R Tutorial : Valuation

DataCamp · Beginner ·💰 FinTech & AI for Finance Professionals ·6y ago

Key Takeaways

This video tutorial demonstrates the valuation of life insurance products in R, covering constant interest rates, discount factors, and cash flow valuation using the discount function.

Full Transcript

with a constant interest i and corresponding discount factor V calculations are pretty simple you will now learn more general notation and setting that is fundamental to go beyond the constant interest rate and to incorporate interest rates that may change over time let's fix two time points s and T and introduce the notation V as T for the value at time s of 1 euro at time T the second argument T refers to the point in time when you consider the 1 euro if the first argument s is smaller than T then VST is a discounting factor but if s is larger than T then vs t is an accumulation factor such that 1 euro at T close to VST euro at time s let's assume an interest rate of 3 percent the corresponding discount factor V is 1 over 1 plus I in the first example set s to 2 and T 2 for V 2 for is then the value at time 2 of 1 euro at time 4 that's 0.94 euro you can calculate this value in two equivalent ways the discount factor V to the power 2 or 1 plus the interest rate I to the power minus to the power 2 in our example corresponds to the number of time units that elapses between time points s and D in the second example set s to 6 and T 2 3 then V 6 3 tells you how one euro at time 3 close to 1.09 euro at time 6 you now have a general mechanism to value a cash flow vector at any point in time you convert each cash flow CK in the vector from time k to time n using discount factor V and gay the sum over all entries in the vector then gives you the value at time n when n is equal to zero you obtain the present value of the cash flow vector when n is equal to capital n the time point that corresponds to the last entry in the vector you obtain the vectors accumulated value let's do this in our you face a cash flow of five hundred zero at time zero three hundred zero at time two and two hundred zero at times seven what is the value of this cash flow vector at time three you create a function discount with three arguments s T and I the interest rate I has a default value of 0.03 or three percent the function returns 1 plus I raised to the negative time difference between T and s thus when T is larger than s the function discounts one euro at time T to time s and when s is larger than T the function accumulates one euro at time T to time pass using this function you convert the cash flows in the time line to time three with an interest rate of three percent the value at time three is thousand thirty three euro instead of repeatedly using the discount function you can vectorize this code you call the function discount with first argument s equal to three the point in time at which you want to value the cash flows the second argument is now a vector running from zero to seven the points considered on the time line thus the vectorized function call returns a vector with the values at time three of one euro at time zero to seven multiplying with the vector cash flows and taking the sum returns the value of the given cash flows at time three that's again thousand thirty three euro now you're ready for some cash flow valuation in our

Original Description

Want to learn more? Take the full course at https://learn.datacamp.com/courses/valuation-of-life-insurance-products-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- With a constant interest i and corresponding discount factor v calculations were pretty simple. You will now learn a more general notation and setting that is fundamental to go beyond a constant interest rate and to incorporate interest rates that may change over time. Let's fix two time points, s and t, and introduce the notation v(s,t) for the value at time s of one euro at time t. The second argument, t, refers to the point in time when you consider the 1 euro. If the first argument, s, is smaller than t, then v(s,t) is a discounting factor. But if s is larger than t, then v(s,t) is an accumulation factor such that 1 euro at t grows to v(s,t) euro at time s. Let's assume an interest rate of 3%. The corresponding discount factor v is 1/(1+i). In the first example set s to 2 and t to 4, v(2,4) is then the value at time 2 of 1 euro at time 4. That's 0-dot-94 euro. You can calculate this value in two equivalent ways: the discount factor v to the power 2, or 1 plus the interest rate i to the power (-2). The power, 2 in our example, corresponds to the number of time units that elapses between time points s and t. In the second example set s to 6 and t to 3, then v(6,3) tells you how 1 euro at time 3 grows to 1-dot-09 euro at time 6. You now have a general mechanism to value a cash flow vector at any point in time. You convert each cash flow ck in the vector from time k to time n using discount factor v(n,k). The sum over all entries in the vector then gives you the value at time n. With n is equal to 0 you obtain the present value of the cash flow vector. When n is equal to capital N, the time point that corresponds to the last entry in the vector, you obtain the vector's accumulated value. Let's do this in R. You face a cash flow
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Playlist

Uploads from DataCamp · DataCamp · 0 of 60

← Previous Next →
1 SQL Server Tutorial: Date manipulation
SQL Server Tutorial: Date manipulation
DataCamp
2 R Tutorial: Intermediate Interactive Data Visualization with plotly in R
R Tutorial: Intermediate Interactive Data Visualization with plotly in R
DataCamp
3 R Tutorial: Adding aesthetics to represent a variable
R Tutorial: Adding aesthetics to represent a variable
DataCamp
4 R Tutorial: Moving Beyond Simple Interactivity
R Tutorial: Moving Beyond Simple Interactivity
DataCamp
5 Python Tutorial: Why use ML for marketing? Strategies and use cases
Python Tutorial: Why use ML for marketing? Strategies and use cases
DataCamp
6 Python Tutorial: Preparation for modeling
Python Tutorial: Preparation for modeling
DataCamp
7 Python Tutorial: Machine Learning modeling steps
Python Tutorial: Machine Learning modeling steps
DataCamp
8 R Tutorial: The prior model
R Tutorial: The prior model
DataCamp
9 R Tutorial: Data & the likelihood
R Tutorial: Data & the likelihood
DataCamp
10 R Tutorial: The posterior model
R Tutorial: The posterior model
DataCamp
11 R Tutorial: An Introduction to plotly
R Tutorial: An Introduction to plotly
DataCamp
12 R Tutorial: Plotting a single variable
R Tutorial: Plotting a single variable
DataCamp
13 R Tutorial: Bivariate graphics
R Tutorial: Bivariate graphics
DataCamp
14 Python Tutorial: Customer Segmentation in Python
Python Tutorial: Customer Segmentation in Python
DataCamp
15 Python Tutorial: Time cohorts
Python Tutorial: Time cohorts
DataCamp
16 Python Tutorial: Calculate cohort metrics
Python Tutorial: Calculate cohort metrics
DataCamp
17 Python Tutorial: Cohort analysis visualization
Python Tutorial: Cohort analysis visualization
DataCamp
18 R Tutorial: Building Dashboards with flexdashboard
R Tutorial: Building Dashboards with flexdashboard
DataCamp
19 R Tutorial: Anatomy of a flexdashboard
R Tutorial: Anatomy of a flexdashboard
DataCamp
20 R Tutorial: Layout basics
R Tutorial: Layout basics
DataCamp
21 R Tutorial: Advanced layouts
R Tutorial: Advanced layouts
DataCamp
22 Python Tutorial: Time Series Analysis in Python
Python Tutorial: Time Series Analysis in Python
DataCamp
23 Python Tutorial: Correlation of Two Time Series
Python Tutorial: Correlation of Two Time Series
DataCamp
24 Python Tutorial: Simple Linear Regressions
Python Tutorial: Simple Linear Regressions
DataCamp
25 Python Tutorial: Autocorrelation
Python Tutorial: Autocorrelation
DataCamp
26 R Tutorial: The gapminder dataset
R Tutorial: The gapminder dataset
DataCamp
27 R Tutorial: The filter verb
R Tutorial: The filter verb
DataCamp
28 R Tutorial: The arrange verb
R Tutorial: The arrange verb
DataCamp
29 R Tutorial: The mutate verb
R Tutorial: The mutate verb
DataCamp
30 R Tutorial: What is cluster analysis?
R Tutorial: What is cluster analysis?
DataCamp
31 R Tutorial: Distance between two observations
R Tutorial: Distance between two observations
DataCamp
32 R Tutorial: The importance of scale
R Tutorial: The importance of scale
DataCamp
33 R Tutorial: Measuring distance for categorical data
R Tutorial: Measuring distance for categorical data
DataCamp
34 Python Tutorial: Plotting multiple graphs
Python Tutorial: Plotting multiple graphs
DataCamp
35 Python Tutorial: Customizing axes
Python Tutorial: Customizing axes
DataCamp
36 Python Tutorial: Legends, annotations, & styles
Python Tutorial: Legends, annotations, & styles
DataCamp
37 Python Tutorial: Introduction to iterators
Python Tutorial: Introduction to iterators
DataCamp
38 Python Tutorial: Playing with iterators
Python Tutorial: Playing with iterators
DataCamp
39 Python Tutorial: Using iterators to load large files into memory
Python Tutorial: Using iterators to load large files into memory
DataCamp
40 SQL Tutorial: Introduction to Relational Databases in SQL
SQL Tutorial: Introduction to Relational Databases in SQL
DataCamp
41 SQL Tutorial: Tables: At the core of every database
SQL Tutorial: Tables: At the core of every database
DataCamp
42 SQL Tutorial: Update your database as the structure changes
SQL Tutorial: Update your database as the structure changes
DataCamp
43 Python Tutorial: Classification-Tree Learning
Python Tutorial: Classification-Tree Learning
DataCamp
44 Python Tutorial: Decision-Tree for Classification
Python Tutorial: Decision-Tree for Classification
DataCamp
45 Python Tutorial: Decision-Tree for Regression
Python Tutorial: Decision-Tree for Regression
DataCamp
46 Python Tutorial: Census Subject Tables
Python Tutorial: Census Subject Tables
DataCamp
47 Python Tutorial: Census Geography
Python Tutorial: Census Geography
DataCamp
48 Python Tutorial: Using the Census API
Python Tutorial: Using the Census API
DataCamp
49 R Tutorial: A/B Testing in R
R Tutorial: A/B Testing in R
DataCamp
50 R Tutorial: Baseline Conversion Rates
R Tutorial: Baseline Conversion Rates
DataCamp
51 R Tutorial: Designing an Experiment - Power Analysis
R Tutorial: Designing an Experiment - Power Analysis
DataCamp
52 R Tutorial: Introduction to qualitative data
R Tutorial: Introduction to qualitative data
DataCamp
53 R Tutorial: Understanding your qualitative variables
R Tutorial: Understanding your qualitative variables
DataCamp
54 R Tutorial: Making Better Plots
R Tutorial: Making Better Plots
DataCamp
55 SQL Tutorial: OLTP and OLAP
SQL Tutorial: OLTP and OLAP
DataCamp
56 SQL Tutorial: Storing data
SQL Tutorial: Storing data
DataCamp
57 SQL Tutorial: Database design
SQL Tutorial: Database design
DataCamp
58 Python Tutorial: Introduction to spaCy
Python Tutorial: Introduction to spaCy
DataCamp
59 Python Tutorial: Statistical Models
Python Tutorial: Statistical Models
DataCamp
60 Python Tutorial: Rule-based Matching
Python Tutorial: Rule-based Matching
DataCamp

Learn to value life insurance products in R by applying constant interest rates and discount factors to cash flows. This tutorial covers the basics of valuation and provides hands-on coding experience.

Key Takeaways
  1. Define the discount function with interest rate and time points
  2. Calculate the discount factor for a given interest rate and time difference
  3. Apply the discount function to a cash flow vector
  4. Vectorize the discount function for efficient computation
  5. Calculate the value of a cash flow vector at a given time point
💡 The discount function can be vectorized to efficiently compute the value of a cash flow vector at a given time point.

Related AI Lessons

Build a Remittance App with the Afriex Business API
Learn to build a remittance app using the Afriex Business API for seamless cross-border transactions
Dev.to · Victory Lucky
Why FinTech Is Growing So Fast — And What the Next 10 Years Could Look Like
FinTech is growing rapidly, enabling fast and convenient financial transactions, and is expected to continue shaping the financial landscape over the next decade
Medium · Startup
$100,000 in Support. Four Weeks. One Goal — Turn African Fintech Founders Into Operators. Bankiffy Fintech Accelerator BFA 2026 Cohort 1 Is Now Open
Learn how the Bankiffy Fintech Accelerator supports African fintech founders in turning their ideas into operational businesses
Techpoint Africa
Nigeria’s fintechs built payments. Now they’re becoming banks.
Nigeria's fintechs are transitioning into banks by obtaining MFB licenses to diversify revenue streams beyond transaction fees
TechCabal
Up next
Ito ang Pinakamalaking WEALTH KILLER | At Ano ang Solusyon?
Chinkee Tan
Watch →