Statistics for Business and Financial Economics

Lee, Cheng-Few; Lee, John C.; Lee, Alice C.

doi:10.1007/978-1-4614-5897-5

Cited by 75 publications

(23 citation statements)

References 0 publications

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“…The higher the CV the higher is the dispersion around the mean. Generally, it is indicated as a percentage [71,72] (displayed at the top of the columns in Figures 1-4). Unfortunately, due to missing data, it was not always possible to retrieve two or more data for each investigated value; in these cases, no statistical elaborations were carried out.…”

Section: Methodsmentioning

confidence: 99%

Assessment of the Space Heating and Domestic Hot Water Market in Europe—Open Data and Results

et al. 2019

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The paper investigates the European space heating (SH) and domestic hot water (DHW) market in order to close knowledge gaps concerning its size. The stimulus for this research arises from incongruences found in SH and DHW market’s data in spite of over two decades of scientific research. The given investigation has been carried out in the framework of the Hotmaps project (Horizon 2020—H2020), which aims at designing an open source toolbox to support urban planners, energy agencies, and public authorities in heating and cooling (H&C) planning on country, regional, and local levels. Our research collects and analyzes SH and DHW market data in the European Union (EU), specifically the amount of operative units, installed capacities, energy efficiency coefficients as well as equivalent full-load hours per equipment type and country, with a bottom-up approach. The analysis indicates that SH and DHW account for a significant portion of the total EU energy utilization (more than 20%), amounting to almost 3900 TWh/y. At the same time, the energy consumption provided by district heating (DH) systems exceeds the one of condensing boilers. While DH systems applications are growing throughout the EU, the replacement of elderly, conventional boilers progresses at a slower pace.

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Section: Methodsmentioning

confidence: 99%

Assessment of the Space Heating and Domestic Hot Water Market in Europe—Open Data and Results

et al. 2019

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“…Hereinafter, angular brackets denote averaging over time (historical values), while bard enotes averaging over index (vector or matrix elements). It is also possible to investigate nonlinear dependence between time series using more sophisticated statistical concepts [30] or nonlinear data transformations, however only the simplest linear case is considered in the current paper. Series variance is autocovariance, σ 2 i ≡ C ii , where σ denotes standard deviation or volatility in finance.…”

Section: Methodsmentioning

confidence: 99%

U.S. stock market interaction network as learned by the Boltzmann machine

2015

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We study historical dynamics of joint equilibrium distribution of stock returns in the U.S. stock market using the Boltzmann distribution model being parametrized by external fields and pairwise couplings. Within Boltzmann learning framework for statistical inference, we analyze historical behavior of the parameters inferred using exact and approximate learning algorithms. Since the model and inference methods require use of binary variables, effect of this mapping of continuous returns to the discrete domain is studied. The presented analysis shows that binarization preserves market correlation structure. Properties of distributions of external fields and couplings as well as industry sector clustering structure are studied for different historical dates and moving window sizes. We found that a heavy positive tail in the distribution of couplings is responsible for the sparse market clustering structure. We also show that discrepancies between the model parameters might be used as a precursor of financial instabilities.

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“…Lee, Lee, and Lee (2013) report “The fourth moment around the mean – kurtosis – which characterizes peakedness, is defined by ... .” Coolican (2013) states “...kurtosis ... refers to the overall shape of the curve in terms of its peak ... .” Katz, Wild, Elmore and Lucan (2013) state, “Kurtosis is characterized by a vertical stretching or flattening of the frequency distribution. .... A kurtotic distribution could appear more peaked or more flattened than the normal bell-shaped distribution.” Sapp (2006) writes “when [kurtosis is] positive the distributions are leptokurtic or peaked.” Taylor (2008) states , “... positive and negative [kurtosis values] refer to whether the peak of the distribution is ‘sharper’ or higher than a normal distribution or if the peak is ‘flatter’ or lower ... .” In McDonald (2007) one finds a misinterpretation of both kurtosis and standard deviation: “Leptokurtic – a high-pointed, narrow-based distribution.…”

Section: Introductionmentioning

confidence: 99%

Kurtosis as Peakedness, 1905–2014.R.I.P.

Westfall¹

2014

The American Statistician

435

229

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Summary The incorrect notion that kurtosis somehow measures “peakedness” (flatness, pointiness or modality) of a distribution is remarkably persistent, despite attempts by statisticians to set the record straight. This article puts the notion to rest once and for all. Kurtosis tells you virtually nothing about the shape of the peak - its only unambiguous interpretation is in terms of tail extremity; i.e., either existing outliers (for the sample kurtosis) or propensity to produce outliers (for the kurtosis of a probability distribution). To clarify this point, relevant literature is reviewed, counterexample distributions are given, and it is shown that the proportion of the kurtosis that is determined by the central μ ± σ range is usually quite small.

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Statistics for Business and Financial Economics

Cited by 75 publications

References 0 publications

Assessment of the Space Heating and Domestic Hot Water Market in Europe—Open Data and Results

Assessment of the Space Heating and Domestic Hot Water Market in Europe—Open Data and Results

U.S. stock market interaction network as learned by the Boltzmann machine

Kurtosis as Peakedness, 1905–2014.R.I.P.

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