1. Pavel Shevchenko works for CSIRO's Quantitative Risk Management group, which develops mathematical models for financial and other risks.
2. CSIRO is Australia's national science agency, with over 6500 staff across various divisions including Mathematical and Information Sciences.
3. The Quantitative Risk Management group applies techniques like extreme value analysis, dependence modelling, and Bayesian methods to areas like financial, infrastructure, environmental and security risks.
Foreign exchange risk and exposure refer to how changes in exchange rates can affect the value of a firm's assets, liabilities, and profits. Exposure is the sensitivity of a firm's value to exchange rate changes, while risk is the variability of a firm's value due to uncertain exchange rate changes. There are three main types of exposures - transaction, translation, and economic. Firms can use hedging strategies like forward contracts and options to manage their foreign exchange risk and exposure by locking in exchange rates for future transactions.
DanTech is a UK-based technology company that is expanding its operations to the US through a joint venture called DanEast Ltd. DanTech will invoice US dollar exports through DanEast and import Japanese yen costs. Changes in the GBP/USD and GBP/JPY exchange rates present foreign exchange risk to DanTech's profits. The treasury manager recommends hedging techniques like money markets, forwards/futures, and options to manage this risk.
This document discusses foreign exchange risk and its management. It defines foreign exchange risk as the risk of an investment's value changing due to currency fluctuations. It identifies the main types of foreign exchange risk as transaction risk, translation risk, and economic risk. Transaction risk arises from currency movements between the signing and execution of contracts. Translation risk occurs when consolidating financial statements in different currencies. Economic risk affects the long-term expected profits and wealth of a company due to currency changes. The document outlines various hedging strategies to manage these risks, including the use of forwards, futures, and money markets.
The document discusses authorized dealers in foreign exchange in India. It states that authorized dealers are banks authorized by the central bank to deal in foreign exchange under the Foreign Exchange Regulation Act of 1947. The functions of authorized dealers include exchanging foreign currencies, making arrangements with foreign correspondents, buying and selling currencies, handling inward and outward remittances, opening letters of credit and settling payments, investing in foreign trade, opening and maintaining foreign bank accounts, and handling export documents. It also discusses the role of banks in foreign trade through correspondent relationships and the types of banks involved in letters of credit.
The Heatmap - Why is Security Visualization so Hard?Raffael Marty
The extent and impact of recent security breaches is showing that current approaches are just not working. But what can we do to protect our business? We have been advocating monitoring for a long time as a way to detect subtle, advanced attacks. However, products have failed to deliver on this promise. Current solutions don't scale in both data volume and analytical insights. In this presentation we will explore why it is so hard to come up with a security monitoring (or shall we call it security intelligence) approach that helps find sophisticated attackers in all the data collected. We are going to explore the question of how to visualize a billion events. We are going to look at a number of security visualization examples to illustrate the problem and some possible solutions. These examples will also help illustrate how data mining and user experience design help us get a handle of the security visualization challenges - enabling us to gain deep insight for a number of security use-cases.
This document discusses managing project cost models, budgets, and actual costs to deliver successful projects. It provides an agenda for a presentation covering: the relationship between initial cost estimates and subsequent budgets/costs; the role of cost models in the product lifecycle; and the role of performance measurement baselines in the project lifecycle. The presentation aims to provide an understanding of how estimated costs are developed, budgets are set, and information is used throughout the project to help ensure successful delivery. It also considers scenarios where products span multiple related projects over their lifecycle.
This document discusses predictive analytics and provides two case studies. It begins with an overview of predictive analytics and the evolution of analytics from business intelligence to big data to intelligent offerings. It then describes two case studies - one on rainfall prediction using machine learning algorithms like SVR and ANN, and another using the WEKA machine learning tool to classify iris flowers by attributes. The document provides details on the methods, datasets, and results of each case study.
1. Pavel Shevchenko works for CSIRO's Quantitative Risk Management group, which develops mathematical models for financial and other risks.
2. CSIRO is Australia's national science agency, with over 6500 staff across various divisions including Mathematical and Information Sciences.
3. The Quantitative Risk Management group applies techniques like extreme value analysis, dependence modelling, and Bayesian methods to areas like financial, infrastructure, environmental and security risks.
Foreign exchange risk and exposure refer to how changes in exchange rates can affect the value of a firm's assets, liabilities, and profits. Exposure is the sensitivity of a firm's value to exchange rate changes, while risk is the variability of a firm's value due to uncertain exchange rate changes. There are three main types of exposures - transaction, translation, and economic. Firms can use hedging strategies like forward contracts and options to manage their foreign exchange risk and exposure by locking in exchange rates for future transactions.
DanTech is a UK-based technology company that is expanding its operations to the US through a joint venture called DanEast Ltd. DanTech will invoice US dollar exports through DanEast and import Japanese yen costs. Changes in the GBP/USD and GBP/JPY exchange rates present foreign exchange risk to DanTech's profits. The treasury manager recommends hedging techniques like money markets, forwards/futures, and options to manage this risk.
This document discusses foreign exchange risk and its management. It defines foreign exchange risk as the risk of an investment's value changing due to currency fluctuations. It identifies the main types of foreign exchange risk as transaction risk, translation risk, and economic risk. Transaction risk arises from currency movements between the signing and execution of contracts. Translation risk occurs when consolidating financial statements in different currencies. Economic risk affects the long-term expected profits and wealth of a company due to currency changes. The document outlines various hedging strategies to manage these risks, including the use of forwards, futures, and money markets.
The document discusses authorized dealers in foreign exchange in India. It states that authorized dealers are banks authorized by the central bank to deal in foreign exchange under the Foreign Exchange Regulation Act of 1947. The functions of authorized dealers include exchanging foreign currencies, making arrangements with foreign correspondents, buying and selling currencies, handling inward and outward remittances, opening letters of credit and settling payments, investing in foreign trade, opening and maintaining foreign bank accounts, and handling export documents. It also discusses the role of banks in foreign trade through correspondent relationships and the types of banks involved in letters of credit.
The Heatmap - Why is Security Visualization so Hard?Raffael Marty
The extent and impact of recent security breaches is showing that current approaches are just not working. But what can we do to protect our business? We have been advocating monitoring for a long time as a way to detect subtle, advanced attacks. However, products have failed to deliver on this promise. Current solutions don't scale in both data volume and analytical insights. In this presentation we will explore why it is so hard to come up with a security monitoring (or shall we call it security intelligence) approach that helps find sophisticated attackers in all the data collected. We are going to explore the question of how to visualize a billion events. We are going to look at a number of security visualization examples to illustrate the problem and some possible solutions. These examples will also help illustrate how data mining and user experience design help us get a handle of the security visualization challenges - enabling us to gain deep insight for a number of security use-cases.
This document discusses managing project cost models, budgets, and actual costs to deliver successful projects. It provides an agenda for a presentation covering: the relationship between initial cost estimates and subsequent budgets/costs; the role of cost models in the product lifecycle; and the role of performance measurement baselines in the project lifecycle. The presentation aims to provide an understanding of how estimated costs are developed, budgets are set, and information is used throughout the project to help ensure successful delivery. It also considers scenarios where products span multiple related projects over their lifecycle.
This document discusses predictive analytics and provides two case studies. It begins with an overview of predictive analytics and the evolution of analytics from business intelligence to big data to intelligent offerings. It then describes two case studies - one on rainfall prediction using machine learning algorithms like SVR and ANN, and another using the WEKA machine learning tool to classify iris flowers by attributes. The document provides details on the methods, datasets, and results of each case study.
This document discusses predictive analytics and provides two case studies. It begins with an overview of predictive analytics and the evolution of analytics from business intelligence to big data to intelligent offerings. Case Study 1 describes a model for rainfall prediction that uses ensemble empirical mode decomposition, support vector regression and artificial neural networks. Case Study 2 discusses using machine learning algorithms like SVR and ANN for prediction and analyzes datasets using the WEKA tool. The document concludes with references.
For the full video of this presentation, please visit:
https://www.edge-ai-vision.com/2021/01/reinforcement-learning-a-practical-introduction-a-presentation-from-microsoft/
Joe Booth, former Vice President of Engineering at Orions Systems (now part of Microsoft, where Booth is a Principal Group Engineering Manager) and an independent researcher, presents the “Reinforcement Learning: a Practical Introduction” tutorial at the September 2020 Embedded Vision Summit.
This talk provides a practical introduction to reinforcement learning. Booth explains how reinforcement learning relates to other machine learning methods, provides examples of real-world deployments, and gives a technical overview of the elements of reinforcement learning.
Booth also presents practical advice on when to use reinforcement learning and how to structure problems to use reinforcement learning effectively. Finally, he provides the recommended resources for learning more about this important technique.
Variation and Quality (2.008x Lecture Slides)A. John Hart
Slides accompanying 2.008x* video module on Variation and Quality, Prof. John Hart, MIT, 2016.
*Fundamentals of Manufacturing Processes on edX: https://www.edx.org/course/fundamentals-manufacturing-processes-mitx-2-008x
This document discusses time series forecasting techniques for multivariate and hierarchical time series data. It presents several cases involving energy consumption forecasting, sales forecasting, and freight transportation forecasting. For each case, it describes the time series data and components, discusses feature generation methods like nonparametric transformations and the Haar wavelet transform to extract features, and evaluates different forecasting models and their ability to generate consistent forecasts while respecting any hierarchical relationships in the data. The focus is on generating accurate forecasts while maintaining properties like consistency, minimizing errors, and handling complex time series structures.
Intro to Quant Trading Strategies (Lecture 1 of 10)Adrian Aley
This document provides an overview of a lecture on quantitative trading strategies given by Dr. Haksun Li. It discusses technical analysis from a scientific perspective and outlines Numerical Method's quantitative trading research process. This includes translating trading intuitions into mathematical models, coding the strategies, evaluating their properties through simulation, and live trading. Moving average crossover is presented as an example strategy and approaches to model it quantitatively are described.
Data is the new oil! Modern analytical methods are a decisive success factor for service-oriented business models in IoT and Industry 4.0. A new white paper explains the state of the art and shows what latest methods can achieve in practice
2008 implementation of va r in financial institutionscrmbasel
The document discusses implementing a value-at-risk (VaR) system in a financial institution. It describes VaR as a single risk measure that incorporates risks across all financial instruments. It outlines methods for calculating VaR, including variance-covariance, historical simulation, and Monte Carlo simulation. It also discusses components of an industry VaR system including market data, pricing engines, and VaR computation. Limitations of VaR are noted.
Mathematics of outlier_detection_and_pattern_recognition_pharmacy_fraud_2013Double Check ĆŐNSULTING
Pharmaceutical drug fraud a 360 degree hurt to US economy that also includes $60 Billion Medicare fraud and Money Laundry. They defeat the drivers of genomics, mHealth, TeleHealth innovations. Random samples are drawn from large samples and analyzed mathematically (calculus, Regression etc)
How the machine understands Korean
기계와 대화를 하려면 어떻게 해야 할까요? 우리는 그 동안 기계가 이해할 수 있는 프로그래밍 언어를 만들어서, 그 언어를 통해 소통해 왔습니다. 하지만 2010년 들어서며 급물살을 탄 AI 연구는 이러한 소통의 영역까지 침투하여, 기계가 인간의 언어를 이해하고, 소통할 수 있는 단계로 다가서고자 노력하고 있습니다. 그 근간에는 선형대수학의 여러 이론들이 사용되고 있는데요, 특히 인간의 언어를 기호화하고 이를 벡터공간에 투영하는 방법들이 핵심으로 여겨지고 있습니다. 이러한 방법을 임베딩(embedding)이라 지칭하고, 단어부터 문장, 문서에 이르기까지 인간의 언어를 다양한 형태로 벡터화하고, 이를 이용해 언어의 의미 유사성, 관계 유사성 등을 벡터 공간에서 벡터 연산을 통해 내재적인 의미를 도출합니다.
이번 세미나에서는 벡터공간모델(Vector Space Model, VSM)의 전통적인 방법(TF-IDF, SVD 등)부터 신경망 방법(word2vec, sent2vec 등)에 이르는 다양한 언어 모델링들을 살펴보고, 이를 한국어에 적용했을 때 기계가 어떻게 의미를 이해하는 것으로 해석할 수 있는지 다양한 관점에서 실험을 통해 살펴보도록 하겠습니다.
This talk builds on recent empirical work addressing the extent to which the transaction graph serves as an early-warning indicator for large financial losses. By identifying certain sub-graphs ('chainlets') with causal effect on price movements, we demonstrate the impact of extreme transaction graph activity on the intraday volatility of the Bitcoin prices series. In particular, we infer the loss distributions conditional on extreme chainlet activity. Armed with this empirical representation, we propose a modeling approach to explore conditions under which the market is stabilized by transaction graph aware agents.
This document discusses using scikit-learn to perform linear predictions on datasets. It begins with an overview of linear regression and classification algorithms in scikit-learn. Then it walks through examples of using scikit-learn to perform linear regression on the Boston housing price dataset and linear classification on the iris dataset. It concludes by demonstrating an example of using linear models to detect insults in social media comments.
Speedup Your Java Apps with Hardware CountersC4Media
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/2n593ji.
Sergey Kuksenko discusses how Performance Monitoring Unit works, what Hardware Counters are, which tools have friendship with Java and how to use HWC for speeding up our Java applications. Filmed at qconsf.com.
Sergey Kuksenko works as Java Performance Engineer at Oracle. His primary goal is making Oracle JVM faster digging into JVM runtime, JIT compilers, class libraries, etc. His favorite area is an interaction of Java with modern hardware what he is doing since 2005 when he worked at Intel in Apache Harmony Performance team.
"New edge prediction and anomaly-detection in large computer networks" by Dr Silvia Metelli, Marie Skołodowska-Curie Individual Fellow
Abstract : Monitoring computer network traffic in search for anomalous behaviour is both a challenging and important task for cyber-security. New edges, i.e. connections from a host or user to a computer that has not been connected to before, provide potentially strong statistical evidence for detecting anomalies and in rare cases might suggest the presence of intruders or malicious activity. In this talk, I will introduce a robust Bayesian model and anomaly detection method for simultaneously characterising network structure and modelling likely new edge formation in a large computer network graph. What constitutes normal behaviour for some hosts might be very unusual for some others and thus examining existing network structure (e.g. clusters of similar clients and servers) is key for accurately predicting likely future interactions. Finally, the model is used to construct an anomaly detection method, which successfully identifies some of the machines known to be compromised when demonstrated on real computer network authentication data.
Online fraud costs the global economy more than $400 billion, with more than 800 million personal records stolen in 2013 alone. Increasingly, fraud has diversified to different digital channels, including mobile and online payments, creating new challenges as innovative fraud patterns emerge. Hence it is still a challenge to find effective methods to mitigate fraud. Existing solutions include simple if-then rules and classical machine learning algorithms.
From an academic perspective, credit card fraud detection is a standard classification problem, in which historical transaction data is used to predict future frauds. However, practical aspects make the problem more complex. Indeed, existent comparison measures lack a realistic representation of monetary gains and losses, which is necessary for effective fraud detection. Moreover, there is an enormous amount of transactions from which only a tiny part are frauds, which implies a huge class imbalance. Additionally, a real fraud detection system is required to give a response in milliseconds. This criterion needs to be taken into account in the modeling process in order for the system to be successfully implemented. To solve these problems, in this presentation two recently proposed algorithms are compared: Bayes minimum risk and example-dependent cost-sensitive decision tree. These methods are compared with state of the art algorithms and shows significant improvements measured by financial savings.
Datastax day 2016 : Cassandra data modeling basicsDuyhai Doan
This document discusses data modeling with Apache Cassandra. It covers:
1. The objectives of data modeling like reducing query latency and avoiding disasters
2. Choosing the right partition key which is the main entry point for queries and helps distribute data
3. Using clustering columns to simulate one-to-many relationships and enable sorting and range queries
4. Other critical details like avoiding huge partitions, sub-partitioning techniques, and how deletes create tombstones
Des exemples de use cases dont vous pourrez vous inspirer, et de plateformes de ML-as-a-Service pour vous faciliter le human learning du machine learning, l'expérimentation, et le déploiement en production!
The document discusses cryptocurrency mining and the cryptocurrency mining supercomputer market. It estimates the annual economic value of mined cryptocurrencies to be $9.1 billion from the top 50 mining pools across the top 6 mined coins. Bitcoin dominates with 62% of the total value, followed by Ethereum at 33%. It also analyzes the top mining rigs, trends in Bitcoin's hashrate growth, leading mining pools and host countries, and provides summaries of the top mining pools for Bitcoin, Ethereum, Litecoin, and other coins.
The document analyzes the cryptocurrency mining supercomputer market. It estimates the top 50 mining pools generate $7.9 billion annually across the top 6 mined coins, with Bitcoin accounting for 62% and Ethereum 33%. China hosts 19 of the top 50 pools and accounts for 39.6% of annual economic value. The largest pool operator is F2Pool, a global pool responsible for 1058 million annually. Bitcoin's hashrate has grown exponentially from 0.2 terahashes to over 100 exahashes in the past decade, representing a 163% compound annual growth rate.
The document discusses load profiling arrangements for the NSW gas market as it transitions to full retail competition (FRC). It summarizes that net system load profiling (NSLP) using regional profiles will be used to facilitate FRC. Key aspects include annual apportionment of loads to retailers, forward reconciliation over 28 days, and global settlements to spread errors across all participants. The arrangements aim to balance objectives like equity, efficiency and minimizing barriers to competition during the transition to FRC.
This document provides an overview of market entry theory and strategies for entering the natural gas market in NSW, Australia. It discusses possible advantages incumbents may have; a case study of the telecommunications industry; growth projections for the gas market; how many competitors the market can support based on other industries; using conjoint analysis to predict market shares; considerations around pricing; and customer price sensitivity segments. The key topics covered are barriers to entry, market saturation points, market share prediction techniques, and pricing strategies.
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This document discusses predictive analytics and provides two case studies. It begins with an overview of predictive analytics and the evolution of analytics from business intelligence to big data to intelligent offerings. Case Study 1 describes a model for rainfall prediction that uses ensemble empirical mode decomposition, support vector regression and artificial neural networks. Case Study 2 discusses using machine learning algorithms like SVR and ANN for prediction and analyzes datasets using the WEKA tool. The document concludes with references.
For the full video of this presentation, please visit:
https://www.edge-ai-vision.com/2021/01/reinforcement-learning-a-practical-introduction-a-presentation-from-microsoft/
Joe Booth, former Vice President of Engineering at Orions Systems (now part of Microsoft, where Booth is a Principal Group Engineering Manager) and an independent researcher, presents the “Reinforcement Learning: a Practical Introduction” tutorial at the September 2020 Embedded Vision Summit.
This talk provides a practical introduction to reinforcement learning. Booth explains how reinforcement learning relates to other machine learning methods, provides examples of real-world deployments, and gives a technical overview of the elements of reinforcement learning.
Booth also presents practical advice on when to use reinforcement learning and how to structure problems to use reinforcement learning effectively. Finally, he provides the recommended resources for learning more about this important technique.
Variation and Quality (2.008x Lecture Slides)A. John Hart
Slides accompanying 2.008x* video module on Variation and Quality, Prof. John Hart, MIT, 2016.
*Fundamentals of Manufacturing Processes on edX: https://www.edx.org/course/fundamentals-manufacturing-processes-mitx-2-008x
This document discusses time series forecasting techniques for multivariate and hierarchical time series data. It presents several cases involving energy consumption forecasting, sales forecasting, and freight transportation forecasting. For each case, it describes the time series data and components, discusses feature generation methods like nonparametric transformations and the Haar wavelet transform to extract features, and evaluates different forecasting models and their ability to generate consistent forecasts while respecting any hierarchical relationships in the data. The focus is on generating accurate forecasts while maintaining properties like consistency, minimizing errors, and handling complex time series structures.
Intro to Quant Trading Strategies (Lecture 1 of 10)Adrian Aley
This document provides an overview of a lecture on quantitative trading strategies given by Dr. Haksun Li. It discusses technical analysis from a scientific perspective and outlines Numerical Method's quantitative trading research process. This includes translating trading intuitions into mathematical models, coding the strategies, evaluating their properties through simulation, and live trading. Moving average crossover is presented as an example strategy and approaches to model it quantitatively are described.
Data is the new oil! Modern analytical methods are a decisive success factor for service-oriented business models in IoT and Industry 4.0. A new white paper explains the state of the art and shows what latest methods can achieve in practice
2008 implementation of va r in financial institutionscrmbasel
The document discusses implementing a value-at-risk (VaR) system in a financial institution. It describes VaR as a single risk measure that incorporates risks across all financial instruments. It outlines methods for calculating VaR, including variance-covariance, historical simulation, and Monte Carlo simulation. It also discusses components of an industry VaR system including market data, pricing engines, and VaR computation. Limitations of VaR are noted.
Mathematics of outlier_detection_and_pattern_recognition_pharmacy_fraud_2013Double Check ĆŐNSULTING
Pharmaceutical drug fraud a 360 degree hurt to US economy that also includes $60 Billion Medicare fraud and Money Laundry. They defeat the drivers of genomics, mHealth, TeleHealth innovations. Random samples are drawn from large samples and analyzed mathematically (calculus, Regression etc)
How the machine understands Korean
기계와 대화를 하려면 어떻게 해야 할까요? 우리는 그 동안 기계가 이해할 수 있는 프로그래밍 언어를 만들어서, 그 언어를 통해 소통해 왔습니다. 하지만 2010년 들어서며 급물살을 탄 AI 연구는 이러한 소통의 영역까지 침투하여, 기계가 인간의 언어를 이해하고, 소통할 수 있는 단계로 다가서고자 노력하고 있습니다. 그 근간에는 선형대수학의 여러 이론들이 사용되고 있는데요, 특히 인간의 언어를 기호화하고 이를 벡터공간에 투영하는 방법들이 핵심으로 여겨지고 있습니다. 이러한 방법을 임베딩(embedding)이라 지칭하고, 단어부터 문장, 문서에 이르기까지 인간의 언어를 다양한 형태로 벡터화하고, 이를 이용해 언어의 의미 유사성, 관계 유사성 등을 벡터 공간에서 벡터 연산을 통해 내재적인 의미를 도출합니다.
이번 세미나에서는 벡터공간모델(Vector Space Model, VSM)의 전통적인 방법(TF-IDF, SVD 등)부터 신경망 방법(word2vec, sent2vec 등)에 이르는 다양한 언어 모델링들을 살펴보고, 이를 한국어에 적용했을 때 기계가 어떻게 의미를 이해하는 것으로 해석할 수 있는지 다양한 관점에서 실험을 통해 살펴보도록 하겠습니다.
This talk builds on recent empirical work addressing the extent to which the transaction graph serves as an early-warning indicator for large financial losses. By identifying certain sub-graphs ('chainlets') with causal effect on price movements, we demonstrate the impact of extreme transaction graph activity on the intraday volatility of the Bitcoin prices series. In particular, we infer the loss distributions conditional on extreme chainlet activity. Armed with this empirical representation, we propose a modeling approach to explore conditions under which the market is stabilized by transaction graph aware agents.
This document discusses using scikit-learn to perform linear predictions on datasets. It begins with an overview of linear regression and classification algorithms in scikit-learn. Then it walks through examples of using scikit-learn to perform linear regression on the Boston housing price dataset and linear classification on the iris dataset. It concludes by demonstrating an example of using linear models to detect insults in social media comments.
Speedup Your Java Apps with Hardware CountersC4Media
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/2n593ji.
Sergey Kuksenko discusses how Performance Monitoring Unit works, what Hardware Counters are, which tools have friendship with Java and how to use HWC for speeding up our Java applications. Filmed at qconsf.com.
Sergey Kuksenko works as Java Performance Engineer at Oracle. His primary goal is making Oracle JVM faster digging into JVM runtime, JIT compilers, class libraries, etc. His favorite area is an interaction of Java with modern hardware what he is doing since 2005 when he worked at Intel in Apache Harmony Performance team.
"New edge prediction and anomaly-detection in large computer networks" by Dr Silvia Metelli, Marie Skołodowska-Curie Individual Fellow
Abstract : Monitoring computer network traffic in search for anomalous behaviour is both a challenging and important task for cyber-security. New edges, i.e. connections from a host or user to a computer that has not been connected to before, provide potentially strong statistical evidence for detecting anomalies and in rare cases might suggest the presence of intruders or malicious activity. In this talk, I will introduce a robust Bayesian model and anomaly detection method for simultaneously characterising network structure and modelling likely new edge formation in a large computer network graph. What constitutes normal behaviour for some hosts might be very unusual for some others and thus examining existing network structure (e.g. clusters of similar clients and servers) is key for accurately predicting likely future interactions. Finally, the model is used to construct an anomaly detection method, which successfully identifies some of the machines known to be compromised when demonstrated on real computer network authentication data.
Online fraud costs the global economy more than $400 billion, with more than 800 million personal records stolen in 2013 alone. Increasingly, fraud has diversified to different digital channels, including mobile and online payments, creating new challenges as innovative fraud patterns emerge. Hence it is still a challenge to find effective methods to mitigate fraud. Existing solutions include simple if-then rules and classical machine learning algorithms.
From an academic perspective, credit card fraud detection is a standard classification problem, in which historical transaction data is used to predict future frauds. However, practical aspects make the problem more complex. Indeed, existent comparison measures lack a realistic representation of monetary gains and losses, which is necessary for effective fraud detection. Moreover, there is an enormous amount of transactions from which only a tiny part are frauds, which implies a huge class imbalance. Additionally, a real fraud detection system is required to give a response in milliseconds. This criterion needs to be taken into account in the modeling process in order for the system to be successfully implemented. To solve these problems, in this presentation two recently proposed algorithms are compared: Bayes minimum risk and example-dependent cost-sensitive decision tree. These methods are compared with state of the art algorithms and shows significant improvements measured by financial savings.
Datastax day 2016 : Cassandra data modeling basicsDuyhai Doan
This document discusses data modeling with Apache Cassandra. It covers:
1. The objectives of data modeling like reducing query latency and avoiding disasters
2. Choosing the right partition key which is the main entry point for queries and helps distribute data
3. Using clustering columns to simulate one-to-many relationships and enable sorting and range queries
4. Other critical details like avoiding huge partitions, sub-partitioning techniques, and how deletes create tombstones
Des exemples de use cases dont vous pourrez vous inspirer, et de plateformes de ML-as-a-Service pour vous faciliter le human learning du machine learning, l'expérimentation, et le déploiement en production!
The document discusses cryptocurrency mining and the cryptocurrency mining supercomputer market. It estimates the annual economic value of mined cryptocurrencies to be $9.1 billion from the top 50 mining pools across the top 6 mined coins. Bitcoin dominates with 62% of the total value, followed by Ethereum at 33%. It also analyzes the top mining rigs, trends in Bitcoin's hashrate growth, leading mining pools and host countries, and provides summaries of the top mining pools for Bitcoin, Ethereum, Litecoin, and other coins.
The document analyzes the cryptocurrency mining supercomputer market. It estimates the top 50 mining pools generate $7.9 billion annually across the top 6 mined coins, with Bitcoin accounting for 62% and Ethereum 33%. China hosts 19 of the top 50 pools and accounts for 39.6% of annual economic value. The largest pool operator is F2Pool, a global pool responsible for 1058 million annually. Bitcoin's hashrate has grown exponentially from 0.2 terahashes to over 100 exahashes in the past decade, representing a 163% compound annual growth rate.
Similar to Options for Managing Foreign Exchange Risk (20)
The document discusses load profiling arrangements for the NSW gas market as it transitions to full retail competition (FRC). It summarizes that net system load profiling (NSLP) using regional profiles will be used to facilitate FRC. Key aspects include annual apportionment of loads to retailers, forward reconciliation over 28 days, and global settlements to spread errors across all participants. The arrangements aim to balance objectives like equity, efficiency and minimizing barriers to competition during the transition to FRC.
This document provides an overview of market entry theory and strategies for entering the natural gas market in NSW, Australia. It discusses possible advantages incumbents may have; a case study of the telecommunications industry; growth projections for the gas market; how many competitors the market can support based on other industries; using conjoint analysis to predict market shares; considerations around pricing; and customer price sensitivity segments. The key topics covered are barriers to entry, market saturation points, market share prediction techniques, and pricing strategies.
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1. Options for Managing Foreign Exchange
Dr Zili Zhu
Quantitative Risk Management
Mathematics, Informatics & Statistics
26th
March 2010
2. CSIRO Mathematics, Informatics & Statistics www.cmis.csiro.au
Background of CSIRO
Organization:
• Commonwealth Scientific and Industrial Research Organization
(7200 staff members)
• Division of Mathematics, Informatics and Statistics (150 Scientists)
• Quantitative Risk Management Group (25 scientists)
Commercial activities
• CSIRO Exotic math for FX markets
• Consulting assignments for major banks
• Development of new options models for hedge
funds.
• Development of major risk-management software.
• Rea-options valuation in energy industries.
3. CSIRO Mathematics, Informatics & Statistics www.cmis.csiro.au
Content
An introduction to common derivative products in
FX
Understanding the key components of pricing
derivatives.
How reliable are the pricing models given recent
and excessive volatility
Other risk valuation methods
4. CSIRO Mathematics, Informatics & Statistics www.cmis.csiro.au
Financial Derivatives
Exchange markets: standardised Futures, swaps and options are
actively traded on exchanges.
Over-the-counter (OTC) market: forwards, exotic options are traded
directly among institutions and outside of exchanges.
Derivative – financial instrument whose value depends on other more
basic variables (stocks, futures, FXs, interest rates), e.g. Vanilla
call/put options on traded shares.
7. CSIRO Mathematics, Informatics & Statistics www.cmis.csiro.au
Some exotic options used in FX
Window barrier options (KO, KI, Touches, Digital)
Basket options
Range accrual
Target-redemption notes
B
8. CSIRO Mathematics, Informatics & Statistics www.cmis.csiro.au
Example: Reverse Knockout Call
Up and Out Call
Payoff is:
V(S,T) = (S – K) if S < B
V(S,T) = 0 if S ≥ B
Barrier is: V(S,t) = 0 if S ≥ B
t
B
K
t
S
K
B
9. CSIRO Mathematical & Information Sciences www.cmis.csiro.au
A Typical Exotic Option: Two-Asset No-Touch
FENICS FX Pricing Page:
12. CSIRO Mathematics, Informatics & Statistics www.cmis.csiro.au
How to Price Derivatives in FX
The price of a derivative should be the hedging cost of the
derivative over its life cycle.
Financial mathematics is well established.
Option-pricing formula and numerical methods are available.
Industry conventions need to be considered.
B
0)(
))()((PutVanilla
0)(
))()((CallVanilla
1
102
1
210
<−−=
∂
∂
=∆
−−−=
>=
∂
∂
=∆
−=
−
−
dN
S
Q
dNeSdKNeprice
dN
S
Q
dKNdNeSeprice
P
P
rTrT
C
C
rTrT
13. CSIRO Mathematics, Informatics & Statistics www.cmis.csiro.au
Currency prices follow stochastic processes:
i
dZ
i
St
i
dt
i
S
ii
dS )(σµ += i=1,2,…..N
j
dZ
i
dZ
ij
=ρ
Methodologies for Pricing Derivatives
$0.6
$0.7
$0.8
$0.9
$1.0
$1.1
$1.2
$1.3
$1.4
$1.5
0 0.2 0.4 0.6 0.8 1
time
stockprice,S(t)
14. CSIRO Mathematics, Informatics & Statistics www.cmis.csiro.au
Example of Pricing a Call Option – delta hedging
Portfolio= S0Δ – call option
Stock price, S0 = $10
Strike=$11
Stock price, ST = $12
Option price = $1
Portfolio1 = 12Δ - 1
Stock price, ST = $8
Option price = $0
Portfolio2 = 8Δ - 0Time period T
optionscall102Portfolio
2Portfolio2Portfolio1..25.0..8112...Portfolio2Portfolio1:wantWe
−∆==
===∆∆=−∆= andsoei
5.02
4
1
10priceoptionCall =−×=
16. CSIRO Mathematics, Informatics & Statistics www.cmis.csiro.au
Using Monte-Carlo Simulations:
]|]0,[max[Pr 0SKSEeAmountemium T
Trd
−×= −
Simulated future carbon prices
1
10
100
1000
10000
2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048
Year
Carbonprice
dtdZdZEtdZtdtttSd ijjiiii
ii
t ρσσµ =+−= ][);()()](5.0)([ln 2)()(
]ˆ)ˆˆexp[( 2
2
1)()(
iiii
i
t
i
tt ZttXX δσδσµδ +−=+
17. CSIRO Mathematics, Informatics & Statistics www.cmis.csiro.au
Finite Difference, Element, Volume Methods
0),()(
),(
])()([
),(
2
),(),(
2
222
=−
∂
∂
−+
∂
∂
+
∂
∂
tSVtr
S
tSV
Stqtr
S
tSVStS
t
tSV σ
S0
S
18. CSIRO Mathematical & Information Sciences www.cmis.csiro.au
An Exotic Option: two-asset options
• 2 asset Black-Scholes equation:
• Payoff function
)max( 2,1 SSPayoff =
S2
∂
∂
σ
∂
∂
σ
∂
∂
ρσ σ
∂
∂ ∂
µ
∂
∂
µ
∂
∂
V
t
S
V
S
S
V
S
S S
V
S S
S
V
S
S
V
S
rV+ + + + + − =
1
2
1
2
01
2 2
1
2
1
2 2
2 2
2
2
2
2 1 2 1 2
2
1 2
1 1
1
2 2
2
S2
S2S1
)0,max( 21 11 KSwSwPayoff −+=
)min( 2,1 SSPayoff =
19. CSIRO Mathematics, Informatics & Statistics www.cmis.csiro.au
How Reliable are Pricing Models?
All models are constructed under certain assumptions.
All models have their limitations.
Model implementations can also have their own limitations.
Computer code can often have bugs.
Market data may not be arbitrage-free.
Market data may be inconsistent.
Models and pricing functions should have been tested for extreme
market conditions.
On-going updates and maintenance are needed.
Market is evolving, and models should too.
20. CSIRO Mathematics, Informatics & Statistics www.cmis.csiro.au
Practical Issues in Pricing Derivatives
Volatility is not constant, vol
skew/smile exists.
Correlation is dependent on ATM
price.
Correlation should be dependent on
strike levels?
How to price basket options with
skew.
How much correction is needed to
get market price?
Compromise between speed and
accuracy.
21. CSIRO Mathematical & Information Sciences www.cmis.csiro.au
Volatility Smile/Skew
),()0(),( 0
VXVXVX Π−=∆•−∆Π−= T
loss
f
}),({min)()( αξξ ξαα
≥Ψ=≡ ∈
XXX R
VaR
][)( tail lossfECVaR −=≡ ααφ X
22. CSIRO Mathematics, Informatics & Statistics www.cmis.csiro.au
Hedging Principles
• Hedging to eliminate risk due to market movements in
asset prices, volatility, interest-rates and correlations.
• The cost of hedging reflects the premium received from
clients.
• Limit large down-side risk to P/L.
• Trading in derivatives without hedging is speculation.
• The objective of hedging is to protect business from
unpredictable market movements on a daily basis.
29. CSIRO Mathematical & Information Sciences www.cmis.csiro.au
Delta hedging is automatically set for each individual option
through the purchase/sell of underlying assets.
Other greek parameters such as gamma, vega, rho are balanced
through the purchase/sell of vanilla and/or more liquid exotic
options at portfolio level.
For options with discontinuous risk profiles or path-dependency
(e.g. barrier options), hedging is difficult.
Portfolio Approach
30. CSIRO Mathematical & Information Sciences www.cmis.csiro.au
Loss Distribution without Hedges
Target portfolio loss distribution
0
10
20
30
40
50
60
70
80
90
100
-6 -3.5 -1 1.5 4 6.5 9 11.5 14 16.5
Loss
Frequency
31. CSIRO Mathematical & Information Sciences www.cmis.csiro.au
A Greek Delta-Gamma Hedge To Reduce Risk
Delta-gamma hedge
0
100
200
300
400
500
600
-0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2
Loss
Frequency
32. CSIRO Mathematics, Informatics & Statistics www.cmis.csiro.au
Hedging Strategy
• Risk can only be reduced but not eliminated via hedging through
greeks even if the Black-Scholes model is appropriate.
• Hedging through greeks is model dependent.
• For commodities and energies (e.g. electricity), model
dependency can make hedging ineffective.
33. CSIRO Mathematical & Information Sciences www.cmis.csiro.au
Hedging Through CVaR Minimisation
CVaR-minimising hedge
0
100
200
300
400
500
-0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2
Loss
Frequency
34. CSIRO Mathematics, Informatics &Statistics www.cmis.csiro.au
Other risk valuation methods
Implied volatility of Black-Scholes model is used for quoting FX
options.
New valuation models are developed and implemented regularly.
Every model has its drawbacks, and no model is perfect.
Speed, accuracy and robustness need to be considered.
35. CSIRO Mathematics, Informatics &Statistics www.cmis.csiro.au
Local volatility surface model
functionyvolatilitlocal),(
).(),()]()([/
tS
tdWtSdttqtrSdS tt
σ
σ+−=
37. CSIRO Mathematical & Information Sciences www.cmis.csiro.au
Summary
Introduction of derivatives in the FX market.
A large number of options are available to accommodate
specific risk appetites and market views of end-users.
The hedging of options can be implemented as part of a
structure.
Full understanding of down-side risk of options is paramount
before trading.
Introduced key concepts in pricing derivatives in the FX
market, and different pricing methods are available.
All models have limitations. Implementation also has
limitations.
Market data can be problematic.
New and sophisticated models are created regularly. No
model is perfect.
38. CSIRO Mathematics, Informatics & Statistics www.cmis.csiro.au
Acknowledgments
• Thanks to FENICS FX, the global standard in FX options
pricing and analysis, for the use of their trading system. The
screenshots of pricing pages and market data pages in this
presentation are from FENICS FX.