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VaR Backtesting using Ox - open to bidding

$30-250 AUD

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Posted about 6 years ago

$30-250 AUD

Paid on delivery
1. In this question we will conduct a backtesting exercise for the 1994 year. For each trading day in 1994 we must graph the 99% VaR that was computed 10 trading days before and we must also graph the realised loss in the portfolio that occurrs over this same period. One is required to produce two graphs. The first graph should be the backtesting of the VaR method under normality. The second graph should be the backtesting of the VaR method under historical sim- ulation of daily changes in prices. Finally, one should interpret the findings from both of these graphical displays. For these exercises, assume that $10,000 dollars was the value of our holdings in each of our nineteen stocks, ten trading days before the first trading day in 1994. i.e. On 17 December 1993, the value of our portfolio is $190,000. Also assume that the number of shares we hold in each of these stocks does not change over the time frame of our back-testing exercise. Finally, in computing the VaR estimates one should use the last 500 changes in prices. The data is located on the fins5542 Moodle page. See last page, for variable names. In addition to printing out the Excel graphs, one should also print out the Ox computer code. 2. In this question we will conduct a backtesting exercise for a portfolio of 4 stocks for the 2006 year. For each trading day in 2006 we must graph the 99% VaR that was computed 10 trading days before and we must also graph the realised loss in the portfolio that occurs over this same period. One is required to produce two graphs. The first graph should be the backtesting of the VaR method under normality. The second graph should be the backtesting of the VaR method under historical sim- ulation of daily changes in prices. Finally, one should interpret the findings from both of these graphical displays. For these exercises, assume that $100,000 dollars was the value of our holdings in each of CISCO, Microsoft, IBM and American Express ten trading days before the first trading day in 2006. Also assume that the number of shares we hold in each of these stocks does not change over the time frame of our back-testing exercise. Finally, in computing the VaR estimates one should use the last 500 changes in prices. In addition to printing out the Excel graphs, one should also print out the Ox computer code. I will provide the data. Feel free to ask me for any doubt.
Project ID: 16851012

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Hello, I have extensive experience in python and financial markets and would love to help you out on this project. I currently work as a consultant for a long/short equity hedge fund part/time in Palo Alto, CA and have a Masters in financial mathematics/engineering. I think I can be of great help to you. My credentials are below: * Responsible for creating proprietary quantitative models and algorithmic trading strategies for long/short equity optimization models with specific risk and return parameters specified by the investor profile, utilizing machine/deep learning, along with Q reinforcement learning agents. •Created predictive vectorized/event-based machine learning models utilizing multivariate/logistic regression, lasso/ridge regression, linear/quadratic discriminant analysis, decision trees, K neighbors, Naive Bayes, random forest, support vector machine, Adaptiveboost, GradientBoost, XGB, and portfolio optimization to maximize return and minimize volatility for various investor risk profiles. •Responsible for deep learning modeling using recurrent neural networks, Tensorflow, nltk, sentiment analyzer, Keras LSTM, and convolutional neural networks, in attempt to predict specific asset class forecasted prices through stocks, forex, bonds, futures, ETFs, and other derivatives. •Designed a proprietary machine/deep learning long/short intraday algorithm utilizing the Interactive Brokers API, IB_Insync python library. Speak soon!
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