A New Approach to Risk Comparison via Uncertain Measure

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  • ABSTRACT

    This paper presents a new approach to risk comparison in uncertain environment. Based on the uncertainty theory, some uncertain risk measures and risk comparison rules are proposed. Afterward the bridges are built between uncertain risk measures and risk comparison rules. Finally, several comparable examples are given.


  • KEYWORD

    Uncertainty Theory , Uncertain Measure , Uncertainty Distribution , Risk Analysis

  • 1. INTRODUCTION

    As we all know, risk is an important factor in making decisions. In many areas, decision makers must often choose a course of action in the face of risks. In order to monitor risks timely and minimize the loss caused by risk, it is necessary to analyze and compare a great variety of risks.

    There exist many ways to depict and measure risk because every decision maker has its own perception of risk. Variance and standard deviation were the traditional risk measures and first applied in the selection of portfolios by Markowitz (1952). After about 40 years, value at risk (VaR) was introduced in financial risk management by Guldimann (1995). Now, it is adopted by many financial institutions to control the risk of loss. Unfortunately, value at risk does not possess subadditivity. Therefore, tail value at risk (TVaR) as a natural remedy for the shortcoming of VaR was proposed by Artzner et al. (1997). These risk measures have become important tools in the stochastic risk analysis fields. In the stochastic environment, risk usually was regarded as a random variable and there were many methods to rank the random variables. The applications of stochastic dominance can be found in welfare, poverty and inequality, such as Shaked and Shanthikumar (1994), Barrett and Donald (2003).

    In fuzzy environment, many researchers regarded risk as a fuzzy variable and adopted fuzzy theory and methodology as an approach to risk analysis (Zmeskal, 2005; Lee and Chen, 2008). From the beginning of the development of fuzzy set theory (Zadeh, 1965), the problem of fuzzy variable dominance was studied. Lee and Li (1988) proposed comparison of fuzzy numbers based on the probability measure of fuzzy events. Tran and Duckstein (2002) compared fuzzy numbers using a fuzzy distance measure. Nojavan and Ghazanfari (2006) presented fuzzy ranking method by desirability index. Peng et al. (2005) and Peng et al. (2007) provided a fuzzy dominance method based on credibility measure. Recently, Peng (2008) presented the concept of credibilistic VaR via credibility theory to measure fuzzy risk. Afterward, Peng (2009b) proposed average value at risk (AVaR) in fuzzy risk analysis. These methods have played important roles in fuzzy risk analysis problems.

    However, many surveys showed that some imprecise phenomena behave neither like randomness nor like fuzziness. In order to deal with this uncertainty different from randomness and fuzziness, Liu (2007) founded uncertainty theory based on normality, self-duality, coun-table subadditivity, and product measure axioms in 2007, and it was refined by Liu (2010a). As a new tool to study the uncertainty in human systems, uncertainty theory motivates a new area of risk analysis called uncertain risk analysis. Here the risk is defined as the accidental loss plus uncertain measure of such loss (Liu, 2010b). The fact that risk is usually not a known constant shows that it is reasonable to represent risk by uncertain variable. Up to now, many significant risk measures have been proposed from different angles in uncertain risk analysis. Peng (2009a) suggested the VaR and TVaR based on uncertainty theory. Liu (2010b) provided the risk index to measure some risk loss. Peng and Li (2010, 2011) studied the distortion risk measure and spectral measure of uncertain risk, respectively. These uncertain measures can be widely used as tools of risk analysis in uncertain environment. In this paper, we aim to define some comparison rules of risks within the framework of uncertainty theory and discuss the relationship between these comparison rules and uncertain risk measures. The risk comparison approach is different from those in stochastic and fuzzy environment. It is worthwhile to compare risks on the basis of these uncertain measures.

    The remainder of this paper is organized as follows. Section 2 presents preliminaries in the uncertainty theory. The concepts of some risk measures based on the uncertainty theory are given in section 3. Section 4 introduces three types of comparison rules of risks. In section 5, we build the bridges between uncertain risk measures and comparison rules of risks. Section 6 gives some comparable illustrations. The last section contains some concluding remarks.

    2. PRELIMINARIES

    Let Γ be a nonempty set. A collection L of subsets of Γ is a σ -algebra. Uncertain measure M introduced by Liu (2007) is a set function if it satisfies the following axioms:

    (1) (Normality) M { Γ } =1;

    (2) (Self-duality) M{Λ} M{Λc} =1 for any Λ ∈ L ;

    (3) (Subadditivity) For every countable sequence of events { Λ i }, we have

    image

    The triplet (Γ L, M) is called an uncertain space. In order to obtain an uncertain measure of compound event, Liu (2009) defined a product uncertain measure which produces the forth axiom of uncertainty theory:

    (4) (Product axiom) Let (Γ k, Lk, Mk) be uncertain space for k = 1, 2, … , n. Then the product uncertain measure M is an uncertain measure on the product σ - algebra Π Lk satisfying

    image

    An uncertain variable is defined as a measurable function from an uncertain space (Γ , L, M ) to the set of real numbers. The uncertainty distribution Φ : ? → [0, 1] of an uncertain variable ξ is defined by Liu (2007) as

    image

    and the inverse function Φ-1 is called the inverse uncertainty distribution of ξ .

    Expected value is the average value of uncertain variable in the sense of uncertain measure, and represents the size of uncertain variable. The expected value of uncertain variable ξ is defined by Liu (2007) as

    image

    provided that at least one of the two integrals is finite. Liu (2010a) has proved that

    image

    if the expected value of uncertain variable ξ exists.

    Let ξ be an uncertain variable with finite expected value E[ξ]. The variance of ξ is defined as V [ξ ] = E

    image

    And the standard deviation of ξ is defined as

    image

    An uncertain variable ξ is called linear if it has a linear uncertainty distribution

    image

    denoted by L(a, b) where a and b are real numbers with a < b, and the inverse uncertainty distribution is

    Φ-1 (α) = (1?α )a αb, 0 < α < 1.

    The expected value and variance are (a + b)/2 and (b?a)2/12.

    An uncertain variable ξ is called normal if it has a normal uncertainty distribution

    image

    denoted by N (e, σ), where e and σ are real numbers with σ > 0, and the inverse uncertainty distribution is

    image

    The expected value and variance are e and σ2.

    3. SOME RISK MEASURES IN UNCERTAINTY ENVIRONMENT

    Risk measure is the core of risk analysis. Here we introduce some uncertain risk measures quoted from Peng (2009a, 2009b).

    Definition 1: (Peng, 2009a) Let ξ be an uncertain variable and α ∈(0,1) be the risk confidence level. Then the function ξVaR : (0, 1) → ? such that

    image

    From this definition, we can see that ξVaR just is the generalized inverse function of Φ-1 (α).

    The VaR measures of linear uncertain variable ξ and normal uncertain variable η are

    image

    and

    image

    Definition 2: (Peng, 2009b) Let ξ be an uncertain variable and α ∈ (0,1) be the risk confidence level. Then the function ξ AVaR : (0, 1) → ? such that

    image

    The AVaR measures of linear uncertain variable ξ and normal uncertain variable η are

    image

    and

    image

    Definition 3: (Peng, 2009a) Let ξ be an uncertain variable and α ∈ (0,1) be the risk confidence level. Then the function ξTVaR : (0,1) → ? such that

    image

    The TVaR measures of linear uncertain variable ξ and normal uncertain variable η are

    image

    , and

    image

    A disadvantage of VaR is that it does not give the information about the severity of loss within and beyond the VaR level. Then, AVaR and TVaR mend this shortcoming. AVaR gives the estimation of loss within the VaR level and TVaR accounts for the severity of loss failure and not only the chance of failure. AVaR and TVaR are considered to provide the better measures of risk. Moreover, it can be verified that they all possess the properties of monotonicity, positive homogeneity, translation invariance, independence additivity.

    4. COMPARISON OF UNCERTAIN RISKS

    Let ξ and η be two risks treated as uncertain variables with distribution functions Φ (x) and Ψ (x), respectively. This section will describe three types of rules to compare risks.

    Definition 4: (First comparison rule) Let ξ and η be two risks with distribution functions Φ ( x) and Ψ ( x), respectively. ξis said to be smaller than η in first-order, if and only if Φ(x) ≤ Ψ(x) for all x∈? , written

    image

    First comparison rule shows the possibility of same loss is small if the distribution function of risk always lies to the lower-right of the other. We can verify that the first comparison rule is reflexive, transitive and antisymmetric. That is, this rule is a partial order. Consequently, it is essential to introduce more applicable rules.

    Definition 5: (Second comparison rule) Let ξ and η be two risks with distribution functions Φ ( x ) and Ψ ( x ), respectively. ξ is said to be smaller than η in secondorder, if and only if

    image

    for all x∈? , written

    image

    Definition 6: (Third comparison rule) Let ξ and η be two risks with distribution functions Φ( x ) and Ψ(x), respectively. ξ is said to be smaller than η in thirdorder, if and only if

    image

    for all x? , written

    image

    It is clear that the second and third comparison rules also meet reflexivity and transitivity. In addition,

    image

    implies both

    image

    and

    image

    5. BRIDGES BETWEEN UNCERTAIN MEASURES AND UNCERTAIN RISK COMPARISONS

    In this section, we will investigate the relationships between uncertain measures and risk comparison rules.

    Theorem 1: Let ξ and η be two risks with continuous distribution functions Φ( x) and Ψ(x), respectively. Then

    image

    iff ξVaR (α) ≥ ηVaR (α) for all α ∈(0, 1)

    Proof: Necessity: If

    image

    i.e., Φ( x) ≤ Ψ(x) for all x∈?, we have inf

    image

    for all α , which means ξ VaR (α) ≥ ηVaR (α)

    Sufficiency: For any x∈?, there is α ∈(0, 1) such that ξ VaR (α) = x. ξVaR (α) ≥ ηVaR (α) shows Φ(x) = α ≤ Ψ(x).

    Theorem 2: Let ξ and η be two risks with continuous distribution functions Φ(x) and Ψ(x), respectively. Then

    image

    iff ξVaR (α) ≥ ηVaR (α) for all α ∈(0, 1)

    Proof: Necessity: Assuming that

    image

    and α ∈(0, 1) , we construct the function

    image

    Next, we will prove that f(a) takes the minimum value at a = Φ-1 (α) = ξVaR (α). On one hand, the inequality

    image

    always holds for a ≥ Φ-1 (α ) . Then,

    image

    i.e.,

    f (a) ≥ f-1 (α )).

    On the other hand, for a ≤ Φ ?1 (α ), we have

    i.e.,

    image

    That is to say, f (a) ≥ f-1 (α )) for any a∈?. Then we have

    image

    Sufficiency: Assume that ξVaR (α) ≥ η VaR (α) for all α ∈ (0, 1). For a such that 0 < Φ(a) < 1, we have

    image

    By the α ≤ Ψ(a) ⇔ a ≥ Ψ -1 (α ) , it is obvious that

    image

    which implies

    image

    for a such that 0 < Φ(a) < 1. It is easy to verify this inequality holds when Φ(a) = 0 and Φ( a) =1. Hence ,

    image

    is proved.

    Theorem 3: Let ξ and η be two risks with continuous distribution functions Φ( x) and Ψ(x), respectively. Then

    image

    iff ξVaR (α) ≤ ηVaR (α) for all α ∈( 0, 1).

    Proof: Similarly, the necessity can be proved by constructing the function

    image

    And, we may prove the sufficiency after noticing

    image

    6. EXAMPLES

    In the uncertainty theory, many types of uncertain variables can be used to describe risks. In this section, we concentrate on the comparison of risks with linear and normal uncertain distributions. First, according to the three risk comparison rules, we can obtain the following results for two risks with linear uncertain distributions.

    Theorem 4: Let ξ ~ L (a1, b1) and η ~ L (a2, b2) be two risks with. b1 ? a1 < b2 ? a2 Then

    image
    image
    image

    Proof: (1) It directly follows from the uncertain distribution of linear uncertain variable and definition of the first comparison rule. The result can be described in Figure 1.

    (2) It follows from the second comparison rule and Theorem 2 that

    image

    Under the hypothesis b1 ? a1 < b2 ? a2 we get

    image

    which yields b1 + a1 ≥ b2 + a2 i.e., E[ξ ] ≥ E[η ] . Then the result can be described in Figure 2 when b1 < b2 and Figure 3 when b2 < b1 .

    (3) By the analysis similar to (2), we have that

    image

    if and only if E[ξ ] ≤ E[η ] . We can describe the result in Figure 4 when a1 < a2 and Figure 5 when a2 < a1 .

    Next, we give the comparison results for two risks with normal uncertain distributions.

    Theorem 5: Let ξ ~ N (e1, σ1 ) and η ~ N (e2, σ2 ). Then

    image
    image
    image

    Proof: (1) It follows from the first comparison rule that

    image

    if and only if Φ( x) ≤ Ψ(x) for all x∈? i. e.,

    image

    for any x∈? and which shows e1 ≥ e2 and σ 1 = σ 2.

    We know that the variance of an uncertain variable provides measure of the spread of the distribution around its expected value. So σ1 = σ2 implies the same spread of the distributions around theirs respective expected values. The result can be described in Figure 6.

    (2) It follows from the second comparison rule and Theorem 2 that for α ∈ (0, 1), f(α)(σ2 ? σ1) ≤ (e1 ? e2) where

    image

    So, we can obtain that

    image

    iff e1 ≥ e2 and σ1 ≤ σ2. In addition, σ1 ≤ σ2 indicates that the distribution of risk ξ is more tightly concentrated around its expected value. The result can be described in Figure 7.

    (3) It follows from the third comparison rule and Theorem 3 that for α ∈ (0, 1), f(α)(σ2 ? σ1) ≤ (e2 ? e1) where

    image

    So, we can obtain the result which can be described in Figure 8.

    7. CONCLUSION

    This paper has proposed a new approach to risk comparison by means of uncertain measures. We introduced the concepts of some uncertain measures and risk comparison rules within the framework of uncertain theory. Special attention is paid to building the bridges between uncertain measures and risk comparison rules. We described the three risk comparison rules by three kinds of uncertain measures: uncertain VaR, uncertain AVaR, and uncertain TVaR, respectively. Moreover, the results are applied in the risks with linear and normal uncertain distributions. This method will be a very helpful tool for studying risks whose characteristic is neither relative to randomness nor fuzziness. It should be emphasized that there are many ways to rank risks according to different uncertain measures. In risk analysis, choosing and finding a suitable risk measure to compare the risks will be constant exploration.

  • 1. Artzner P., Delbaen F., Eber J., Heath D. (1997) Thinking coherently [Risk] Vol.10 P.68-71 google
  • 2. Barrett G. F., Donald S. G. (2003) Consistent tests for stochastic dominance [Econometrica] Vol.71 P.71-104 google
  • 3. Guldimann T. M. (1995) Risk Metrics TM: Technical Document google
  • 4. Lee E. S., Li R.-J. (1988) Comparison of fuzzy numbers based on the probability measure of fuzzy events [Computers and Mathematics with Applications] Vol.15 P.887-896 google
  • 5. Lee L.-W., Chen S.-M. (2008) Fuzzy risk analysis based on fuzzy numbers with different shapes and different deviations [Expert Systems with Applications] Vol.34 P.2763-2771 google
  • 6. Liu B. (2007) Uncertainty Theory (2nd ed.) google
  • 7. Liu B. (2009) Some research problems in uncertainty theory [Journal of Uncertain Systems] Vol.3 P.3-10 google
  • 8. Liu B. (2010a) Uncertainty Theory: A Branch of Mathematics for Modeling Human Uncertainty google
  • 9. Liu B. (2010b) Uncertain risk analysis and uncertain reliability analysis [Journal of Uncertain Systems] Vol.4 P.163-170 google
  • 10. Markowitz H. (1952) Portfolio selection [The Journal of Finance] Vol.7 P.77-91 google
  • 11. Nojavan M., Ghazanfari M. (2006) A fuzzy ranking method by desirability index [Journal of Intelligent and Fuzzy Systems] Vol.17 P.27-34 google
  • 12. Peng J. (2008) Measuring fuzzy risk by credibilistic value at risk [Proceedings of the 3rd International Conference on Innovative Computing Information and Control] P.270 google
  • 13. Peng J. (2009a) Value at risk and tail value at risk in uncertain environment [Proceedings of the 8th International Conference on Information and Management Sciences] P.787-793 google
  • 14. Peng J. (2009b) Average value at risk in fuzzy risk analysis [Advances in Intelligent and Soft Computing] Vol.62 P.1303-1313 google
  • 15. Peng J., Li, S. (2010) Spectral measure of uncertain risk [Proceedings of the 1st International Conference on Uncertain Theory] P.1-7 google
  • 16. Peng J., Li S. (2011) Distortion risk measures of uncertain systems [Proceedings of the 9th International Conference on Reliability, Maintainability and Safety] P.460-467 google
  • 17. Peng J., Jiang Q., Rao C. (2007) Fuzzy dominance: a new approach for ranking fuzzy variables via credibility measure [International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems] Vol.15 P.29-41 google
  • 18. Peng J., Mok H. M. K., Tse W.-M. (2005) Fuzzy dominance based on credibility distributions [Proceedings of the 2nd International Conference on Fuzzy Systems and Knowledge Discovery] P.295-303 google
  • 19. Shaked M., Shanthikumar J. G. (1994) Stochastic Orders and Their Applications google
  • 20. Tran L., Duckstein L. (2002) Comparison of fuzzy numbers using a fuzzy distance measure [Fuzzy Sets and Systems] Vol.130 P.331-341 google
  • 21. Zadeh L. A. (1965) Fuzzy sets [Information and Control] Vol.8 P.338-353 google
  • 22. Zmeskal Z. (2005) Value at risk methodology of international index portfolio under soft conditions (fuzzy- stochastic approach) [International Review of Financial Analysis] Vol.14 P.263-275 google
  • [Figure 1.] First comparison of linear risks.
    First comparison of linear risks.
  • [Figure 2.] Second comparison of linear risks; a2 < a1 , b2 > b1 .
    Second comparison of linear risks; a2 <
 a1 , b2 >
 b1 .
  • [Figure 3.] Second comparison of linear risks; a2 < a1 , b1 < b2 .
    Second comparison of linear risks; a2 <
 a1 , b1 <
 b2 .
  • [Figure 4.] Third comparison of linear risks; a1 < a2, b1 < b2.
    Third comparison of linear risks; a1 <
 a2, b1 <
 b2.
  • [Figure 5.] Third comparison of linear risks; a2 < a1, b1 < b2.
    Third comparison of linear risks; a2 <
 a1, b1 <
 b2.
  • [Figure 6.] First comparison of normal risks.
    First comparison of normal risks.
  • [Figure 7.] Second comparison of normal risks.
    Second comparison of normal risks.
  • [Figure 8.] Third comparison of normal risks.
    Third comparison of normal risks.