We employ a GARCH-type model to jointly estimate returns, conditional variance and skewness and show that conditional skewness outperforms sample skewness and conditional and sample variance in ...predicting future Bitcoin returns. Interestingly, the results show that the relationship between conditional skewness and future Bitcoin returns is different depending on the sample period. In the first subsample (2018–2020), a period of relative calm in the Bitcoin market, the relationship is negative, which is in line with that found in the literature. However, in the second subsample (2021–2022), a period of major turmoil in the Bitcoin market, the relationship is positive, which is consistent with that found in previous papers on the relationship between conditional market skewness and future index returns during crisis periods. Based on these results, a dynamic buy and sell strategy of buying or selling Bitcoin based on the estimated conditional skewness is proposed. This dynamic strategy outperforms a static buy-and-hold strategy. The profitability of this strategy can be viewed as the reward that investors demand for bearing the risk associated with the changing conditions in the cryptocurrency market that generate time-varying expected returns.
•Conditional skewness performs well at predicting future Bitcoin returns.•In periods of major crises the relation Bitcoin skewness-future returns is positive.•A dynamic strategy based on Bitcoin skewness outperforms a buy-and-hold strategy.•Profitability found is the reward for bearing risk of time-varying expected returns.
We obtain all the leading-twist quark generalized parton distributions (GPDs) inside the proton at nonzero skewness within the basis light-front quantization framework. We employ the light-front wave ...functions of the proton from a light-front quantized Hamiltonian in the valence Fock sector consisting of a three-dimensional confinement potential and a one-gluon exchange interaction with fixed coupling. We find that the qualitative behaviors of our GPDs are similar to those of other theoretical calculations. We further examine the GPDs within the boost-invariant longitudinal coordinate, σ=12b−P+, which is identified as the Fourier conjugate of the skewness. The GPDs in the σ-space show diffraction patterns, which are akin to the diffractive scattering of a wave in optics.
Motivated by existing evidence of a preference among investors for assets with lottery-like payoffs and that many investors are poorly diversified, we investigate the significance of extreme positive ...returns in the cross-sectional pricing of stocks. Portfolio-level analyses and firm-level cross-sectional regressions indicate a negative and significant relation between the maximum daily return over the past one month (MAX) and expected stock returns. Average raw and risk-adjusted return differences between stocks in the lowest and highest MAX deciles exceed 1% per month. These results are robust to controls for size, book-to-market, momentum, short-term reversals, liquidity, and skewness. Of particular interest, including MAX reverses the puzzling negative relation between returns and idiosyncratic volatility recently shown in
Ang, Hodrick, Xing, and Zhang (2006, 2009).
Since the introduction of the Autoregressive Conditional Heteroscedasticity (ARCH) model, the literature on modeling the time‐varying second‐order conditional moment has become increasingly popular ...in the last four decades. Its popularity is partly due to its success in capturing volatility in financial time series, which is useful for modeling and predicting risk for financial assets. A natural extension of this is to model time variation in higher‐order conditional moments, such as the third and fourth moments, which are related to skewness and kurtosis (tail risk). This leads to an emerging literature on time‐varying higher‐order conditional moments in the last two decades. This paper outlines recent developments in modeling time‐varying higher‐order conditional moments in the economics and finance literature.
Using the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) framework as a foundation, this paper provides an overview of the two most common approaches for modeling time‐varying higher‐order conditional moments: autoregressive conditional density (ARCD) and autoregressive conditional moment (ARCM). The discussion covers both the theoretical and empirical aspects of the literature. This includes the identification of the associated skewness–kurtosis domain by using the solutions to the classical moment problems, the structural and statistical properties of the models used to model the higher‐order conditional moments and the computational challenges in estimating these models. We also advocate the use of a maximum entropy density (MED) as an alternative method, which circumvents some of the issues prevalent in these common approaches.
This study investigates the impact of commercial bank affiliation on mutual funds' lottery-like characteristics. Existing literature has extensively explored the benefits or costs of commercial bank ...affiliation in the asset management industry. Using a sample of Chinese mutual funds between 2003 and 2019, we reveal that bank affiliation significantly decreases these characteristics, and this phenomenon exhibits better performance. Our evidence suggests that affiliated funds benefit from information advantages. Notably, affiliated funds attract cash inflows by enhancing their performance rather than catering to investors’ lottery preferences. These results emphasize the role of gambling behavior in the mutual fund industry by focusing on the significance of ownership, in this case, by commercial banks.
Understanding sediment transport processes on natural sandy beaches is essential for gaining insights into beach recovery and making effective coastal management decisions. This study examines ...surfzone sediment transport rates related to beachface morphological variations on an embayed mesotidal sandy beach located on the northwestern coast of the Baja California Peninsula in Mexico. Data were collected during a week-long field experiment conducted in June 2016 under low-to-moderate wave energy conditions (Hs=0.4−1.3m). Daily topographical surveys and continuous measurements of near-bottom suspended sediment fluxes were conducted alongside the application of an extended energetics-based model that accounted for velocity and acceleration skewness. Results reveal contrasting accretionary and erosive patterns in the inner surfzone, along with consistent sediment deposition in the swash zone throughout the study period. Onshore sediment transport is found to be related to short-period calm wave conditions (Hs<0.7 m; Tp<10 s) and a weak undertow (<0.2 ms−1). Alongshore nonuniform wave breaking, influenced by irregular bathymetry and moderate-energetic oblique waves from the northwest, contributes to an alongshore gradient in sediment transport rate, leading to erosion in the northern part of the intertidal beach and accretion in the southern part. Suspended sediment flux measurements at 0.2 m above the bed suggest offshore mean transport predominates over oscillatory transport throughout the field experiment. Nevertheless, this observation should be interpreted with caution, as the flux is not vertically integrated across the water column and does not consider fluid acceleration. The model predictions effectively replicate sediment transport rates and consequent volumetric changes (Accuracy = 55–63%; RMSE = 44–69 m3; Bias=−2 to −61 m3), although they underestimate observed accretion by a factor of three and overestimate erosion by a factor of two. Overall, this research highlights the complexities of natural sandy beach recovery processes in mesotidal environments and emphasizes the importance of considering both cross-shore and longshore components in sediment transport assessments.
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•Beachface accretion remains consistent under low-to-moderate wave conditions.•Combined velocity- and acceleration-skewness model reproduces onshore sediment transport, but underestimates beachface accretion.•Erosive and accretive surfzone patterns are linked to nonuniform wave breaking influenced by bathymetry.•Undertow significantly governs offshore sediment flux near the seabed within the surfzone.
Skewness is a well-established statistical concept for continuous and, to a lesser extent, for discrete quantitative statistical variables. However, for ordered categorical variables, limited ...literature concerning skewness exists, although this type of variables is common for behavioral, educational, and social sciences. Suitable measures of skewness for ordered categorical variables have to be invariant with respect to the group of strictly increasing, continuous transformations. Therefore, they have to depend on the corresponding maximal-invariants. Based on these maximal-invariants, we propose a new class of skewness functionals, show that members of this class preserve a suitable ordering of skewness and derive the asymptotic distribution of the corresponding skewness statistic. Finally, we show the good power behavior of the corresponding skewness tests and illustrate these tests by applying real data examples.
Accurate estimation of skewness and kurtosis is crucial for addressing issues related to non-Gaussian wind pressure distribution fitting, signal simulation, and extreme estimation. The skewness and ...kurtosis determined from finite-length signals are inherent random variables characterized by obvious value fluctuations. Very limited research has been conducted on the sampling distributions of skewness and kurtosis for non-Gaussian wind pressure, motivating the present study. Firstly, the investigation focuses on the non-Gaussian white noise. The expressions of main statistical indicators such as the variance, covariance, correlation coefficient, skewness, and kurtosis of the two sampling distributions are derived and then verified using the Hermite polynomial model. The primary factors influencing the sampling distribution variance (SDV) of white noise are analyzed, including the signal length and marginal moments of the parent distribution. Secondly, the concentration shifts to colored non-Gaussian processes. The composition of SDV is analyzed through theoretical derivation. On this basis, a method for estimating SDV values using the Gaussian process regression model is proposed, with accuracy and feasibility verified based on the long-duration wind pressure data measured in wind tunnel. Furthermore, the relationship between the SDVs of wind pressure processes, signal length, higher-order correlation functions, and marginal moments of the parent process is discussed.
The robustness of F-test to non-normality has been studied from the 1930s through to the present day. However, this extensive body of research has yielded contradictory results, there being evidence ...both for and against its robustness. This study provides a systematic examination of F-test robustness to violations of normality in terms of Type I error, considering a wide variety of distributions commonly found in the health and social sciences.
We conducted a Monte Carlo simulation study involving a design with three groups and several known and unknown distributions. The manipulated variables were: Equal and unequal group sample sizes; group sample size and total sample size; coefficient of sample size variation; shape of the distribution and equal or unequal shapes of the group distributions; and pairing of group size with the degree of contamination in the distribution.
The results showed that in terms of Type I error the F-test was robust in 100% of the cases studied, independently of the manipulated conditions.
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