Advances in Decision Sciences

Volume 2012 (2012), Article ID 973173, 8 pages

doi:10.1155/2012/973173

Research Article

Optimal Portfolio Estimation for Dependent Financial Returns with Generalized Empirical Likelihood

Hiroaki Ogata

School of International Liberal Studies, Waseda University, Tokyo 169-8050, Japan

Received 16 February 2012; Accepted 10 April 2012

Academic Editor: Junichi Hirukawa

Copyright © 2012 Hiroaki Ogata. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

This paper proposes to use the method of generalized empirical likelihood to find the optimal portfolio weights. The log-returns of assets are modeled by multivariate stationary processes rather than i.i.d. sequences. The variance of the portfolio is written by the spectral density matrix, and we seek the portfolio weights which minimize it.

1. Introduction

The modern portfolio theory has been developed since circa the 1950s. It is common knowledge that Markowitz [1, 2] is a pioneer in this field. He introduced the so-called mean-variance theory, in which we try to maximize the expected return (minimize the variance) under the constant variance (the constant expected return). After that, many researchers followed, and portfolio theory has been greatly improved. For a comprehensive survey of this field, refer to Elton et al. [3], for example.

Despite its sophisticated paradigm, we admit there exists several criticisms against the early portfolio theory. One of them is that it blindly assumes that the asset returns are normally distributed. As Mandelbrot [4] pointed out, the price changes in the financial market do not seem to be normally distributed. Therefore, it is appropriate to use the nonparametric estimation method to find the optimal portfolio. Furthermore, it is empirically observed that financial returns are dependent. Therefore, it is unreasonable to fit the independent model to it.

One of the nonparametric techniques which has been capturing the spotlight recently is the empirical likelihood method. It was originally proposed by Owen [5, 6] as a method of inference based on a data-driven likelihood ratio function. Smith [7] and Newey and Smith [8] extended it to the generalized empirical likelihood (GEL). GEL can be also considered as an alternative of generalized methods of moments (GMM), and it is known that its asymptotic bias does not grow with the number of moment restrictions, while the bias of GMM often does.

From the above point of view, we consider to find the optimal portfolio weights by using the GEL method under the multivariate stationary processes. The optimal portfolio weights are defined as the weights which minimize the variance of the return process with constant mean. The analysis is done in the frequency domain.

This paper is organized as follows. Section 2 explains about a frequency domain estimating function. In Section 3, we review the GEL method and mention the related asymptotic theory. Monte Carlo simulations and a real-data example are given in Section 4. Throughout this paper, and indicate the transposition and adjoint of a matrix , respectively.

2. Frequency Domain Estimating Function

Here, we are concerned with the -dimensional stationary process with mean vector , the autocovariance matrix

and spectral density matrix

Suppose that information of an interested -dimensional parameter exists through a system of general estimating equations in frequency domain as follows. Let , be matrix-valued continuous functions on satisfying and . We assume that each satisfies the spectral moment condition

where is the true value of the parameter. By taking an appropriate function for , (2.3) can express the best portfolio weights as shown in Example 2.1.

Example 2.1 (portfolio selection). In this example, we set . Let be the log-return of th asset at time and suppose that the process is stationary with zero mean. Consider the portfolio , where is a vector of weights, satisfying . The process is a linear combination of the stationary process, hence is still stationary, and, from Herglotz’s theorem, its variance is

Our aim is to find the weights that minimize the variance (the risk) of the portfolio under the constrain of . The Lagrange function is given by

where and is Lagrange multiplier. The first order condition leads to

where is an identity matrix. Now, for fixed , consider to take

Then, (2.3) coincides with the first order condition (2.6), which implies that the best portfolio weights can be solved with the framework of the spectral moment condition.

Besides, we can express other important indices for time series. In what follows, several examples are given.

Example 2.2 (autocorrelation). Denote the autocovariance and the autocorrelation of the process (th component of the process ) with lag by and , respectively. Suppose that we are interested in the joint estimation of and . Take

Then, (2.3) leads to

From Herglotz’s theorem, , and . Then, corresponds to the desired autocorrelations . The idea can be directly extended to more than two autocorrelations.

Example 2.3 (Whittle estimation). In this example, we set . Consider fitting a parametric spectral density model to the true spectral density . The disparity between and is measured by the following criterion:

which is based on Whittle likelihood. The purpose here is to seek the quasi-true value defined by

Assume that the spectral density model is expressed by the following form:

where each is an matrix ( is defined as identity matrix), and is an symmetric matrix. The general linear process has this spectral form, so this assumption is not so restrictive. The key of this assumption is that the elements of the parameter do not depend on . We call such a parameter innovation-free. Imagine that you fit the VARMA process, for example. Innovation-free implies that the elements of the parameter depend on only AR or MA coefficients and not on the covariance matrix of the innovation process. Now, let us consider the equation:

to find quasi-true value. The Kolmogorov’s formula says

This implies that if is innovation-free, the quantity is independent of and (2.13) leads to

This corresponds to (2.3), when we set

so the quasi-true value can be expressed by the spectral moment condition.

Based on the form of (2.3), we set the estimating function for as

where is the periodogram, defined by

where , . Then, we have

3. Generalized Empirical Likelihood

Once we construct the estimating function, we can make use of the method of GEL as in the work by Smith [7] and Newey and Smith [8]. GEL is introduced as an alternative to GMM and it is pointed out that its asymptotic bias does not grow with the number of moment restrictions, while the bias of GMM often does.

To describe GEL let be a function of a scalar which is concave on its domain, an open interval containing zero. Let . The estimator is the solution to a saddle point problem

The empirical likelihood (EL) estimator (cf. [9]) the exponential tilting (ET) estimator (cf. [10]), and the continuous updating estimator (CUE) (cf. [11]) are special cases with , and , respectively. Let , , and . The following assumptions and theorems are due to Newey and Smith [8].

Assumption 3.1. (i) is the unique solution to (2.3).

(ii) is compact.

(iii) is continuous at each with probability one.

(iv) for some .

(v) is nonsingular.

(vi) is twice continuously differentiable in a neighborhood of zero.

Theorem 3.2. Let Assumption 3.1 hold. Then .

Assumption 3.3. (i) .

(ii) is continuously differentiable in a neighborhood of and .

(iii) .

Theorem 3.4. Let Assumptions 3.1 and 3.3 hold. Then .

4. Monte Carlo Studies and Illustration

In the first part of this section, we summarize the estimation results of Monte Carlo studies of the GEL method. We generate 200 observations from the following two-dimensional-AR(1) model

where is an i.i.d. innovation process, distributed to two-dimensional -distribution whose correlation matrix is identity, and degree of freedom is 5. The true autocorrelations with lag 1 of this process are and , respectively. As described in Example 2.2, we estimate and by using three types of frequency domain GEL method (EL, ET, and CUE). Table 1 shows the mean and standard deviation of the estimation results whose repetition time is 1000. All types work properly.

Table 1: Estimated autocorrelations for two-dimensional-AR(1) model.

EL
ET
CUE

Mean
s.d.
Mean
s.d.
Mean
s.d.

(1)
0.3779
0.0890
0.3760
0.0900
0.3797
0.0911

(1)
0.4680
0.0832
0.4660
0.0817
0.4650
0.0855

Next, we apply the proposed method to the returns of market index data. The sample consists of 7 weekly indices (S&P 500, Bovespa, CAC 40, AEX, ATX, HKHSI, and Nikkei) having 800 observations each: the initial date is April 30, 1993, and the ending date is August 22, 2008. Refer to Table 2 for the market of each index.

Table 2: Market.

Index
Market

S&P 500
NYSE

Bovespa
São Paulo Stock Exchange

CAC 40
Bourse de Paris

AEX
Amsterdam Stock Exchange

ATX
Wiener Börse

HKHSI
Hong Kong Exchanges and Clearing

Nikkei
Tokyo Stock Exchange

As shown in Example 2.1, we use frequency domain GEL method to estimate the optimal portfolio weights, and the results are shown in Table 3. Bovespa and ATX account for large part in the optimal portfolio.

Table 3: Estimated portfolio weights.

EL
ET
CUE

S&P 500
0.0759
0.0617
0.0001

Bovespa
0.6648
0.6487
0.6827

CAC 40
0.0000
0.0000
0.0000

AEX
0.0000
0.0000
0.0000

ATX
0.2593
0.2558
0.3168

HKHSI
0.0000
0.0000
0.0000

Nikkei
0.0000
0.0338
0.0004

Acknowledgment

This work was supported by Grant-in-Aid for Young Scientists (B) (22700291).

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