The post Theory of Estimation Or What is Estimation appeared first on StepUp Analytics.

]]>**Population**: A group of individuals
under study is called population. The population may be finite or infinite. Eg.
All the registered voters in India.

**Sample**: A finite subset of statistical
individuals in a population. Eg. Selecting some voters from all registered
voters.

**Parameter**: The statistical constants of the
population such as mean (μ), variance (σ^{2}) etc. Eg. Mean of income of all the
registered voters.

**Statistic**: The statistical constants of the sample such as mean (X̄), variance (s^{2}) etc. In other words, any function of the random sample x_{1}, x_{2},…, _{n} that are being observed say, T_{n} is called a statistic. Eg. Mean

**Estimator**: If a statistic is used to estimate an
unknown parameter θ of the distribution,
then it is called an estimator. Eg. Sample mean is an estimator of population
mean.

**Estimate:** A particular value of the estimator is
called an estimate of an unknown parameter. Eg. Mean income of selected voters
is ₹25000 which represents mean income of all
the registered voters.

**Sampling Distribution**: When the total probability is distributed according to the value of statistic then the distribution is said to be sampling distribution. Eg. If we want the average height of a voter, we can randomly select some of them and use the sample mean to estimate the population mean.

**Standard Error**: The standard deviation of the sampling distribution of a statistic is known as its standard error and is denoted by ‘s.e.’ Eg. If we want to know the variability of the height of voters, then standard error is used.

Now, before discussing about different methods of finding estimates of unknown population parameter, it is important to know the characteristics of a good estimator. Here, “a good estimator” is one which is close to the true value of the parameter as much as possible. The following are some of the criterion that should be satisfied by a good estimator:

- Unbiasedness
- Consistency
- Efficiency
- Sufficiency

**Unbiasedness**

This is a desirable property of a good estimator. An estimator T_{n }is said to be an unbiased estimator of γ (θ), where γ (θ) is a function of unknown parameter θ, if the expectation of the estimator is equal to the population parameter, i.e.,

**E [T**_{n}**] = γ (θ)**

Example: If X ~ N (μ,σ^{2}),

**Consistency**

An estimator is said to be consistent if increasing the sample size produces an estimate with smaller standard error (standard deviation of sampling distribution of a statistic). In other words, if the sample size increases, it becomes almost certain that the value of a statistic will be very close to the true value of the parameter. Example: Sample mean is a consistent estimator of the population mean, since as sample size n→∞, the sample means converges to the population mean in probability and variability of the sample mean tends to 0.

**Efficiency**

There is a necessity of some further criterion which will enable us to choose between the estimators, with the common property of consistency. Such a criterion which is based on the variances of the sampling distribution of estimators is usually known as efficiency.

It refers to the size of the standard error of the statistic. If two statistic are compared from a sample of same size and we try to decide which one a good estimator is, the statistic that has a smaller standard error or standard deviation of the sampling distribution will be selected.

If T_{1} is the most efficient estimator with variance V_{1} and T_{2}, any other estimator with variance V_{2}, then the efficiency E of T_{2} is given by:

[∵ Efficiency and Variances are inversely proportional]

**Sufficiency**

An estimator is said to be sufficient for a parameter, if it contains all the information in the sample regarding the parameter.

If T_{n} is an estimator of parameter θ, based on a sample x_{1}, x_{2},…, x_{n} of size n from the population with density f(x,θ), such that the conditional distribution of x_{1}, x_{2},…, x_{n} given T_{n}, is independent of θ, then T_{n} is sufficient estimator for θ.

**Methods of Point Estimation**

So far we have been discussing the requisites of a good estimator. Now we shall briefly outline some of the important methods of obtaining such estimators. Commonly used methods are:

- Method of Moments
- Method of Maximum Likelihood Estimation
- Method of Minimum Variance
- Method of Least Squares

**Method of Moments (MoM)**

The basic principle is to equate population moments (i.e. the means, variances, etc. of the theoretical model) to the corresponding sample moments (i.e. the means, variances, etc. of the sample data observed) and solve for the parameter(s).

Let x_{1}, x_{2}, …, x_{n} be a random sample from any distribution f(x,θ) which has m unknown parameters θ_{1}, θ_{2}, …, θ_{m}, where m ≤ n. Then the moment estimators θ ̂ _{1}, θ ̂ _{2}, …, θ ̂ _{m }are obtained by equating the first m sample moments to the corresponding m population moments and then solving for θ_{1}, θ_{2}, …, θ_{m}.

**Method of Maximum Likelihood Estimation (MLE) **

MLE is widely regarded as the best general method of finding estimators. In particular, MLE’s usually have easily determined asymptotic properties and are especially good in the large sample situations. “Asymptotic’’ here just means when the samples are very large.

Let x_{1}, x_{2}, …, x_{n} be a random sample from a population with density f(x,θ). The likelihood function of the observed sample at the function of θ is given by:

Notice that the likelihood function is a function of the unknown parameter θ. So different values of θ would give different values for the likelihood. The maximum likelihood approach is to find the value of θ that would have been most likely to give us the particular sample we got. In other words, we need to find the value of θ that maximizes the likelihood function. In most cases, taking logs greatly simplifies the determination of the MLE θ ̂. Differentiating the likelihood or log likelihood with respect to the parameter and setting the derivative to 0 gives the MLE for the parameter.

It is necessary to check, either formally or through simple logic, that the turning point is a maximum. The formal approach would be to check that the second derivative is negative.

**Method of Minimum Variance**

It is also known as Minimum Variance Unbiased Estimator (MVUE). As the name itself depicts, estimator which is unbiased as well as having minimum variance.

If a statistic T_{n} based on a sample of size n is such that:

- T
_{n}is unbiased - It has the smallest variance among the class of all unbiased estimators
- then T
_{n}is called MVUE of θ.

**Method of Least Squares**

The principle of least squares is used to fit a curve of the form:

where θ_{i}’s are unknown parameters, to a set of n sample observations (x_{i}, y_{i}); i=1,2,…,n from a bivariate population. It consists of minimizing the sum of squares of residuals,

subject to variations in θ_{1}, θ_{2}, …, θ_{n}. The normal equations for estimating θ_{1}, θ_{2}, …, θ_{n} are given by:

**Confidence Intervals and Confidence Limits**

Confidence interval provides an ‘interval estimate’ for an unknown population parameter. It is designed to contain the parameter’s value with some stated probability. The width of the interval provides a measure of the precision accuracy of the estimator involved.

Let
x_{i}, i = 1, 2, … n be a random sample of size n from f(x,θ). If T_{1}(x) and T_{2}(x)
be any two statistics such that T_{1}(x) ≤
T_{2}(x) then,

**P(T**_{1}**(x) < θ < T**_{2}**(x)) = 1 – α**

where α is level of
significance, then the random interval (T_{1}(x), T_{2}(x)) is
called 100(1-α)% confidence interval for θ.

Here, T_{1} is called lower confidence limit and T_{2}
is called upper confidence limit. (1-α) is called the confidence coefficient.

Usually, the value of α is taken as 5% in the testing of hypothesis. Thus, if α = 5%, then there is a 95% chance of the estimate to be in the confidence interval.

*Interval estimate =
Point estimate ± Margin of Error*

The margin of error is the amount of random sampling error. In other words, the range of values above and below the sample statistic.

*Margin of Error =
Critical Value * Standard Error of the statistic*

Here, a critical value is the point (or points) on the scale of the test statistic beyond which we reject the null hypothesis, and is derived from the level of significance α of a particular test into consideration.

Confidence intervals are not unique. In general, they should be obtained via the sampling distribution of a good estimator, in particular, the MLE. Even then there is a choice between one-sided and two-sided intervals and between equal-tailed and shortest length intervals although these are often the same.

So, we have learned what the estimation is, i.e., the process of providing numerical value to unknown population parameter. To test whether an estimate is a good estimator of the population parameter, an estimate should have the following characteristics:

- Unbiasedness
- Consistency
- Efficiency
- Sufficiency

There are different methods of finding estimates such as method of moments, MLE, minimum variance and least squares. Of these methods, MLE is considered as the best general method of finding estimates.

Also, there are two types of estimations, point and interval estimation. Point estimation provides a single value to the estimate, whereas, interval estimation provides confidence interval which is likely to include the unknown population parameter.

Hence, now you have the basic understanding about the theory of estimation.

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]]>The post How to take exemption under new curriculum for IFoA appeared first on StepUp Analytics.

]]>To read more on **How To Take Exemption Under New Actuarial Curriculum For IAI** refer the below link: **Click**

If you are a student pursuing some course from the university accredited by IFoA based on the existing curriculum, no need to worry because the institute has provided enough time to complete your degree programme and claim for the exemption(s). Students who begin their degree based on the existing curriculum will have until December 2023 to complete their university course. In addition to that, the relevant examinations should be passed by December 2018 and will have until December 2023 to claim for their exemption.

To avail an exemption(s), one needs to be the member of IFoA. Candidates eligible for an exemption should have:

- A university course graduate certificate, with which the IFoA has an accreditation or exemption agreement with.
- Exam passes or exemptions from other international actuarial association.
- A qualification from the professional body that IFoA has an agreement with.
- An actuarial or numerate degree graduate certificate, which covered the majority of the syllabuses of the relevant IFoA exams.
- Someone who has completed a postgraduate thesis or an MBA on an actuarial or financial subject.

If you have completed a numerate degree on a programme that is not accredited by the IFoA, then you will be eligible to get exemptions under the new curriculum too. The applications for these exemptions are particularly dedicated for the subjects CT1, CT2, CT3, and CT7 and are supposed to be made by December 2018.

Those submitted after this date will be accepted only for CS1, CB1, and CB2 and will be needed to map against the programme that you have completed. In order to gain an exemption from CM1 and CS2, students must have obtained passes in both CT1 and CT5, and both CT4 and CT6 from the IAI respectively and claim it from the IFoA by 31 December 2023.

Exemption applications from people who hold passes from IAI’s December 2018 examinations, the IFoA will not be recognizing the passes achieved for the same. Moreover, the institute is unable to confirm yet whether they will be recognizing IAI paper passes achieved after January 2019 or not.

Applications from individuals of other actuarial associations that IFoA have agreements with for the subjects passed before December 2018 will be considered and you can claim the exemptions until December 2023. The exams sat after December 2018 will lead to further discussions with those associations.

Take a leap of good faith, get ready for the changes and challenges coming with the new curriculum and do not forget to apply for your exemptions as early as possible.

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]]>The post Actuarial Techniques in Pensions and Employee Benefits appeared first on StepUp Analytics.

]]>That’s where the actuaries come into the picture and the various actuarial techniques play a significant role. For a layman, employee benefits are simply the non-salary compensation provided by the employer to create a competitive package for the potential employees. Employee Benefits include medical insurance, retirement plans, child care spending, paid leaves, paid vacations and the list goes on.

Among all the above benefits, pension given to employees posts their retirement is very crucial and needs to be modeled accurately so that the employer is able to meet his liability at the time when it’s due.

In this article, we will see how the actuaries design the company’s pension schemes.

**Actuarial cost** is simply the money that the company sets aside periodically to meet the future pension liabilities. This approach takes into consideration an employee’s current salary, the number of years to retirement, annual rate of salary increment, probable number of years the individual will live to continue getting the benefit etc. It has two approaches:

**Cost Approach:**It estimates the total retirement benefits to be paid in the future and then determines the equal annual payments that are necessary to fund those benefits.**Benefit Approach:**This calculates the benefits that the employees have already received based on the length of their employment. In other words, we can calculate the amount of benefit associated with employees’ service to date and use a discount factor to reduce the benefit to its present value.

Another approach to valuation is** Defined benefit (DB) and Defined Contribution Scheme (DS)**.

**Defined benefit:** It is the amount that the employer promises to pay to the employee at the time of retirement which is predetermined by a formula. The pensionable salary is derived using either of the three ways:

- null
- Current Salary
- Average salary of 3 or 5 years
- Career average salary

Once the pensionable salary is derived appropriate formula is used to get the benefit.

**Defined Contribution:** Here the employer and the employee both contribute to the pension fund according to a specified percentage. Here the benefit is not known beforehand.

**Annual Contribution= Contribution x (Current Pensionable Earnings) **

In general, the above is the pictorial view of the employee defined benefits (eg – gratuity, pension etc) actuarial valuation process –

**Step 1:** Collection of data- This involves fetching information such as employee joining date, DOB, current salary, employee’s contribution made during the period etc.

**Step 2:** Actuarial assumptions are made for the discount rate, expected future salary, mortality rate, withdrawal rate.

**Step 3:** This includes calculations of the present value of all the payments to be made from the date of retirement until the death of the employee.

**Step 4:** It involves meeting the various disclosures that are required to be adhered according to Indian Accounting Standard (IAS) 19 by the companies.

**Step 5:** This requires the companies to identify the key risks and keep an eye on the various assumptions that were initially taken before.

**Step 6:** Carrying out the actuarial gain/loss analysis.

Thus, this is how the actuary works in modeling the pension liability of the firm.

Also, it should be noted that when the companies make the pension reserves, it is reflected in it’s financial statements. It generally records the funding cost as an expense and the total accumulated future pension payments as a liability.

The above are some of the ways in which the pension models are designed. There are yet another complex models that the actuaries use to help companies value their employee benefits and make appropriate decisions to fund those liabilities.

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]]>The post Is Actuarial Science Right Profession For Me? appeared first on StepUp Analytics.

]]>But, you will truly never know if Actuarial Science is really your thing until you try it. Despite saying this, let’s consider some points on the basis of which you’ll get to decide whether it is a right choice for you or not.

**Science v/s Commerce**

“Actuarial Science”, as the word depicts, has nothing to do much with the subjects from the science background. Rather, it is a profession which can be pursued by both commerce as well as science students. Though students who are savvy with subjects like mathematics, statistics, economics, finance, probability and so on, find it pretty much easier to grasp the basics of Actuarial Science.

**Actuarial Science is all about Mathematics**

“Are you passionate about Mathematics?”

Even though you may be passionate about Math, it does not mean actuarial science would be the right fit for you. Being good at Math is just a requirement to study actuarial science. If you hate Math, actuarial science is not for you.

Actuarial science focuses on the practical side of Math instead of the theoretical side. If you are someone who just wants to derive formulae and prove mathematical solutions, actuarial science will not be a good fit for you. Practical Math is completely different from theoretical Math because practical Math is all about applying mathematical concepts to solve problems.

Actuarial Science is the application of mathematical and technical skills to solve problems. Actuaries not only have strong Math skills, they are also able to apply them well in the business context and communicate their results to both actuaries and non-actuaries.

**Passion about Numbers**

“Are you passionate about numbers or are you just interested in numbers?”

There is a big difference between being passionate and being interested. Being interested means you are good at numbers, you are comfortable with them and you do well in your Math courses, but you get tired of it when you do too much Math. Someone who is truly passionate about numbers never gets tired of it. They treat it as a hobby. Which one are you?

**Learning Programming Languages**

“Are you a computer geek?”

Being good at programming is an essential part of actuarial science. Programming languages are very helpful in analyzing and organizing data. Some of the commonly used programming languages are SQL, SAS, R, and Python.

**Long-term Thinker**

“Can you adapt a long-term view when making decisions? Or are you more prone towards making impulsive decisions?”

You might be familiar with the work of actuaries. They are always looking at the future, making forecasts of future events and developing strategies to reduce the risks associated with these future events. They ask questions such as “What are the potential risks the company may encounter within the next five years?”; “Which project would be more profitable?” and so on. To answer these questions, you must be a long-term thinker.

**Problem Solver**

“Are you someone who is open to problems or do you try to avoid problems in general?”

As an actuarial student, you’ll learn how to break down large and complex problems into smaller pieces. Attack the smaller pieces and you’ll reach a solution much faster. It’s ok if you are not good at it. This skill comes with experience and practice.

**Discipline – A long-term commitment**

“Are you willing to devote the time and effort required to become a qualified actuary? Can you remain disciplined in the long run?”

You must understand that the actuarial journey requires a lot of commitment from your end. Self-studying is crucial in actuarial science. That is why you have to be much disciplined to stick to your study schedule.

**Insurance and financial concepts**

“Do you have an interest in finance or economics or insurance in general?”

Actuarial students must have a good foundation in insurance and financial concepts. That is why students are required to take insurance related and finance courses during the actuarial course.

**Money**

“Are you pursuing actuarial science just because you think that you can make more money as an actuary?” Yes, it’s true that you can make more money as an actuary. But, the reward or recognition will only come after years and years of hard work, dedication, and commitment. Using money as a motivation to do well in this field will not work. Never decide to study actuarial science solely with the thought of “I can make more money as an actuary” in mind.

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]]>The post Actuarial Science Modern Portfolio Theory appeared first on StepUp Analytics.

]]>So is there any formula to maximize the returns and minimize risk or Is there a way to reduce the investor’s investment risk by creating a diversified portfolio so that the portfolio as a whole can produce strong returns in any economic climate?

That’s where the idea of Modern Portfolio Theory also called as Mean-Variance Portfolio Theory comes into the picture. MPT, a hypothesis put forth by Dr. Harry Markowitz in his paper “Portfolio Selection” in the year 1952.

**What is MPT?**

Modern Portfolio Theory specifies a method to an investor to construct a portfolio that gives the maximum return for a specified risk or the minimum risk for a specified return. It specifies how investment decisions affect the entire portfolio. This theory also gives emphasis to the words of Paul Heyman, ”I’ve always been a big believer in diversification for anybody. It’s never good to put off your efforts and all of your time and all of your financial resources into just one project. Diversification is the key for any individual and any business.” Thus, it advocates how diversification can reduce the inherent risk present in a portfolio.

For instance, if you invest in 5 stocks individually without even giving a thought of how these stocks affect your entire portfolio then you are at mercy of the stock market alone. If stocks in the general drop, you could face a serious risk without anything to offset that risk. If instead of putting all the eggs in one basket, one diversifies the portfolio and invests partially in the bond market or any other asset category which does not have any direct relation with the stock market then one stands up a chance to offset that risk. So, in case even if the share price plunges there might occur again in the different asset class which would, in turn, set off the loss due to fall in the share prices. As someone rightly said, ”Every portfolio benefits from bonds; they provide a cushion when the stock market hits a rough patch.”

**Assumptions:**

Every theory that is put into practice works only with certain assumptions. The MPT assumes that:

- Investor decides to invest only on the basis of 2 parameters-Risk and Return.
- All the sets of variances, covariance and expected return are known in advance.
- Investor has a single time horizon.
- Investor prefers more to less. Thus, at a given level of income investor will always prefer those assets that give them higher returns.
- Investor dislikes risk.
- Markets are efficient and there are no transaction costs and taxes.
- There is unlimited buying and selling in the market including the short selling.
- Investors can hold their assets in fractions as well.

**How does it work?****Step** **1:** To find the optimal portfolio, one needs to find the opportunity set which is a set of all possible combinations of assets that are taken.

**Step 2:** The next task is to find the efficient frontier which is nothing but removing all the inefficient parts of the opportunity set.

**Step 3:** The last step is to find an efficient portfolio which is the point of tangency of the indifference curve and the efficient frontier.

This is how the efficient portfolio is identified. The above diagrams tell the efficient portfolio in case of 2 assets. It may happen that an investor has more than 2 assets in his portfolio. In such a case, Lagrangian Multiplier is used to identify the different proportion of assets that are to be invested to maximize portfolio returns.

- It helps investors construct portfolios to maximize returns while limiting risk as much as possible.
- It also helps in diversification thus reducing considerate risk.

No theory is without its faults or naysayers, and Modern Portfolio Theory is no exception.

- Experts say that technical analysis gives a better picture.
- There are investors who prefer to manage their portfolio by actively trading and maximizing returns.

Even with the above flaws, MPT is widely used in managing portfolios and maximizing returns.

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]]>The post Behavioral Finance – A Field of Cognitive Biases appeared first on StepUp Analytics.

]]>In this article, we will explore the field of Behavioural Finance, which is a small topic in CM2 but resides deep in us. The CM2 material has more detailed content on this topic than CT8 material. A 5-10 marks question is expected in IFOA exam. Through this article, I intend to make Behavioural Finance simple to understand and easy to score.

**What is it ?**

Behavioral Finance is the intersection of Psychology and Finance. It seeks to explain stock market movements by looking into emotions and behavior of investors. It explains investor behavior in situations where their decisions seem inconsistent with the traditional finance theory. The traditional economic theory assumes that all investors are rational and Behavioral Finance questions this assumption and studies the cognitive biases that cause deviation from rationality.

**Daniel Kahneman**, a psychologist notable for his work in Judgement and decision making, introduced the concept of *System 1* and *System 2* to explain the cause of these biases. System 1 and System 2 are simply the two parts of your brain, the intuitive and the rational part respectively, responsible for slow and fast thinking respectively. System 1 generates suggestions, impressions, intentions, and feelings.

It operates automatically and cannot be turned off at will, for example, you cannot prevent yourself from knowing that 2+2=4. System 2 allocates attention to effortful activities. It is responsible for rational decision making… *System 1 is prone to systematic errors in intuition that causes cognitive biases*

The biases that are studied in Behavioural Finance are not only faced by people when making investment decisions but also while making routine – life decisions.

We will look at a few biases below,

**Anchoring:** Answer these questions

Is the height of the tallest redwood more or less than 1100 feet?

What is your best guess about the height of the tallest redwood?

The response to the second question has values close to 1100 feet. The true height might not be close to 1100 ft but the estimates that we provide get influenced by the anchor-value, 1100 ft and we adjust our estimate to it. This is Anchoring and Adjustment.

**Availability bias:** Answer this,

Which job do you think is more dangerous, police officer or a logger? The answer that comes to our mind is the police officer’s job but statistically, the job of a logger is more precarious. This happens due to the decision being made by our brain based on the limited data available to it. Judgments are influenced by the ease with which something can be brought to mind.

**Framing
bias: **Answer the following questions sequentially,

How happy are you these days?

Now look the questions this way,

How many dates did you have last month?

How happy are you these days?

While answering the second set of questions, a bias was introduced when you were first asked about the dates, the answer to the next question depended on if you had a good dating period. In the first set of questions, both the answers were independent. The effect was due to the framing heuristic.

**Prospect
Theory **(developed by Daniel Kahneman and Amos
Tversky):Consider these questions,

**Problem 1:** Which do you choose?

Get $900 for sure or 90% chance to get $1000**Problem 2:** Which do you choose? Lose $900 for sure or 90% chance to lose $1000

A deviation from economic utility theory was observed when investors wouldn’t choose the option that maximized their expected wealth. It was found that they ride on losses and book their profits when rationally, they must book their loss and ride on profits. It talks about how the decisions are influenced by the gains or losses from a *reference point*.

**Representativeness: **Representativeness occurs because it is easier and quicker for our brain to compare a situation to a similar one (System 1) than assess it probabilistically on its own merits (System 2). This is also related to the law of small numbers, where people assess the probability of something occurring based on its occurrence in a small, statistically-unrepresentative sample due to a desire to make sense of the uncertain situation.

**Myopic loss aversion: **As the name suggests, investors are averse to short term losses. In an

**Herd mentality: **Herd behavior describes the tendency of people to follow or mimic the actions and decisions taken by others, as a mechanism to deal with uncertain situations. The underlying rationale may be that others must know better (safety in numbers), learning or conformity preferences. Stock bubbles are a result of herd behavior.

**Endowment effect: **the endowment effect occurs when a person’s preferences depend upon what they already possess. This implies that a person’s preferences depend upon a certain reference point, perhaps determined by the person’s possessions. Ownership itself creates satisfaction. This is similar to Prospect Theory.

**Self-serving bias:** Occurs when people credit favorable or positive outcomes to their own capabilities or skills while blaming external forces or others for any negative outcomes. This may be done in order to maintain a positive self-image and avoid what psychologists call ‘cognitive-dissonance’, which is the discomfort felt when there is a discrepancy between the perceived self and the actual self – as evidenced by outcomes. It is similar to confirmation bias under overconfidence.

**Overconfidence:** Overconfidence occurs when people systematically overestimate their own capabilities, judgment and abilities. Moreover, studies show that the discrepancy between accuracy and overconfidence increases (in all but the simplest tasks) as the respondent becomes more knowledgeable! Accuracy increases to a modest degree but confidence increases to a much larger degree.

The biases are driven by intuition and emotions. And Anthony Bolton said, *“If you are very emotional you may not make a good investment as you will be influenced by the prevailing investment climate.”*

The question that arises is can the biases be overcome?

The answer is biases cannot always be avoided consistently because System 2 may have no clue to the error.

What’s important is that people should be aware of the biases they go through and then accept it. People who are overconfident wouldn’t readily accept that they are overconfident. Acceptance is the first step towards

Some techniques that can be applied while making investment decisions are:

- Have quantitative approach – Read the financial statements of companies you are investing in.
- Take care to avoid bubbles
- If the numbers don’t stack up, try not to make the investment fit – it won’t, because it’s illogical.
- Taking a disciplined approach to portfolio rebalancing or commitment to regular monthly savings
- Remember, ‘a watched kettle never boils’, to overcome myopic loss aversion.
- Go for counter-stereotyping activities and counter stereotypes like male nurses, female scientists, elderly athletes and the like.
- Identify the anchors
influencing your decision and try to avoid following some expert’s comment on a
stock to avoid making irrational decision. As Rakesh Jhunjhunwala said, “
*If you hear me buying a share then either it’s too late or it’s rumour.”*

It is important to be aware of the biases and not get swayed away by the market news.* As rightly said by Benjamin Graham,*

*“Investing isn’t about beating others at their game. It’s about controlling yourself at your own game.”* The article has been greatly inspired by Daniel Kahneman’s book, “Thinking Fast and Slow”. You must check it out if this topic interests you.

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]]>The post Upcoming Challenges Of Actuarial Science Curriculum 2019 appeared first on StepUp Analytics.

]]>Actuarial Science Curriculum 2019 is all set to go. The IFoA is all set to bring a paradigm shift in its educational programme to make the student’s industry ready whilst giving their actuarial exams. With this motive in hand, IFoA has introduced the online based test to make the students handy with the various software that is used in the actuarial world.

Welcoming the 2019 curriculum I would like to throw light on the varied benefits and challenges that this new curriculum imposes

** STUDY HOURS**: The study hours under the new arrangement are far more than that of the earlier CT subjects. To specify a few:

**(i)** Under the CM1 the recommended study hours are 250 as compared to 125-150 hours in CT1&CT5.

**(ii)** The study hours for CS1 has increased to 200 hours.

**(iii)** For the updated exam CS2,250 hours are suggested to study under the same institution. This would

**EXAMINATION FEES**: The standard examination fee for the new subjects is shown below:

**COURSE NOTES:** Since two subjects are merged into one, this has increased the number of chapters significantly. Thus, more focus needs to be laid on every concept.

**EXAMINATION PATTERN:** The pattern of assessment has changed drastically.

**(i) Core Principles:** One needs to sit for a 1 hour 15 minutes online exam in addition to conventional 3 hours 15 minutes paper-pen test. This online test is a major challenge for the ones who are not tech savvy and are not abreast with the software used.

**(ii)** ** Core Practices:** At this stage, there will be three main modules. The Actuarial Practice (CP1) module will be examined by two paper-based exams. The Modelling Practice (CP2) module will be examined by two three hour computer-based examinations. These will cover the modeling of data, documentation of work, analysis of methods, communication to an actuarial audience and data analytics. The Communication Practice (CP3) module will be examined by one three hours computer-based exam.

**(iii) Specialist Principles: **The Specialist Principles modules (SP) require you to be able to demonstrate an understanding of the concepts of specific specialist subjects. Each subject will be examined by a three-hour paper-based exam.

**(iv) Specialist Advanced**: The Specialist Advanced modules (SA) require you to apply specific knowledge of principles of actuarial practice to the provision of specific practice areas. Each subject will be examined by a three-hour paper-based exam.

**EXEMPTIONS**: As of now there hasn’t any agreement signed by the IFoA with the Indian Institute of Actuaries (IAI) which is a major threat to the students who used to sit for examination with both the institutes. Thus, getting exemptions would be a serious challenge. One can get exemptions for the exams passed before December 2018 from other actuarial societies such as The Actuarial Society Of South Africa, The Actuaries Institute Australia, The Casualty Actuarial Society, China Association of Actuaries and Society of Actuaries Students.

Examinations sat after December 2018 will be subject to further discussions with these associations. Individuals will have until December 2023 to claim their exemptions.

Perhaps, the brighter side to this change is that the new curriculum is set to benefit the students in the following ways:

**UP to DATE:**The curriculum so developed caters to the industry’s requirements. The students will be aware of the practical application of the theoretically learned concepts. This would make them job ready and would increase their chances of landing into their dream jobs.

**COMPLIANT:**The change is in compliance with the International Actuarial Association which is the international body that all actuarial associations are a part of. Thus, the IFoA changes are in line with the changes happening across all the world.

**INTERNATIONALLY FOCUSSED:**The best of all is that the new curriculum is internationally focussed so it won’t be necessarily around the UK rules and regulations. So whilst a student who is based in the UK can write the answer based around the UK rules. A student in India, for example, will be able to answer around the Indian rules and regulations so they should be able to relate it more to what actually goes on where they’re working and not necessarily having to learn a lot of material that they don’t come across in their day to day working life.

Thus, we hope that this IFoA’s initiative for change will help us change the entire Actuarial profession and make it more competent.

All latest updates on **Actuarial Science Curriculum 2019**

The post Upcoming Challenges Of Actuarial Science Curriculum 2019 appeared first on StepUp Analytics.

]]>The post Actuarial Science: Difficulty Level of Exams Under the New Curriculum appeared first on StepUp Analytics.

]]>In this article, we will see how the difficulty level of Actuarial exams has changed under the 2019 Curriculum and the opportunity that lies in it for us.

For starters, the subjects that have been combined are as follows:

CM1: The combination of CT1 and CT5 is trickier and more challenging. Besides, students have to get their hands on MS Excel.

CS2: Subjects CT4 and CT6 which were, individually, quite conceptual and difficult to deal with, have been combined in CS2 paper. These subjects have advanced statistics and concepts that students struggle with. In addition, R programming has to be dealt with.

CA1: Officially, there is no change in the syllabus but the format of the questions has changed. Students retaking the exam under the new curriculum will face difficulties. SA7 (Investment and Finance): Two paper SA5 (Investment) and SA6 (Finance) have been combined reducing it to one specialisation, thereby increasing the difficulty level.

An Actuarial student, to prepare for these subjects in one diet with simultaneously appreciating the concepts of these subjects, will have to work harder and put in more studying hours than previous exams. Majorly, the change is challenging for the students who will have to re-take an exam, failed in 2018 or couldn’t give the exam in 2018 for various other reasons, under the new curriculum. It is disrupting for them as the format for every subject has changed. In CS1, chapters from CT6 have been added and an exam on R programming has been introduced. In CM2, chapters from CT6 have been incorporated. CA1, CA2 has undergone a format change. The difficulty is more for the repeaters than the students who will have the first attempt on a subject under the revised curriculum.

As for the chapters that have been added in and removed from the revised syllabus, Members who had cleared CT4 and CT6 before the new Curriculum was adopted, lose out on additional topics like Machine Learning, Copulas. Despite having cleared these papers, they will have to take extra efforts to learn these concepts to have the same knowledge as a member clearing CS2. Members who had cleared CT3 and CT8, but not CT6, before 2018, will lose out on the topics that have been shifted to CS1 and CM2, respectively. The software skills (Excel and R) introduced in CM1, CM2, CS1, and CS2 will have to learn separately by the members who cleared these subjects under the old curriculum.

Personally, I feel victimize for losing out on concepts that have been removed or shifted. I cleared CT3 and CT8 before the change and I was left with CT4 and CT6. With many chapters of CT6 being shifted to CS1 and CM2 and chapters like Monte-Carlo Simulation that just got removed from CS2, I feel a loss. My passed papers are not being questioned but I know what I am missing out on. I will take to put in extra work to learn CT6 separately to have better knowledge. But my friend, who had cleared the CT series, will have to learn all the software skills that the new members will be taught. The challenges are more for the members who had cleared under the previous curriculum than for the members who will have the first attempt under the revised curriculum. Nevertheless, the efforts and challenges will be worth it if it makes us work-ready and helps us to excel in our domain.

With the introduction of programming (R and excel), the exams have become tougher to clear. Now, besides preparing for 3 hour paper-based assessment, students are required to take additional computer-based assessment which would require hours of practice. Even the assessment result will be single mark weighted average of the two exams with 70% weight assigned to paper-based assessment and 30% weight to computer-based assessment. The students run the risk of having to retake both the elements of the exam on performing below average in either one.

Work-based skills have been replaced by the PPD (Personal and Professional Development) scheme in the new curriculum. PPD scheme started on 1st September 2018. To get detailed knowledge of PPD, read https://stepupanalytics.com/actuarial-science-ppd-under-new-curriculum/

Change in qualification requirements:

Associate: Two additional tests OPAT (Online Professional Awareness Test) and PSC (Professional Skills Course) have been added and one year of PPD is required.

Fellow: 3 years of PPD in addition to associate requirements. Recommended study hours: Under the new curriculum, the study hours required for each exam has increased.

With all this, it is quite clear that qualifying has become challenging and demands proper work and understanding. Also, with IAI holding 2019 exams in June and November has caused uncertainty and stress among the students regarding exemptions.

*Challenges
are not sent to destroy you. They are sent to promote, increase and strengthen
you. *

And the new Actuarial Curriculum comes with the same purpose. The new Curriculum has focused on making the study more application-based. The introduction of software in the curriculum is a big advantage. Actuarial employers expect their staff to have the same skill set as Data Scientists. Interesting topics like machine learning and copulas have been introduced. Machine learning automatically finds the best algorithms for our data, applying the best technique and avoiding overtraining. As the rumor has it, Data Science being better than Actuarial Science, learning programming along with our course defend Actuaries. The best we can do is accept and develop. *Difficulty does not leave until you learn from it.*

For detailed knowledge on the new curriculum, read https://stepupanalytics.com/actuarial-science-curriculum-2019-a-step-forward/

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]]>The post Actuarial Science Profit Testing appeared first on StepUp Analytics.

]]>Profit testing is applied to two types of contracts:

- It is the traditional life insurance policy on which benefit is fixed and known at the start of the policy.
- Example: Whole Life Assurance, Term Insurance, Pure endowment, Endowment assurance, etc.

- In Unit linked contract minimum sum assured is guaranteed on death and no sum assured is guaranteed at maturity.
- The unit Linked contract comprises two types of funds: Unit fund and Non-unit fund. The unit fund is managed by the company on behalf of the policyholder and is the part of the policy that is visible to the policyholder. The non-unit fund is the insurance company’s money and this fund is not visible to the policyholder.

Proﬁt is assured by taking a margin on the basis of the main assumptions- interest rates and mortality. Firstly **Profit Vector**, i.e. profits per policy in force at the start of each year is determined. Then the **Profit Signature**, i.e. profits per policy in force at inception and **Net Present Value (NPV)** is calculated. Finally, we get the **PROFIT MARGIN** which is the expected NPV of the profit signature expressed as a percentage of the expected NPV of the premium income.

**PROFIT TEST MODELLING IN LIFE ASSURANCE USING SPREADSHEETS
**If you have already studied CT5, you must have done profit testing on paper. But the new curriculum requires us to do it on spreadsheets. So, if you are appearing for CM1 – Actuarial Mathematics, you should be aware of how to do it in Excel. Not to worry more, this article will teach us how to do it.

The aim of this article is to demonstrate the profit test models in life assurance using excel. This article presents an example of a conventional contract and a unit-linked contract.

The profit testing technique starts with the calculation of the __premiums__. The premiums charged by a life insurance company are calculated in such a way that the present value of the premiums should be equal to or exceed the present value of the future benefits and expenses. In this article, we will work through basic problems of Profit Testing. Hence, we will calculate premiums __using the equation of value__.

Let us first start with a unit-linked policy as the first example.

**EXAMPLE 1**

**Formulas used are as follows**

- Cost of allocation= Premium allocated (1- B/O Spread).
- Premium allocation = Premium received x allocation percentage.
- FV before growth = Bid value at end + Cost of allocation.
- FV after growth = FV before growth (1+ UF growth rate).
- Management charges = FV after growth x Mng. Charges.
- Bid value at end = FV after growth – Mng. Charges.

Now let’s have a look at an example of a conventional policy.

**EXAMPLE 2**

Every business needs to know if the products it is selling are proﬁtable. However, it is very hard to determine the profitability of a business. Hence some technique is required to assess the proﬁtability before writing the business and profit testing is widely used as a process of determining the profitability of an insurance contract in advance. Nevertheless, the actual experience may not be the same as the expected experience. But profit testing gives us results which are almost similar to the actual result.

Profit testing is a very important and high scoring topic of CT5 (New Curriculum CM1). I hope this article helped you understand how to incorporate profit testing models in Excel. That’s all from our side.

Read more about Actuarial Science New Curriculum **Click**

Thank you!

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]]>The post Actuarial Science: Introduction To MS Excel appeared first on StepUp Analytics.

]]>Microsoft Excel plays a key role in many sectors among the computer programs which exist. It is the most extensively used spreadsheet program in several business activities, personal data organization, and class work. In this article, we will go through Introduction To MS Excel in Actuarial Science perspective.

It was first uncanned in the year 1985. Since then, this software has come a long way in performing comprehensive formula based arithmetic and calculations and other operations that require mathematical interpretation.

Almost all the businesses, personal enterprises, and educational institutions have embraced the pertinence of MS Excel for its utility and the potential to serve as a visual basic for a wide range of applications. And the same goes for our actuarial community. MS Excel is our oxygen that fuels our interpretation and valuation using mathematical & statistical techniques and economics approach to solve our real-life actuarial based problems.

Seeing this spunk liveliness of MS Excel in our actuarial profession at work, our institutional bodies, IFoA and IAI have come up with the updated actuarial science curriculum in 2019 which comprises MS Excel-based practical exams lodged on the theory and application of the actuarial techniques to the real data sets. What a remarkable move to ensure the fit of the actuarial science coursework and overcoming challenges in today’s evolving world!

The significance of MS Excel can be seen in the distinctive units it is used as follows:

**Graphical Interpretation****Data Organization****Programming**

And these distinctive units can be learned to use and apply at three different levels:

**Microsoft Excel- BASIC (or BEGINNER) LEVEL
**You will perform simple arithmetic operations on the data whose formulae are already there in Excel. Additionally, you will be able to use a few keyboard shortcuts of various key features to help you get around the sheet.

The content briefly includes:

- Basic Spreadsheet Skills
- Understanding Workbooks and Worksheets
- Selecting Cells
- Auto-Sum and Auto-Fill Function
- Arithmetic Operations
- Formatting Cells
- Cells, Rows and Columns Adjustment
- Placing Cell Alignment
- Edit, Copy and Move Cells
- Basic Options, Ribbons, and Toolbar
- Cell Referencing
- Proofing Workbooks

**Microsoft Excel- INTERMEDIATE LEVEL
**You can apply efficient ways to manipulate and draw inferences from the data. You can effortlessly write your own complex formulae, construct graphs and will eventually land up working on MS Excel more with the keyboard than the mouse.

The content briefly includes:

- Define Names and Sort Data
- Insert Table
- Filter Data
- Insert Charts
- Chart Design Options and Format Tools
- Combo Charts
- Insert Date and Time Functions
- Information, Logical and Financial Functions
- Find and Replace
- Headers and Footers
- Adding Comments
- Conditional Formatting

**Microsoft Excel- ADVANCED LEVEL
**You will actively try to automate user experience using Macros to create your own function and will come across various functions and graphics to master data design of tables, pivot tables, and charts. Advanced techniques include super-functions, array formulae, advanced range names and problem-solving.

The content briefly includes:

- Use Text to Columns
- Paste Special Function
- Data Validation
- Subtotals and Grouping
- Scenario Analysis
- What-if Analysis
- Text Functions
- Lookup Functions
- Statistical Functions
- Insert Hyperlink
- Insert Pivot Tables
- Protecting and Sharing Worksheets
- Understanding and using Macros
- Data Encrypting and Finalizing Workbooks
- Working with Templates

Being an actuarial aspirant, if you have decided to learn software skills then trying your hands on MS Excel is the best head start to aspire for. It is a versatile software and universally used spreadsheet. The sooner you learn, the better for you. You will now not just be using it in the corporate sector but will also be giving MS Excel-based exams too. So, working on your proficiency in MS Excel will be a sure shot leverage to your actuarial jaunt.

This is all from my side and most certainly I would like to end this article highlighting the importance of MS Excel as it is the most vital preliminary software in most of the traditional as well as the modern actuarial organizations for the purpose of efficiency and problem-solving. Organizations aim to keep the systematic and up-to-date records of their products, the order of affairs and activities so that Actuaries are able to predict the future and risks involved on the basis of the data available. In addition to that, individuals who are skillful in creating Excel programming i.e. VBA (Visual Basic for Applications) are considered assets to an organization.

To know more on how to solve actuarial problems based on real data sets in MS Excel, stay tuned with StepUp Analytics website.

Read more about Actuarial Science New Curriculum **Click**

**I wish you All The Best! Happy learning!! **

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