A note on interdependent happiness

Based on two models of interdependent utilities [Becker, G., 1974. A theory of social interaction, Journal of Political Economy 82, 1064–1093; Fehr, E., Schmidt, K., 1999. A theory of fairness, competition, and cooperation, Quarterly Journal of Economics 114, 817–868] we derive a functional relationship between average happiness and the standard deviation of happiness within a country. This hypothesis is supported by an empirical investigation of 71 countries which shows that the average happiness in these countries depends only on income and on the standard deviation of happiness σ. The latter may be partly based on influences beyond income, for which no data are available. Income has the expected positive influence and σ has the expected negative influence, i.e. large differences in “autonomous” happiness have a dampening influence on “effective” happiness which also takes into account the happiness of others.     

Comparison of different estimation techniques for portfolio selection

The main problem in applying the mean-variance portfolio selection consists of the fact that the first two moments of the asset returns are unknown. In practice the optimal portfolio weights have to be estimated. This is usually done by replacing the moments by the classical unbiased sample estimators. We provide a comparison of the exact and the asymptotic distributions of the estimated portfolio weights as well as a sensitivity analysis to shifts in the moments of the asset returns. Furthermore we consider several types of shrinkage estimators for the moments. The corresponding estimators of the portfolio weights are compared with each other and with the portfolio weights based on the sample estimators of the moments. We show how the uncertainty about the portfolio weights can be introduced into the performance measurement of trading strategies. The methodology explains the bad out-of-sample performance of the classical Markowitz procedures.     

New characteristics for portfolio surveillance

This paper elaborates the tools for the surveillance of the global minimum variance portfolio weights. Golosnoy and Schmid [V. Golosnoy and W. Schmid, EWMA control charts for optimal portfolio weights, Sequential Anal. 26 (2007), pp. 195–224] introduced exponentially weighted moving average-type control charts for this task based on the processes of the estimated weights as well as of their first differences. This paper proposes the new approximations to these processes exhibiting better stochastic properties for sequential monitoring purposes. The control schemes for the new processes are compared for different types of economically relevant changes using Monte Carlo simulations. The suggested procedures appear to be superior for the considered performance measures.     

Distributional properties of continuous time processes: from CIR to bates

In this paper, we compute closed-form expressions of moments and comoments for the CIR process which allows us to provide a new construction of the transition probability density based on a moment argument that differs from the historic approach. For Bates’ model with stochastic volatility and jumps, we show that finite difference approximations of higher moments such as the skewness and the kurtosis are unstable and, as a remedy, provide exact analytic formulas for log-returns. Our approach does not assume a constant mean for log-price differentials but correctly incorporates volatility resulting from Ito’s lemma. We also provide R, MATLAB, and Mathematica modules with exact implementations of the theoretical conditional and unconditional moments. These modules should prove useful for empirical research.

     

De copulis non est disputandum

Normal distribution of residuals is a traditional assumption in multivariate models. It is, however, not very often consistent with real data. Copulae allow for an extension of dependency models to nonellipticity and for separation of margins from the dependency. This paper provides a survey of copulae where different copula classes, estimation and simulation techniques and goodness-of-fit tests are considered. In the empirical section we apply different copulae to the static and dynamic Value-at-Risk of portfolio returns and Profit-and-Loss function.     

Statistical surveillance of the mean vector and the covariance matrix of nonlinear time series

The purpose of this paper is to jointly monitor the mean vector and the covariance matrix of multivariate nonlinear times series. The underlying target process is assumed to be a constant conditional correlation process Bollerslev (Rev Econ Stat 72:498–505, 1990) or a dynamic conditional correlation model Engle (J Bus Econ Stat 20:339–350, 2002). We introduce several EWMA and CUSUM control charts. These control schemes are based on univariate EWMA statistics, multivariate EWMA recursions, and different types of cumulative sums. The recursions are applied to local measures for means and covariances, e.g. the present observations and the conditional covariances. Further, they are applied to means and covariances of residuals. The control statistics are obtained by computing the Mahalanobis distance between the EWMA or CUSUM statistics and their expectations if no change occurs. Via Monte Carlo simulation the performance of the proposed charts is compared. Our empirical study illustrates an application of these control procedures to bivariate logarithmic returns of the European indices FTSE100 and DAX. In order to assess the performance of the introduced schemes we apply the average run length and the maximum conditional expected delay.     

Editorial

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