By David Ruppert
The re-creation of this influential textbook, geared in the direction of graduate or complex undergraduate scholars, teaches the information important for monetary engineering. In doing so, it illustrates techniques utilizing monetary markets and monetary information, R Labs with real-data routines, and graphical and analytic equipment for modeling and diagnosing modeling error. those tools are severe simply because monetary engineers now have entry to 1000's of information. to use this information, the robust equipment during this ebook for operating with quantitative details, relatively approximately volatility and dangers, are crucial. Strengths of this fully-revised variation contain significant additions to the R code and the complex themes coated. person chapters conceal, between different themes, multivariate distributions, copulas, Bayesian computations, chance administration, and cointegration. prompt must haves are simple wisdom of information and likelihood, matrices and linear algebra, and calculus. there's an appendix on chance, information and linear algebra. working towards monetary engineers also will locate this ebook of curiosity.
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Extra resources for Statistics and Data Analysis for Financial Engineering: with R examples
Treasury rates of three maturities. Weekly time series. The data were taken from the website of the Federal Reserve Bank of Chicago. • • prices of zero-coupon bonds of maturities 1-year, 2-years, . . , n-years are denoted here by P (1), P (2), . . , P (n); spot rates (yields of maturity of zero-coupon bonds) of maturities 1-year, 2-years, . . , n-years are denoted by y1 , . . , yn ; 28 • 3 Fixed Income Securities forward rates r1 , . . , rn , where ri is the forward rate that can be locked in now for borrowing in the ith future year (i = 1 for next year, and so on).
You should study each line of code, understand what it is doing, and convince yourself that the code estimates the probability being requested. Note that anything that follows a pound sign is a comment and is used only to annotate the code. Suppose the hedge fund will sell the stock for a proﬁt of at least $100,000 if the value of the stock rises to at least $1,100,000 at the end of one of the ﬁrst 100 trading days, sell it for a loss if the value falls below $950,000 at the end of one of the ﬁrst 100 trading days, or sell after 100 trading days if the closing price has stayed between $950,000 and $1,100,000.
Problem 14 Perform a t-test to compare the means of the returns and the log returns. Comment on your ﬁndings. Do you reject the null hypothesis that they are the same mean at 5 % signiﬁcance? Or do you accept it? ] What are the assumptions behind the t-test? Do you think that they are met in this example? If the assumptions made by the t-test are not met, how would this aﬀect your interpretation of the results of the test? Problem 15 After looking at return and log return data for McDonald’s, are you satisﬁed that for small values, log returns and returns are interchangeable?