Reference no: EM132678459

Objective of Assessment

The assessment below is designed to introduce you to the technologies and platforms heavily relied upon in investing banking, hedge funds and derivatives financial modeling. Excel is undoubtedly the standard work horse in any finance house or bank or insurance business. In the TU318 Derivatives module, we rely heavily on Excel for graphing and valuation. To enhance a bit your experience, we provide training in VBA, (although not exclusively), so as to automate your work and bring it to an industry standard. We will also present other technologies that are commonly relied upon in the finance/banking arena. Both R and Python will be introduced as modeling tools, and we assume zero knowledge your end. The approach adapted here is "learning-by-doing" and again no priors/pre-requisites are demanded/expected. We will also make use of C++ which is another stalwart of the fintech/banking world and again we assume no priors from students. In the Derivatives module, we introduce these technologies gently so you can develop your finance skills in a way that dove tails with your own pace to learn and absorb. Our objective here is to ensure that you can ultimately transfer your knowledge into a wide set of finance-based careers. The hyperlinks below will bring you to online learning tools where the basics are explained, and you are provided with the shell of each solution.

Question 1
Calculate the time value of a European call and a put option using Black Scholes. Download the spreadsheet and follow video links embedded in spreadsheet

Select your own parameter values. Investigate the effects of changing each of the parameter inputs on the time value of the option i.e. investigate the effect by changing S, K, r, q, T, Sigma. Use excel graphing to demonstrate parameter sensitivity. Interpret your results.

Verify the excel model results against VBA generated results.

Build an Excel userform that estimates the value of both a call and put

Show that put Call parity holds.

Verify the excel model results using the C++ code. Use the OnlineGBD compiler to estimate your results. Use share link in the C++ OnlineGBD compiler as part of the project submission.

Download Anaconda Python/Google Colab and install on your Machine. Download and install RStudio on your machine.

Estimate the value of the Back Scholes Call and Put using Python. (use the Anaconda Spyder Application/Google Colab). Use the same parameters as before

Estimate the same Call and Put using the Jupyter Notebook/Google Colab. Generate a series of values for the underlying and from these estimate the Intrinsic value and Black Scholes Parabola using python code provided.

Interpret your output. Explain the difference between the time value parabola and the intrinsic value.

Question 2
With reference to Shiller's lecture on Options - how does the Binomial Model differ from the Black Scholes (1973)?

Explain how and under what circumstances does the Cox, Ross and Rubinstein (1979) Binomial Model converge to Black Scholes (1973).

Set out A European option in Excel.

Using the same parameters set out the same example for an American Option. Describe the convergence of the CRR and Tian models to BAW.

Interpret your results. What are your key findings?

Explain what is the difference between European and American Options? Use the C++ code for Cox, Ross and Rubinstein (1979) and implement in OnlineGDB compiler:

Explain under what circumstances does the value of the European call differ from the American call? Consider different parameter values. Use a step size n = 100 for estimation.

Explain how to implement an 8-step Binomial tree in Excel. Set the lattice tree up in a spreadsheet and estimate the value of a European call. How closely does the 8-step tree approximate the Black Scholes (1973) call? (Use your own parameters value inputs). What is the effect of making the option American? Illustrate that is never optimal to exercise an American Call Option early if the dividend is zero. Under what circumstances is it optimal to exercise an American put early? Verify results using VBA code for Cox, Ross and Rubinstein (1979).

Question 3

Explain what techniques can be applied to optimize the Cox, Ross and Rubinstein estimation.

Run the three snippets of C++ code. What observations can you make with regard to efficiency that can be obtained by make use of Dynamic memory and truncation. Interpret your results.

Attachment:- Objective of Assessment.rar