Moment Condition Failure Australian Evidence (original) (raw)
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Investment management & financial innovations, 2005
This paper examines the issue of stock return moments in the Australian stock market. The existence of at least second moments is a fundamental assumption of underlying finance theory. We determine, using characteristic exponent point estimates, that the population variance may be infinite but on the same data, we also find that Hill-estimates are above 2 for all stocks, indicating that second moments do exist. This conflicting result is resolved by setting up a simulation experiment in which we show that the empirical combination of the Hill-estimate and the characteristic exponent lies outside the simulated confidence intervals for sum stables. This enhances the evidence for the existence of second moments in Australian stock returns.
An investigation of higher order moments of empirical financial data and the implications to risk
2021
Here, we analyse the behaviour of the higher order standardised moments of financial time series when we truncate a large data set into smaller and smaller subsets, referred to below as time windows. We look at the effect of the economic environment on the behaviour of higher order moments in these time windows. We observe two different scaling relations of higher order moments when the data sub sets’ length decreases; one for longer time windows and another for the shorter time windows. These scaling relations drastically change when the time window encompasses a financial crisis. We also observe a qualitative change of higher order standardised moments compared to the gaussian values in response to a shrinking time window. We extend this analysis to incorporate the effects these scaling relations have upon risk. We decompose the return series within these time windows and carry out a Value-at-Risk calculation. In doing so, we observe the manifestation of the scaling relations thro...
An investigation of higher order moments of empirical financial data series
2021
Here we analyse the behaviour of the higher order moments of financial series when we truncate a large data set into smaller and smaller subsets, referred to below as time windows. Additionally, we look at the effect of the economic environment on the behaviour of higher order moments in these time windows. We observe two different nontrivial scaling relations of higher order moments when the data sub sets' length decreases; one for longer time windows and another for the shorter time windows. The scaling relations drastically change when the time window encompasses a financial crisis. We also observe a qualitative change of higher order standardised moments compared to the gaussian values in response to a shrinking time window.
An investigation of higher order moments of empirical financial data and their implications to risk
Heliyon, 2022
Higher order standardised moments Value-at-risk Gaussian mixtures Here, we analyse the behaviour of the higher order standardised moments of financial time series when we truncate a large data set into smaller and smaller subsets, referred to below as time windows. We look at the effect of the economic environment on the behaviour of higher order moments in these time windows. We observe two different scaling relations of higher order moments when the data sub sets' length decreases; one for longer time windows and another for the shorter time windows. These scaling relations drastically change when the time window encompasses a financial crisis. We also observe a qualitative change of higher order standardised moments compared to the gaussian values in response to a shrinking time window. Moreover, we model the observed scaling laws by analysing the hierarchy of rare events on higher order moments. We extend the analysis of the scaling relations to incorporate the effects these scaling relations have upon risk. We decompose the return series within these time windows and carry out a Value-at-Risk calculation. In doing so, we observe the manifestation of the scaling relations through the change in the Value-at-Risk level.
Generalized Methods of Moments: Applications in Finance
Journal of Business & Economic Statistics, 2002
We provide a brief overview of applications of generalized method of moments in nance. The models examined in the empirical nance literature, especially in the asset pricing area, often imply moment conditions that can be used in a straight forward way to estimate the model parameters without making strong assumptions regarding the stochastic properties of variables observed by the econometrician. Typically the number of moment conditions available to the econometrician would exceed the number of model parameters. This gives rise to overidentifying restrictions that can be used to test the validity of the model speci cations. These advantages have led to the widespread use of the generalized method of moments in the empirical nance literature.
Stock Return Distributions: Tests of Scaling and Universality from Three Distinct Stock Markets
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Semi-moments based tests of normality and the evolution of stock returns towards normality
2004
Testing for normality is of paramount importance in many areas of science since the Gaussian distribution is a key hypothesis in many models. As the use of semi–moments is increasing in physics, economics or finance, often to judge the distributional properties of a given sample, we propose a test of normality relying on such statistics. This test is proposed in three different versions and an extensive study of their power against various alternatives is conducted in comparison with a number of powerful classical tests of normality. We find that semi–moments based tests have high power against leptokurtic and asymmetric alternatives. This new test is then applied to stock returns, to study the evolution of their normality over different horizons. They are found to converge at a “log-log” speed, as are moments and most semi–moments. Moreover, the distribution does not appear to converge to a real Gaussian.
A Reconsideration of the Properties of the Generalized Method of Moments in Asset Pricing Models
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2016
The aim of this paper is to assess the existence and the sign of moment risk premia. To this end, we use methodologies ranging from swap contracts to portfolio sorting techniques in order to obtain robust estimates. We provide empirical evidence for the European stock market for the 2008-2015 time period. Evidence is found of a negative volatility risk premium and a positive skewness risk premium, which are robust to the different techniques and cannot be explained by common risk-factors such as market excess return, size, book-to-market and momentum. Kurtosis risk is not priced in our dataset. Furthermore, we find evidence of a positive risk premium in relation to the firm's size.