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Published in: The Journal of Real Estate Finance and Economics 4/2015

01-05-2015

Cornish-Fisher Expansion for Commercial Real Estate Value at Risk

Authors: Charles-Olivier Amédée-Manesme, Fabrice Barthélémy, Donald Keenan

Published in: The Journal of Real Estate Finance and Economics | Issue 4/2015

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Abstract

The computation of Value at Risk has traditionally been a troublesome issue in commercial real estate. Difficulties mainly arise from the lack of appropriate data, the non-normality of returns, and the inapplicability of many of the traditional methodologies. As a result, calculation of this risk measure has rarely been done in the real estate field. However, following a spate of new regulations such as Basel II, Basel III, NAIC and Solvency II, financial institutions have increasingly been required to estimate and control their exposure to market risk. As a result, financial institutions now commonly use “internal” Value at Risk (V a R) models in order to assess their market risk exposure. The purpose of this paper is to estimate distribution functions of real estate V a R while taking into account non-normality in the distribution of returns. This is accomplished by the combination of the Cornish-Fisher expansion with a certain rearrangement procedure. We demonstrate that this combination allows superior estimation, and thus a better V a R estimate, than has previously been obtainable. We also show how the use of a rearrangement procedure solves well-known issues arising from the monotonicity assumption required for the Cornish-Fisher expansion to be applicable, a difficulty which has previously limited the useful of this expansion technique. Thus, practitioners can find a methodology here to quickly assess Value at Risk without suffering loss of relevancy due to any non-normality in their actual return distribution. The originality of this paper lies in our particular combination of Cornish-Fisher expansions and the rearrangement procedure.

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Appendix
Available only for authorised users
Footnotes
1
Note that V a R does not give any information about the likely severity of a loss. An associated measure that solves this difficulty is the expected shortfall (ES), which measures the expected loss of a portfolio, conditional on the portfolio loss exceeding the chosen quantile. More information about ES is reported in Rockafellar and Uryasev (2002)
 
2
See Appendix A for the formal definition of a quantile function, the inverse of a distribution function.
 
3
In terms of gains rather than losses, the V a R at confidence level α for a market rate of return X whose distribution function is denoted F X (x)≡P[Xx] and whose quantile at level α is denoted q α (X) is:
$$-VaR_{\alpha}(X) = \text{sup} \left\{x:F_{X}(x) \leq {1 - \alpha} \right\}\equiv q_{1 - \alpha}(\textit{X}). $$
 
4
An ideal risk measure ought to be coherent; i.e., it should obey the properties of monotonicity, sub-additivity, homogeneity, and translational invariance. However, V a R does not always respect the sub-additivity property, meaning that the risk of a portfolio can be larger than the sum of the stand-alone risks of its components, as measured by V a R. This failing has been emphasized by Artzner et al. (1999) and Acerbi (2002), who have consequently proposed such alternatives to V a R as ES. However, it should be noted that the practical consequences of this potential difficulty with V a R have been minimized by the findings of Daníelsson et al. (2013), who show that the violation of subadditivity by V a R seldom occurs in practice, and that “ V a R is [generally] subadditive in the relevant tail region.” In any case, the methodology proposed here can easily be adapted to most of the alternative risk measures that have been proposed, and in particular to ES.
 
5
For instance, the former encompasses REITs.
 
6
This approximation is based on the Taylor series developed, for example, in Kendall et al. (1994)
 
7
At the third order, the approximation is: \(\forall \alpha \in (0,1), z_{CF,\alpha }=z_{\alpha }+\frac {1}{6}(z_{\alpha }^{2}-1)S \).
 
8
Notice that in presence of a Gaussian distribution (S=0 and K=3), Eq. (2) reduces to the Gaussian quantile z α .
 
9
For example, inequality (5) implies a kurtosis coefficient higher than 3 (a positive excess of kurtosis), which indicates a leptokurtic distribution. Thus, unadjusted CF expansion is not appropriate in the presence of thin tails.
 
10
See the first figure presented in Chernozhukov et al. (2010). Note that the non-rearranged quantile function encountered might be even more severely non-monotonic (and therefore could provide poorer approximations of the distribution function) than the one presented in Fig. 2. We note that \(\tilde {z}_{CF,0.001}=-1.4\), whereas z C F,0.001 is clearly biased, being equal to −0.3.
 
11
An extensive presentation of the desmoothing technique can be found in Geltner et al. (2007), p.682. However, we note that the impact of the desmoothing is negligible in our case because of offsetting impacts on each of the first four moments
 
12
Index issues have already been discussed in the Solvency II calibration paper CEIOPS-SEC-40-10, as well as in many articles; among others: Fisher et al. (1994), Edelstein and Quan (2006), Booth et al. (2002) and Cho et al. (2003).
 
13
This raises a questions concerning the window length of the data set chosen by regulators. A shorter window length leads to higher skewness and kurtosis coefficients nearer the present, since the subprime and mortgage crisis then have more weight.
 
14
The halt in estimation is due to the lack of data required for kernel estimation.
 
15
In mathematics, the notion of rearrangement derives from the notion of permutation and is reported in the work of Bóna (2004). Lorentz (1953) can also be consulted.
 
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Metadata
Title
Cornish-Fisher Expansion for Commercial Real Estate Value at Risk
Authors
Charles-Olivier Amédée-Manesme
Fabrice Barthélémy
Donald Keenan
Publication date
01-05-2015
Publisher
Springer US
Published in
The Journal of Real Estate Finance and Economics / Issue 4/2015
Print ISSN: 0895-5638
Electronic ISSN: 1573-045X
DOI
https://doi.org/10.1007/s11146-014-9476-x

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