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Risk of the DARWIN asset is tracked in terms of VaR (Value at Risk). Our VaR evaluates the potential loss in the worst out of 20 months, in terms of percentage of the equity. Do you want to know more about VaR? We suggest you the following article: http://help.darwinex.com/darwinex-algorithms/metrics-and-charts/var-metric Furthermore, would you like to know what VaR and a DARWIN have in common? Check it out here: http://help.darwinex.com/darwinex-for-investors/what-is-a-darwin Darwinex - The Trader Exchange: https://goo.gl/p7TGRY
Views: 76539 Darwinex

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MIT 18.S096 Topics in Mathematics with Applications in Finance, Fall 2013 View the complete course: http://ocw.mit.edu/18-S096F13 Instructor: Kenneth Abbott This is an applications lecture on Value At Risk (VAR) models, and how financial institutions manage market risk. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 203069 MIT OpenCourseWare

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Views: 13714 The Audiopedia

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In this short video, we'll explain how an investment's risk gets measured. Closed-captions-only tutorial, please activate CC.
Views: 3834 Darwinex

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Views: 25059 Bionic Turtle

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This example is a portfolio of three stocks: GOOG, YHOO, and MSFT. Process is: 1. I calculated for each stock the historical series of daily periodic returns (bottom left, below). 2. For each historical day (e.g., Friday 7/18), I calculate the portfolio gain/loss as if I held the current portfolio on that day. This is the essence of the idea: run historical returns through the current portfolio allocation. 3. This produces an historical series (right column, green) of simulated portfolio returns. Now I can treat as with the single-asset; e.g., if I want 95% VaR, then I need = PERCENTILE(range, 5%). For more financial risk videos, please visit our website! http://www.bionicturtle.com
Views: 155844 Bionic Turtle

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Views: 1117 Coursera

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Value at Risk or VaR is one of the most used risk management tools by the investors and traders. It is a statistical tool. Value at Risk or VaR tells the probability of loss with 99% or 95% accuracy over a period of time. Value at Risk or VaR is critical when the market is in downtrend or bear phase. It basically tells how much money you can lose in a particular stock. In the stock market, risk management is important for risk-averse retail investors. Secondly, it also helps in stock selection depending on the risk appetite. As a thumb rule, i invest only in stocks with the Value at Risk or VaR of less than 7.5%. It reduces my loss in the stock market. If you liked this video, You can "Subscribe" to my YouTube Channel. The link is as follows https://goo.gl/nsh0Oh By subscribing, You can daily watch a new Educational and Informative video in your own Hindi language. For more such interesting and informative content, join me at: Website: http://www.nitinbhatia.in/ T: http://twitter.com/nitinbhatia121 G+: https://plus.google.com/+NitinBhatia #NitinBhatia
Views: 46743 Nitin Bhatia

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Description of historical and normal distribution methods for computing Value at Risk (VAR) of a portfolio
Views: 120062 westofvideo

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This is an excerpt from the IFT Level II Port Mgmt on Measuring and Managing Market Risk. Here we understand the definition of VaR. For more videos, notes, practice questions, mock exams and more visit: http://www.ift.world/inbound-signup Facebook: facebook.com/Pass.with.IFT
Views: 2079 IFT

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Value at Risk describes the probability of a loss threshold over a time horizon. At Darwinex, Value at Risk describes a 5% likely loss over a 1 month investment threshold.
Views: 906 Darwinex

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Hi Guys, This video will show you how to find the expected return and risk of a single portfolio. This example will show you the higher the risk the higher the return. Please watch more videos at www.i-hate-math.com Thanks for learning !
Views: 224172 I Hate Math Group, Inc

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Training on VAR and Risk Budgeting in Investment Management by Vamsidhar Ambatipudi

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https://alphabench.com/data/excel-value-at-risk-tutorial.html -- Also see my Monte Carlo Simulation of VaR Value at Risk (VaR) is a statistical measurement of downside risk applied to current portfolio positions. It represents downside risk going forward a specified amount of time, with no changes in positions held. VaR can be calculated for any time period however, since uncertainty increases with time it is often calculated for a single day or several days into the future. There are two major methods for calculating VaR: 1. using historical data or empirical data, referred to as non-parametric. 2. using an approximation based on some theoretical probability distribution such as the normal distribution. This method is discussed in the tutorial. VaR is supposed to represent a worst case scenario such that there is a low probability that actual losses will exceed the calculated VaR. So for a 95% confidence level VaR represents a downside movement of 1.645 std deviations and for a 99% confidence level it represents a downside move of 2.33 std deviations. When calculating VaR using the method in this tutorial, we are actually calculating a mean VaR based on some pre-specified confidence level. The drawback is it is not possible to estimate how large a loss may be if the downside move exceeds the confidence level.
Views: 1648 Matt Macarty

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Watch the next finance lesson: https://bluebookacademy.com/courses

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Surplus as risk is value at risk (VaR) for a pension fund. For more financial risk videos, visit our website! http://www.bionicturtle.com
Views: 6521 Bionic Turtle

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Sateesh Bolloju, Principal Architect, Thales Avionics Inc. Many security professionals are challenged with limited cybersecurity budgets, unlimited threats, and are unable to articulate the value of cybersecurity investments to executives. How do we overcome this challenge? The answer is “Value-at-Risk” measure, which helps executives in answering “what, where and how much” to invest in cybersecurity by quantifying the cyber-risks in terms of business value. Learning Objectives: 1: Understand the “what, where and how much” to invest in cybersecurity. 2: Learn the “Value-at-Risk” (VaR) framework and how to quantify cyber-risks. 3: Learn how to make effective decisions in cyber-investments with the VaR model. https://www.rsaconference.com/videos/value-at-risk-decision-making-in-cybersecurity-investments-overflow
Views: 261 RSA Conference

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This is a brief introduction to the three basic approaches to value at risk (VaR): Historical simulation, Monte Carlo simulation, Parametric VaR (e.g., delta normal). For more financial risk videos, visit our website at http://www.bionicturtle.com!
Views: 193419 Bionic Turtle

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How much collateral to set aside against "crowded trades"? - Lutfey Siddiqi Lecture
Views: 1011 Lutfey Siddiqi

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Training on Value at Risk by Vamsidhar Ambatipudi

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Views: 264033 Preston Pysh

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Views: 27 Patrick Boyle

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You are asked to estimate the VaR of an investment in Big Pharma Inc. The company's stock is trading at USD 23 and the stock has a daily volatility of 1.5%. Using the delta-normal method, what is the 1-day (holding period) 95% confident VaR of a long position in an at-the-money put on this stock , if the put has a delta of -0.50? Bonus: what is the put option's 10-day VaR? For more financial risk videos, visit our website! http://www.bionicturtle.com
Views: 9610 Bionic Turtle

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Learn how to calculate VAR and CVAR in Excel. We'll also teach you the difference between VAR and CVAR. Not enough for you? Want to learn more R? Our friends over at DataCamp will whip you into shape real quick if you need help: https://www.datacamp.com/courses/free-introduction-to-r?tap_a=5644-dce66f&tap_s=84932-063f71 Or if you're more of a Python guy, we have an intro to finance for Python course live on DataCamp right now: https://www.datacamp.com/courses/introduction-to-portfolio-analysis-in-r?tap_a=5644-dce66f&tap_s=84932-063f71 Join the Quants by taking our Quant Course at http://quantcourse.com
Views: 109116 QuantCourse

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ACCA P4 Value at risk Free lectures for the ACCA P4 Advanced Financial Management Exams
Views: 17157 OpenTuition

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Andrew Howard, Head of Sustainable Research, explains how we are modelling the impacts of tougher climate regulations on investments; a process known as Carbon Value at Risk (VaR).
Views: 1090 Schroders

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Views: 5597 Bionic Turtle

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[my xls is here https://trtl.bz/2ErWQl8] Coherence requires that a risk measure meets all four of the following conditions unconditionally: 1. Translation invariance (aka, adding cash reduces risk), 2. Positive homogeneity (aka, risk is proportional to size"), 3. Monotonicity (aka, If Y dominates X, then Y is less risky than X), and 4. Subadditivity (aka, the risk measure should not penalize diversification). Value at risk (VaR) is a popular risk measure but VaR is NOT coherent because it is not necessarily sub-additive (instead, VaR is only subadditive if the returns are normally distributed). We can illustrate VaR's lack of subadditivity by observing that the VaR of a single bond can easily be zero, yet when combined into a portfolio of identical bonds, the portfolio VaR is greater than zero. VaR is often not subadditive when such a property is most desired: when the tails are heavy. Lack of subadditivity is of practical significance. Discuss this video here in our from forum: https://trtl.bz/2VLNiL6.
Views: 1676 Bionic Turtle

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Dr Jessica Stauth: Portfolio and Risk Analytics in Python with pyfolio PyData NYC 2015 Pyfolio is a recent open source library developed by Quantopian to support common financial analyses and plots of portfolio allocations over time. Pyfolio is a tear sheet that consists of various individual plots that provide a comprehensive image of the performance of a trading algorithm and features advanced statistical analyses using Bayesian modeling. (http://quantopian.github.io/pyfolio/). Python is quickly establishing itself as the lingua franca for quantitative finance. The rich stack of open source tools like Pandas, the Jupyter notebook, and Seaborn, provide quants with a rich and powerful tool belt to analyze financial data. While useful for Quantitative Finance, these general purpose libraries lack support for common financial analyses like the computation of certain risk factors (Sharpe, Fama-French), or plots of portfolio allocations over time. Pyfolio is a recent open source tool developed by Quantopian to fill this gap. At the core of pyfolio is a so-called tear sheet that consists of various individual plots that provide a comprehensive image of the performance of a trading algorithm/portfolio. In addition, the library features advanced statistical analyses using Bayesian modeling. The software can be used stand-alone, w**ith our open-source backtesting library Zipline and is available on the Quantopian platform. This talk will be a tutorial on how to get the most out of this library (http://quantopian.github.io/pyfolio/). Slides available here: http://www.slideshare.net/JessStauth/pydata-nyc-2015 Relevant GitHub repos: https://github.com/quantopian/pyfolio https://github.com/quantopian/zipline www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 40185 PyData

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An introduction to Value at Risk using components of the corresponding module found under Optimal MRM's market risk e-Learning service. The full presentation includes risk measurement exercises in Excel and guides subscribers as they practice the concepts and techniques presented in a hands-on manner. We invite you to attend a complimentary e-Learning demo module (https://www.optimalmrm.com/services/elearning-catalog/17-banks/22-basel/) to experience how Optimal MRM delivers a practical understanding of risk in a rich and interactive manner.
Views: 13714 Optimal MRM

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In todays video we learn about Value at Risk (VaR) and how is it calculated? Buy The Book Here: https://amzn.to/2CLG5y2 Follow Patrick on Twitter Here: https://twitter.com/PatrickEBoyle What Is Value at Risk (VaR)? Value at risk (VaR) is a calculation that aims to quantify the level of financial risk within a firm, portfolio or position over a specific time frame. This metric is most commonly used by investment and commercial banks to determine the extent and occurrence ratio of potential losses in their institutional portfolios. Risk managers use VaR to measure and control the level of risk exposure. One can apply VaR calculations to specific positions or whole portfolios or to measure firm-wide risk exposure. VaR modeling aims to calculate the potential for loss in the portfolio being assessed and the probability of occurrence for the defined loss. One measures VaR by assessing the amount of potential loss, the probability of occurrence for the amount of loss, and the timeframe involved. A VaR calculation based on data from a period of low volatility may understate the potential for risk events to occur and the magnitude of those events. Risk may be further understated using normal distribution probabilities, which rarely account for extreme or black-swan events. The financial crisis of 2008 exposed many of the problems with VaR as relatively benign VaR calculations understated the potential occurrence of loss events posed by portfolios of subprime mortgages. Risk was underestimated, which resulted in extreme leverage ratios within subprime portfolios. As a result, the underestimations of occurrence and risk magnitude left institutions unable to cover billions of dollars in losses as subprime mortgage values collapsed. Risk Management
Views: 39 Patrick Boyle

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Views: 6219 Uris Stats

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https://alphabench.com/data/monte-carlo-simulation-tutorial.html Demonstration of a simple Monte Carlo simulation technique or Monte Carlo method that utilizes the Excel Data Table feature to replicate iterations. This tutorial models annual investments in an S&P 500-like environment. No add-ins are used; 100% pure Excel. Monte Carlo simulation in Excel typically makes use of add-in software for Excel like Palisades Decision Suite or Oracle's Crystal Ball, but we can do a reasonable job modeling Monte Carlo Simulation with Excel, using just Excel. Monte Carlo Simulation is one of the most highly used and important numerical techniques used in finance. A dynamic histogram can be added to further characterize a return profile. See my video on the topic if interested: https://youtu.be/WsQH3AtJqxY For a Monte Carlo simulation to approximate individual stock price movement see: https://youtu.be/1ot7HOI3wQE
Views: 226247 Matt Macarty

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I this weeks class we learn about Conditional Value at Risk and Stress Testing. Buy The Book Here: https://amzn.to/2CLG5y2 Follow Patrick on Twitter Here: https://twitter.com/PatrickEBoyle What Is Conditional Value at Risk (CVaR)? Conditional Value at Risk (CVaR), also known as the expected shortfall, is a risk assessment measure that aims to quantify the amount of risk an investment portfolio has. CVaR is derived by taking a weighted average of the “extreme” losses in the tail of the distribution of possible returns, beyond the value at risk (VaR) cutoff point. Conditional value at risk is used in portfolio optimization as part of risk management. Understanding Conditional Value at Risk (CVaR) Conditional Value at Risk (CVaR) attempts to address the shortcomings of the VaR model, which is a statistical technique used to measure the level of financial risk within a firm or an investment portfolio over a specific time frame. While VaR represents a worst-case loss associated with a probability and a time horizon, CVaR is the expected loss if that worst case threshold is ever crossed. CVaR, in other words, quantifies the expected losses that occur beyond the VaR breakpoint.
Views: 16 Patrick Boyle

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Dr. Emanuele Canegrati explains the future of Portfolio Optimization Techniques which respects Basle II Protocol to manage the market risks of banks and financial institutions
Views: 7658 quantsfinance

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An introduction to Stressed VaR, using components of the corresponding module found under Optimal MRM's e-Learning service. The full presentation includes measurement exercises in Excel and guides subscribers as they practice the concepts and techniques presented in a hands-on manner. We invite you to attend a complimentary e-Learning demo module (https://www.optimalmrm.com/services/elearning-catalog/17-banks/22-basel/) to experience how Optimal MRM delivers a practical understanding of risk in a rich and interactive manner.
Views: 13194 Optimal MRM

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This video demonstrates the risk management tool I wrote in Matlab to calibrate parametric VaR models for use in financial risk management.
Views: 1177 Alexander Ockenden

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In Part 1b, we continue with our discussion of Value at Risk, VaR, starting with the difference between Price and Rate VaR. We move onto another VaR Case study which looks at the determination of VaR using the historical simulation approach. Next we review in detail the processes behind the calculation of each of the three VaR methods, issues with each method and comparisons between them. We see how the calculation is impacted for a change in the liquidation or holding period assumption. Lastly we look at Nicholas Nassim Talebs views on VaR in particular his rules for risk management. Website: http://financetrainingcourse.com/
Views: 1777 FinanceTrainingVideo

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To get portfolio variance, we post-multiply the vector of positions (x) by the covariance matrix, then pre-multiply the transposed vector (x'). For more financial risk videos, visit our website! http://www.bionicturtle.com
Views: 53978 Bionic Turtle

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Rival Risk’s VaR calculation utilizes Monte Carlo simulation, the most advanced and accurate way to calculate VAR, generating thousands of possible future market scenarios to determine the risk of loss in a set timeframe and confidence level. Rival’s Monte Carlo simulation is able to calculate VaR for an account in seconds and provides a unique view into the calculation with graphical displays of the distribution and statistical output. To learn more about Rival Risk's VaR feature and our other smart features, visit www.rivalsystems.com/request-a-demo today.
Views: 174 Rival Systems

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"Try my "Hands-on Python for Finance" course on Udemy free for the first 100 people with code: HPFF0975 https://www.udemy.com/hands-on-python-for-finance/ " http://alphabench.com/data/monte-carlo-simulation-python.html Introductory Monte Carlo simulation, or Monte Carlo method, concepts using investing in an S&P 500-like portfolio as an example. Link to Jupyter notebook (there is a link to download the file in the upper-right corner): https://alphabench.com/data/monte-carlo-simulation-python.html
Views: 7336 Matt Macarty

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Learn what impact today's energy transformation has on utilities and their assets, and how asset investment decisions based on value and risk can help utilities adapt to rapidly changing conditions. Making the right asset investment decisions is a key focus as utilities move to what’s next. They are facing profound changes—from distributed energy resources to energy efficiency to demands for clean energy—that are challenging traditional business models. Utilities must adapt to changing regulations, maintain the reliability of their infrastructure, and focus on activities that deliver shareholder value—all while investing in new technologies and services to build new revenue streams. This webinar features speakers from Duke Energy, Avista Utilities, and Copperleaf.
Views: 160 Copperleaf

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Download a trial: https://goo.gl/PSa78r See what's new in the latest release of MATLAB and Simulink: https://goo.gl/3MdQK1 In this webinar, you will learn how to use MATLAB to verify and validate complex investment strategies. The approach seeks to model an event-driven strategy through Monte Carlo simulation at the instrument level, and to use the portfolio optimization tools - specifically the Conditional Value-at-Risk tools - to identify optimal trading strategies at the portfolio level. In particular, the case study in this webinar determines the conditions needed to successfully implement a covered-call or buy-write strategy. Through simulation and subsequent optimization, it is possible to conclude that covered-call strategies are appropriate under a limited and unexpected set of circumstances. At a higher level, this webinar demonstrates a workflow to analyze general investment strategies that exploits the powerful features available in the MATLAB environment. Webinar highlights: • Conditional Value-at-Risk portfolio optimization • Monte Carlo simulation • Event-driven strategy modeling About the Presenter: Bob Taylor is a developer at MathWorks for computational finance products. View example code from this webinar here: http://www.mathworks.com/matlabcentral/fileexchange/39449
Views: 1062 MATLAB

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Crack CA Final in the 1st attempt. Get India's best faculty video classes for best study at home. Give missed call @9980100288. International students - visit https://www.cakart.in and chat. A smart decision today can save you a lot of time (years) in your career. Give missed call @9980100288 now. This lecture sample from Ideal Classes covers the topic -Financial Derivative from the subject - - Strategic Financial Management of CA Final G1 .For the Video Lecture + eBooks + Question bank package please visit http://www.cakart.in
Views: 72 CA KART

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This video explains the procedure to calculate value at risk (VaR) in a very simple and easily understandable method.
Views: 57691 Ns Toor

01:09:44
Financial Markets (2011) (ECON 252) Professor Shiller introduces basic concepts from probability theory and embeds these concepts into the concrete context of financial crises, with examples from the financial crisis from 2007-2008. Subsequent to a historical narrative of the financial crisis from 2007-2008, he turns to the definition of the expected value and the variance of a random variable, as well as the covariance and the correlation of two random variables. The concept of independence leads to the law of large numbers, but financial crises show that the assumption of independence can be deceiving, in particular through its impact on the computation of Value at Risk measures. Moreover, he covers regression analysis for financial returns, which leads to the decomposition of a financial asset's risk into idiosyncratic and systematic risk. Professor Shiller concludes by talking about the prominent assumption that random shocks to the financial economy are normally distributed. Historical stock market patterns, specifically during crises times, establish that outliers occur too frequently to be compatible with the normal distribution. 00:00 - Chapter 1. Financial Crisis of 2007-2008 and Its Connection to Probability Theory 05:51 - Chapter 2. Introduction to Probability Theory 09:58 - Chapter 3. Financial Return and Basic Statistical Concepts 26:29 - Chapter 4. Independence and Failure of Independence as a Cause for Financial Crises 38:58 - Chapter 5. Regression Analysis, Systematic vs. Idiosyncratic Risk 58:59 - Chapter 6. Fat-Tailed Distributions and their Role during Financial Crises Complete course materials are available at the Yale Online website: online.yale.edu This course was recorded in Spring 2011.
Views: 211536 YaleCourses

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MIT 18.S096 Topics in Mathematics with Applications in Finance, Fall 2013 View the complete course: http://ocw.mit.edu/18-S096F13 Instructor: Jake Xia This lecture focuses on portfolio management, including portfolio construction, portfolio theory, risk parity portfolios, and their limitations. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 596911 MIT OpenCourseWare