scholarly journals Model-independent pricing with insider information: a skorokhod embedding approach

2021 ◽  
Vol 53 (1) ◽  
pp. 30-56
Author(s):  
Beatrice Acciaio ◽  
Alexander M. G. Cox ◽  
Martin Huesmann
Keyword(s):  
Asset Prices ◽  
Insider Information ◽  
Additional Information ◽  
Model Independent ◽  
Pricing Bounds ◽  
Modelling Assumptions ◽  
Types Of Information ◽  
Time Invariant ◽  
The Impact ◽  
Monotonicity Principle

AbstractIn this paper we consider the pricing and hedging of financial derivatives in a model-independent setting, for a trader with additional information, or beliefs, on the evolution of asset prices. In particular, we suppose that the trader wants to act in a way which is independent of any modelling assumptions, but that she observes market information in the form of the prices of vanilla call options on the asset. We also assume that both the payoff of the derivative, and the insider’s information or beliefs, which take the form of a set of impossible paths, are time-invariant. In this way we accommodate drawdown constraints, as well as information/beliefs on quadratic variation or on the levels hit by asset prices. Our setup allows us to adapt recent work of [12] to prove duality results and a monotonicity principle. This enables us to determine geometric properties of the optimal models. Moreover, for specific types of information, we provide simple conditions for the existence of consistent models for the informed agent. Finally, we provide an example where our framework allows us to compute the impact of the information on the agent’s pricing bounds.

How much is a calorie worth? A study of willingness to pay for calorie labels in restaurant menus

2019 ◽  
pp. 1-24
Author(s):  
SANA RAFIQ
Keyword(s):  
Willingness To Pay ◽  
Well Being ◽  
Economic Benefits ◽  
Individual Level ◽  
Additional Information ◽  
Health Related ◽  
The Individual ◽  
Types Of Information ◽  
Caloric Consumption ◽  
The Impact

AbstractWe asked individuals about their willingness to pay (WTP) either: (1) for a mandate requiring restaurants to post calorie information on their menus; or (2) to avoid such a mandate. On average, more people were in in favor of the mandate and were willing to pay four times more than those who were against it, thereby leading to a Kaldor–Hicks improvement from this policy. To ensure robustness, we tested the impact of providing three types of information during individuals’ WTP determinations: (1) visual examples of the proposed calorie labels; (2) data on their effectiveness at the individual level; and (3) data on their wider social and economic benefits. For those in favor, providing a simple visual of the label had no impact on WTP. Data on the individual effectiveness of the labels increased the WTP, while evidence on broader obesity reduction and economic benefits reduced it. For opponents, WTP did not change with provision of additional information except when provided with information on social and economic benefits. Under this condition, the opponents increased their WTP 12-fold to avoid a mandate of this policy. Finally, we measured individual well-being under this policy and found directionally similar results, confirming a net improvement in aggregate welfare. Our results suggest that messaging that focuses on private benefits (providing calorie information so that individuals can effectively choose to reduce excessive caloric consumption) rather than wider public benefits (reduction in overall health-related costs and obesity) is more likely to be effective.

News and Idiosyncratic Volatility: The Public Information Processing Hypothesis*

2020 ◽  
Author(s):  
Robert F Engle ◽  
Martin Klint Hansen ◽  
Ahmet K Karagozoglu ◽  
Asger Lunde
Keyword(s):  
Information Processing ◽  
Public Information ◽  
Selection Procedure ◽  
Autoregressive Process ◽  
Return Volatility ◽  
Market Outcomes ◽  
The Public ◽  
Additional Information ◽  
Financial News ◽  
The Impact

Abstract Motivated by the recent availability of extensive electronic news databases and advent of new empirical methods, there has been renewed interest in investigating the impact of financial news on market outcomes for individual stocks. We develop the information processing hypothesis of return volatility to investigate the relation between firm-specific news and volatility. We propose a novel dynamic econometric specification and test it using time series regressions employing a machine learning model selection procedure. Our empirical results are based on a comprehensive dataset comprised of more than 3 million news items for a sample of 28 large U.S. companies. Our proposed econometric specification for firm-specific return volatility is a simple mixture model with two components: public information and private processing of public information. The public information processing component is defined by the contemporaneous relation with public information and volatility, while the private processing of public information component is specified as a general autoregressive process corresponding to the sequential price discovery mechanism of investors as additional information, previously not publicly available, is generated and incorporated into prices. Our results show that changes in return volatility are related to public information arrival and that including indicators of public information arrival explains on average 26% (9–65%) of changes in firm-specific return volatility.

scholarly journals Grading Investment Diversification Options in Presence of Non-Historical Financial Information

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 692
Author(s):  
Clara Calvo ◽  
Carlos Ivorra ◽  
Vicente Liern ◽  
Blanca Pérez-Gladish
Keyword(s):  
Risk Measure ◽  
Statistical Information ◽  
Expected Returns ◽  
Financial Assets ◽  
Expected Return ◽  
Additional Information ◽  
Subjective Risk ◽  
The Impact ◽  
Statistical Risk ◽  
Reduction Index

Modern portfolio theory deals with the problem of selecting a portfolio of financial assets such that the expected return is maximized for a given level of risk. The forecast of the expected individual assets’ returns and risk is usually based on their historical returns. In this work, we consider a situation in which the investor has non-historical additional information that is used for the forecast of the expected returns. This implies that there is no obvious statistical risk measure any more, and it poses the problem of selecting an adequate set of diversification constraints to mitigate the risk of the selected portfolio without losing the value of the non-statistical information owned by the investor. To address this problem, we introduce an indicator, the historical reduction index, measuring the expected reduction of the expected return due to a given set of diversification constraints. We show that it can be used to grade the impact of each possible set of diversification constraints. Hence, the investor can choose from this gradation, the set better fitting his subjective risk-aversion level.

scholarly journals Tools for the Recognition of Sorting Signals and the Prediction of Subcellular Localization of Proteins From Their Amino Acid Sequences

2020 ◽  
Vol 11 ◽  
Author(s):  
Kenichiro Imai ◽  
Kenta Nakai
Keyword(s):  
Amino Acid ◽  
Subcellular Localization ◽  
Amino Acid Sequences ◽  
Additional Information ◽  
Sorting Signals ◽  
Specific Alternative ◽  
Cell Type Specific ◽  
Future Direction ◽  
The Impact ◽  
New Algorithms

At the time of translation, nascent proteins are thought to be sorted into their final subcellular localization sites, based on the part of their amino acid sequences (i.e., sorting or targeting signals). Thus, it is interesting to computationally recognize these signals from the amino acid sequences of any given proteins and to predict their final subcellular localization with such information, supplemented with additional information (e.g., k-mer frequency). This field has a long history and many prediction tools have been released. Even in this era of proteomic atlas at the single-cell level, researchers continue to develop new algorithms, aiming at accessing the impact of disease-causing mutations/cell type-specific alternative splicing, for example. In this article, we overview the entire field and discuss its future direction.

Public perception of COVID-19 vaccines from the digital footprints left on Twitter: analyzing positive, neutral and negative sentiments of Twitterati

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Aasif Ahmad Mir ◽  
Sevukan Rathinam ◽  
Sumeer Gul
Keyword(s):  
Social Media ◽  
Public Perception ◽  
English Language ◽  
Social Issues ◽  
Application Programming Interface ◽  
Content Type ◽  
The Public ◽  
Public Reaction ◽  
Types Of Information ◽  
The Impact

PurposeTwitter is gaining popularity as a microblogging and social networking service to discuss various social issues. Coronavirus disease 2019 (COVID-19) has become a global pandemic and is discussed worldwide. Social media is an instant platform to deliberate various dimensions of COVID-19. The purpose of the study is to explore and analyze the public sentiments related to COVID-19 vaccines across the Twitter messages (positive, neutral, and negative) and the impact tweets make across digital social circles.Design/methodology/approachTo fetch the vaccine-related posts, a manual examination of randomly selected 500 tweets was carried out to identify the popular hashtags relevant to the vaccine conversation. It was found that the hashtags “covid19vaccine” and “coronavirusvaccine” were the two popular hashtags used to discuss the communications related to COVID-19 vaccines. 23,575 global tweets available in public domain were retrieved through “Twitter Application Programming Interface” (API), using “Orange Software”, an open-source machine learning, data visualization and data mining toolkit. The study was confined to the tweets posted in English language only. The default data cleaning and preprocessing techniques available in the “Orange Software” were applied to the dataset, which include “transformation”, “tokenization” and “filtering”. The “Valence Aware Dictionary for sEntiment Reasoning” (VADER) tool was used for classification of tweets to determine the tweet sentiments (positive, neutral and negative) as well as the degree of sentiments (compound score also known as sentiment score). To assess the influence/impact of tweets account wise (verified and unverified) and sentiment wise (positive, neutral, and negative), the retweets and likes, which offer a sort of reward or acknowledgment of tweets, were used.FindingsA gradual decline in the number of tweets over the time is observed. Majority (11,205; 47.52%) of tweets express positive sentiments, followed by neutral (7,948; 33.71%) and negative sentiments (4,422; 18.75%), respectively. The study also signifies a substantial difference between the impact of tweets tweeted by verified and unverified users. The tweets related to verified users have a higher impact both in terms of retweets (65.91%) and likes (84.62%) compared to the tweets tweeted by unverified users. Tweets expressing positive sentiments have the highest impact both in terms of likes (mean = 10.48) and retweets (mean = 3.07) compared to those that express neutral or negative sentiments.Research limitations/implicationsThe main limitation of the study is that the sentiments of the people expressed over one single social platform, that is, Twitter have been studied which cannot generalize the global public perceptions. There can be a variation in the results when the datasets from other social media platforms will be studied.Practical implicationsThe study will help to know the people's sentiments and beliefs toward the COVID-19 vaccines. Sentiments that people hold about the COVID-19 vaccines are studied, which will help health policymakers understand the polarity (positive, negative, and neutral) of the tweets and thus see the public reaction and reflect the types of information people are exposed to about vaccines. The study can aid the health sectors to intensify positive messages and eliminate negative messages for an enhanced vaccination uptake. The research can also help design more operative vaccine-advocating communication by customizing messages using the obtained knowledge from the sentiments and opinions about the vaccines.Originality/valueThe paper focuses on an essential aspect of COVID-19 vaccines and how people express themselves (positively, neutrally and negatively) on Twitter.

scholarly journals The BioCyc collection of microbial genomes and metabolic pathways

2017 ◽  
Vol 20 (4) ◽  
pp. 1085-1093 ◽  
Author(s):  
Peter D Karp ◽  
Richard Billington ◽  
Ron Caspi ◽  
Carol A Fulcher ◽  
Mario Latendresse ◽  
...  
Keyword(s):  
Sequence Alignment ◽  
Metabolic Pathways ◽  
Biomedical Literature ◽  
Analysis Software ◽  
New Developments ◽  
Microbial Genomes ◽  
Additional Information ◽  
Alignment Tool ◽  
Extensive Range ◽  
Types Of Information

Abstract BioCyc.org is a microbial genome Web portal that combines thousands of genomes with additional information inferred by computer programs, imported from other databases and curated from the biomedical literature by biologist curators. BioCyc also provides an extensive range of query tools, visualization services and analysis software. Recent advances in BioCyc include an expansion in the content of BioCyc in terms of both the number of genomes and the types of information available for each genome; an expansion in the amount of curated content within BioCyc; and new developments in the BioCyc software tools including redesigned gene/protein pages and metabolite pages; new search tools; a new sequence-alignment tool; a new tool for visualizing groups of related metabolic pathways; and a facility called SmartTables, which enables biologists to perform analyses that previously would have required a programmer’s assistance.

Toward functional proteomics of alveolar macrophages

2005 ◽  
Vol 288 (4) ◽  
pp. L585-L595 ◽  
Author(s):  
Haifeng M. Wu ◽  
Ming Jin ◽  
Clay B. Marsh
Keyword(s):  
Alveolar Macrophages ◽  
Large Scale ◽  
Cell Types ◽  
Mononuclear Phagocyte ◽  
Detection Sensitivity ◽  
Current Status ◽  
Blood Monocytes ◽  
Protein Patterns ◽  
Additional Information ◽  
The Impact

Alveolar macrophages (AM) belong to a phenotype of macrophages with distinct biological functions and important pathophysiological roles in lung health and disease. The molecular details determining AM differentiation from blood monocytes and AM roles in lung homeostasis are largely unknown. With the use of different technological platforms, advances in the field of proteomics have made it possible to search for differences in protein expression between AM and their precursor monocytes. Proteome features of each cell type provide new clues into understanding mononuclear phagocyte biology. In-depth analyses using subproteomics and subcellular proteomics offer additional information by providing greater protein resolution and detection sensitivity. With the use of proteomic techniques, large-scale mapping of phosphorylation differences between the cell types have become possible. Furthermore, two-dimensional gel proteomics can detect germline protein variants and evaluate the impact of protein polymorphisms on an individual's susceptibility to disease. Finally, surface-enhanced laser desorption and ionization (SELDI) time-of-flight mass spectrometry offers an alternative method to recognizing differences in protein patterns between AM and monocytes or between AM under different pathological conditions. This review details the current status of this field and outlines future directions in functional proteomic analyses of AM and monocytes. Furthermore, this review presents viewpoints of integrating proteomics with translational topics in lung diseases to define the mechanisms of disease and to uncover new diagnostic and therapeutic targets.

Design capacities of joints with laterally loaded nails

2001 ◽  
Vol 28 (2) ◽  
pp. 282-290 ◽  
Author(s):  
Ian Smith ◽  
Steven T Craft ◽  
Pierre Quenneville
Keyword(s):  
Limit State ◽  
Ultimate Limit State ◽  
Joint Models ◽  
Yield Model ◽  
Rigid Plastic ◽  
Double Shear ◽  
Modelling Assumptions ◽  
Ultimate Limit State Design ◽  
Laterally Loaded ◽  
The Impact

Capacities of joints with laterally loaded nails may be predicted using "European yield" type models (EYMs) with various levels of complexity. EYMs presume that a nail and the wood on which it bears exhibit a rigid–plastic stress–strain response. Consideration is given in this paper to the "original" model published by K.W. Johansen in 1949, an empirical approximation proposed by L.R.J. Whale and coworkers in 1987, and a curtailed and "simplified" model proposed by H.J. Blass and coworkers in 1999. Predictions from the various EYMs are compared with experimentally determined ultimate capacities of single and double shear joints. Experiments covered a range of combinations of member thicknesses and two nail sizes. The impact of modelling assumptions is illustrated in the context of the Canadian timber design code. Suggestions are made regarding the necessary level of complexity for nailed joint models used in design.Key words: timber, joints, nails, yield model, ultimate limit state, design.

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