Aniseikonia tests: The role of viewing mode, response bias, and size-color illusions.

PURPOSE To identify the factors responsible for the poor validity of the most common aniseikonia tests, which involve size comparisons of red-green stimuli presented haploscopically. METHODS Aniseikonia was induced by afocal size lenses placed before one eye. Observers compared the sizes of semicircles presented haploscopically via color filters. The main factor under study was viewing mode (free viewing versus short presentations under central fixation). To eliminate response bias, a three-response format allowed observers to respond if the left, the right, or neither semicircle appeared larger than the other. To control decisional (criterion) bias, measurements were taken with the lens-magnified stimulus placed on the left and on the right. To control for size-color illusions, measurements were made with color filters in both arrangements before the eyes and under binocular vision (without color filters). RESULTS Free viewing resulted in a systematic underestimation of lens-induced aniseikonia that was absent with short presentations. Significant size-color illusions and decisional biases were found that would be mistaken for aniseikonia unless appropriate action is taken. CONCLUSIONS To improve their validity, aniseikonia tests should use short presentations and include control conditions to prevent contamination from decisional/response biases. If anaglyphs are used, presence of size-color illusions must be checked for. TRANSLATIONAL RELEVANCE We identified optimal conditions for administration of aniseikonia tests and appropriate action for differential diagnosis of aniseikonia in the presence of response biases or size-color illusions. Our study has clinical implications for aniseikonia management.

Testing co-volatility spillovers for natural gas spot, futures and ETF spot using dynamic conditional covariances

There is substantial empirical evidence that energy and financial markets are closely connected. As one of the most widely-used energy resources worldwide, natural gas has a large daily trading volume. In order to hedge the risk of natural gas spot markets, a large number of hedging strategies can be used, especially with the rapid development of natural gas derivatives markets. These hedging instruments include natural gas futures and options, as well as Exchange Traded Fund (ETF) prices that are related to natural gas stock prices. The volatility spillover effect is the delayed effect of a returns shock in one physical, biological or financial asset on the subsequent volatility or co-volatility of another physical, biological or financial asset. Investigating volatility spillovers within and across energy and financial markets is a crucial aspect of constructing optimal dynamic hedging strategies. The paper tests and calculates spillover effects among natural gas spot, futures and ETF markets using the multivariate conditional volatility diagonal BEKK model. The data used include natural gas spot and futures returns data from two major international natural gas derivatives markets, namely NYMEX (USA) and ICE (UK), as well as ETF data of natural gas companies from the stock markets in the USA and UK. The empirical results show that there are significant spillover effects in natural gas spot, futures and ETF markets for both USA and UK. Such a result suggests that both natural gas futures and ETF products within and beyond the country might be considered when constructing optimal dynamic hedging strategies for natural gas spot prices.

Realized Matrix-Exponential Stochastic Volatility with Asymmetry, Long Memory and Spillovers

The paper develops a novel realized matrix-exponential stochastic volatility model of multivariate returns and realized covariances that incorporates asymmetry and long memory (hereafter the RMESV-ALM model). The matrix exponential transformation guarantees the positivedefiniteness of the dynamic covariance matrix. The contribution of the paper ties in with Robert Basmann’s seminal work in terms of the estimation of highly non-linear model specifications (“Causality tests and observationally equivalent representations of econometric models”, Journal of Econometrics, 1988, 39(1-2), 69–104), especially for developing tests for leverage and spillover effects in the covariance dynamics. Efficient importance sampling is used to maximize the likelihood function of RMESV-ALM, and the finite sample properties of the quasi-maximum likelihood estimator of the parameters are analysed. Using high frequency data for three US financial assets, the new model is estimated and evaluated. The forecasting performance of the new model is compared with a novel dynamic realized matrix-exponential conditional covariance model. The volatility and co-volatility spillovers are examined via the news impact curves and the impulse response functions from returns to volatility and co-volatility.