The Analytics of the Intervaling Effect on Skewness and Kurtosis of Stock Returns

Lau, Hon‐Shiang; Wingender, John R.

doi:10.1111/j.1540-6288.1989.tb00340.x

Cited by 18 publications

(9 citation statements)

References 30 publications

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“…Moreover, we find that the parameter sign associated with gamma differs from interval to interval. This finding is consistent with previous empirical evidence on the changing signs of financial returns' skewness, when different sampling intervals are considered (Lau and Wingender, 1989;Hawawini, 1980a).…”

Section: Table 1 About Heresupporting

confidence: 82%

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The Intervaling Effect on Higher-Order Co-Moments

2016

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This paper investigates the sensitivity of higher-order co-moments for different return measurement intervals. The levels of systematic skewness and kurtosis are found to be significantly influenced by the length of return interval. An asset preferred because of its positive co-skewness and low co-kurtosis when measured in one particular interval may have negative co-skewness or high co-kurtosis for another interval. We find the intervaling effect varies according to the level of price adjustment delay as proxied by market capitalization and illiquidity. Findings persist for intervals of up to twelve months, and are consistent during both volatile and stable periods.

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Section: Table 1 About Heresupporting

confidence: 82%

“…Specifically, the literature suggests that the sign of skewness will also change due to different sampling intervals used (Lau and Wingender, 1989;Hawawini, 1980b). Building on this, we test whether the sign of systematic co-skewness changes from one interval to another.…”

Section: Test Hypothesesmentioning

confidence: 99%

The Intervaling Effect on Higher-Order Co-Moments

2016

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“…The statistics in panel (c) are the averages of the statistics for each of the ten possible starting days. Implied coefficients of skewness and of excess kurtosis for the aggregation of k returns are calculated by scaling the non-aggregated coefficients by 1/ √ k and 1/k, based on Lau and Wingender (1989). The correlations in panel (b) are the averages of the correlations based on weekly returns for each day of the week.…”

Section: Resultsmentioning

confidence: 99%

“…Lau and Wingender (1989) derive that when k i.i.d. returns are aggregated, the skewness coefficient is scaled by 1/ √ k and the coefficient of excess kurtosis by 1/k.…”

Section: Datamentioning

confidence: 99%

Forecasting Value-at-Risk under Temporal and Portfolio Aggregation*

Kole

Markwat²,

Opschoor

et al. 2017

Journal of Financial Econometrics

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in December 2015 AbstractWe examine the impact of temporal and portfolio aggregation on the quality of Valueat-Risk (VaR) forecasts over a horizon of ten trading days for a well-diversified portfolio of stocks, bonds and alternative investments. The VaR forecasts are constructed based on daily, weekly or biweekly returns of all constituent assets separately, gathered into portfolios based on asset class, or into a single portfolio. We compare the impact of aggregation to that of choosing a model for the conditional volatilities and correlations, the distribution for the innovations and the method of forecast construction. We find that the degree of temporal aggregation is most important. Daily returns form the best basis for VaR forecasts. Modelling the portfolio at the asset or asset class level works better than complete portfolio aggregation, but differences are smaller. The differences from the model, distribution and forecast choices are also smaller compared to temporal aggregation.

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“…That is, as the holding interval h increases, e 3 and e 4 decline at a rate of h 1=2 and h 1 respectively. This is the so-called intervalling e¤ect on skewness and kurtosis that were studied by Hawawini (1980) and Lau and Wingender (1989). Before we proceed to derive the required tests, it is worthwhile to consider the following example to illustrate why the higher order relations may not hold.…”

Section: Higher-order Ratio Relationsmentioning

confidence: 99%

A GMM Skewness and Kurtosis Ratio Test for Higher Moment Dependence

Wong

2019

Journal of Financial Econometrics

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This article extends the variance ratio test of Lo and MacKinlay (1988) to tests of skewness and kurtosis ratios using the generalized methods of moments. In particular, overlapping observations are used in which dependencies are explicitly modeled to make the tests more powerful and have better size properties. The proposed higher-order ratio tests can be useful in risk management where risk models are estimated using daily data but multiperiod forecasts of tail risks are required for the determination of risk capital. Application of the tests finds significant higher moment dependence in the U.S. stock market returns.

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The Analytics of the Intervaling Effect on Skewness and Kurtosis of Stock Returns

Cited by 18 publications

References 30 publications

The Intervaling Effect on Higher-Order Co-Moments

The Intervaling Effect on Higher-Order Co-Moments

Forecasting Value-at-Risk under Temporal and Portfolio Aggregation*

A GMM Skewness and Kurtosis Ratio Test for Higher Moment Dependence

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