进展和现在与新疆啤酒花股份有限公司的资产组合，已初步形成了现代化的治理雏形；制定了严格的产品治理、监测体系，培养造就了一批善于经营、销售、开发的专业队伍。生产规模由最初的年产230吨进展至今年的100万吨。 公司的产品包括胡萝卜汁，蟠桃汁，番茄汁，鲜杏汁等八大品种，其中，在胡萝卜汁方面，神内公司是国家行业标准的制定者。在疆内，神内胡萝卜汁的饮料市场占有率达到14％，品牌认知率为67．4％，平均年销售增长率为301％。同时，神内产品还销往南京、西安、成都、上海、湖南等市场，市场阻碍力逐步向全国各地延伸。
2.公司营销现状分析及策划问题的提出 1)产品 神内公司的产品包括胡萝卜汁，蟠桃汁，番茄汁，鲜杏汁等八大品种，其中胡萝卜汁是企业的重点产品。公司产品质量较好并具有一定的技术优势，并在特定的市场区域内形成了一定的美誉度。同时，神内的产品符合“绿色、环保、健康”的消费主流并在特定的市场区域内占有一定的市场份额。然而神内处在一个大品牌尚未注意的细分市场，该细分市场尽管规模不大，却有着十分快的增长速度。持续的增长率必定引起大品牌的注意。神内公司尽管产品过硬，同时在果蔬汁市场临时处于领先地位。然而公司的产品没有形成较强的产品特色，没有一个明确的产品定位和对目标消费者的定位，产品诉求“绿色、环保、健康”，与大多数产品无差异性。同时品牌知名度也不够大，在大品牌进入市场之后全然没任何品牌上的竞争优势。 2)价格 在定价方面，神内公司的定价策略在一定程度上达到了调动经销商的积极性、吸引顾客购买、战胜竞争对手、开发和巩固市场、实现市场中产品通畅循环的目的。然而公司采取的差价定价法造成了价格上的混乱，不但不能够幸免窜货问题，而且有可能
年度营销工作计划书
( 工作计划) 单位：____________________ 姓名：____________________ 日期：____________________ 编号：YB-BH-092011 年度营销工作计划书Annual marketing plan
年度营销工作计划书 以下是以某杏仁露饮料公司XX年度营销计划为例，提供了年度营销计划书的范文。 1.计划概要 XX本公司要继续保持销售和利润高速增长，销量目标为10万吨，合销售额10亿元，利润目标为1亿元，分别比去年增长50%。这一目标实现的途径是，继续扩大北方根据地市场家庭消费市场和注重营养保健人群的销量，以及对华东、西南三省市(上海、江苏、四川)新市场的开拓，同时用“热了更好喝”创造一个饮料消费的“冬季市场”(把淡季变成旺季)。因此，本年度的营销费用预算为1.5亿元，比去年增长60%(费用率比去年增长2%)，增长部分主要用于开拓新的地理市场和创造“冬季市场”的广告宣传。 2.营销状况 ①市场需求状况：营养型饮料(包括水果饮料、植物蛋白饮料、茶饮和液态奶)约占整体饮料市场的20%，即160亿元，其中植物蛋白饮料约占整体饮料市场的5%，即40亿元，预计未来几年营养型饮料(及植物蛋白饮料)均将以年30%左右的增长率增长。营养型饮料的主要消费者是大中型城市及沿海发达地区中小城镇的老人、少年、儿童、青年女性，因为人们收入的增长，消费场所已由餐饮
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