This document presents a thesis on lattice approximations for Black-Scholes type models in option pricing. The thesis introduces derivatives, securities and options, and discusses option pricing via discrete and continuous time models. It then covers lattice approaches like binomial trees and trinomial trees. The thesis also examines the convergence of binomial models to geometric Brownian motion in continuous time. Key topics analyzed include binomial models, trinomial models, and the case of equivalence between discrete and continuous approaches.
This document discusses properties of the complement of fuzzy graphs. It begins by introducing fuzzy graphs and defining the complement of a fuzzy graph. It then presents three theorems about the structure of complements:
1) If a fuzzy graph G is a cycle with 5 or more vertices, then the underlying graph of G's complement (Gc)* will be a block.
2) If a fuzzy graph G satisfies a certain connectivity property, then (Gc)* will be a block.
3) If a fuzzy graph G satisfies a certain degree and connectivity property, then Gc will be a fuzzy block.
The document concludes by stating that the results are sufficient but not necessary conditions for the structural properties of
Vershinin 2013_about presentations of braid groups and theirPanagiote Ligouras
In the paper we give a survey of rather new notions and results which generalize
classical ones in the theory of braids. Among such notions are various inverse monoids of
partial braids. We also observe presentations different from standard Artin presentation for
generalizations of braids. Namely, we consider presentations with small number of generators,
Sergiescu graph-presentations and Birman-Ko-Lee presentation. The work of V. V. Chaynikov
on the word and conjugacy problems for the singular braid monoid in Birman-Ko-Lee generators
is described as well.
ON ALGORITHMIC PROBLEMS CONCERNING GRAPHS OF HIGHER DEGREE OF SYMMETRYFransiskeran
Since the ancient determination of the five platonic solids the study of symmetry and regularity has always
been one of the most fascinating aspects of mathematics. One intriguing phenomenon of studies in graph
theory is the fact that quite often arithmetic regularity properties of a graph imply the existence of many
symmetries, i.e. large automorphism group G. In some important special situation higher degree of
regularity means that G is an automorphism group of finite geometry. For example, a glance through the
list of distance regular graphs of diameter d < 3 reveals the fact that most of them are connected with
classical Lie geometry. Theory of distance regular graphs is an important part of algebraic combinatorics
and its applications such as coding theory, communication networks, and block design. An important tool
for investigation of such graphs is their spectra, which is the set of eigenvalues of adjacency matrix of a
graph. Let G be a finite simple group of Lie type and X be the set homogeneous elements of the associated
geometry.
On algorithmic problems concerning graphs of higher degree of symmetrygraphhoc
Since the ancient determination of the five platonic solids the study of symmetry and regularity has always
been one of the most fascinating aspects of mathematics. One intriguing phenomenon of studies in graph
theory is the fact that quite often arithmetic regularity properties of a graph imply the existence of many
symmetries, i.e. large automorphism group G. In some important special situation higher degree of
regularity means that G is an automorphism group of finite geometry. For example, a glance through the
list of distance regular graphs of diameter d < 3 reveals the fact that most of them are connected with
classical Lie geometry. Theory of distance regular graphs is an important part of algebraic combinatorics
and its applications such as coding theory, communication networks, and block design. An important tool
for investigation of such graphs is their spectra, which is the set of eigenvalues of adjacency matrix of a
graph. Let G be a finite simple group of Lie type and X be the set homogeneous elements of the associated
geometry. The complexity of computing the adjacency matrices of a graph Gr on the vertices X such that
Aut GR = G depends very much on the description of the geometry with which one starts. For example, we
can represent the geometry as the totality of 1 cosets of parabolic subgroups 2 chains of embedded
subspaces (case of linear groups), or totally isotropic subspaces (case of the remaining classical groups), 3
special subspaces of minimal module for G which are defined in terms of a G invariant multilinear form.
The aim of this research is to develop an effective method for generation of graphs connected with classical
geometry and evaluation of its spectra, which is the set of eigenvalues of adjacency matrix of a graph. The
main approach is to avoid manual drawing and to calculate graph layout automatically according to its
formal structure. This is a simple task in a case of a tree like graph with a strict hierarchy of entities but it
becomes more complicated for graphs of geometrical nature. There are two main reasons for the
investigations of spectra: (1) very often spectra carry much more useful information about the graph than a
corresponding list of entities and relationships (2) graphs with special spectra, satisfying so called
Ramanujan property or simply Ramanujan graphs (by name of Indian genius mathematician) are important
for real life applications (see [13]). There is a motivated suspicion that among geometrical graphs one
could find some new Ramanujan graphs.
Tensor-based models of natural language semantics provide a conceptually motivated procedure to compute the meaning of a sentence, given its grammatical structure and a vectorial representation of the meaning of its parts. The main characteristic of these models is that words with relational nature, such as adjectives and verbs, become (multi-)linear maps acting on vectors representing words of atomic types, e.g. nouns and noun phrases. On the practical side, the tensor-based framework has been proved useful in a number of NLP tasks. On the theoretical side, its rigorous mathematical foundations provide a test-bed for studying compositional aspects of language at a level deeper than most practically-oriented approaches would allow; for example, mathematical structures such as Frobenius algebras and bialgebras have been used to allow the explication of functional words such as relative pronouns, to model linguistic aspects such as coordination and intonation, and to provide accounts of quantification in distributional models. Furthermore, the deep structural similarity of the framework to concepts that explain the behaviour of quantum-mechanical systems has enabled a unique perspective in approaching language-related problems, such as lexical ambiguity and entailment, by leveraging the model to the realm of density operators and complete positive maps via Selinger's CPM construction. This talk aims at providing a comprehensive introduction to this emerging field by presenting the mathematical foundations, discussing important extensions and recent work, and (time permitted) touching implementation issues and practical applications.
- Kruskal's algorithm finds a minimum spanning tree by greedily adding edges to a forest in order of increasing weight, as long as it does not form a cycle.
- It runs in O(m log m + n) time by sorting edges first and then using efficient data structures to test for cycles in constant time per edge.
- Prim's algorithm grows a minimum spanning tree from a single vertex by always adding the lowest weight edge that connects a new vertex. It runs in O(n^2) time with basic implementations but can be optimized.
EVEN GRACEFUL LABELLING OF A CLASS OF TREESFransiskeran
A labelling or numbering of a graph G with q edges is an assignment of labels to the vertices of G that
induces for each edge uv a labelling depending on the vertex labels f(u) and f(v). A labelling is called a
graceful labelling if there exists an injective function f: V (G) → {0, 1,2,......q} such that for each edge xy,
the labelling │f(x)-f(y)│is distinct. In this paper, we prove that a class of Tn trees are even graceful.
Singh gordon-unified-factorization-ecmlHuỳnh Thông
This document presents a unified view of matrix factorization methods that frames the differences among popular methods like NMF, SVD, and probabilistic models in terms of a small number of modeling choices. Many approaches can be viewed as minimizing a generalized Bregman divergence. The authors show that an alternating projection algorithm can be applied to most models in this framework, and the Hessian for each projection has a special structure enabling efficient Newton updates. This allows incorporating constraints like non-negativity or clustering while factorizing matrices.
This document discusses properties of the complement of fuzzy graphs. It begins by introducing fuzzy graphs and defining the complement of a fuzzy graph. It then presents three theorems about the structure of complements:
1) If a fuzzy graph G is a cycle with 5 or more vertices, then the underlying graph of G's complement (Gc)* will be a block.
2) If a fuzzy graph G satisfies a certain connectivity property, then (Gc)* will be a block.
3) If a fuzzy graph G satisfies a certain degree and connectivity property, then Gc will be a fuzzy block.
The document concludes by stating that the results are sufficient but not necessary conditions for the structural properties of
Vershinin 2013_about presentations of braid groups and theirPanagiote Ligouras
In the paper we give a survey of rather new notions and results which generalize
classical ones in the theory of braids. Among such notions are various inverse monoids of
partial braids. We also observe presentations different from standard Artin presentation for
generalizations of braids. Namely, we consider presentations with small number of generators,
Sergiescu graph-presentations and Birman-Ko-Lee presentation. The work of V. V. Chaynikov
on the word and conjugacy problems for the singular braid monoid in Birman-Ko-Lee generators
is described as well.
ON ALGORITHMIC PROBLEMS CONCERNING GRAPHS OF HIGHER DEGREE OF SYMMETRYFransiskeran
Since the ancient determination of the five platonic solids the study of symmetry and regularity has always
been one of the most fascinating aspects of mathematics. One intriguing phenomenon of studies in graph
theory is the fact that quite often arithmetic regularity properties of a graph imply the existence of many
symmetries, i.e. large automorphism group G. In some important special situation higher degree of
regularity means that G is an automorphism group of finite geometry. For example, a glance through the
list of distance regular graphs of diameter d < 3 reveals the fact that most of them are connected with
classical Lie geometry. Theory of distance regular graphs is an important part of algebraic combinatorics
and its applications such as coding theory, communication networks, and block design. An important tool
for investigation of such graphs is their spectra, which is the set of eigenvalues of adjacency matrix of a
graph. Let G be a finite simple group of Lie type and X be the set homogeneous elements of the associated
geometry.
On algorithmic problems concerning graphs of higher degree of symmetrygraphhoc
Since the ancient determination of the five platonic solids the study of symmetry and regularity has always
been one of the most fascinating aspects of mathematics. One intriguing phenomenon of studies in graph
theory is the fact that quite often arithmetic regularity properties of a graph imply the existence of many
symmetries, i.e. large automorphism group G. In some important special situation higher degree of
regularity means that G is an automorphism group of finite geometry. For example, a glance through the
list of distance regular graphs of diameter d < 3 reveals the fact that most of them are connected with
classical Lie geometry. Theory of distance regular graphs is an important part of algebraic combinatorics
and its applications such as coding theory, communication networks, and block design. An important tool
for investigation of such graphs is their spectra, which is the set of eigenvalues of adjacency matrix of a
graph. Let G be a finite simple group of Lie type and X be the set homogeneous elements of the associated
geometry. The complexity of computing the adjacency matrices of a graph Gr on the vertices X such that
Aut GR = G depends very much on the description of the geometry with which one starts. For example, we
can represent the geometry as the totality of 1 cosets of parabolic subgroups 2 chains of embedded
subspaces (case of linear groups), or totally isotropic subspaces (case of the remaining classical groups), 3
special subspaces of minimal module for G which are defined in terms of a G invariant multilinear form.
The aim of this research is to develop an effective method for generation of graphs connected with classical
geometry and evaluation of its spectra, which is the set of eigenvalues of adjacency matrix of a graph. The
main approach is to avoid manual drawing and to calculate graph layout automatically according to its
formal structure. This is a simple task in a case of a tree like graph with a strict hierarchy of entities but it
becomes more complicated for graphs of geometrical nature. There are two main reasons for the
investigations of spectra: (1) very often spectra carry much more useful information about the graph than a
corresponding list of entities and relationships (2) graphs with special spectra, satisfying so called
Ramanujan property or simply Ramanujan graphs (by name of Indian genius mathematician) are important
for real life applications (see [13]). There is a motivated suspicion that among geometrical graphs one
could find some new Ramanujan graphs.
Tensor-based models of natural language semantics provide a conceptually motivated procedure to compute the meaning of a sentence, given its grammatical structure and a vectorial representation of the meaning of its parts. The main characteristic of these models is that words with relational nature, such as adjectives and verbs, become (multi-)linear maps acting on vectors representing words of atomic types, e.g. nouns and noun phrases. On the practical side, the tensor-based framework has been proved useful in a number of NLP tasks. On the theoretical side, its rigorous mathematical foundations provide a test-bed for studying compositional aspects of language at a level deeper than most practically-oriented approaches would allow; for example, mathematical structures such as Frobenius algebras and bialgebras have been used to allow the explication of functional words such as relative pronouns, to model linguistic aspects such as coordination and intonation, and to provide accounts of quantification in distributional models. Furthermore, the deep structural similarity of the framework to concepts that explain the behaviour of quantum-mechanical systems has enabled a unique perspective in approaching language-related problems, such as lexical ambiguity and entailment, by leveraging the model to the realm of density operators and complete positive maps via Selinger's CPM construction. This talk aims at providing a comprehensive introduction to this emerging field by presenting the mathematical foundations, discussing important extensions and recent work, and (time permitted) touching implementation issues and practical applications.
- Kruskal's algorithm finds a minimum spanning tree by greedily adding edges to a forest in order of increasing weight, as long as it does not form a cycle.
- It runs in O(m log m + n) time by sorting edges first and then using efficient data structures to test for cycles in constant time per edge.
- Prim's algorithm grows a minimum spanning tree from a single vertex by always adding the lowest weight edge that connects a new vertex. It runs in O(n^2) time with basic implementations but can be optimized.
EVEN GRACEFUL LABELLING OF A CLASS OF TREESFransiskeran
A labelling or numbering of a graph G with q edges is an assignment of labels to the vertices of G that
induces for each edge uv a labelling depending on the vertex labels f(u) and f(v). A labelling is called a
graceful labelling if there exists an injective function f: V (G) → {0, 1,2,......q} such that for each edge xy,
the labelling │f(x)-f(y)│is distinct. In this paper, we prove that a class of Tn trees are even graceful.
Singh gordon-unified-factorization-ecmlHuỳnh Thông
This document presents a unified view of matrix factorization methods that frames the differences among popular methods like NMF, SVD, and probabilistic models in terms of a small number of modeling choices. Many approaches can be viewed as minimizing a generalized Bregman divergence. The authors show that an alternating projection algorithm can be applied to most models in this framework, and the Hessian for each projection has a special structure enabling efficient Newton updates. This allows incorporating constraints like non-negativity or clustering while factorizing matrices.
2024 State of Marketing Report – by HubspotMarius Sescu
https://www.hubspot.com/state-of-marketing
· Scaling relationships and proving ROI
· Social media is the place for search, sales, and service
· Authentic influencer partnerships fuel brand growth
· The strongest connections happen via call, click, chat, and camera.
· Time saved with AI leads to more creative work
· Seeking: A single source of truth
· TLDR; Get on social, try AI, and align your systems.
· More human marketing, powered by robots
ChatGPT is a revolutionary addition to the world since its introduction in 2022. A big shift in the sector of information gathering and processing happened because of this chatbot. What is the story of ChatGPT? How is the bot responding to prompts and generating contents? Swipe through these slides prepared by Expeed Software, a web development company regarding the development and technical intricacies of ChatGPT!
Product Design Trends in 2024 | Teenage EngineeringsPixeldarts
The realm of product design is a constantly changing environment where technology and style intersect. Every year introduces fresh challenges and exciting trends that mold the future of this captivating art form. In this piece, we delve into the significant trends set to influence the look and functionality of product design in the year 2024.
How Race, Age and Gender Shape Attitudes Towards Mental HealthThinkNow
Mental health has been in the news quite a bit lately. Dozens of U.S. states are currently suing Meta for contributing to the youth mental health crisis by inserting addictive features into their products, while the U.S. Surgeon General is touring the nation to bring awareness to the growing epidemic of loneliness and isolation. The country has endured periods of low national morale, such as in the 1970s when high inflation and the energy crisis worsened public sentiment following the Vietnam War. The current mood, however, feels different. Gallup recently reported that national mental health is at an all-time low, with few bright spots to lift spirits.
To better understand how Americans are feeling and their attitudes towards mental health in general, ThinkNow conducted a nationally representative quantitative survey of 1,500 respondents and found some interesting differences among ethnic, age and gender groups.
Technology
For example, 52% agree that technology and social media have a negative impact on mental health, but when broken out by race, 61% of Whites felt technology had a negative effect, and only 48% of Hispanics thought it did.
While technology has helped us keep in touch with friends and family in faraway places, it appears to have degraded our ability to connect in person. Staying connected online is a double-edged sword since the same news feed that brings us pictures of the grandkids and fluffy kittens also feeds us news about the wars in Israel and Ukraine, the dysfunction in Washington, the latest mass shooting and the climate crisis.
Hispanics may have a built-in defense against the isolation technology breeds, owing to their large, multigenerational households, strong social support systems, and tendency to use social media to stay connected with relatives abroad.
Age and Gender
When asked how individuals rate their mental health, men rate it higher than women by 11 percentage points, and Baby Boomers rank it highest at 83%, saying it’s good or excellent vs. 57% of Gen Z saying the same.
Gen Z spends the most amount of time on social media, so the notion that social media negatively affects mental health appears to be correlated. Unfortunately, Gen Z is also the generation that’s least comfortable discussing mental health concerns with healthcare professionals. Only 40% of them state they’re comfortable discussing their issues with a professional compared to 60% of Millennials and 65% of Boomers.
Race Affects Attitudes
As seen in previous research conducted by ThinkNow, Asian Americans lag other groups when it comes to awareness of mental health issues. Twenty-four percent of Asian Americans believe that having a mental health issue is a sign of weakness compared to the 16% average for all groups. Asians are also considerably less likely to be aware of mental health services in their communities (42% vs. 55%) and most likely to seek out information on social media (51% vs. 35%).
AI Trends in Creative Operations 2024 by Artwork Flow.pdfmarketingartwork
Creative operations teams expect increased AI use in 2024. Currently, over half of tasks are not AI-enabled, but this is expected to decrease in the coming year. ChatGPT is the most popular AI tool currently. Business leaders are more actively exploring AI benefits than individual contributors. Most respondents do not believe AI will impact workforce size in 2024. However, some inhibitions still exist around AI accuracy and lack of understanding. Creatives primarily want to use AI to save time on mundane tasks and boost productivity.
Organizational culture includes values, norms, systems, symbols, language, assumptions, beliefs, and habits that influence employee behaviors and how people interpret those behaviors. It is important because culture can help or hinder a company's success. Some key aspects of Netflix's culture that help it achieve results include hiring smartly so every position has stars, focusing on attitude over just aptitude, and having a strict policy against peacocks, whiners, and jerks.
PEPSICO Presentation to CAGNY Conference Feb 2024Neil Kimberley
PepsiCo provided a safe harbor statement noting that any forward-looking statements are based on currently available information and are subject to risks and uncertainties. It also provided information on non-GAAP measures and directing readers to its website for disclosure and reconciliation. The document then discussed PepsiCo's business overview, including that it is a global beverage and convenient food company with iconic brands, $91 billion in net revenue in 2023, and nearly $14 billion in core operating profit. It operates through a divisional structure with a focus on local consumers.
Content Methodology: A Best Practices Report (Webinar)contently
This document provides an overview of content methodology best practices. It defines content methodology as establishing objectives, KPIs, and a culture of continuous learning and iteration. An effective methodology focuses on connecting with audiences, creating optimal content, and optimizing processes. It also discusses why a methodology is needed due to the competitive landscape, proliferation of channels, and opportunities for improvement. Components of an effective methodology include defining objectives and KPIs, audience analysis, identifying opportunities, and evaluating resources. The document concludes with recommendations around creating a content plan, testing and optimizing content over 90 days.
How to Prepare For a Successful Job Search for 2024Albert Qian
The document provides guidance on preparing a job search for 2024. It discusses the state of the job market, focusing on growth in AI and healthcare but also continued layoffs. It recommends figuring out what you want to do by researching interests and skills, then conducting informational interviews. The job search should involve building a personal brand on LinkedIn, actively applying to jobs, tailoring resumes and interviews, maintaining job hunting as a habit, and continuing self-improvement. Once hired, the document advises setting new goals and keeping skills and networking active in case of future opportunities.
A report by thenetworkone and Kurio.
The contributing experts and agencies are (in an alphabetical order): Sylwia Rytel, Social Media Supervisor, 180heartbeats + JUNG v MATT (PL), Sharlene Jenner, Vice President - Director of Engagement Strategy, Abelson Taylor (USA), Alex Casanovas, Digital Director, Atrevia (ES), Dora Beilin, Senior Social Strategist, Barrett Hoffher (USA), Min Seo, Campaign Director, Brand New Agency (KR), Deshé M. Gully, Associate Strategist, Day One Agency (USA), Francesca Trevisan, Strategist, Different (IT), Trevor Crossman, CX and Digital Transformation Director; Olivia Hussey, Strategic Planner; Simi Srinarula, Social Media Manager, The Hallway (AUS), James Hebbert, Managing Director, Hylink (CN / UK), Mundy Álvarez, Planning Director; Pedro Rojas, Social Media Manager; Pancho González, CCO, Inbrax (CH), Oana Oprea, Head of Digital Planning, Jam Session Agency (RO), Amy Bottrill, Social Account Director, Launch (UK), Gaby Arriaga, Founder, Leonardo1452 (MX), Shantesh S Row, Creative Director, Liwa (UAE), Rajesh Mehta, Chief Strategy Officer; Dhruv Gaur, Digital Planning Lead; Leonie Mergulhao, Account Supervisor - Social Media & PR, Medulla (IN), Aurelija Plioplytė, Head of Digital & Social, Not Perfect (LI), Daiana Khaidargaliyeva, Account Manager, Osaka Labs (UK / USA), Stefanie Söhnchen, Vice President Digital, PIABO Communications (DE), Elisabeth Winiartati, Managing Consultant, Head of Global Integrated Communications; Lydia Aprina, Account Manager, Integrated Marketing and Communications; Nita Prabowo, Account Manager, Integrated Marketing and Communications; Okhi, Web Developer, PNTR Group (ID), Kei Obusan, Insights Director; Daffi Ranandi, Insights Manager, Radarr (SG), Gautam Reghunath, Co-founder & CEO, Talented (IN), Donagh Humphreys, Head of Social and Digital Innovation, THINKHOUSE (IRE), Sarah Yim, Strategy Director, Zulu Alpha Kilo (CA).
Trends In Paid Search: Navigating The Digital Landscape In 2024Search Engine Journal
The search marketing landscape is evolving rapidly with new technologies, and professionals, like you, rely on innovative paid search strategies to meet changing demands.
It’s important that you’re ready to implement new strategies in 2024.
Check this out and learn the top trends in paid search advertising that are expected to gain traction, so you can drive higher ROI more efficiently in 2024.
You’ll learn:
- The latest trends in AI and automation, and what this means for an evolving paid search ecosystem.
- New developments in privacy and data regulation.
- Emerging ad formats that are expected to make an impact next year.
Watch Sreekant Lanka from iQuanti and Irina Klein from OneMain Financial as they dive into the future of paid search and explore the trends, strategies, and technologies that will shape the search marketing landscape.
If you’re looking to assess your paid search strategy and design an industry-aligned plan for 2024, then this webinar is for you.
5 Public speaking tips from TED - Visualized summarySpeakerHub
From their humble beginnings in 1984, TED has grown into the world’s most powerful amplifier for speakers and thought-leaders to share their ideas. They have over 2,400 filmed talks (not including the 30,000+ TEDx videos) freely available online, and have hosted over 17,500 events around the world.
With over one billion views in a year, it’s no wonder that so many speakers are looking to TED for ideas on how to share their message more effectively.
The article “5 Public-Speaking Tips TED Gives Its Speakers”, by Carmine Gallo for Forbes, gives speakers five practical ways to connect with their audience, and effectively share their ideas on stage.
Whether you are gearing up to get on a TED stage yourself, or just want to master the skills that so many of their speakers possess, these tips and quotes from Chris Anderson, the TED Talks Curator, will encourage you to make the most impactful impression on your audience.
See the full article and more summaries like this on SpeakerHub here: https://speakerhub.com/blog/5-presentation-tips-ted-gives-its-speakers
See the original article on Forbes here:
http://www.forbes.com/forbes/welcome/?toURL=http://www.forbes.com/sites/carminegallo/2016/05/06/5-public-speaking-tips-ted-gives-its-speakers/&refURL=&referrer=#5c07a8221d9b
ChatGPT and the Future of Work - Clark Boyd Clark Boyd
Everyone is in agreement that ChatGPT (and other generative AI tools) will shape the future of work. Yet there is little consensus on exactly how, when, and to what extent this technology will change our world.
Businesses that extract maximum value from ChatGPT will use it as a collaborative tool for everything from brainstorming to technical maintenance.
For individuals, now is the time to pinpoint the skills the future professional will need to thrive in the AI age.
Check out this presentation to understand what ChatGPT is, how it will shape the future of work, and how you can prepare to take advantage.
The document provides career advice for getting into the tech field, including:
- Doing projects and internships in college to build a portfolio.
- Learning about different roles and technologies through industry research.
- Contributing to open source projects to build experience and network.
- Developing a personal brand through a website and social media presence.
- Networking through events, communities, and finding a mentor.
- Practicing interviews through mock interviews and whiteboarding coding questions.
Google's Just Not That Into You: Understanding Core Updates & Search IntentLily Ray
1. Core updates from Google periodically change how its algorithms assess and rank websites and pages. This can impact rankings through shifts in user intent, site quality issues being caught up to, world events influencing queries, and overhauls to search like the E-A-T framework.
2. There are many possible user intents beyond just transactional, navigational and informational. Identifying intent shifts is important during core updates. Sites may need to optimize for new intents through different content types and sections.
3. Responding effectively to core updates requires analyzing "before and after" data to understand changes, identifying new intents or page types, and ensuring content matches appropriate intents across video, images, knowledge graphs and more.
A brief introduction to DataScience with explaining of the concepts, algorithms, machine learning, supervised and unsupervised learning, clustering, statistics, data preprocessing, real-world applications etc.
It's part of a Data Science Corner Campaign where I will be discussing the fundamentals of DataScience, AIML, Statistics etc.
Time Management & Productivity - Best PracticesVit Horky
Here's my presentation on by proven best practices how to manage your work time effectively and how to improve your productivity. It includes practical tips and how to use tools such as Slack, Google Apps, Hubspot, Google Calendar, Gmail and others.
The six step guide to practical project managementMindGenius
The six step guide to practical project management
If you think managing projects is too difficult, think again.
We’ve stripped back project management processes to the
basics – to make it quicker and easier, without sacrificing
the vital ingredients for success.
“If you’re looking for some real-world guidance, then The Six Step Guide to Practical Project Management will help.”
Dr Andrew Makar, Tactical Project Management
2024 State of Marketing Report – by HubspotMarius Sescu
https://www.hubspot.com/state-of-marketing
· Scaling relationships and proving ROI
· Social media is the place for search, sales, and service
· Authentic influencer partnerships fuel brand growth
· The strongest connections happen via call, click, chat, and camera.
· Time saved with AI leads to more creative work
· Seeking: A single source of truth
· TLDR; Get on social, try AI, and align your systems.
· More human marketing, powered by robots
ChatGPT is a revolutionary addition to the world since its introduction in 2022. A big shift in the sector of information gathering and processing happened because of this chatbot. What is the story of ChatGPT? How is the bot responding to prompts and generating contents? Swipe through these slides prepared by Expeed Software, a web development company regarding the development and technical intricacies of ChatGPT!
Product Design Trends in 2024 | Teenage EngineeringsPixeldarts
The realm of product design is a constantly changing environment where technology and style intersect. Every year introduces fresh challenges and exciting trends that mold the future of this captivating art form. In this piece, we delve into the significant trends set to influence the look and functionality of product design in the year 2024.
How Race, Age and Gender Shape Attitudes Towards Mental HealthThinkNow
Mental health has been in the news quite a bit lately. Dozens of U.S. states are currently suing Meta for contributing to the youth mental health crisis by inserting addictive features into their products, while the U.S. Surgeon General is touring the nation to bring awareness to the growing epidemic of loneliness and isolation. The country has endured periods of low national morale, such as in the 1970s when high inflation and the energy crisis worsened public sentiment following the Vietnam War. The current mood, however, feels different. Gallup recently reported that national mental health is at an all-time low, with few bright spots to lift spirits.
To better understand how Americans are feeling and their attitudes towards mental health in general, ThinkNow conducted a nationally representative quantitative survey of 1,500 respondents and found some interesting differences among ethnic, age and gender groups.
Technology
For example, 52% agree that technology and social media have a negative impact on mental health, but when broken out by race, 61% of Whites felt technology had a negative effect, and only 48% of Hispanics thought it did.
While technology has helped us keep in touch with friends and family in faraway places, it appears to have degraded our ability to connect in person. Staying connected online is a double-edged sword since the same news feed that brings us pictures of the grandkids and fluffy kittens also feeds us news about the wars in Israel and Ukraine, the dysfunction in Washington, the latest mass shooting and the climate crisis.
Hispanics may have a built-in defense against the isolation technology breeds, owing to their large, multigenerational households, strong social support systems, and tendency to use social media to stay connected with relatives abroad.
Age and Gender
When asked how individuals rate their mental health, men rate it higher than women by 11 percentage points, and Baby Boomers rank it highest at 83%, saying it’s good or excellent vs. 57% of Gen Z saying the same.
Gen Z spends the most amount of time on social media, so the notion that social media negatively affects mental health appears to be correlated. Unfortunately, Gen Z is also the generation that’s least comfortable discussing mental health concerns with healthcare professionals. Only 40% of them state they’re comfortable discussing their issues with a professional compared to 60% of Millennials and 65% of Boomers.
Race Affects Attitudes
As seen in previous research conducted by ThinkNow, Asian Americans lag other groups when it comes to awareness of mental health issues. Twenty-four percent of Asian Americans believe that having a mental health issue is a sign of weakness compared to the 16% average for all groups. Asians are also considerably less likely to be aware of mental health services in their communities (42% vs. 55%) and most likely to seek out information on social media (51% vs. 35%).
AI Trends in Creative Operations 2024 by Artwork Flow.pdfmarketingartwork
Creative operations teams expect increased AI use in 2024. Currently, over half of tasks are not AI-enabled, but this is expected to decrease in the coming year. ChatGPT is the most popular AI tool currently. Business leaders are more actively exploring AI benefits than individual contributors. Most respondents do not believe AI will impact workforce size in 2024. However, some inhibitions still exist around AI accuracy and lack of understanding. Creatives primarily want to use AI to save time on mundane tasks and boost productivity.
Organizational culture includes values, norms, systems, symbols, language, assumptions, beliefs, and habits that influence employee behaviors and how people interpret those behaviors. It is important because culture can help or hinder a company's success. Some key aspects of Netflix's culture that help it achieve results include hiring smartly so every position has stars, focusing on attitude over just aptitude, and having a strict policy against peacocks, whiners, and jerks.
PEPSICO Presentation to CAGNY Conference Feb 2024Neil Kimberley
PepsiCo provided a safe harbor statement noting that any forward-looking statements are based on currently available information and are subject to risks and uncertainties. It also provided information on non-GAAP measures and directing readers to its website for disclosure and reconciliation. The document then discussed PepsiCo's business overview, including that it is a global beverage and convenient food company with iconic brands, $91 billion in net revenue in 2023, and nearly $14 billion in core operating profit. It operates through a divisional structure with a focus on local consumers.
Content Methodology: A Best Practices Report (Webinar)contently
This document provides an overview of content methodology best practices. It defines content methodology as establishing objectives, KPIs, and a culture of continuous learning and iteration. An effective methodology focuses on connecting with audiences, creating optimal content, and optimizing processes. It also discusses why a methodology is needed due to the competitive landscape, proliferation of channels, and opportunities for improvement. Components of an effective methodology include defining objectives and KPIs, audience analysis, identifying opportunities, and evaluating resources. The document concludes with recommendations around creating a content plan, testing and optimizing content over 90 days.
How to Prepare For a Successful Job Search for 2024Albert Qian
The document provides guidance on preparing a job search for 2024. It discusses the state of the job market, focusing on growth in AI and healthcare but also continued layoffs. It recommends figuring out what you want to do by researching interests and skills, then conducting informational interviews. The job search should involve building a personal brand on LinkedIn, actively applying to jobs, tailoring resumes and interviews, maintaining job hunting as a habit, and continuing self-improvement. Once hired, the document advises setting new goals and keeping skills and networking active in case of future opportunities.
A report by thenetworkone and Kurio.
The contributing experts and agencies are (in an alphabetical order): Sylwia Rytel, Social Media Supervisor, 180heartbeats + JUNG v MATT (PL), Sharlene Jenner, Vice President - Director of Engagement Strategy, Abelson Taylor (USA), Alex Casanovas, Digital Director, Atrevia (ES), Dora Beilin, Senior Social Strategist, Barrett Hoffher (USA), Min Seo, Campaign Director, Brand New Agency (KR), Deshé M. Gully, Associate Strategist, Day One Agency (USA), Francesca Trevisan, Strategist, Different (IT), Trevor Crossman, CX and Digital Transformation Director; Olivia Hussey, Strategic Planner; Simi Srinarula, Social Media Manager, The Hallway (AUS), James Hebbert, Managing Director, Hylink (CN / UK), Mundy Álvarez, Planning Director; Pedro Rojas, Social Media Manager; Pancho González, CCO, Inbrax (CH), Oana Oprea, Head of Digital Planning, Jam Session Agency (RO), Amy Bottrill, Social Account Director, Launch (UK), Gaby Arriaga, Founder, Leonardo1452 (MX), Shantesh S Row, Creative Director, Liwa (UAE), Rajesh Mehta, Chief Strategy Officer; Dhruv Gaur, Digital Planning Lead; Leonie Mergulhao, Account Supervisor - Social Media & PR, Medulla (IN), Aurelija Plioplytė, Head of Digital & Social, Not Perfect (LI), Daiana Khaidargaliyeva, Account Manager, Osaka Labs (UK / USA), Stefanie Söhnchen, Vice President Digital, PIABO Communications (DE), Elisabeth Winiartati, Managing Consultant, Head of Global Integrated Communications; Lydia Aprina, Account Manager, Integrated Marketing and Communications; Nita Prabowo, Account Manager, Integrated Marketing and Communications; Okhi, Web Developer, PNTR Group (ID), Kei Obusan, Insights Director; Daffi Ranandi, Insights Manager, Radarr (SG), Gautam Reghunath, Co-founder & CEO, Talented (IN), Donagh Humphreys, Head of Social and Digital Innovation, THINKHOUSE (IRE), Sarah Yim, Strategy Director, Zulu Alpha Kilo (CA).
Trends In Paid Search: Navigating The Digital Landscape In 2024Search Engine Journal
The search marketing landscape is evolving rapidly with new technologies, and professionals, like you, rely on innovative paid search strategies to meet changing demands.
It’s important that you’re ready to implement new strategies in 2024.
Check this out and learn the top trends in paid search advertising that are expected to gain traction, so you can drive higher ROI more efficiently in 2024.
You’ll learn:
- The latest trends in AI and automation, and what this means for an evolving paid search ecosystem.
- New developments in privacy and data regulation.
- Emerging ad formats that are expected to make an impact next year.
Watch Sreekant Lanka from iQuanti and Irina Klein from OneMain Financial as they dive into the future of paid search and explore the trends, strategies, and technologies that will shape the search marketing landscape.
If you’re looking to assess your paid search strategy and design an industry-aligned plan for 2024, then this webinar is for you.
5 Public speaking tips from TED - Visualized summarySpeakerHub
From their humble beginnings in 1984, TED has grown into the world’s most powerful amplifier for speakers and thought-leaders to share their ideas. They have over 2,400 filmed talks (not including the 30,000+ TEDx videos) freely available online, and have hosted over 17,500 events around the world.
With over one billion views in a year, it’s no wonder that so many speakers are looking to TED for ideas on how to share their message more effectively.
The article “5 Public-Speaking Tips TED Gives Its Speakers”, by Carmine Gallo for Forbes, gives speakers five practical ways to connect with their audience, and effectively share their ideas on stage.
Whether you are gearing up to get on a TED stage yourself, or just want to master the skills that so many of their speakers possess, these tips and quotes from Chris Anderson, the TED Talks Curator, will encourage you to make the most impactful impression on your audience.
See the full article and more summaries like this on SpeakerHub here: https://speakerhub.com/blog/5-presentation-tips-ted-gives-its-speakers
See the original article on Forbes here:
http://www.forbes.com/forbes/welcome/?toURL=http://www.forbes.com/sites/carminegallo/2016/05/06/5-public-speaking-tips-ted-gives-its-speakers/&refURL=&referrer=#5c07a8221d9b
ChatGPT and the Future of Work - Clark Boyd Clark Boyd
Everyone is in agreement that ChatGPT (and other generative AI tools) will shape the future of work. Yet there is little consensus on exactly how, when, and to what extent this technology will change our world.
Businesses that extract maximum value from ChatGPT will use it as a collaborative tool for everything from brainstorming to technical maintenance.
For individuals, now is the time to pinpoint the skills the future professional will need to thrive in the AI age.
Check out this presentation to understand what ChatGPT is, how it will shape the future of work, and how you can prepare to take advantage.
The document provides career advice for getting into the tech field, including:
- Doing projects and internships in college to build a portfolio.
- Learning about different roles and technologies through industry research.
- Contributing to open source projects to build experience and network.
- Developing a personal brand through a website and social media presence.
- Networking through events, communities, and finding a mentor.
- Practicing interviews through mock interviews and whiteboarding coding questions.
Google's Just Not That Into You: Understanding Core Updates & Search IntentLily Ray
1. Core updates from Google periodically change how its algorithms assess and rank websites and pages. This can impact rankings through shifts in user intent, site quality issues being caught up to, world events influencing queries, and overhauls to search like the E-A-T framework.
2. There are many possible user intents beyond just transactional, navigational and informational. Identifying intent shifts is important during core updates. Sites may need to optimize for new intents through different content types and sections.
3. Responding effectively to core updates requires analyzing "before and after" data to understand changes, identifying new intents or page types, and ensuring content matches appropriate intents across video, images, knowledge graphs and more.
A brief introduction to DataScience with explaining of the concepts, algorithms, machine learning, supervised and unsupervised learning, clustering, statistics, data preprocessing, real-world applications etc.
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The six step guide to practical project managementMindGenius
The six step guide to practical project management
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Beginners Guide to TikTok for Search - Rachel Pearson - We are Tilt __ Bright...
Bachelor's PP Presentation
1. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
Lattice Approximations for Black-Scholes type
models in Option Pricing
Hossein Nohrouzian
Anne Karl´en
March 16, 2014
Bachelor thesis in mathematics
2. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
Agenda
1 About Our Thesis
2 Introduction
3 Lattice
Binomial Tree
Trinomial Tree
4 Convergence of Binomial Models to GBM
Part i
Part ii
Part iii
5 Lattice Approaches in Discrete Time
Binomial Models
Trinomial Models
6 Case of Equivalence
7 Conclusion
3. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
About Our Thesis
• Why did we choose our topic?
4. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
About Our Thesis
• Why did we choose our topic?
• Knowledge and understanding
5. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
About Our Thesis
• Why did we choose our topic?
• Knowledge and understanding
• Ability to search, collect, evaluate and interpret
6. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
About Our Thesis
• Why did we choose our topic?
• Knowledge and understanding
• Ability to search, collect, evaluate and interpret
• Identify, formulate and solve problems
7. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
About Our Thesis
• Why did we choose our topic?
• Knowledge and understanding
• Ability to search, collect, evaluate and interpret
• Identify, formulate and solve problems
• Communication of our project to different groups
8. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
Introduction
• Derivatives, Securities and Options
9. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
Introduction
• Derivatives, Securities and Options
• Option Pricing Via Discrete and Continuous Time
10. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
Introduction
• Derivatives, Securities and Options
• Option Pricing Via Discrete and Continuous Time
• Lattice Approach in Discrete Time
11. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
Introduction
• Derivatives, Securities and Options
• Option Pricing Via Discrete and Continuous Time
• Lattice Approach in Discrete Time
• Geometric Brownian Motion in Continuous Time
12. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
Binomial Tree
S0
S0u
S0d
S0u2
S0ud
S0d2
S0u3
S0u2d
S0ud2
S0d3
p
1 − p
∆T
∆t ∆t ∆t
t0 t1 t2 T
Figure : Three-Step Binomial Tree
13. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
Trinomial Tree
S0
S0u
S0pm
S0d
S0u2
S0u
S0
S0d
S0d2
S0u3
S0u2
S0u
S0
S0d
S0d2
S0d3
pu
pd
Figure : Three-Step Trinomial Tree
14. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
Convergence of Binomial Models
to Geometric Brownian Motion
• The sequence of Binomial Models and its Convergence to
Geometric Brownian Motion (Part i)
15. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
Convergence of Binomial Models
to Geometric Brownian Motion
• The sequence of Binomial Models and its Convergence to
Geometric Brownian Motion (Part i)
• The sequence of Binomial Models and its Convergence to
Black-Scholes Formulae Under Risk-Neutral Probability
(Part ii)
16. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
Convergence of Binomial Models
to Geometric Brownian Motion
• The sequence of Binomial Models and its Convergence to
Geometric Brownian Motion (Part i)
• The sequence of Binomial Models and its Convergence to
Black-Scholes Formulae Under Risk-Neutral Probability
(Part ii)
• Mean and Variance of a Random Variable Which is
Log-normally Distributed (Part iii)
17. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
Central Limit Theorem
Let Y1, Y2,. . . , Yn be independent and identically distributed
random variables with E[Yi ] = µ and V [Yi ] = σ2 < ∞. Define
Un =
n
i=1 Yi − nµ
σ
√
n
=
Y − µ
σ/
√
n
whereY =
1
n
n
i=1
Yi
Then the distribution function of Un converges to the standard
normal distribution function as n → ∞. That is
lim
n→∞
P(Un ≤ u) =
u
−∞
1
√
2π
e−t2/2
dt for allu
18. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
The sequence of Binomial Models
and its Convergence to Geometric
Brownian Motion
•
E[Y ] = E
t
k=1
Yn,k = E ln
Sn,t
Sn,0
= E [Yn,1 + Yn,2 + ... + Yn,t] , 1 ≤ t ≤ n
19. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
The sequence of Binomial Models
and its Convergence to Geometric
Brownian Motion
•
E[Y ] = E
t
k=1
Yn,k = E ln
Sn,t
Sn,0
= E [Yn,1 + Yn,2 + ... + Yn,t] , 1 ≤ t ≤ n
•
E [Yn,t] = p ln un + (1 − p) ln dn
Y = µt + σW (t) 0 ≤ t ≤ T
E[Y ] = µT V [Y ] = σ2
T
20. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
The sequence of Binomial Models
and its Convergence to Geometric
Brownian Motion
•
E[Y ] = E
t
k=1
Yn,k = E ln
Sn,t
Sn,0
= E [Yn,1 + Yn,2 + ... + Yn,t] , 1 ≤ t ≤ n
•
E [Yn,t] = p ln un + (1 − p) ln dn
Y = µt + σW (t) 0 ≤ t ≤ T
E[Y ] = µT V [Y ] = σ2
T
• Denoting xn = ln un and yn = ln dn.
E[Y ] = n [pxn + (1 − p)yn] = µT
V [Y ] = np(1 − p)(xn − yn)2
= σ2
T
21. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
The sequence of Binomial Models
and its Convergence to Geometric
Brownian Motion
•
xn = µT
n + σ 1−p
p
T
n
yn = µT
n − σ p
1−p
T
n
⇒
22. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
The sequence of Binomial Models
and its Convergence to Geometric
Brownian Motion
•
xn = µT
n + σ 1−p
p
T
n
yn = µT
n − σ p
1−p
T
n
⇒
•
lim
n→∞
P
Yn,1 + Yn,2 + ... + Yn,n − nE[Yn,1]
nV [Yn,1]
≤ x
=p
ln(ST /S0) − µT
σ
√
T
≤ x = Φ(x)
23. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
The sequence of Binomial Models
and its Convergence to Geometric
Brownian Motion
•
xn = µT
n + σ 1−p
p
T
n
yn = µT
n − σ p
1−p
T
n
⇒
•
lim
n→∞
P
Yn,1 + Yn,2 + ... + Yn,n − nE[Yn,1]
nV [Yn,1]
≤ x
=p
ln(ST /S0) − µT
σ
√
T
≤ x = Φ(x)
• This proves that binomial models at time T, follow the
normal distribution with mean µT and σ2T.
24. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
The sequence of Binomial Models
and its Convergence to
Black-Scholes Model Under
Risk-Neutral Probability
•
lim
n→∞
E∗
[Y ] = lim
n→∞
n[p∗
xn + (1 − p∗
)yn] = r −
σ2
2
T
25. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
The sequence of Binomial Models
and its Convergence to
Black-Scholes Model Under
Risk-Neutral Probability
•
lim
n→∞
E∗
[Y ] = lim
n→∞
n[p∗
xn + (1 − p∗
)yn] = r −
σ2
2
T
•
lim
n→∞
V ∗
[Y ] = lim
n→∞
np ∗ (1 − p∗
)(xn − yn)2
= σ2
T
26. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
The sequence of Binomial Models
and its Convergence to
Black-Scholes Model Under
Risk-Neutral Probability
•
lim
n→∞
P∗ Y − nµn]
σn
√
n
≤ x
=p∗ ln(ST /S0) − (r − σ2
2 )T
σ
√
T
≤ x = Φ(x)
27. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
The sequence of Binomial Models
and its Convergence to
Black-Scholes Model Under
Risk-Neutral Probability
•
lim
n→∞
P∗ Y − nµn]
σn
√
n
≤ x
=p∗ ln(ST /S0) − (r − σ2
2 )T
σ
√
T
≤ x = Φ(x)
• which means, under risk-neutral probability measure, our
stochastic process (binomial models) at time T converges
to normal distribution with mean (r − σ2
2 )T and variance
σ2T.
28. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
Mean and Variance of a Random
Variable Which is Log-normally
Distributed
• Random variable Y is normally distributed
E[Y ] = (r −
σ2
2
)T V [Y ] = σ2
T
29. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
Mean and Variance of a Random
Variable Which is Log-normally
Distributed
• Random variable Y is normally distributed
E[Y ] = (r −
σ2
2
)T V [Y ] = σ2
T
• Random variable X = eY or Y = ln X is log-normally
distributed
E[X] = E[eY
] = e(µ+1
2
σ2)T
V [X] = e(2µ+σ2)T
eσ2T
− 1
30. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
Different Binomial Models
• Cox-Ross-Rubinstein Model
31. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
Different Binomial Models
• Cox-Ross-Rubinstein Model
• Jarrow-Rudd Model
32. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
Different Binomial Models
• Cox-Ross-Rubinstein Model
• Jarrow-Rudd Model
• Tian Model
33. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
Different Binomial Models
• Cox-Ross-Rubinstein Model
• Jarrow-Rudd Model
• Tian Model
• Trigeorgis Model
34. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
Different Binomial Models
• Cox-Ross-Rubinstein Model
• Jarrow-Rudd Model
• Tian Model
• Trigeorgis Model
• Leisen-Reimer Model
35. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
Different Binomial Models
p
CRR
u
d
eσ
√
∆t
e−σ
√
∆t
er∆t −d
u−d
JR
u
d
e(r−σ2
2
)∆t+σ
√
∆t
e(r−σ2
2
)∆t−σ
√
∆t
e
σ2
2 ∆t
−e−σ
√
∆t
eσ
√
∆t −e−σ
√
∆t
Ti
u
d
MV
2 [V + 1 +
√
V 2 + 2V + 3]
MV
2 [V + 1 −
√
V 2 + 2V + 3]
M−d
u−d
Tri ∆X σ2∆t + r − σ2
2
2
(∆t)2 1
2 1 + r − σ2
2
∆t
∆X
LR
u
d
un = rn
pn
pn
dn = rn−pnun
1−pn
pn = h−1(d1)
pn = h−1(d2)
Where in Tian’s Model, M = er∆t and V = eσ2∆t.
36. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
Different Trinomial Models
• Boyle’s Approach
37. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
Different Trinomial Models
• Boyle’s Approach
• The Replicating Portfolio
38. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
Different Trinomial Models
• Boyle’s Approach
• The Replicating Portfolio
• Log-normal Transformation (Kamrad-Ritchken Model)
39. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
Different Trinomial Models
• Boyle’s Approach
• The Replicating Portfolio
• Log-normal Transformation (Kamrad-Ritchken Model)
• The Explicit Finite Difference Approach
(Brennan-Schwartz Approach)
40. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
Different Trinomial Models
pi
B u λeσ
√
∆t pu
pd
(V + M2 − M)u − (M − 1)
(u − 1)(u2 − 1)
(V + M2 − M)u2 − u3(M − 1)
(u − 1)(u2 − 1)
KR v λσ
√
∆t
pu
pd
1
2λ2
+
µ
√
∆t
2λσ
1
2λ2
−
µ
√
∆t
2λσ
BS
pu
pd
−
1
2
rj∆t +
1
2
σ2j2∆t
1
2
rj∆t +
1
2
σ2j2∆t
Where in Boyle’s Model M = er∆t and V = M2 eσ2∆t − 1 .
Further, pm = 1 − pu − pd .
41. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
The Case of Equivalence Between
Binomial and Trinomial Models
Static binomial and trinomial trees with equal ∆t and T
coincide, if we choose:
• u = e
√
σ2h−µ2h
• p = 1
2
1
2(σ2h−µ2h2)
+ 1√
σ2h−µ2h2
µ
√
2h
σ
1
2
(Other models exist, e.g. Derman)
42. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
Conclusion
• Knowledge and understanding
43. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
Conclusion
• Knowledge and understanding
• Ability to search, collect, evaluate and interpret
44. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
Conclusion
• Knowledge and understanding
• Ability to search, collect, evaluate and interpret
• Identify, formulate and solve problems
45. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
Conclusion
• Knowledge and understanding
• Ability to search, collect, evaluate and interpret
• Identify, formulate and solve problems
• Communication of our project to different groups
46. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
Conclusion
• Knowledge and understanding
• Ability to search, collect, evaluate and interpret
• Identify, formulate and solve problems
• Communication of our project to different groups
• Ability to put our work into a societal context and its
value within it
47. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
Conclusion
• Knowledge and understanding
• Ability to search, collect, evaluate and interpret
• Identify, formulate and solve problems
• Communication of our project to different groups
• Ability to put our work into a societal context and its
value within it
• Plans to continue and develop this research
48. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
Conclusion
• Knowledge and understanding
• Ability to search, collect, evaluate and interpret
• Identify, formulate and solve problems
• Communication of our project to different groups
• Ability to put our work into a societal context and its
value within it
• Plans to continue and develop this research
• Questions?
49. Lattice Ap-
proximations
for
Black-Scholes
type models in
Option Pricing
Hossein
Nohrouzian
Anne Karl´en
About Our
Thesis
Introduction
Lattice
Binomial Tree
Trinomial Tree
Convergence
of Binomial
Models to
GBM
Part i
Part ii
Part iii
Lattice
Approaches in
Discrete Time
Binomial Models
Trinomial
Models
Case of
Equivalence
Conclusion
Conclusion
• Knowledge and understanding
• Ability to search, collect, evaluate and interpret
• Identify, formulate and solve problems
• Communication of our project to different groups
• Ability to put our work into a societal context and its
value within it
• Plans to continue and develop this research
• Questions?
• Thanks!