Optimal Asset Allocation Under Linear Loss Aversion

Growing experimental evidence suggests that loss aversion plays an important role in asset allocation decisions. We study the asset allocation of a linear loss-averse (LA) investor and compare the optimal LA portfolio to the more traditional optimal mean-variance (MV) and conditional value-at-risk (CVaR) portfolios. First we derive conditions under which the LA problem is equivalent to the MV and CVaR problems. Then we analytically solve the twoasset problem, where one asset is risk-free, assuming binomial or normal asset returns. In addition we run simulation experiments to study LA investment under more realistic assumptions. In particular, we investigate the impact of different dependence structures, which can be of symmetric (Gaussian copula) or asymmetric (Clayton copula) type. Finally, using 13 EU and US assets, we implement the trading strategy of an LA investor assuming assets are reallocated on a monthly basis and find that LA portfolios clearly outperform MV and CVaR portfolios.LOss aversion, portfolio optimization, MV and CVaR portfolios, copula, investment strategy

The Day-of-the-Week Effect Revisited: An Alternative Testing Approach

This paper questions traditional approaches for testing the day-of-the-week effect on stock returns. We propose an alternative approach based on the closure test principle introduced by Marcus, Peritz and Gabriel (1976), which has become very popular in Biometrics and Medical Statistics. We test all pairwise comparisons of daily expected stock returns, while the probability of committing any type I error is always kept smaller than or equal to some prespecified level a for each combination of true null hypotheses. We confirm day-of-theweek effects for the S&P 500, the FTSE 30 and the DAX 30 found in earlier studies, but find no evidence for the 1990's.Day-of-the-week effect, Multiple hypotheses testing, Multiple comparisons, Closed test procedures, Multiple level a test

Optimal Bandwidth Selection in Non-Parametric Spectral Density Estimation

This paper deals with optimal window width choice in non-parametric lag- or spectral window estimation of the spectral density of a stationary zero-mean process. Several approaches are reviewed: the cross-validation based methods described by Hurvich (1985), Beltrao & Bloomfield (1987) and Hurvich & Beltrao (1990), an iterative procedure due to Buehlmann (1996), and a bootstrap approach followed by Franke & Haerdle (1992). These methods are compared in terms of the mean square error, the mean square percentage error, and a third measure of distance between the true spectral density and its estimate. The comparison is based on a small simulation study. The processes that are simulated are in the class of ARMA (5,5) processes. Based on the simulation evidence, we suggest to use a slightly modified version of Buehlmann's (1996) iterative method.Window Width, Bandwidth, Non-Parametric Spectral Estimation, Simulation

Financial instability and economic activity

We estimate new indices measuring financial and economic (in)stability in Austria and in the euro area. Instead of estimating the level of (in)stability in a financial or economic system we measure the degree of predictability of (in)stability, where our methodological approach is based on the uncertainty index of Jurado, Ludvigson and Ng (2015). We perform an impulse response analysis in a vector error correction framework, where we focus on the impact of uncertainty shocks on industrial production, employment and the stock market. We and that financial uncertainty shows a strong significantly negative impact on the stock market, for both Austria and the euro area, while economic uncertainty shows a strong significantly negative impact on the economic variables for the euro area. We also perform a forecasting analysis, where we assess the merits of uncertainty indicators for forecasting industrial production, employment and the stock market, using different forecast performance measures. The results suggest that financial uncertainty improves the forecasts of the stock market while economic uncertainty improves the forecasts of macroeconomic variables. We also use aggregate banking data to construct an augmented financial uncertainty index and examine whether models including this augmented financial uncertainty index outperform models including the original financial uncertainty index in terms of forecasting

Evaluation of economic forecasts for Austria

In this paper, we evaluate macroeconomic forecasts for Austria and analyze the effects of external assumptions on forecast errors. We consider the growth rates of real GDP and the demand components as well as the inflation rate and the unemployment rate. The analyses are based on univariate measures like RMSE and Theil’s inequality coefficient and also on the Mahalanobis distance, a multivariate measure that takes the variances of and the correlations between the variables into account. We compare forecasts generated by the two leading Austrian economic research institutes, the Institute for Advanced Studies (IHS) and the Austrian Institute of Economic Research (WIFO), and additionally consider the forecasts produced by the European Commission. The results indicate that there are no systematic differences between the forecasts of the two Austrian institutes, neither for the traditional measures nor for the Mahalanobis distance. Generally, forecasts become more accurate with a decreasing forecast horizon, as expected; they are unbiased for forecast horizons of less than a year considering traditional measures and for the shortest forecast horizon considering the Mahalanobis distance. Finally, we find that mistakes in external assumptions, in particular regarding EU GDP and the oil price, translate into forecast errors for GDP and inflation

Prospect theory and asset allocation

We study the asset allocation of an investor with prospect theory (PT) preferences. First, we solve analytically the two-asset problem of the PT investor for one risk-free and one risky asset and find that loss aversion and the reference return affect differently less ambitious investors and more ambitious investors. Second, we empirically investigate the performance of a PT portfolio when diversifying among a stock market index, a government bond and gold, in Europe and the US. We focus on investors with PT preferences under different scenarios regarding the reference return and the degree of loss aversion and compare their portfolio performance with the performance of investors under CVaR, risk neutral, linear loss averse and in particular mean-variance (MV) preferences. We find that, in the US, PT portfolios signiffcantly outperform (in terms of returns) mean-variance portfolios in the majority of cases. Also with respect to riskadjusted performance, PT investment outperforms MV investment in the US. Similar results, however, can not be observed in Europe. Finally, we analyze asymmetric effects along economic uncertainty and observe that PT investment leads to higher returns than MV investment in times of larger economic uncertainty, especially in the US

Regime-dependent commodity price dynamics: a predictive analysis

We develop an econometric modelling framework to forecast commodity prices taking into account potentially different dynamics and linkages existing at different states of the world and using different performance measures to validate the predictions. We assess the extent to which the quality of the forecasts can be improved by entertaining different regime-dependent threshold models considering different threshold variables. We evaluate prediction quality using both loss minimization and profit maximization measures based on directional accuracy, directional value, the ability to predict adverse movements and returns implied by a trading strategy. Our analysis provides overwhelming evidence that allowing for regime-dependent dynamics leads to improvements in predictive ability for the Goldman Sachs Commodity Index, as well as for its five sub-indices (energy, industrial metals, precious metals, agriculture, livestock). Our results suggest the existence of a trade-off between predictive ability based on loss and profit measures, which implies that the particular aim of the prediction exercise carried out plays a very important role in terms of defining which set of models is the best to use

Regime-dependent nowcasting of the Austrian economy

We nowcast and forecast Austrian economic activity, namely real gross domestic product (GDP), consumption and investment, which are available at a quarterly frequency. While nowcasting uses data up to (and including) the quarter to be predicted, forecasting uses only data up to the previous quarter. We use a large number of monthly indicators to construct early estimates of the target variables. For this purpose we use different mixed-frequency models, namely the mixed-frequency vector autoregressive model according to Ghysels (2016) and mixed data sampling approaches, and compare their forecast and nowcast accuracies in terms of the root mean squared error. We also consider traditional benchmark models which rely only on quarterly data. We are particularly interested in whether explicitly considering different regimes improves the nowcast. Thus we examine regime-dependent models, taking into account business cycle regimes (recession/non-recession) or financial/economic uncertainty regimes (high/low uncertainty) driven by global and Austrian economic and financial uncertainty indicators. We find that taking explicit account of regimes clearly improves nowcasting, and different regimes are important for GDP, consumption and investment. While the recession/non-recession regimes seem to be important to nowcast GDP and consumption, high/low global financial uncertainty regimes are important to nowcast investment. Also, some variables seem to be important only in certain regimes, like tourist arrivals in the non-recession regime when nowcasting consumption, while other variables are important in both regimes, like order books in the high and low global financial uncertainty regimes when nowcasting investment. In addition, nowcasting indeed provides a value added to forecasting, and the new information available in the first month seems to be most important. However, sometimes also the forecast performs quite well, and then it mostly comes from a mixed-frequency model. So monthly information seems to be helpful also in forecasting, not only in nowcasting. Finally, we do not find a clear winner among the different mixed-frequency models

Regime‐dependent commodity price dynamics: A predictive analysis

We develop an econometric modelling framework to forecast commodity prices taking into account potentially different dynamics and linkages existing at different states of the world and using different performance measures to validate the predictions. We assess the extent to which the quality of the forecasts can be improved by entertaining different regime‐dependent threshold models considering different threshold variables. We evaluate prediction quality using both loss minimization and profit maximization measures based on directional accuracy, directional value, the ability to predict turning points, and the returns implied by a simple trading strategy. Our analysis provides overwhelming evidence that allowing for regime‐dependent dynamics leads to improvements in predictive ability for the Goldman Sachs Commodity Index, as well as for its five sub‐indices (energy, industrial metals, precious metals, agriculture, and livestock). Our results suggest the existence of a trade‐off between predictive ability based on loss and profit measures, which implies that the particular aim of the prediction exercise carried out plays a very important role in terms of defining which set of models is the best to use

The Day-of-the-Week Effect Revisited: An Alternative Testing Approach

Abstract: This paper questions traditional approaches for testing the day-of-the-week effect on stock returns. We propose an alternative approach based on the closure test principle introduced by Marcus, Peritz and Gabriel (1976), which has become very popular in Biometrics and Medical Statistics. We test all pairwise comparisons of daily expected stock returns, while the probability of committing any type I error is always kept smaller than or equal to some prespecified level a for each combination of true null hypotheses. We confirm day-of-theweek effects for the S&P 500, the FTSE 30 and the DAX 30 found in earlier studies, but find no evidence for the 1990's.