Empirical Characteristics Research Articles

Mixed interval realized variance: A robust estimator of stock price volatility

An ex post volatility estimator, called mixed interval realized variance (MIRV), is proposed. The estimator uses high-frequency price data to provide measurements robust to the idiosyncratic noise of stock markets caused by the bid-ask bounce. The theoretical properties of the new volatility estimator are illustrated and compared with those of the two canonical realized measures: realized volatility and realized range. A simulation study adds to this comparison and highlights some favorable robustness properties of the new estimator when subject to market microstructures. The main finding is that mixed interval realized variance is robust to the presence of microstructures, but inconsistent in the hypothetical ideal scenario. The empirical illustration features Australian stocks from the ASX 20 and provides evidence that for a number of stocks the mixed interval realized variance is competitive with other realized measures under predictive likelihood when it is included in a Realized GARCH model.

Non-Contact Human Motion Recognition Based on UWB Radar

Human motion recognition is crucial for surveillance, search and rescue operation, smart homes, and senior care. In daily life, there exists various kinds of human motions with widely different characteristics and meanwhile they also exhibit some clustering features, which make it difficult for recognition. In this paper, a multiple-layer classification method is introduced for comprehensive human motion recognition, including the largest number of motion types ever studied with an ultra-wideband radar system. First, in the pre-screening layer, information in the time-range domain is used to distinguish in situ motions and non-in situ motions. According to different kinds of human motions, the weighted range-time-frequency transform method is proposed to obtain corresponding spectrograms. Then physical empirical features and principal component analysis-based features are extracted for the classifiers to achieve the specific in situ and non-in situ motions, respectively. Extensive experiments have been conducted, achieving the highest accuracy rate of up to 94.4% and 95.3% for in situ motions and non-in situ motions, respectively. The interferences of individual diversity on the proposed method are also investigated. The proposed method could be beneficial in smart homes and senior care.

Anatomy of Chinese Futures Markets

In this study, the fundamental empirical characteristics of the Chinese futures markets, which includes all the liquid financial and commodity futures traded in mainland China, are analyzed at different time scales. The comprehensive results for the whole range of products provide valuable insight for the market practitioners, academics, and regulators. Stylized facts from the stock markets such as serial correlation, volatility clustering, non-normality, gain/loss asymmetry, risk characteristics and structural dependences are characterized. Futures returns in the Chinese futures markets show certain similarities and also differences from the stock markets in terms of the stylized facts.

Volume effects in the London housing market

PurposeThis paper aims to extend existing research in relation to both the importance of volume effects within housing markets and the specific behaviour of the London housing market. A detailed borough-level examination is undertaken of the relationships between volume, house prices and house price volatility. Support for alternative housing market theories, the degree of heterogeneity in house price behaviour across boroughs and the extent to which housing displays differing properties to other financial assets are examined.Design/methodology/approachCorrelation analyses, causality testing and volatility modelling are undertaken in extended forms which synthesise and extend approaches within the housing, economics and finance literatures. The various modelling and testing techniques are supplemented via the use of alternative variable transformations to evaluate housing market behaviour in detail.FindingsNovel findings are provided concerning both volume effects within housing markets generally and the specific properties of London housing market. Evidence concerning bubbles, the volatility-reducing effects of volume, the importance of geographical and price-related factors underlying the relationship between volume and both house price growth and volatility and the presence of asymmetric adjustment in the London housing market are all provided. The extent and nature of the support available for alternative housing market theories are evaluated.Originality/valueThe volatility-reducing effects of volume within housing markets, along with volume effects and the presence of asymmetric adjustment within the London housing market are examined for the first time. New empirical evidence on the support for alternative housing market theories and the differing empirical characteristics of housing relative to other financial assets are presented.

Fracturing dams, fractured data: Empirical trends and characteristics of existing and removed dams in the United States

AbstractDam removals in the United States continue to accelerate in pace and scope, but no national analyses have examined how removed dams compare with existing dam stock. Here, we review and analyse the best available national data on dams from the National Inventory of Dams (NID), dam removals from American Rivers, the U.S. Geological Survey, and the National River Restoration Science Synthesis databases to compare trends and characteristics of removed versus existing dams in the United States. If historical trends continue, by 2050 the United States can expect between 4,000 and 36,000 total removals, including 2,000–10,000 removals of NID dams. Best‐fit regression models estimate total costs between $50.5 million and $25.1 billion (mean $10.5 billion, median $416.5 million) for all removals and $29.6 million to $18.9 billion (mean $7.2 billion, median $285 million) for NID removals, a significant cost savings over present stated dam rehabilitation needs. Structural characteristics and ages of documented removals are not representative of existing dams, with privately owned hydroelectric dams subject to public oversight and water supply dams the most disproportionately removed. We conclude that dam removal science would benefit from the creation of an interdisciplinary framework for studying dams as environmental, social, and technological interventions, facilitated by transparent datasets around dams and removals and reflexive research approaches that combine statistical approaches with place‐based analyses.

Exchange rates and monetary spillovers

When do flexible exchange rates prevent monetary and financial conditions from spilling over across currencies? We examine a model in which international investors strategically supply capital to a small inflation-targeting economy with flexible exchange rates. For some combination of parameters, the unique equilibrium exhibits the observed empirical feature of prolonged episodes of capital inflows and appreciation of the domestic currency, followed by reversals where capital outflows go hand-in-hand with currency depreciation, a rise in domestic interest rates, and inflationary pressure. Arbitrarily small shocks to global financial conditions suffice to trigger these dynamics.

Formation of the n = 0 Landau level in hybrid graphene

The minimum of 4-terminal conductance occurring as carrier density is tuned through its charge neutral point has proven to be a robust empirical feature of graphene, persisting with changes to temperature, applied magnetic field, substrate, and layer thickness, though the theoretical mechanisms involved in transport about this point—vanishing density of states, conventional band gap opening, and broken-symmetry quantum Hall mobility gaps—vary widely depending on the regime. In this paper, we report on observations of a regime where the 4-terminal conductance minimum ceases to exist: transport in monolayer graphene connected to bilayer graphene during the onset of the quantum Hall effect. This observed increase in conductance is accompanied by decreases in conductance at the half-filling of the Landau levels adjacent to the charge-symmetric, zero-energy level. As monolayer and bilayer graphene have distinct zero-energy levels that form about the charge neutral point, our observations suggest that competitions between the differing many-body orderings of these states as they emerge may underlie these anomalous conductances.

INTEGRATED MODEL OF STATIONS WITH ADEQUATE 3D VISUALIZATION OF OBJECTS

For the English abstract and full text of the article please see the attached PDF-File (English version follows Russian version). ABSTRACT The author suggests a 3D model of a railway station, which is the closest to reality in terms of external perception and the essence of the processes. It is shown that an attempt to simplify model operations, not to take into account the technological links of individual processes, basing only on physical analogies, leads to development of non-functional reconstructions. This is a claimed system of model images that only can reproduce the dynamics of movement of cars along the station tracks, as well as can visualize the successive states of objects in their interaction. Keywords: 3D model, railway station, system of model images, physical processes, synergetic properties, empirical characteristics.

Rethinking the Study of “Religion” and Media from an Existential Perspective

The broadening of the concept of religion from a substantive, anthropological definition to a more cultural, functional definition has enabled expansion of the study of media, technology and religion into a much wider field of social phenomena. It has Ben argued that this expansion has been so broad and unbounded that the more appropriate question in this field of study is no longer “What is religion?”, but “What isn’t religion?” This paper contends that the time is ripe to set aside a dualistic lens of religion and secular and look instead at embodie human reality as incorporating not only material, empirical and instrumental characteristics but also tanscendental, metaphysical and non-empirical characteristics that also need to be theorised in secular terms.

Identification of spin maneuver aerodynamic nonlinear model by applying ensemble empirical mode decomposition and extended multipoint modeling

Identification of the “aircraft aerodynamic model” in some unusual flight conditions such as spin maneuver provides critical information to the flight controller to retake the “dynamic stability” after it has been disturbed by the systematic, natural or environmental oscillations. Hence, a method for identifying the appropriate aerodynamic model in spin maneuvers is presented in this paper. We present an innovative systematic method for aerodynamic modeling of spin maneuvers, which combines the ensemble empirical mode decomposition technique and extended multipoint modeling approach, using flight data. In ensemble empirical mode decomposition, in addition to having all the empirical mode decomposition features, the original signal is collected with the white noise, and by using its statistical characteristics, the ensemble empirical mode decomposition solves the mode mixing problem. By applying the ensemble empirical mode decomposition to the flight parameter data, their intrinsic mode frequencies are extracted and are used as inputs to the extended multipoint modeling model. The extended multipoint modeling structure includes some parameters describing the distribution of aerodynamic forces and moments along each of the aircraft components. Moreover, this method allows coupling between the forces and moments. Unlike conventional methods, which consider the average forces obtained by plane surfaces relative to the center of mass, in the extended multipoint modeling technique, the force generated by each plane of the aircraft is allowed to appear independently in the motion equations. For identifying the aerodynamic model with extended multipoint modeling structure, the equation error method is used with a maximum likelihood optimizer inside. The obtained algorithm has been applied to two sets of spin maneuver flight data which were recorded in actual spin flight. The results demonstrate that the proposed method is able to reproduce the aerodynamic forces and moments for the second spin flight inputs with high accuracy by using a model which is derived from the first spin data identification.

Analyzing competing demands in organizations: a systematic comparison

Organizational scholars have shown increasing interest in the ways in which managers enact and respond to competing demands and the tensions they prompt as constitutive elements of their organizations. There is now a proliferation of conceptualizations of such competing demands that can be somewhat confusing. We will enhance conceptual clarity by identifying seven constitutive empirical characteristics of competing demands: these consist of the existence of dyadic relations, contradiction, interrelatedness, complementarity, compatibility, simultaneity, and the existence of push-pull forces. We construct a comparative classification of competing demands using these characteristics as our distinguishing features. The result is a more nuanced understanding of how managers approach competing demands that can help scholars to minimize arbitrariness, interpret results, and compare contributions in the area in a much-needed step toward understanding and designing organizations.

죽음의 공동체로서의 국가 : 총력전기 국민문학을 통해본 일본 제국

죽음의 공동체로서의 국가 : 총력전기 국민문학을 통해본 일본 제국

'Outlier Blindness': Efficient Coding Generates an Inability to Represent Extreme Values

How do people record information about the economic outcomes they observe in their environment? Building on a well-established neuroscientific framework, we propose a model in which people are not attuned to making distinctions between realized outcomes that they seldom expect to encounter. We provide evidence for such `outlier blindness' in a series of controlled laboratory experiments and discuss important implications of our model for economic decision-making. In particular, we show how outlier blindness provides a microfoundation for well-established empirical features of risk perception such as tail risk neglect and context-dependent risk attitude.

Mini-Flash Crashes, Model Risk, and Optimal Execution

Oft-cited causes of mini-flash crashes include human errors, endogenous feedback loops and the nature of modern liquidity provision. We develop a mathematical model which captures aspects of these explanations. Empirical features of recent mini-flash crashes are present in our framework. For example, there are periods when no such events will occur. If they do, even just before their onset, market participants may not know with certainty that a disruption will unfold. Our mini-flash crashes can materialize in both low and high trading volume environments and may be accompanied by a partial synchronization in order submission. Instead of adopting a classically-inspired equilibrium approach, we borrow ideas from the optimal execution literature. Each of our agents begins with beliefs about how his own trades impact prices and how prices would move in his absence. They, along with other market participants, then submit orders which are executed at a common venue. Naturally, this leads us to explicitly distinguish between how prices actually evolve and our agents’ opinions. In particular, every agent’s beliefs will be expressly incorrect.

Ultra-Sparse Classifiers Through Minimizing the VC Dimension in the Empirical Feature Space

Sparse representations have gained much interest due to the rapid growth of intelligent embedded systems, the need to reduce the time to mine large datasets, and for reducing the footprint of recognition based applications on portable devices. Computational learning theory tells us that the Vapnik–Chervonenkis (VC) dimension of a learning model directly impacts the structural risk and the generalization ability of a learning model. The minimal complexity machine (MCM) was recently proposed as a way to learn a hyperplane classifier by minimizing a tight bound on the VC dimension; results show that it learns very sparse representations that yield test set accuracies that are comparable to the state-of-the-art. The MCM formulation works in the primal itself, both when the classifier is learnt in the input space and when it is learnt implicitly in a higher dimensional feature space. In the latter case, the hyperplane is constructed in the empirical feature space (EFS). In this paper, we examine the hyperplane restricted to the EFS. The EFS is a finite dimensional vector space spanned by the image vectors in a higher dimensional feature space. Since the VC dimension of a linear hyperplane classifier is exactly the number of features used by the classifier, the dimension of the EFS is a direct measure of both the sparsity of the model and the VC dimension. This allows us to formulate optimization problems that focus on learning sparse representations, and yet generalize well. We derive an EFS version of the MCM, that allows us to minimize the model complexity and improve sparsity. We also propose a novel least squares version of the MCM in the EFS. Experimental results demonstrate that the EFS variants yield sparse models with generalization comparable to the state-of-the-art.

Reporting tax evasion

I study the anonymous tax evasion reports collected by the website evasori.info for Italy. I find that tax morale, measured by the number of reports per unit of irregular activity, is negatively associated with the median reported amount. If the utility of reporting evasion is increasing in the amount of the transaction, then, for a fixed cost of reporting, there exists a threshold value above which tax evasion is reported. Stronger tax morale implies a bigger utility for the individual that reports the transaction and, therefore, a lower threshold value above which tax evasion is reported. The empirical analysis features a regression of the median reported evaded amount per economic activity on tax morale, controlling for province fixed effects, economic activity fixed effects and time dummies. I also propose a simulation exercise to estimate the threshold below which tax evasion is reported and its dependence on tax morale. The simulation also shows that the empirical results are not the byproduct of oversampling of small transactions. I conclude that tax morale makes it more difficult to overlook petty evasion.

Risk-Based Investing in the German Stock Market

The article shows properties of risk-based portfolios in the German stock market. Those systematic strategies use different approaches to weight stocks in portfolios. We present theoretical and empirical characteristics of five risk-based equity investments: the equal-weighted, minimum variance, maximum diversification and risk parity (equal risk budgeting and equal risk contribution) portfolios. Risk-based portfolios outperformed the market-cap weighted CDAX index with a lower level of risk in the period 2002–2015. Their excess returns relative to the CDAX index can be explained with Scherer’s five-factor model; with Fama-French and low-risk anomaly factors. R2s of different strategies range from 77% to 92%.

A New Prediction Model Based on Cascade NN for Wind Power Prediction

This paper presents a new prediction model based on empirical mode decomposition, feature selection and hybrid forecast engine. The whole structure of proposed model is based on nonstationarity and non-convex nature of wind power signal. The hybrid forecast engine consists of three main stages as; empirical mode decomposition, an intelligent algorithm and three stage neural network. All parameters of proposed neural network will be optimized by intelligent algorithm. Effectiveness of the proposed model is tested with real-world hourly data of wind farms in Canada, Spain and Texas. In order to demonstrate the validity of the proposed model, it is compared with several other wind speed and power forecast techniques where, different prediction horizons are considered from day-ahead to one week forecasting. Obtained results confirm the validity of the developed approach in prediction model for different forecast horizons.

Automated diagnosis of focal liver lesions using bidirectional empirical mode decomposition features

Liver is the heaviest internal organ of the human body and performs many vital functions. Prolonged cirrhosis and fatty liver disease may lead to the formation of benign or malignant lesions in this organ, and an early and reliable evaluation of these conditions can improve treatment outcomes. Ultrasound imaging is a safe, non-invasive, and cost-effective way of diagnosing liver lesions. However, this technique has limited performance in determining the nature of the lesions. This study initiates a computer-aided diagnosis (CAD) system to aid radiologists in an objective and more reliable interpretation of ultrasound images of liver lesions. In this work, we have employed radon transform and bi-directional empirical mode decomposition (BEMD) to extract features from the focal liver lesions. After which, the extracted features were subjected to particle swarm optimization (PSO) technique for the selection of a set of optimized features for classification. Our automated CAD system can differentiate normal, malignant, and benign liver lesions using machine learning algorithms. It was trained using 78 normal, 26 benign and 36 malignant focal lesions of the liver. The accuracy, sensitivity, and specificity of lesion classification were 92.95%, 90.80%, and 97.44%, respectively. The proposed CAD system is fully automatic as no segmentation of region-of-interest (ROI) is required.

Satellite tagging highlights the importance of productive Mozambican coastal waters to the ecology and conservation of whale sharks.

The whale shark Rhincodon typus is an endangered, highly migratory species with a wide, albeit patchy, distribution through tropical oceans. Ten aerial survey flights along the southern Mozambican coast, conducted between 2004–2008, documented a relatively high density of whale sharks along a 200 km stretch of the Inhambane Province, with a pronounced hotspot adjacent to Praia do Tofo. To examine the residency and movement of whale sharks in coastal areas around Praia do Tofo, where they may be more susceptible to gill net entanglement, we tagged 15 juveniles with SPOT5 satellite tags and tracked them for 2–88 days (mean = 27 days) as they dispersed from this area. Sharks travelled between 10 and 2,737 km (mean = 738 km) at a mean horizontal speed of 28 ± 17.1 SD km day−1. While several individuals left shelf waters and travelled across international boundaries, most sharks stayed in Mozambican coastal waters over the tracking period. We tested for whale shark habitat preferences, using sea surface temperature, chlorophyll-a concentration and water depth as variables, by computing 100 random model tracks for each real shark based on their empirical movement characteristics. Whale sharks spent significantly more time in cooler, shallower water with higher chlorophyll-a concentrations than model sharks, suggesting that feeding in productive coastal waters is an important driver of their movements. To investigate what this coastal habitat choice means for their conservation in Mozambique, we mapped gill nets during two dedicated aerial surveys along the Inhambane coast and counted gill nets in 1,323 boat-based surveys near Praia do Tofo. Our results show that, while whale sharks are capable of long-distance oceanic movements, they can spend a disproportionate amount of time in specific areas, such as along the southern Mozambique coast. The increasing use of drifting gill nets in this coastal hotspot for whale sharks is likely to be a threat to regional populations of this iconic species.