scholarly journals Forecasting the Volatility of the Cryptocurrency Market by GARCH and Stochastic Volatility

Mathematics ◽  
2021 ◽  
Vol 9 (14) ◽  
pp. 1614
Author(s):  
Jong-Min Kim ◽  
Chulhee Jun ◽  
Junyoup Lee
Keyword(s):  
Stochastic Volatility ◽  
Financial Data ◽  
Garch Models ◽  
Market Capitalization ◽  
Time Horizons ◽  
Forecast Models ◽  
Forecast Time ◽  
Forecasting Errors ◽  
Insight Into ◽  
Better Than

This study examines the volatility of nine leading cryptocurrencies by market capitalization—Bitcoin, XRP, Ethereum, Bitcoin Cash, Stellar, Litecoin, TRON, Cardano, and IOTA-by using a Bayesian Stochastic Volatility (SV) model and several GARCH models. We find that when we deal with extremely volatile financial data, such as cryptocurrencies, the SV model performs better than the GARCH family models. Moreover, the forecasting errors of the SV model, compared with the GARCH models, tend to be more accurate as forecast time horizons are longer. This deepens our insight into volatility forecast models in the complex market of cryptocurrencies.

scholarly journals The Effectiveness of Multi-Label Classification and Multi-Output Regression in Social Trait Recognition

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4127
Author(s):  
Will Farlessyost ◽  
Kelsey-Ryan Grant ◽  
Sara R. Davis ◽  
David Feil-Seifer ◽  
Emily M. Hand
Keyword(s):  
Social Interactions ◽  
First Impressions ◽  
Social Traits ◽  
Judicial Proceedings ◽  
Facial Attributes ◽  
Future Work ◽  
Automated Methods ◽  
Insight Into ◽  
Social Trait ◽  
Better Than

First impressions make up an integral part of our interactions with other humans by providing an instantaneous judgment of the trustworthiness, dominance and attractiveness of an individual prior to engaging in any other form of interaction. Unfortunately, this can lead to unintentional bias in situations that have serious consequences, whether it be in judicial proceedings, career advancement, or politics. The ability to automatically recognize social traits presents a number of highly useful applications: from minimizing bias in social interactions to providing insight into how our own facial attributes are interpreted by others. However, while first impressions are well-studied in the field of psychology, automated methods for predicting social traits are largely non-existent. In this work, we demonstrate the feasibility of two automated approaches—multi-label classification (MLC) and multi-output regression (MOR)—for first impression recognition from faces. We demonstrate that both approaches are able to predict social traits with better than chance accuracy, but there is still significant room for improvement. We evaluate ethical concerns and detail application areas for future work in this direction.

scholarly journals Acoustic-Prosodic and Lexical Cues to Deception and Trust: Deciphering How People Detect Lies

2020 ◽  
Vol 8 ◽  
pp. 199-214
Author(s):  
Xi (Leslie) Chen ◽  
Sarah Ita Levitan ◽  
Michelle Levine ◽  
Marko Mandic ◽  
Julia Hirschberg
Keyword(s):  
Deception Detection ◽  
Human Perception ◽  
Lie Detection ◽  
Challenging Problem ◽  
Cues To Deception ◽  
Lexical Cues ◽  
Insight Into ◽  
Large Corpus ◽  
Better Than

Humans rarely perform better than chance at lie detection. To better understand human perception of deception, we created a game framework, LieCatcher, to collect ratings of perceived deception using a large corpus of deceptive and truthful interviews. We analyzed the acoustic-prosodic and linguistic characteristics of language trusted and mistrusted by raters and compared these to characteristics of actual truthful and deceptive language to understand how perception aligns with reality. With this data we built classifiers to automatically distinguish trusted from mistrusted speech, achieving an F1 of 66.1%. We next evaluated whether the strategies raters said they used to discriminate between truthful and deceptive responses were in fact useful. Our results show that, although several prosodic and lexical features were consistently perceived as trustworthy, they were not reliable cues. Also, the strategies that judges reported using in deception detection were not helpful for the task. Our work sheds light on the nature of trusted language and provides insight into the challenging problem of human deception detection.

Modeling CO2 Emissions of Top 5 Emitters with Combination Technique

2012 ◽  
Vol 616-618 ◽  
pp. 1143-1147
Author(s):  
Wei Sun ◽  
Jing Min Wang ◽  
Jun Jie Kang
Keyword(s):  
Forecast Error ◽  
Combination Method ◽  
Future Projection ◽  
Forecasting Accuracy ◽  
Combination Technique ◽  
Combination Forecast ◽  
Forecast Models ◽  
Model Time Series ◽  
Grey Verhulst Model ◽  
Better Than

In this paper, the performance of combination forecast methods for CO2 emissions prediction is investigated. Linear model, time series model, GM (1, 1) model and Grey Verhulst model are selected in study as the separate models. And, four kinds of combination forecast models, i.e. the equivalent weight (EW) combination method, variance-covariance (VACO) combination method, regression combination (R) method, and discounted mean square forecast error (MSFE) method are chosen to employ for top 5 CO2 emitters. The forecasting accuracy is compared between these combination models and single models. This research suggests that the combination forecasts are almost certain to outperform the worst individual forecasts and maybe even better than most individual ones. Furthermore the combination forecasts can avoid the risk of model choosing in future projection. For CO2 emissions forecast with many uncertain factors in the future, combining the single forecast would be safer in such forecasting situations.

The Military Physician in Captivity

1969 ◽  
Vol 9 (99) ◽  
pp. 295-303
Author(s):  
E. Reginato
Keyword(s):  
Medical Officer ◽  
Red Cross ◽  
Prisoner Of War ◽  
Geneva Conventions ◽  
Peace And Justice ◽  
In Captivity ◽  
Military Physician ◽  
The Military ◽  
Insight Into ◽  
Better Than

In his introductory address at the third International Refresher Course for Junior Medical Officers, Dr. H. Meuli, member of the ICRC, said “No one knows war better than the military medical officer, nor measures its horror, nor hates it more. No one has greater insight into war to enable him to take a stand for peace and against war”. From its very beginnings the Red Cross has been linked to medicine; it was the ICRC which obtained for doctors the means of exercising their profession in war, which are laid down in the Geneva Conventions.It therefore seems appropriate to quote extensively from a communication submitted at the Course by an Italian doctor, bearing moving testimony to the difficulties facing the medical officer, the noble character of his mission and the principles underlying his activity in the prisoner of war camp. These principles were summed up in his conclusion : “Like peace and justice, medicine loses its significance if not accompanied by charity. If it is to stay universal, it must not lose its humanity”. (Ed.).

scholarly journals Impact of Unusual Features in Credit Scoring Problem

2020 ◽  
Author(s):  
Luiz Felipe Vercosa ◽  
Rodrigo Lira ◽  
Rodrigo Monteiro ◽  
Kleber Silva ◽  
Jailson Magalhaes ◽  
...  
Keyword(s):  
Multilayer Perceptron ◽  
Demographic Data ◽  
Credit Scoring ◽  
Financial Data ◽  
Unusual Feature ◽  
The Real ◽  
Kolmogorov Smirnov ◽  
Small Improvement ◽  
Better Than

Standard features used for Credit Scoring includes mainly registration and financial data from customers. However, exploring new features is of great interest for financial companies, since slight improvements in the person score directly impact the company revenue. In this work, we categorize features from open credit scoring datasets and compare them with the features found in a real company dataset. The company dataset contains unusual feature groups such as historical, geolocation, web behavior, and demographic data. We performed bivariate tests using the Kolmogorov-Smirnov metric and features to assess the performance of the particular feature groups. We also generated a score of good payer by using AdaBoost, Multilayer Perceptron, and XGBoost algorithms. Then, we analyzed the results with different metrics and compared them with the real company results. Our main finding was that these features added a small improvement to current datasets. We also identified the most promising feature groups and noticed that the tuned XGBoost performed better than the company solution in three out of four deployed metrics.

scholarly journals Design and Demonstration of Large Scale Cu2O Photocathodes with Metal Grid Structure for Photoelectrochemical Water Splitting

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7422
Author(s):  
Min-Kyu Son
Keyword(s):  
Water Splitting ◽  
Large Scale ◽  
Grid Structure ◽  
Large Area ◽  
Intrinsic Conductivity ◽  
Metal Grid ◽  
Splitting System ◽  
Insight Into ◽  
Better Than

Upscaling of photoelectrode for a practical photoelectrochemical (PEC) water splitting system is still challenging because the PEC performance of large-scale photoelectrode is significantly low, compared to the lab scale photoelectrode. In an effort to overcome this challenge, sputtered gold (Au) and copper (Cu) grid lines were introduced to improve the PEC performance of large-scale cuprous oxide (Cu2O) photocathode in this work. It was demonstrated that Cu grid lines are more effective than Au grid lines to improve the PEC performance of large-scale Cu2O photocathode because its intrinsic conductivity and quality of grid lines are better than ones containing Au grid lines. As a result, the PEC performance of a 25-cm2 scaled Cu2O photocathode with Cu grid lines was almost double than one without grid lines, resulting in an improved charge transport in the large area substrate by Cu grid lines. Finally, a 50-cm2 scaled Cu2O photocathode with Cu grid lines was tested in an outdoor condition under natural sun. This is the first outdoor PEC demonstration of large-scale Cu2O photocathode with Cu grid lines, which gives insight into the development of efficient upscaled PEC photoelectrode.

scholarly journals Asymmetries in mutual understanding: People with low status, power, and self-esteem understand better than they are understood

2020 ◽  
Author(s):  
Sanaz Talaifar ◽  
Michael Buhrmester ◽  
Ozlem Ayduk ◽  
william swann
Keyword(s):  
Low Power ◽  
Conceptual Analysis ◽  
Self Esteem ◽  
Mutual Understanding ◽  
Insight Into ◽  
Better Than

NOW PUBLISHED: https://doi.org/10.1177/1745691620958003. All too often, people who develop exceptionally astute insights into others remain mysterious to these others. Evidence for such asymmetric understanding comes from several independent domains. Striking asymmetries occur among those who differ in status and power, such that low status, low power individuals understand more than they are understood. We show that this effect extends to people who merely perceive that they have low status: individuals with low self-esteem. Whereas people with low self-esteem display insight into people with high self-esteem, their high self-esteem counterparts fail to reciprocate. Conceptual analysis suggests that asymmetries in mutual understanding may be reduced by addressing deficits in information and motivation among perceivers. Nevertheless, evidence from several interventions were unsuccessful, indicating that the path to symmetric understanding is a steep and thorny one. Further research is needed to develop strategies for fostering understanding of those who are most misunderstood: people with low self-esteem, low status, and low power.

scholarly journals Informative Frailty Indices from Binarized Biomarkers

2020 ◽  
Author(s):  
Garrett Stubbings ◽  
Spencer Farrell ◽  
Arnold Mitnitski ◽  
Kenneth Rockwood ◽  
Andrew Rutenberg
Keyword(s):  
Medical Knowledge ◽  
Data Sets ◽  
Canadian Study ◽  
Adverse Health Outcomes ◽  
Health And Nutrition ◽  
Health Measure ◽  
Upper Limits ◽  
Insight Into ◽  
Better Than ◽  
Biomarker Data

AbstractFrailty indices (FI) based on continuous valued health data, such as obtained from blood and urine tests, have been shown to be predictive of adverse health outcomes. However, creating FI from such biomarker data requires a binarization treatment that is difficult to standardize across studies. In this work, we explore a “quantile” methodology for the generic treatment of biomarker data that allows us to construct an FI without preexisting medical knowledge (i.e. risk thresholds) of the included biomarkers. We show that our quantile approach performs as well as, or even slightly better than, established methods for the National Health and Nutrition Examination Survey (NHANES) and the Canadian Study of Health and Aging (CSHA) data sets. Furthermore, we show that our approach is robust to cohort effects within studies as compared to other data-based methods. The success of our binarization approaches provides insight into the robustness of the FI as a health measure, the upper limits of the FI observed in various data sets, and highlights general difficulties in obtaining absolute scales for comparing FI between studies.

The Met Office operational global ocean forecast system FOAM-ORCA12

2021 ◽  
Author(s):  
Ana Barbosa Aguiar ◽  
Jennifer Waters ◽  
Martin Price ◽  
Gordon Inverarity ◽  
Christine Pequignet ◽  
...  
Keyword(s):  
Computational Cost ◽  
Absolute Error ◽  
Global Ocean ◽  
Background Error ◽  
Verification Methods ◽  
Climate Simulations ◽  
Forecast Models ◽  
The Mean ◽  
Lower Continuous ◽  
Better Than

<div> <p>The importance of oceans for atmospheric forecasts as well as climate simulations is being increasingly recognised with the advent of coupled ocean / atmosphere forecast models. Having comparable resolutions in both domains maximises the benefits for a given computational cost. The Met Office has recently upgraded its operational global ocean-only model from an eddy permitting 1/4 degree tripolar grid (ORCA025) to the eddy resolving 1/12 degree ORCA12 configuration while retaining 1/4 degree data assimilation. </p> </div><div> <p>We will present a description of the ocean-only ORCA12 system, FOAM-ORCA12, alongside some initial results. Qualitatively, FOAM-ORCA12 seems to represent better (than FOAM-ORCA025) the details of mesoscale features in SST and surface currents. Overall, traditional statistical results suggest that the new FOAM-ORCA12 system performs similarly or slightly worse than the pre-existing FOAM-ORCA025. However, it is known that comparisons of models running at different resolutions suffer from a double penalty effect, whereby higher-resolution models are penalised more than lower-resolution models for features that are offset in time and space. Neighbourhood verification methods seek to make a fairer comparison using a common spatial scale for both models and it can be seen that, as neighbourhood sizes increase, ORCA12 consistently has lower continuous ranked probability scores (CRPS) than ORCA025. CRPS measures the accuracy of the pseudo-ensemble created by the neighbourhood method and generalises the mean absolute error measure for deterministic forecasts. </p> </div><div> <p>The focus over the next year will be on diagnosing the performance of both the model and assimilation. A planned development that is expected to enhance the system is the update of the background-error covariances used for data assimilation. </p> </div>

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