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There exists recent formal theoretical evidence that, relative to true ages, Carbon-14 (¹⁴C) ages for fossils of living organisms (man or animals) are lacking in qualitative and quantitative robustness. This study examines the extent to which highlighted recent evidence extends to ¹⁴C ages for Paper, Ink that is derived from wood, Papyri, and Parchments. The formal axiomatic (mathematically theoretical) evidence establishes, relative to true ages for paper and ink, that ¹⁴C ages for either of paper or ink are qualitatively and quantitatively robust. While ¹⁴C ages for Papyri are not quite as robust as ¹⁴C ages for Paper, formal axioms establish that, asymptotically, and relative to true ages for Papyrus plants, ¹⁴C ages for Papyri are qualitatively and quantitatively robust. While ¹⁴C ages for Parchments originating within the same spatial location are shown to be (asymptotically) qualitatively and quantitatively robust, absent adjustments for spatial differences in rates of young carbon formation, the entire universe of ¹⁴C ages for Parchments is lacking in qualitative and quantitative robustness. The preliminary empirical evidence provides unequivocal support for study predictions. In aggregate, while there exist some concerns about robustness of ¹⁴C ages for Parchments, the evidence reveals boundedness of the concerns, with outcome it safely can be asserted that inferences derived from ¹⁴C ages for Paper, Ink that comes from wood, Papyri, and Parchments can be deemed to be robust.

We revisit well-known stochastic volatility models with constant coefficients for single asset driven by one factor stochastic volatility as homogeneous diffusion and demonstrate an alternative to the classifications provided in Albanese and Lawi, and Henry-Labord`ere, to deduce asset price distribution function, at a fixed time, in an analytic form.

In this short paper, we outline computationally efficient formulae for zero coupon swap pricing.

This paper provides invariance theorems that facilitate testing for the existence of an asset price bubble in a market where the price evolves as a Markov diffusion process. The test involves only the properties of the price process’ quadratic variation under the statistical probability. It does not require an estimate of either the equivalent local martingale measure or the asset’s drift. To augment its use, a new family of stochastic volatility price processes is also provided where the processes’ strict local martingale behavior can be characterized.

Fil: Galvan, Maria Jose. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Cordoba. Centro de Investigaciones y Transferencia de Villa Maria. Universidad Nacional de Villa Maria. Centro de Investigaciones y Transferencia de Villa Maria; Argentina

Full paper is available at: https://ssrn.com/abstract=3193697 Most papers in the financial literature control for Type I errors (false positive rate), while ignoring Type II errors (false negative rate). This is a mistake, because a low Type I error can only be achieved at the cost of a high Type II error. In this presentation we derive analytical expressions for both, after correcting for Non-Normality, Sample Length and Multiple Testing.

Full paper available at: https://ssrn.com/abstract=2840730 This supplementary material to "Which Risk Factors Drive Oil Futures Price Curves?" includes the derivation of the futures price expression, details of the Kalman Filter utilised, and the equation for the sensitivity of the expected backwardation to parameter shocks. Results plots for other data periods, and descriptive statistics of the data are also presented. Furthermore, results for the HMF model and stepwise HMF model fits are given for other data periods. Finally, results for the beta and gamma Versions of the HMF model are detailed.

In this short paper, we review various (non)-parametric regression methods, mainly k-nearest neighbors, Nadaraya-Watson, LP(p)-estimators, spline regressor and random forest. They are then compared when calibrating local stochastic volatility models using the particle method.

Full paper available at https://ssrn.com/abstract=3141041 We first present simulations investigating some properties of the Inverse Product Differentiation Logit (IPDL) model. Next, we provide a range of general methods for building Generalized Logit (GL) models along with illustrative examples that go beyond the IPDL model.

This paper develops a theoretical framework to assess the feasibility of global environmental sustainability solutions based on one or more value changes. The framework represents four sustainability paradigms (weak sustainability WS, a-growth AG, de-growth DG, strong sustainability SS) and five value changes (i.e. a sense of responsibility for nature, future generations, or the current generation in developing countries; aversion to inequality for the current generation or future generations). It defines solutions in terms of consumption, environment use, and welfare for representative individuals in both developed (OECD) and developing (non-OECD) countries. Solutions are characterised by efficiency (i.e. Pareto and Kaldor–Hicks) with respect to welfare and by intra- and inter-generational equality for consumption, environment use, and welfare, by confirming internal consistency and consistency with alternative equity approaches for utilitarianism (i.e. Harsanyi), egalitarianism (i.e. Arneson for welfare; Dworkin for consumption or environment use; Sen for consumption and environment use), and contractarianism (i.e. Rawls). Theoretical and operational insights are described for alternative sustainability paradigms and equity approaches. In terms of feasibility based on improved technology, decreased population, and modified consumption, the ordering is responsibility for future generations > responsibility for the current generation in developing countries > aversion to inequality for the current generation > aversion to inequality for future generations and AG > SS > DG > WS: responsibility for nature is unfeasible. In terms of internal consistency, responsibility for future generations > responsibility for the current generation in developing countries = aversion to inequality for the current generation = aversion to inequality for future generations and SS > AG > DG; WS is internally inconsistent. In terms of consistency with an equity approach, responsibility for future generations > responsibility for the current generation in developing countries = aversion to inequality for future generations > aversion to inequality for the current generation and SS > AG > DG > WS.

Synthetic dyes have a wide variety of colors with wide color range, guaranteed, stable, easy to wear, strong dyeing power, easy to obtain, cheap, and easy to use. Nevertheless the use of synthetic dyes can cause health and environmental problems. Therefore it is necessary to explore various potential sources of natural dyes from various plants for batik. The sources of natural dyes is often used from the plant that take from stem, flower, fruit or leaf. One of the important process of batik making is fixation to lock the color. There are many types of material for fixation that influence coloring. This research aims to explore the suitable fixer for natural dyes from various leaves of fresh and fallen plants. Natural dyes are extracted using a heating method with a water solvent. Natural dye sources are taken from Glodogan Tiang Leaves (Polyathea longifolia), Teak Leaves (Tectona grandis L. F), Sono Leaves (Pterocarpus indicus), and Mango Leaves (Mangifera indica). The Fixer will be used lime, alum and tunjung. Batik will be tested with preference consumer test.. The result showed that consumer preferred batik from tunjung fixer.

Random utility models are widely used to estimate preference parameters. In the case of intertemporal choice, the two most common models are the discounted logit and one we call the discounted Luce. Due to their apparent similarity, the choice to use one model or the other seems irrelevant. In this paper, we argue that the discounted Luce is superior to the discounted logit in two significant aspects. First, in relevant applications, the discounted Luce is monotone in the sense of Apesteguia and Ballester (2018), while the discounted logit is not. Second, we show that the discounted logit is incompatible with a property of choice probabilities we call “weak stationarity”. The latter is compatible with common assumptions on the random nature of choices and is often not falsifiable with commonly available data. Fitting a logit model to weakly stationary choice probabilities biases the time-preference estimates. On the contrary, the discounted Luce can be safely used when choice probabilities are weakly stationary. An application to an existing dataset supports the theoretical results.

In this study, I developed operational versions of Hirsch’s H index that can be applied to each researcher’s curriculum vitae (CV) to allow cross-disciplinary comparisons. The revised indices account for anomalies that potentially arise from tactical or opportunistic citation and publication behaviours by authors and editors, and can be calculated from readily available information. I split the original H index into nested indices to isolate networking activity, distinguish scientific production and productivity, and used nested Gini indices to identify intentional and successful inter-topical and inter-disciplinary research. I applied the most popular normalisations (i.e., per author and per year) using simple methodologies (i.e., least-squares linear and cubic interpolation fitting, whole-career vs. sub-periods, two-dimensional graphs) to solve empirical problems (e.g., sensitivity to citations, the “fashion” effect, attribution to disciplines, life cycle of articles) as well as open questions (e.g., the attribution of an article to a given discipline) associated with the original H index. I provided three numerical examples based on a representative heterodox, a representative orthodox multi-disciplinary, and a representative orthodox uni-disciplinary CV: the first CV includes 17 Scopus publications, and shows a highly heterodox (i.e., 5.8%), but no interdisciplinary research career, with a tiny networking component (i.e., 0.9%); the second CV includes 24 Scopus publications, and shows a slightly heterodox (i.e., 0.3%), but highly interdisciplinary (i.e., 53.9%) research career, with a small networking component (i.e., 14.3%); the third CV includes 16 Scopus publications, and shows slightly heterodox (i.e., 0.1%) and no interdisciplinary research career, with no networking component.

In this short paper, we explain how to build all correlation matrices when some subcorrelations are fixed. The set of solutions is mapped to a particular n-transitivity group of the orthogonal group that we construct explicitly.

Present conventional vermicomposting entails a greater portion of production cost due to the long duration of harvesting and high labor requirement. A vermireactor device was designed, fabricated and evaluated with the aim of reducing time and labor requirements during harvesting. Testing and evaluation were done to establish optimum screen size at the different moisture content (MC) of vermicompost. Power was measured at different loading depths and the relationship was then subsequently analyzed by regression analysis. The effectiveness of the device was compared to conventional vermicomposting in terms of processing capacity, efficiency, and labor requirements. Results revealed that capacity was affected by screen size and moisture content (MC) while, efficiency was affected by the interaction of screen size and moisture content. The optimum screen size of 50 x 50 mm at 35 % MC of vermicompost yielded the highest capacity and efficiency of 346.8 kg/min and 85.57 %, respectively. The relationship of power and depth was analyzed using regression analysis resulting in a linear equation Y = 0.0091x + 0.0759 and an R2 value of 0.9674, where y and x are power and depth respectively. Labor reduction was achieved during vermicomposting with the use of the vermireactor device requiring only 74.5 man-hours while 98 man-hour on conventional vermicomposting. The cost of fabricating the device is P25, 703.09 and production of cost of P1.42/kg with a break-even weigh of 3703 kg against current unloading practice of P2.35/kg.

Mean-Variance portfolios are optimal in-sample, however they tend to perform poorly out-of-sample (even worse than the 1/N naive portfolio!) We introduce a new portfolio construction method that substantially improves the Out-Of-Sample performance of diversified portfolios.The full paper is available at: http://ssrn.com/abstract=2708678.

This Appendix contains additional empirical results with respect to the published article. In Section 1, the posterior results for the HDP parameters of the IHMS- ARMA models are presented for the U.S. GDP growth rate and inflation series. In Section 2, we report additional in-sample and forecasting results for the same series. In Section 3, some results for a different truncation choice of the number of regimes in the approximate model are reported. Full paper available at: https://ssrn.com/abstract=2965441

In this paper, we studied the population behaviour in the Zika virus spread system. Several authors have discussed the mathematical model of the spread of the Zika virus. This paper will modify the transmission pattern that is inhibited by the presence of educational factors for vulnerable populations to inhibited the transmission rate. The model consists of five nonlinear differential equations and it is reduced to three nonlinear equations. Then we analyzed the equilibrium points and reproduction number, to describe the stability of the system. Analytically, the disease-free equilibrium it can be ascertained that the mortality indicates that the disease-free balance is locally asymptotically stable when the reproduction number is less than one. Otherwise, the endemic equilibrium point is locally asymptotically stable. Numerical analysis is also performed to show the equilibrium behaviour of the system on certain parameters. The first section in your paper

In this paper, we consider the optimal investment problem with both probability distor- tion/weighting and general non-concave utility functions with possibly finite number of inflection points. Our model contains the model under cumulative prospect theory (CPT) as a special case, which has inverse S-shaped probability weighting and S-shaped utility function (i.e. one inflection point). Existing literature have shown the equivalent relationships (strong duality) between the concavified problem and the original one by either assuming the presence of probability weighting or the non-concavity of utility functions, but not both. In this paper, we combine both features and propose a step-wise relaxation method to handle general non-concave utility functions and probability distortion functions. The necessary and sufficient conditions on eliminating the duality gap for the Lagrange method based on the step-wise relaxation have been provided under this circumstance. We have applied this solution method to solve in closed-form several representative examples in mathematical behavioral finance including the CPT model, Value-at-Risk based risk management (VAR-RM) model with probability distortions, Yarri’s dual model and the goal reaching model. We obtain a closed-form optimal trading strategy for a special example of the CPT model, where a “distorted” Merton line has been shown exactly. The slope of the “distorted” Merton line is given by an inflation factor multiplied by the standard Merton ratio, and an interesting finding is that the inflation factor is solely dependent on the probability distortion rather than the non-concavity of the utility function.

This paper studies the pairs trading strategy of No.1 soybean futures, soymeal futures, and soyoil futures on the DCE (Dalian Commodity Exchange). Pairs trading strategies are comprised of two stages. First, the Co-integration method (CA) is applied to form pairs and the formation period for pairs is divided into one year, two years and three years. Second, the criteria for opening and closing positions are established using two methods to give the trading rules applied in the decision-making process, the Simple Threshold Method (ST) and Deep Reinforcement Learning (DRL). In this paper, we adopt four models, including the ST (Simple Threshold Method), the CA-ST (Co-integration method and Simple Threshold Method), the DRL and the CA-DRL (Co-integration method and Deep Reinforcement Learning). The experimental results show that the CA-DRL model has the best performance, whether the pair’s formation period is one year, two years or three years, and China’s non-genetically modified soybean futures can implement the pairs trading strategy between commodities.

The paper presents a more complete theory of utility. In order to do so, the paper begins with Jon Von Neumann's original method of correspondences found in Theory of Games. Then by means of different correspondences between objects we define the entire economic game space as a pseudo-Euclidean space-time continuum. Using Green's method for ellipsoids of variable densities, we are then able to create a utility function which contains a removable hole discontinuity at the origin, and is continuous for returns bounded from negative one to infinity.

Bartik regressions use locations' differential exposure to nationwide sector-level shocks as an instrument to estimate the effect of a location-level treatment on an outcome. In the canonical Bartik design, locations' differential exposure to industry-level employment shocks are used as an instrument to measure the effect of their employment evolution on their wage evolution. Some recent papers studying Bartik designs have assumed that the sector-level shocks are exogenous and all have the same expectation. This second assumption may sometimes be implausible. For instance, there could be industries whose employment is more likely to grow than that of other industries. We replace that second assumption by parallel trends assumptions. Under our assumptions, Bartik regressions identify weighted sums of location-specific effects, with weights that may be negative. Accordingly, such regressions may be misleading in the presence of heterogeneous effects, an issue that was not present under the assumptions maintained in previous papers. Estimating the weights attached to Bartik regressions is a way to assess their robustness to heterogeneous effects. We also propose an alternative estimator that is robust to location-specific effects. Finally, we revisit two applications. In both cases, Bartik regressions have fairly large negative weights attached to them. Our alternative estimator is substantially different from the Bartik regression coefficient in one application.

This paper is based on the concept of mathematical axiomatic physics. This means that through the structure of the things critical conclusions could be extracted about the spherical and the cyclic shapes in the universe. The concept has as mainstream the theoretical background of the theory “Count Theory – The Role of Zero in the Existence of Numbers and Things”, about the role of one and zero to the structure of the things and the paper “Time travel - According to Mathematical Axiomatic Physics”, which includes also the role one and zero, for the function of dimensions to time travel and teleportation.

This paper estimates the elasticity of space-time and its impact on the space-time pattern. According to this estimation, space has more elasticity than time. To the macro universe level, this means that at some point the universe will stop expand and will stay in a stable condition. The paper of “Gravity Control” estimated the relation between space and time dimension. Then, gravity does not affect finally the space-time pattern. It is an attribute of the objects because of gravity and light as this showed to the paper “Black Balls are the Sources of Big Bangs”.

This paper establishes bounds on the performance of empirical risk minimization for large-dimensional linear regression. We generalize existing results by allowing the data to be dependent and heavy-tailed. The analysis covers both the cases of identically and heterogeneously distributed observations. Our analysis is nonparametric in the sense that the relationship between the regressand and the regressors is assumed to be unknown. The main results of this paper indicate that the empirical risk minimizer achieves the optimal performance (up to a logarithmic factor) in a dependent data setting.

This paper models Aumann structures with complete lattices and discusses unawareness. In multi-attribute state space models, although previous studies discussing unawareness assume a family of spaces with a complete lattice, there is no model in which a standard state space is a complete lattice. Without formulating the family of spaces, this paper models a state space with a complete lattice using a set-theoretic and a constructive approach. However, in the presented model, although almost all the properties of the unawareness operator as those of previous studies hold, Symmetry with non-trivial unawareness does not. Consequently, this paper proposes a property, the Reverse Symmetry, to show that non-trivial unawareness holds if and only if this Reverse Symmetry holds, and discusses the implications.

Measurement of price change is based on averages - also, hedonic price index numbers. As NSIs obtain scannerdata having varying content, question arise: what is correct average to use? We compare in this paper price change estimates based on most known averages and index numbers including quality adjusted methods. All analysis is done in logarithmic scale. The statistical analysis of our data is based on graphical presentations and mathematical analysis is presented mostly in our earlier papers.

This paper studies the effects of different productivity on current and future period output under a nonseparable production function with multiple productivity. This model relaxes two widely used assumptions in production function literature: the parametric production function structure and one single productivity. This paper establishes a novel nonparametric identification of all the unspecified elements. Concerning estimation, this paper proposes easy-to-implement kernel estimators and shows they are consistent and asymptotically normal by the functional delta method. The model is then applied to study Spanish firms' behaviors. I find that change in current period capital productivity plays a more crucial role than change in current period Hicksian productivity on current period output. Their effects on next period output become smaller and similar. Moreover, I find such effects depend on the levels of productivity.

In this paper we use risk-free short-term interest rates (OISTIRs) derived from equity derivatives prices to make the following four contributions: (i) We introduce and compare different methods how to extract these rates and show that a method based on box spreads performs best. (ii) We compare the results with other benchmark short-term interest rates and show that OISTIRs are higher than BUND rates (30 bps for 3-month tenors), lower than EURIBOR rates (16 bps for 3-month tenors), and almost equal to EUREX repo rates. (iii) In terms of informational sensitivity we find OISTIRs to Granger-cause EURIBOR rates, at least since the year 2013. (iv) We use OISTIRs to de- rive the BUND convenience yield. We identify bond offerings and expansive monetary policy decision to negatively impact these yields, while the risk-free maturity premium has a positive impact. Finally, we think this paper may also contribute to the discussion on how to further develop EURIBOR rates.

In this paper, we first develop some properties to state the relationships among central moments, stochastic dominance (SD), risk-seeking stochastic dominance (RSD), and integrals for the general utility functions and the polynomial utility functions of both risk averters and risk seekers. We then introduce the moment rule and establish the necessary and/or sufficient conditions between stochastic dominance and the moment rule for the general utility functions and the polynomial utility functions of both risk averters and risk seekers with and without the same-location-scale-family condition. Thereafter, we apply the moment rules to develop some properties of portfolio diversification for the general utility functions and the polynomial utility functions of both risk averters and risk seekers. The extensions in our paper enable academics and practitioners to draw preferences of both risk averters and risk seekers on their choices of the portfolios or assets by using different moments.

The aim of this paper is to provide a modeling of capital transfer between a portfolio consisted by two assets. For this purpose we use the Arrhenius Equation, which is a modeling tool for the specific modeling. We provide a stochastic differential equation of the Arrhenuis equation. We consider a unique uncertainty factor for this purpose, which arises from a generalization of It$\hat{o}$ stochastic integral. The stochastic integral established in this paper, may become a tool of substitution in any application of the It$\hat{o}$ stochastic integral in Finance.

Context: Machine learning is forming an algorithm that proposes a model that can learn from data and do predictions. It is used to know why something works and why one model is better than another. Machine learning is a built-in mathematical prerequisite. Machine learning is powered by a combination of statistics, calculus, linear algebra, and probability. Objective: This paper aims to examine the mathematics behind machine learning. Method: Different digital repositories are searched for research articles. Studies that are most relevant to the objective of the paper are selected for analysis. Result: Selected studies show that machine learning models are designed to perform calculation operations like matrix manipulation of the data for which linear algebra is used. The concept of probability is the dominant framework for dealing with uncertainty. Learning algorithms use the concept of probability to create an analysis on a given training dataset for creative prediction. Machine learning is intrinsically data-driven. The initial data acquired from different sources is in the form of junks, to search out the right data, statistical analysis is performed. Calculus helps to find the direction of change. In which direction should the unknown variable be changed such that the prediction is more optimal with minimum error? Conclusion: Findings of this paper are that the mathematical rules of designing models of machine learning are based on concepts of linear algebra, calculus, statistics, and probability. Statistics is widely used for the estimation of the value of a population parameter. Calculus tells us how to learn and optimize models. Linear algebra ensures the feasibility of algorithms to run on massive data sets. Probability predicts the likelihood of the occurrence of an event.

The paper discusses the moments of Wishart matrices, in both the central and noncentral cases. The first part of the paper shows that the expectation map has certain homogeneity and equivariance properties which impose considerable structure on the moments, hitherto unrecognised. The second part of the paper explains how the moments may be computed efficiently. The two parts of the paper are completely independent, but the computations produce precisely the algebraic structure predicted in the first part, as well as reproducing all previously known formulae. A number of examples are given for the more manageable cases.

Protein sequence clustering is very vast and challenging since number of protein sequences are increasing and their unpredicted protein structures. Once we will be able to cluster the protein sequence which is similar to each other we can easily find their structure. Protein sequences with their physico-chemical properties had been used in this paper. In this paper soft partitioning clustering algorithm are applied i.e. Fuzzy C-Means (FCM) and Possibilistic C-Means (PCM). Thirteen properties had been used to analyze the performance of fuzzy c-means and possibilistic c-means algorithm. Four properties (Pi, Hydropathy, Solubility and Charge) with their experiment results had been discussed in detail. Performance of Fuzzy C-means and Possibilistic C-means algorithm had been evaluated based on Partition Entropy (PE), Partition Coefficient (PC), Modified Partition Coefficient (MPC) and Fuzzy Silhouette Index (SIL.F) metrics using R Programming. The results show that the PCM performs better than FCM.

This paper provides a general framework to model bounded rationality in dynamic stochastic general equilibrium models with infinitely lived heterogeneous agents. A boundedly rational agent is associated with an information set $I$ and an extra parameter $\epsilon$, which can be interpreted as the ``level of irrationality''. To make decisions, the boundedly rational agent forms a belief of a stationary joint distribution of the exogenous and endogenous variables and uses the marginal distribution (conditional on $I$) to form forecasts. If the equilibrium distribution stays within $\epsilon$ of the forecasted next-period distribution, the agent would consider it as $\epsilon$-stationary. In equilibrium, each agent maximizes utility with an $\epsilon$-stationary belief and markets clear. The main theorem of this paper shows that for any strictly positive $\epsilon$, a recursive equilibrium exists. With a quantifiable ``level of irrationality'', the model incorporates many behavioral economics models as well as rational-expectations models with computational approximations into a unified framework. An illustration example is presented to show that the boundedly rational recursive equilibrium may substantially enrich the asset pricing dynamics of the rational-expectations model even for small $\epsilon$.

In addition to conventional sample size tables, few formulae were developed according to risk-based approaches and used for calculating the size of food commodity samples for inspection purposes. The current paper hypothesized the dependency of the sample size on both the risk level of the commodity or establishment and the confidence level of sampling. Accordingly, a sampling formula was developed using the commonly used 95% confidence level as fixed attribute. Application of the developed formula on populations and lots selected from three different sources, sample size tables, official authorities’ information, and calculated number of units for a fixed lot of hay (2,400 tonnes), revealed no significant difference between the sample sizes at the two selected risk probabilities 0.99 and 0.75. The findings of the current paper strongly support the use of lot units as basis for calculating the sample size rather than the lot weight, further the use of individual lot sizes to calculate sample sizes is more realistic than the common use of groups of lot and populations. The developed formula could confidently be used for calculating sample sizes for commodities of known risk probabilities.

This paper proposes a procedure for the determination of the minimal length of the historical time series of daily deposit variations in accordance with an institution’s specific risk tolerance. In a previously released paper we developed a methodology to ascertain an institutional specific confidence level to be used for the same purposes. As the formula for the determination of the confidence level depends on the length of the deposit variations time series, we complete the procedural construct by proposing a means to estimating the minimal length of the daily deposit volume variations vector in accordance with Principle 9 of the EBA recommendations for liquidity risk management best practices. As the formula determining the minimal length of the volume time series will depend on the institutional specific confidence level to be used (as described in the previous paper), we will augment our procedure with a recursive method which converges to optimal values for both parameters. The procedure centers on the identification of structural breaks in the volume dynamics distributions. The resulting vectors of constant maturities are identified via distributional power statistical tests. We further illustrate the application of the proposed procedure using a dataset of daily demand deposit volumes from a fictional European savings bank.

The top-1% most-highly-cited articles are watched closely as the vanguards of the sciences. Using Web-of-Science data, one can find that China had overtaken the USA in the relative participation in the top-1% (PP-top1%) in 2019, after outcompeting the EU on this indicator in 2015. However, this finding contrasts with repeated reports of Western agencies that the quality of China’s output in science is lagging other advanced nations, even as it has caught up in numbers of articles. The difference between the results presented here and the previous results depends mainly upon field normalizations, which classify source journals by discipline. Average citation rates of these subsets are commonly used as a baseline so that one can compare among disciplines. However, the expected value of the top-1% of a sample of N papers is N / 100, ceteris paribus. Using the average citation rates as expected values, however, errors are introduced by (1) using the mean of highly skewed distributions and (2) a specious precision in the delineations of the subsets. Classifications can be used for the decomposition, but not for the normalization. When the data is thus decomposed, the USA ranks ahead of China in biomedical fields such as virology. Although the number of papers is smaller, China outperforms the US in the field of Business and Finance (in the Social Sciences Citation Index; (p

Differences between chain and base methods (or strategies) are analyzed. This is a preliminary paper that concentrates on numerical results and new ideas. Omitted proofs will appear in later papers. As in title, we use the term circular or chain error. We do not use milder terms as drift or deviation in the identity or circular test, because it is definitely an error of the chain index. Chain error is a norm of all proper index numbers formulas (except Lowe, Jevons and their derivatives which are not proper) and it vanishes only in special circumstances. Our main claim is that chain error is almost zero only if all its links are described by the same almost ideal demand theory. This is demonstrated for a comprehensible set of both contingently biased and excellent index numbers.

I present two independent proofs of the Riemann Hypothesis considered by many the greatest unsolved problem in mathematics. I find that the admissible domain of complex zeros of the Riemann Zeta Function is the critical line. The methods and results of this paper are based on well-known theorems on the number of zeros for complex value functions (Jensen, Titchmarsh, Rouche theorems), with the Riemann Mapping Theorem acting as a bridge between the Unit Disk on the complex plane and the critical strip. By primarily relying on well-known theorems of complex analysis our approach makes this paper accessible to a relatively wide audience permitting a fast check of its validity. Both proofs do not use any functional equation of the Riemann Zeta Function, except leveraging its implied symmetry for non-trivial zeros on the critical strip., Subj-Class: Number Theory; submitted to the Annals of Mathematics for publication

This research paper proposes a new indicator to evaluate the COVID-19 responds worldwide. This new indicator is entitled “The COVID-19 National Respond Index (CNR-Index)”. Basically, the CNR-Index is using five new indicators such as (i) the total recovered cases from COVID-19 marginal rate (R’); (ii) the total number of deaths from COVID-19 marginal rate (D’); (iii) the total number of non-recovered from COVID-19 marginal rate (NR’); (iv) the total cases from COVID-19 marginal rate (TC’); (v) the CNR-Index. However, this research paper is willing to evaluate the marginal changes daily of each indicator simultaneously under the application of growth rates. Finally, the final results from these four growth rates make possible to find the CNR-Index to analyze the responds of different regions and countries in the case of COVID-19 through time and space.

The purpose of this paper is to examine the causality between DUST, CO2 and temperature for the Vostok ice core data series [Vostok Data Series], dating from 420 000 years ago, and the EPICA C Dome data going back 800 000 years. In addition, the time-varying volatility and coefficient of variation in the CO2, dust and temperature is examined, as well as their dynamic correlations and interactions. We find a clear link between atmospheric C02 levels, dust and temperature, together with a bi-directional causality effects when applying both Granger Causality Tests (1969) and multi-directional Non-Linear analogues, i.e. Generalized Correlation. We apply both parametric and non-parametric statistical measures and testing. Linear interpolation with 100 years and 1000 years is applied to the three variables, in order to solve the problem of data points mismatch among them. The visualizations and descriptive statistics of the interpolated variables (using the two periods) show robustness in the results. The data analysis points out that variables are volatile, but their respective rolling mean and standard deviation remain stable. Additionally, 1000 years interpolated data suggests positive correlation between temperature and CO2, while dust is negatively correlated with both temperature and CO2. The application of the non-parametric Generalized Measure of Correlation to our data sets, in a pairwise fashion suggested that CO2 better explains temperature than temperature does CO2, that temperature better explains dust than dust does temperature, and finally that CO2 better explains dust than vice -versa. The latter two pairs of relationships are negative. The summary of the paper presents some avenues for further research, as well as some policy relevant suggestions.

There are hundreds and thousands of schools of thought in social science and humanities, while there is only one school of thought in natural science. This paper investigates the question how to unify all schools of thought in social science and humanities into one scientific school of thought by transforming social science and humanities into subfields of physics. By being parts of physics, the scientific version of social science and humanities should allow only one school of thought logically and naturally. This paper will discuss what this scientific version of social science and humanities will look like and how it can be uniquely defined in terms of physics of human society, mathematics, methodology, history, facts, forecasting models, equilibrium values systems, and equilibrium solutions to normative problems. This paper should be useful for reconciling the current extremely polarized policy debates between the left and right around the world.

Analogous to Stambaugh (1999), this paper derives the small sample bias of estimators in J-horizon predictive regressions, providing a plug-in adjustment for these estimators. A number of surprising results emerge, including (i) a higher bias for overlapping than nonoverlapping regressions despite the greater number of observations, and (ii) particularly higher bias for an alternative long-horizon predictive regression commonly advocated for in the literature. For large J, the bias is linear in (J/T) with a slope that depends on the predictive variable’s persistence. The bias adjustment substantially reduces the existing magnitude of long-horizon estimates of predictability. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

We prove that the assertion of Genest (2020) is incorrect and irrelevant. First, there is no claim (in the paper he is referring to) regarding the dependence of the non-arbitrary constant. That paper did not make any claim that would justify the emergence of Genest (2020). Furthermore, these are ”linearized functions” based on convergence as n → ∞. Moreover, though it becomes irrelevant and needless given the above discussion, the author still made incorrect claims and we provide refuting examples.

In this paper, the Grey Wolf Optimizer (GWO) is used for tuning the parameters of PID (Proportional, Integral and Derivative) controller. The GWO is a meta-heuristic approach inspired by grey wolves (Canis lupus). The GWO algorithm consists of four types of grey wolves known as alpha (∝) as a leader or decision maker, beta (𝛽) as to assist alpha in decision making, delta (𝛿) as a subordinate wolf and omegas (𝜔) the followers. The hunting behavior of GWO continues with the three main following steps and they are searching for prey, encircling the prey and attacking the prey. Firstly, the parameters of the PID controller are tuned using the Ziegler-Nichols (Z-N) technique. Then these tuned parameters along with some randomly assumed coefficients are used as the first iteration step for the GWO algorithm. The tuning principle of GWO ensures the minimization of performance index. The performance index used in this paper is Integral Square error (ISE). The GWO algorithm for tuning the parameters of PID controller is validated by controlling the highly nonlinear in-verted pendulum system. Simulation results showed that the GWO algorithm minimizes the ISE of the system and stabilized the system within the stipulated time.

We investigate the behaviour of cryptocurrencies' return data. Using return data for bitcoin, ethereum and ripple which account for over 70% of the cyrptocurrency market, we demonstrate that $\alpha$-stable distribution models highly speculative cryptocurrencies more robustly compared to other heavy tailed distributions that are used in financial econometrics. We find that the Maximum Likelihood Method proposed by DuMouchel (1971) produces estimates that fit the cryptocurrency return data much better than the quantile based approach of McCulloch (1986) and sample characteristic method by Koutrouvelis (1980). The empirical results show that the leptokurtic feature presented in cryptocurrencies' return data can be captured by an ${\alpha}$-stable distribution. This papers covers predominant literature in cryptocurrencies and stable distributions., Comment: LaTeX MikTex 2.9 Texworks, 13 pages, 10 figures (in total). Paper presented at The 4th PKU-NUS International Conference in Quantitative Finance and Economics, 2019

This paper aims to establish asymptotic normality of the local linear kernel estimator for quantile regression under near epoch dependence, a useful concept in characterising time series dependence of extensive interests in Econometrics. In particular, near epoch dependence can cover a wide range of linear or nonlinear time series models that are even not of strong or $\alpha$-mixing property (a property usually assumed in the nonlinear time series literature). Under the mild conditions, the Bahadur representation of the quantile regression estimators is established in weak convergence sense. The method provides much richer information than mean regression and covers much more processes, which do not satisfy general mixing conditions. Simulation and application to a real data set are studied, which demonstrate the usefulness of the introduced method for analysis of time series. The theoretical results of this paper will be of widely potential interest for time series econometric semiparametric quantile regression modelling.

This paper uses five pairs trading strategies to conduct in-sample training and backtesting on 35 commodities in the major commodity markets from 1980 to 2018. The Distance Method (DM) and the Co-integration Approach (CA) are used for pairs formation. The Simple Thresholds (ST) strategy, Genetic Algorithm (GA) and Deep Reinforcement Learning (DRL) are used to determine trading actions. Traditional DM-ST, CA-ST and CA-DM-ST are used as benchmark models. The GA is used to optimize the trading thresholds in ST strategy, and this is known as the CA-GA-ST strategy. This paper proposes a novel DRL structure for determining trading actions, which replaces the ST decision method based on the standard deviation of the in-sample spread. This novel DRL structure is then combined with CA and called the CA-DRL trading strategy. The average annualized returns of the traditional DM-ST, CA-ST and CA-DM-ST methods are not high, at 0.12%, 0.37% and 0.43%, respectively. Average out-of-sample performance of CA-GA-ST only improves slightly, with an average annual return of 1.84% but an increased risk. CA-DRL provides a satisfactory trading performance: the average annualized return reaches 12.49%; the Sharpe Ratio reaches 1.853; the portfolio has a low possibility (only 5.41%) of generating negative returns.

We propose a joint distribution that decomposes asset returns into two independent components: an elliptical innovation (Gaussian) and a systematic non-elliptical latent process. The paper provides a tractable approach to estimate the underlying parameters and, hence, the assets' exposures to the latent non-elliptical factor. Additionally, the framework incorporates higher-order moments, such as skewness and kurtosis, for portfolio selection. Taking into account estimation risk, we investigate the economic contribution of the non-elliptical term. Overall, we find weak empirical evidence to support the inclusion of the non-elliptical term and, hence, the higher-order comoments. Nonetheless, our findings support the mean-variance (MV) decision rule that incorporates the elliptical term alone. Excluding the non-elliptical term results in more robust mean-variance estimates and, thus, enhanced out-of-sample performance. This evidence is significant among stocks that exhibit a strong deviation from the Gaussian property. Moreover, it is most pronounced during market turmoils, when exposures to the latent factor are highest. Overall, our paper advocates for shrinking away from the non-elliptical term, which is associated with higher estimation risk.