FRM Level 1 & 2: Strategy

FRM Levels  1&2  Strategy

Abstract: This article will be about the strategies and areas of FRM for the FRM exam. Strategy to crack both exams on the same day in one go, or a single.

Free recordings on FRM Part 2 -  http://qcfinance.in/frm-part-ii-videos/

Link to one on one Course - http://www.wiziq.com/course/48106-one-on-one-training-to-prepare-for-frm-part-ii-exam

Introduction: If you have registered for the FRM exam then this post will talk about some strategies you should take.

FRM 1:

Tough areas that are important for exam are:

• Cheapest to deliver.
• Bound quotation and how to use the conversion factor and other things in cheapest to deliver the entire concept.
• Euro Dollar Futures.
• Beta changing and hedge effectiveness using index futures.
• Stack and Roll strategy.
• Areas of 2nd Level when comes in First level like MBS duration (from old question bank).
• Pension funds based long questions on liabilities and asset movements.
• Most of the people who give this exam are very prepared and professional and each question count in this exam.

Hence It is all about exam management.

I will be developing hybrid questions which can be helpful which are 2 times as tough as the real exam where I will try to use real data:

1. EMWA + GARCH on real data.
2. Portfolio Management (taken from CFA L3, sortorio, information ratio, etc)..
3. Regression based on ab initio methods (summations rules).
4. More complex swaps, floating rate vs. fixed.
5. Unexpected loss mixed with other's areas (and with junk data).
6. Bigger portfolio for VAR Calculations.
7. Multiple regression for more beta and many regression equations build on data.
8. Questions which taken into detail Brownian motion in a more detailed way of delta-t.
9. Greeks Management including many options and Vega+Gamma hedge using options and shares (book talks very simple questions about them).
10. Cheapest to deliver derivations on how it all came into existence.
11. All stories of fraud in risk management.
12. Real case studies on Stack and Strip hedge in commodities.
13. 1 and 2 error and hypothesis testing in more detail.
14. Margin and maintenance variation calls and namings.
15. Expected loss formula and the derivation of unexpected loss.
16. convexity basic problem using common numbers.
17. calculator advanced functions.
18. memory cards for FRM, and mind maps.

Similar to this, these are areas of the CFA exam which are seen here so for those who are giving both exams can focus on:

1. Types 1 and 2 errors.
2. Hypothesis testing of non zero.
3. Chi square test.
4. Bayes theorem.
5. Hardcore theory questions in corporate Finance.

Getting info about Valuation at risk models and foundation of risk management.

1. We can start with the easiest topic "Foundation of Risk Management", which is nearly one-fourth of the exam. I will read the last few chapters i.e. ERP, Financial disaster, failures, GARP conduct codes.
2. Quantitative part is simple, so this can be done easily.
3. Financial Markets and product are the heart and soul of the exam respectively. It is mostly made up of derivatives.
4. Valuation and Risk model is again theoretical and easy.

The only thing in FRM Level 1 which gives me trouble is the distributions where using and applying them is very important to understand the essence of the subject. Pareto, Beta, Extreme value, Weibull are some which are tough to understand and requires some patience. 

Foundation of Risk Management can be divided into 4 major blocks:

1. MPT.
2. APT Arbitrage pricing theory vs. CAPM is an important area to understand.
3. Risk and failures.
4. GARP Code.

This is also the most easy and scoring one.

Financial Markets and product are the toughest as well as most important part of the FRM. There are many things that gives a lot of resistance in this area to those who have not studied any of this part before, but I will be sharing all what I can on this area.

1. Interest rate instruments options, swaps.
2. Stock options are very easy.
3. Commodity is easy but new.

In total, this is the most fascinating area, and most difficult.

Valuation and Risk Models

Now this chapter is very important in terms of models like Black-Scholes, binomial and other important models for valuations. When I was doing it, the most interesting was the subject of Greeks in options. It took some time to get things in. The best part about this part of FRM is that you can read more and more and understand as there is a lot of depth attached to this area. For example, the derivation from Kinetic theory gases, stochastic calculus of blacks-scholes.

Quantitative Techniques is similar to all other Quant you might have done in CFA, Engineering, Modeling so it will be skipped in my discussions.

FRM Level 1 Nov 2012 Strategy

Level 2 of FRM has been talked about very less in all forums and blogs. I will be taking about some of the course material.

Resources: Readings Core (1444 pages old of 2009), Handbook for FRM (800 pages), Scz (book and questions).

No Videos on Youtube: There were no videos on youtube so I will add videos on the same in 1 month. Bionic still remains only option.

Core Reading: Are from various books, also see the AIM statements. Does not have a lot of questions.

Research Areas: How did these distributions came into play: F stat, t sats, Chi... these three are to be known by common sense and as well from their roots. comparing variance of 2 population and formula derivation of chi-square distribution. Derivation of standard error and that root term in denominator. How and when did DoF started and how it is imp? Derivation of integration of variance formula with the denominator 12, how to convert that limit into integration? Story of Normal distribution.

Book to check out: Jogn Hull, chap. 11.

FRM Part 2: No idea about Part 2 as still looking at the resources for the exam. I saw the model papers of 2011 and 2010 and the Level 2 was very different. Questions were:

• Theoretical and broad
• Longer and detailed
• Has many statements and we have to select the right combinations
• Less Quant
• Checking good broad knowledge
• Requires experience and consistent reading habits

Based on these parameters you can understand that Part 2 is more about theory than quant, but this is my view based on 3-4 papers I have seen and read on internet.

There are 63 Chapters in the readings which are taken from various books. Interesting areas and overlap:

1. Operations Risk and Sarbanes Oxleys Act (Allied areas have 25% weightage).
2. Portfolio (maths at Level 2).
3. MBS mortgage back securities (may be Quant).
4. Credit Risk Quant is an imp area that is quite fun.

Reading in general and watching generic lectures will help. There is no Quant in L-2 which I was expecting.

In last 2 months you can plan to study for Operation Risk in FRM-2 and Foundations of RM in FRM-1.

Strategy for FRM is to:

Step 1: Read summary or key concepts.

Step 2: Videos youtube.

Step 3: Content of scz.

Step 4: Studying the book.

Step 5: Doing questions.

Topics for each month (FRM Level 1):

4th last 2012: VAR and Valuation and risk models.

3rd last 2012: Foundations of Risk.

2nd last 2012: Financial Market and Products.

last 2012: Quantitative techniques.

Summary of interesting things in var valuation and risk models (FRM Level 1) related to Quant:

1. Single step binomial trees and risk neutral method.
2. e to power sd*root(t).
3. Delta hedging.
4. Coherent Risk.
5. Worst case distributions.
6. 7 Distributions.

Tough areas that I find very interesting:

1. Interest rate swap and 2 methods.
2. Brownian Motion and monte carlo simulations.
3. Formulas of Green that are derived from Black Scholes.
4. Euro Dollar futures.
5. Inverse normal distributions, random number generations, selecting number from 0-1 in random fashion and then taking inverse normal methods.
6. Floating rate adjustment.
7. Poisson distribution derivations.
8. Merton Model for Credit.

FRM Level 2

Quantitative Areas in FRM Level 2

Copulas is an area that I came across which was followed by extreme value theorem and Pareto distribution. Then there is a much talked about VAR back testing. These are few things that you should research in general and read from wiki.

FRM from Kaplan:

The Book 2 of Scz on FRM Level is very easy, whereas the last 3 chapters of book 1 (on VAR) are toughest in the entire FRM Level 1. Some areas of book 1 especially VAR, the 3 chapters help you to get Level 2 and you should look at Level 2 to understand them more even while giving Level 1 to get them inside your brains.

Future Work: I will introduce to you tricky but interesting area and try to develop some motivation for you to study them. Because to study these areas you need to have some pre- idea, motivations, real examples, news items so that you can create right mental frame to study them. The exam is very simple if you work on the things systematically and ready to spend some time.

Important links to read from Wikipedia for FRM level 2:

http://en.wikipedia.org/wiki/Extreme_value_theorem

http://en.wikipedia.org/wiki/Basel_II

Operation Risk

http://en.wikipedia.org/wiki/Merton_Model

http://en.wikipedia.org/wiki/Kernel_density_estimation

http://en.wikipedia.org/wiki/Exotic_option

MBS, cdo and all types teaching securitization etc.

http://en.wikipedia.org/wiki/Mortgage-backed_security

http://en.wikipedia.org/wiki/Collateralized_debt_obligation

Correlation and Copula

http://en.wikipedia.org/wiki/Copula_(probability_theory)

Operation risk

http://en.wikipedia.org/wiki/Standardized_approach_(operational_risk)

Jensons Aplha and information ratio CAPM revision.

KMV model not on wiki.

Crediting scoring model.

Trading gamma (interview question).

Fixed income arbitrage.

Portfolio of CFA and FRM are same.

--------------------------------------------------------------------------------------

Please contact at shivgan@qcfinance.in/arpit@qcfinance.in in case of any suggestions or feedback.

Raw List of Interesting and core areas to search online are:

1. Hypothesis testing.
2. delta of option.
3. Different distribution.
4. degree of freedom.
5. Greeks and partial derivatives.
6. Monte carlo simulation.
7. black scholes model.
8. Boot strapping and spot rates.
9. Regression and beta.
10. Put call parity.
11. types of duration.
12. calculator mastery.
13. currency.
14. correlation matrix.
15. technical analysis.
16. Black Swarm (Movie and book).
17. VB and excel modeling for Risk.
18. Poisson and pareto distribution.
19. Dollar value.
20. LTCM Russian default.
21. Interest for AAA AA A.
22. UBS false trading of billion.

One on One Customized Training:

qcfinance.in believes in personalized touch so that our clients are completely satisfied with our service. In this regard, we offer One on One Customized Training to our clients.

These Training are provided on requests by our clients & are customized according to their individual needs.

The course structure & timings for these training are highly flexible, classes are scheduled as per the convenience of our clients. 

Contact Us for More details: info@qcfinance.in

SAS Certification Base/Advanced/Predictive-Modeling: Study Strategy (About Statistical Software)

SAS certifications Strategy (Programming vs. Predictive)

Check out the Link: http://qcfinance.in/sas-business-analytic-course/

I heard about this software by some people on LinkedIn, and also saw its requirements on various job portals. The exam seems simple and doable, now will look into more details. We have 2 approaches one is learning database commands and other is the real thing which is prediction like that on decision trees.

Introduction: I have been reading about SAS and found it one of the most powerful tools which include power of C++ and MATLAB (linking to things I know). I will be discussing about SAS base certificate, studies, strategies, syllabus and how much time I will be allocated to study this area. The first part is about base programmer which is just like database management.

SAS Base (easy and data management): In this regard I had a look at the book "SAS-Base-Certification-Preparation" which is a 500 page big book and found it quite useful, I will try to join a coaching where I can do hands on practice on SAS and also try to implement my older models on research in SAS. The courses that I saw are of around 50 hours for Base, and hence I think I need around 100 hours in total to prepare for this exam. The 7 chapters in the book does not include any financial or complex models, but rather are very very basic and describes the overall framework, hence there is no fin in the initial stages to give this exam.

There are 2 levels in this course for software engineering:

1. Base Exam is about introduction (OOC, managing data, generating reports, managing input output, acquaintances to platform etc).
2. Advanced Exam which is to be given after the base exam includes Macros and SQL integration.

How to start preparation, and general matters. All programming languages has these things:

1. OOC remains the same.
2. Data type follows general ways.
3. Input-output.
4. Referencing.
5. Library.
6. Database extraction.

Coming to more important part i.e. Predictive Modeling Using SAS. This is a tough but a very important which has decision tree and regression.  This is the tool for finance people and risk managers. 

Broad areas at stat packages that you need to work independently on SAS not covered in certification:

1. ANOVA
2. Regression
3. Time series analysis.

Ref: http://en.wikipedia.org/wiki/Comparison_of_statistical_packages

Current strategy:

I have downloaded some presentations and trying to look for books to start with. I am having moderate exposure to stat, but know all the stat that will be used in Pred Modeling. I plan to give the exam in Jan. last week. I plan to join some coaching where I can be taught by SAS Pred Model certified teachers and practice on SAS installed at his coaching.

Broad areas that I need to master in Stat Analysis, if possible using SAS:

Linear regression, logistic regression, survival analysis, neural network, time series analysis, conjoint analysis, clustering techniques, decision trees and linear programming etc.

Financial Modeling  on other platforms (Excel, Macros, VBA, SAS, MATLAB):

I was looking at 2 options on financial modeling: SAS and Excel. I will taught by the current insti over the next 2 months on Excel, so I am working to get the SAS base certificate. It seems to be a 180 USD exam, and will explore more on that. SAS is mentioned at Berk, NYu, Qnet as useful so I think moving into it will be worth.

I also read about SAS CFA interlinking for a CFO on the CFA website: SAS for the CFO: Helping CFOs Adjust to an Expanding Role. And it did make a lot of sense because CFO should be able to link the performance matrix.

SAS Predictive Modeling contents and Strategy:

1. Accessing and Assaying Prepared Data (typical data management activity).
2. Decision Trees (here it is your own decision tree).
3. Regressions (you know this).
4. Neural Networks and Other Modeling Tools (highly researched topic).
5. Model Assessment and Implementation (requires tool).
6. Pattern Discovery (theoretical topic).
7. Memory based reasoning (this is something new, I will add notes on this).

The thing here is that you can still understand many of things without even giving your hand on Predictive modeling. The things here that are important are mostly derived from statistics. Neural network was a subject in engineering especially for CSE people, and it was a topic of 8th sem of RGTU people. I focus on how this thing is used in financial engineering. The tool that most people use in this regard is the MATLAB Toolbox.

Conclusion of the visits to over 4 coaching and official training provider of SAS in Jan 12:

• SAS can be learned quickly if you know the maths and stat.
• SAS base and advanced are more of programming type, SAS predictive is specific to engineering.
• SAS predictive modeling can be learned quickly if you know the major 4 areas that are also a part of the engineering syllabus.
• SAS predictive modeling course is 30 hours by these institutes, and teachers are scarce to find, and for these 30 hours they charge huge money. This money is 2.5 times my PGDF fee, 2.5 times my CFA coaching fee, 2.5 times of what I earn (all stat in per hour), thus it is an expensive program by all means.
• The part which can give trouble is macros, SQL which is used in Predictive modeling as well.
• For resume purpose, I think you can still give Base SAS which is programming, but does it help really in the Quant job that one needs to get?

MATLAB Finance toolbox:

I have some experience about this software when I did reliability engineering, and also in electrical modeling. Now I will check out the MATLAB Fin toolbox and see if it works on not.

Conclusion: Language not tough for those acquainted with MATLAB and C++, and can be learned quickly. Base has no great thing and has to cover both levels. SAS predictive is the one that makes the most sense in Financial Engineering, but SAS base talks about basics of data handling. In the long term SAS, predictive modeling will help, but for now even SAS base certificate will be good enough to prove that you are acquainted with the platform.

Versions at friends and coaching institutes:

I went to check but I found SAS Data miner for Predictive modeling no where, it is in fact the best software I want to learn today. But to learn Base and Advanced SAS is very easy, also because it is something related to database management and nothing related to forecasting.

Here is a brief summary of the software tools that I liked to work on in Finance:

1. For derivative pricing: MATLAB and Mathematica.
2. For Stat: SAS and R (There are certifications in SAS but R is open source and no certi exists as per my findings).
3. For common day to day use: VB and Excel.

Other tools for predictive modeling (only):

Enterprise miner, Knowledge seeker, Treenet.

Free Trial :http://www.salford-systems.com/downloadspm.html.

Target Date: Jan 12 Last week or July 12; Fee 180 USD for base, 250 for Predictive Modeling, similarities with OOC and MATLAB.

R vs. SAS Dilemma and my views:

1. About R Software: This software is open source and is having a big community to do research. There is no certification in this exam, otherwise this package is very good as it is open source.
2. The problem with R is that unlike SAS it has no certification course but has got  a huge support online. SAS is more seen as requirement than R and SAS seems to be more user friendly. Hence things are bit tricky, personally I like open source and things which has got a lot of help and effort online. But here I will prefer SAS due to job requirements and proof of knowledge as SAS certifications. 
3. I still don't know the answer of whether predictive modeling can be done in R, in the same way it is done in SAS. I think there are ways to do all things in R but the paths can be longer.

Will try to compare it with Oracle Hyperion Financial Management.

What training has to offer and how to do that on your own?

Training is important part, but SAS training is very expensive and you can still learn it or go for open source options like R. CFA Case is about data management, I had a look and it was all playing with data, no maths involved. Advanced looked into Macros and SQL, and this is again hardcore programming. For these 2 things, you can do on your own as you can find a place to practice yourself.

Total training time = 3 months full time for SAS B+A+P which means around 300 hours.

120 for Base, 120 for Adv, 80 for Predictive. Looking at this you can also make a chart and do it on your own.

Another parallel strategy is to do it on R.

Sample STAT-Quantitative Job requirements:

-Statistical expertise and knowledge of few of the following techniques – linear regression, logistic regression,   survival analysis, neural network, time series analysis, conjoint analysis, clustering techniques, decision trees   and linear programming etc.,

- Expertise in the usage of SAS for both data manipulation and model development.

- Experience with modeling software – Eg: Enterprise miner, Knowledge seeker, Treenet etc.

Some areas of Artificial Intelligence that are used:

• Fuzzy logic
• Artificial Intelligence
• Genetic Algorithm
• Neural Network
• Monte-Carlo Method
• ACO (Ant Colony Optimization)
• AHP(Analytic Hierarchy Process).

Some Derivatives pricing Models:

• Rational pricing assumptions
• Moneyness
• Pricing models
• SABR Volatility Model
• Markov Switching Multifractal
• The Greeks
• Finite difference methods for option pricing
• Trinomial tree
• Optimal stopping (Pricing of American options)
• Interest rate derivatives
• Short rate model
• Hull–White model
• Cox–Ingersoll–Ross model
• Chen model
• LIBOR Market Model
• Heath–Jarrow–Morton framework.

Risk Analytics has SAS Analytics Statistics includes but not limited to:

• PD, LGD models with hands on experience in creating models.
• Basel framework and regulations and experience in creating related models. 
• Running SAS queries to prepare datasets used in analysis and predictive modeling. 
• Using SQL from SAS to extract and aggregate data from larger data sources. 
• Performing ad-hoc analysis/statistical analysis and generation actionable reports. 
• Idea about SAS/SPSS/MS Excel/MS Access and VBA etc. 
• Major tools include analysis tools like SPSS, R.
• IT data management tools and BI platforms.

Links:

http://www.puzha.com/sasbook/sas%20examples.html

http://www.ats.ucla.edu/stat/sas/webbooks/reg/chapter1/sasreg1.html.

SAS Base Course (making it relevant to Financial Risk)

Uploaded by Shivgan on WizIQ Tutorials

One on One Customized Training:

qcfinance.in believes in personalized touch so that our clients are completely satisfied with our service. In this regard, we offer One on One Customized Training to our clients.

These Trainings are provided on request by our clients & are customized according to their individual needs.

The course structure & timings for these training are highly flexible, classes are scheduled as per the convenience of our clients. 

Contact Us for More details: info@qcfinance.in.

Will be adding content and updating this post frequently.

Prospective Research areas in Financial Engineering at Qcfinance

Abstract: I have discussed about areas in Finance pertinent to old areas that I have been working and trying to expand and add value to that work with the help of my new knowledge.

Working White Papers (At QCFinance.in):
1) Applying Quantitative Game Theory in International Business and novel methods for computing Nash Equilibria
2) Monte Carlo Simulation for Investment Banking / exotic derivative pricing in MATLAB/R
3) Recent Trends in International business and Game Theory (theory)
4) Strategic Finance for Commercialization of CNT and Decision Tree Analysis (90% complete for Tri Nano)
5) Copula Modeling in MATLAB/R
6) Review of Research Trends in HPC / CUDA for Financial Engineering
7) Merton Model on R / MATLAB

Research areas of Financial Engineering to be applied:

1. Game Theory
2. Energy Risk
3. Nanotech
4. SMC in Finance
5. FRA for Nanotech companies
6. Nano solar
7. Intentional Business and International politics

There has been good research done in the area of Energy risk and financial aspects of energy, but less has been discovered about the implications of alternative energy solutions like Solar and Fuel Cells. to understand the implications of this new type of energy one needs to know about the conventional financial instruments used and also about the research trends in solar and fuel cells. There are various parameters that define what I company can do to hedge its risk from conventional energy and its instruments to this new area, but this has be done in compatibility with the current financial policy of the company. When I was looking around on the internet there is a very good course called the "Energy Risk Management" which has an excellent amount of reading for the oil and other energy sources.What I will be doing in my research is to add the new Nanotech-energy solutions and its implications. For this one needs to understand the current models and instruments.

SMS(Secure Multi Party computations) is a way of performing secure computations when parties donot want to reveal their data, this becomes very important in the world where no one trust no other and information is the major tool for competitiveness.There are some research papers available on this subject but a comprehensive research is still lacking.

Nanotech is an area where a lot of research is not realizable and it becomes very tough for an company to understand how to use it in the most efficient way. I will be looking ahead to add the findings in this area to my old work.

Game theory which involves a lot of Maths and computations is very rare in financial instruments and there is a lot of scope of imbibing this subject with the current models and how to make it relevant to current research and analysis.

Much of the research of Quant Games are not applied and used in real life, because of many reasons, and in this work I will be working on how these Quant games can be used in the subject of international business. I will link the maths and define some parameters with real modeling options for the subject. Nash Equilibrium is the heart of Game theory. 

This Nash equilibrium takes into account all moves that the opposite party can take, and hence reduces your loss  due to wrong moves. Thus if our competitor is not working on the same he will move to different direction and keep on losing.


The areas that I will show in real sense of Quant Game theory in international business are:

1. Geometric games
2. Isovalue surfaces
3. Differential Games
4. Multi stage games
5. Optimality

Their linkages with various real examples are shown to develop a strategy for international business.



International Business & Politics are also closely linked with this idea, because war in middle east always triggers oil prices, and which has a very wide effect on countries like India. So Game theory and strategy can be applied to do Business keeping all these scenarios in mind. Not only game theory but the models of Financial engineering can also be applied to these scenarios of International strategy making.

Will revisit Articles on Game theory + Finance, Nanotechnology + Finance, SMC and Finance, Energy Alt + Finance in the month of Dec 2011.

Green Energy Strategies for implementations:
My research includes working on these parameters:

• Energy solar cell, financial derivatives on how each movement will affect the expected use and pricing
• Some breakthrough points in research in solar cells that may trigger the future expected device realization
• Decision making tree, taking data from neural network using regression
• Neural network and system and signals engineering, in itself this topic is immense
• Regression coefficient selection for Green energy models
• ROE, profit loss models, ratios, etc
• Game theory on how to move ahead to diversify the scenarios, because other competetiors are working using the same models
• Demands of oil and crude price and its effect on alternative energy market like solar cell and fuel cells, this can be modeled using regression coefficient
• Not just technology, politics, wars, crude prices, recessions will also effect the scnerio which needs to be taken into consideration
• Game theory for research in solar and fuel cells for energy
• Next research breakthrough in Nano solar and its possible impact, on productions and solar
• Making the decision tree and pointing the probability
• Effect of extreme events and research implications on solar energy
• And also time to commercialize and strategy from a company perspective
• Important research that will effect are on special type of solar cell and reliability and modeling and manufacturing and making a decision tree on how these things adds up to commercialization

These are the points I am expanding in my research paper.

Strategic Finance in Green Energy

Energy made from solar cell and other alternative forms will be preferred by the consumers of the future. If one can study this behavior and use the financial derivatives on the current energy options and how each movement will affect the expected use and pricing of alternative energy; then he/she could make some good prediction on the usage of alternative energy. This is because energy derivatives and other models can help you to predict the demand of energy as well as price of energy markets of the future.  Macroeconomic factors and their operators: Economy plays an effect on each of the models that we make in the predictive modeling.

Suppose as the price of oil increases there is shift toward the sales of solar cells or fuel cells or wind turbines then these changes can be modeled empirically. As price of oil increases the demand of old decreases and the demand of alt energy product increases [derive Maths relations Sales= ax-by, for simplicity reasons this is taken linear].

Demand = Function (Energy futures, consumer demands, macro economy, technology, growth in current companies, extreme events)

STEP 1: Looking at Energy Derivatives (Crude Oil Prices, Energy, coal)

STEP 2: Inferring expected price and demands & Macro economic factors

STEP 3: Looking at technology breakthroughs, like reliability packaging

STEP 4: Linking both to make a model to find out demand for alternative energy solutions

STEP 5: Making of decision trees and neural networks, using right distributions

STEP 6: Observing financial ratios of current companies and predicting future

STEP 7: Looking for extreme events (Financial Risk Management)

STEP 8: Use Game theory to understand competitors Behavior

STEP 9: Implementation models: HPC, Multi Scale, Reverse Reliability Models

Demands of oil and crude price and its effect on alternative energy market like solar cell and fuel cells; this can also be modeled using regression coefficient using appropriate data, but in this work we have not focused on that. Regression coefficient selection for Green energy models. Energy options vs. investing in green energy alternatives.

Some breakthrough points in research in solar cells that may trigger the future expected device realization. Example of nanotech enabled solar cells, packaging, etc. A. Vora[2] has described the possible breakthrough in technology.  Modeling using HPC[3], multi scale properties, understand things in more details will be an important area. This research can be linked down to the same in modeling the future. This will be one part of the model which will be discussed in detail in this work on how much the research work is going to affect the entire model and to quantify that. We will be using our earlier models like TTC model[4].

Decision making tree and taking data to form neural network using regression models. Smaller version of decision trees are shown in our earlier papers [4]. In this part we will make regression models on how to move to various branches. We will also associate probability distribution to each event in the appropriate manner. Neural network and system and signals engineering, in itself this topic is immense. Because in Neural network we have system (boxes) with input and outputs. Again make a tree on commercialization of Green nano solutions.

ROE, profit loss models, ratios, etc which are the part of any financial analysis. Optimum ratios for green energy models from FRA. Next research breakthrough in Nanotechnology solar and its possible impact, on productions and solar. And I we will list down all areas that will help. And also time to commercialize and strategy from a company perspective: future and current. Energy project finance is another important area. For this data can be collected from the current companies, now after observing the sales of the past we can get some trend about how things have behaved in the past.

Game theory for research in solar and fuel cells for energy. Game theory on how to move ahead to diversify the scenarios, because other competitors are working using the same models. Hence price and strategy adjusts as new and current players take some steps or take a move. We can define some of the games from our readings of game theory, for example if we analyze some special type of games and the Nash Equilibrium of the games then analysis becomes easy. We will be expanding our models of GT from [4].

Effect of extreme events and research implications on solar energy: defined on financial, political, environmental, wars, research breakthroughs etc. Not just technology, politics, wars, crude prices, recessions will also affect the scenario which needs to be taken into consideration. Also international business will play a very important role with this regard. Operators for all such event needs to included. This includes today’s political scenario like wars or anarchies in the middle east which is described by Arpit[2].

Important research that will effect are on special type of solar cell and reliability and modeling and manufacturing and making a decision tree on how these things adds up to commercialization. These technical areas will be discussed in details. Technical knowhow’s on its relation with reliability and packaging.

Proposal on implementing these models on HPC, use of multi scale modeling to find abstraction layers of research.

Contemporary solutions for the perception of the people, and how this would affect the change or sales and which of this part is perception or price movements or other parameters.

Selection of probability distribution: Normal, log normal, tale adjustment, skewness.

Expected implementation: SAS Predictive modeling tools, other options as well

Complexity of research and applicability of nanotechnology in solar cells. Kind of derivative pricing model for the same like Brownian motion models. Linking down physics of nano levels. Linking stochastic calculus for deriving right black sholes models.

 

 [1] A. Vora’s research on breakthrough points on solar cells

[2] A. Ludhiyani’s research on international politics

[3] HPC Research related to this area of R. Patak

[4] Linking Reliability engineering to this area, application of risk old papers, my old research, esp the 2

 

Important points to look into:

1. Sample games in extreme scenarios, risk cases, game type, maths of games, linking it down to business. Like wars in middle east. 
2. I am looking at different games that are there so that we can put in some more light in the political international business. 
3. Decision tree vs game theory or adding the research done in both areas?
4. Diagrams of decision trees that are used in the system, making diagrams of hypothetical cases.
5.  Deriving Research from Financial Risk to Political Risk in business.. Quantification of political risk, and adjusting quant methods of derivatives in this place
6. Polto-Monte-Carlo model for International Business: Where we can put these equations and look at political movements. Brownian motion derivation form stochastic calculus.

References:

[1] springer.game.theory.decisions.interaction.and.evolution.2007.1846284236

[2] Quantitative_and_Qualitative_Games__Volume_58__Mathematics_in_Science_and_Engineering_

[3] 20081188511397467 GAMES AND INFORMATION  FOURTH EDITION

[4] Compleat_Strategyst__Being_a_Primer_on_the_Theory_of_Games_of_Strategy

Recent Trends in International business and Game Theory:

This paper is about a survey of game theory and its application in Finance

How to use research of game theory in here
Decision tree for international business, exploiting the new research

Politics and energy, and politics of energy

Trade and politics, what are the future linkages

Define game types[1,3], game based on number of players [4], change decision tree

Statistics inferences, regression models, probability distributions, and using them in the GT models [2]      Review of research and adding modern tools to international business and Finance

Linking down effects of Financial and Political Scenarios using game theory
 
International Politics & Game Theory Model outlook: How do international games looks like, mathematical games:

Step1: Define the game time

Step 2: Decide the probabilities on each point, and games of cooperation

Step 3: Make the decision tree with the branches (Monte Carlo simulations fittings and motivations)

Step 4: Also check Value at Risk, Extreme events and Distributions

Step 5: Use neural networking

Step 6: Re-check the model

Step 7: Decide the modeling tools

How to make good research presentations.

http://qcfinance.in/research/

Important points includes citing the right reference, using mathematical equations, graphs should be properly made and cited, history and present of the thing should be discussed, online reference of the detailed presentation should be given (ex. Blog entry).

Strategy for CFA Level 2 June 2013

CFA Level 2 strategy June 2013


My Targets:
Quant 22
Port 23
Fi/Der 24 25
Copr 26
Ai 27
Eq 28
FSA 29
Ethics 30

200 questions each day.

Last month strategy. single minded focus needed.



In this article I will outline some strategy for the CFA level 2 exam. To start with, I personally would advice to go through Economics and FSA again as they are some of the areas which are very interesting and very broad, also they will take a lot of time to settle in your mind.

• Economics is the most interesting topic today for the entire world in the light of currency and debt collapses so I think it will be the easiest to start with slowly. And the other one which is FSA is the largest and most interesting subject that helps you to understand the pillars of finance.
• I checked Scz of Fixed income and derivatives, now if the things are done in momentum the under lying logics can be imbibed the mind. The glimpse that I had in Fixed income and Derivatives made me feel that they will not give me trouble
• Portfolio is nearly the same as I studied and things looked fair.

I will be writing about some of the intuitive ways to understand and apply and see in the real world some concepts of economics, part of which I will write in my research post.



Resources that are very good:
Scz & All for CFA L2 esp videos
Sas materials are provided by coaching

Monthly Strategy as planned on Dec5th:
Dec 2011: Eco and FSA (CFA)
Dec 2011: Foundation of Risk Management (20% of FRM and a total theory topic with no Quant)
Thinking of SAS certificates
Dec 2011: SAS software, MATLAB Finance toolbox, and other modeling tools like Excel finance
Jan 2011: CFA and Modeling

Carry over to Jan: Eco+FSA (CFA-L2) & Foundation of Risk Management


Monthly strategy for the exam:
Jan 2011: Registrations

Feb: Financial statements analysis + Corporate Finance (Economics, Equity touching)
March: Portfolio Management + Fixed income
April: Derivative + Quantitative
May (Practice the tests)
June (Exam on 3rd June 2012)


Financial Statement analysis of CFA level 2 consists of broadly these areas:

1. Inter-corporate investment
2. Pensions
3. Merger and Acquisitions
4. Multinational Operations
5. Analysis in details of Cash flows

This is something very interesting and will require some time to get in.
When I looked into the Analysis of operating cash flows, the topics and areas covered and taken are very very fascinating and will help in understanding the games that are played with Cash flows. I will be taking this subject as the first to start with in the L2 study.

Economics for CFA Level 2:
This is a very short area, and has topics related to global/international economics. There are small small topics picked up and made up into a chapter. And these topics are taken from various sources, which are cited clearly. The most interesting area is International asset pricing. Overall it is not as interesting as Level 1, no marginal graphs, no complexities, and not so much depth. But this is an opinion after going through it once.
Interest rate parity
Currency conversion
International CAPM

Synchronizing my effort from Level 1 to Level 2(for Dec 2nd week):

1. Portfolio is one are directly taken as it is form 1 to 2, and it is also bit imp for FRM
2. Derivative is same as FRM Level 1 and CFA Level 1
3. Equity is again taken directly from 1 to 2 so it highly related
4. Eco seems to be very different, and of-course quant is not a problem (less linked up)
5. Fixed income remains normal,
6. Ethics is again as it is from there to here
7. Corporate is same

Rank from toughest to easiest for me:

1. Economics
2. Financial Reporting
3. Corporate
4. Rest are same, Derivatives Fixed income covered in both
5. Easiest being Equity

Independent areas:

• International asset pricing (Eco)
• CDO (quite independent)

I am trying to make some presentation on these areas. Strategy to make Videos of CFA Level 2, priority list:

1. Portfolio Management
2. Derivative
3. Equity

Forex is covered in Economics, and will start this to know bit about currency.

Areas covered in Excel Financial modeling pertinent to CFA level 2:
FCFF (Equity)
FI Bond Portfolio and Z spreads (best understood when talked on excel)
Corporate Wacc question of optimizations
Waiting for Derivatives in Excel, macros in excel, VBA to cover some part of level 2
Most of the features of excel are covered in this regard

Jan end: The Modeling done on Excel for all Equity valuations are taken from CFA level 2, hence this is one thing that gets linked, and some of the M&A and Investment banking areas comes from Corporate which makes this area interesting.

Whereas if I think of difficulty then FSA becomes most tricky, hence keeping an eye on it is a better idea.

Pages linked to time to read:
On an average these books vary from 300-600 pages, thus 300 pages books requires 300*5 minutes and so on. You can break it down to like 50 pages to be done in one sitting of 2 hours, and note making.

FRM will cover a lot of derivatives and fixed income so the only thing left will be FRA. I can do quant side by side using financial time series and regression books so those things will not be a problem.

Conclusion: Strategy for the 2 exams are discussed, my personal views on strategy is described.

Last resort of Memorization for some areas of Portfolio/Quant/FRA CFA Level 1 Exam

About some of the formulas in Finance that you  need to memorize for the moment.

Exam Prep Portfolio consist of understanding, memorizing and speed, and you need to balance all these 3 parameters as each one will help you minimize your risk and maximize output. My personal weights used are: understanding & research = 50%, speed is 15%, and memory is 35%. But I have observed that in my class people who are getting higher marks than me have a portfolio (my guess) of 70% memory, 20% speed and 10% understanding.

It has been my attempt to keep things in the most logical and common sensesical way. But there are somethings which requires a lot of research which time doesn't allow. In the later stage you can understand these formulas but for now you need to memorize them.




Other areas that need to be memorized include:

1. Beta is sensitivity of asset with respect to market. It can be also looked upon as Cov-im/sigma-square-m. beta= Pim (sigma-i/sigma-m)
2. M-squared is like sharpe ratio (rp2-rf)(sigma-m/sigma-p2)-(rm-rf), (Trenoy measure, Jensen alpha both are for SML excess and slope): Details about these 3 measures, probably they will come in some later stage with more detailed derivations on my Portfolio thread.
3. Different yield formulas of quant where hpy =(bdy*n/360)/(1-bdy*n/360) , CFO shortcuts (increase in asset means reduced cash flows)
4. Cash convergence cycle in corporate finance, and its derivation
5. Technical Analysis from core readings
6. Enron and Sunbeam Scandal
7. Sale type lease vs other types
8. Difference in USGAP vs IFRS example in held for sale/ for trade/ till maturity
9. Cost of trade credit numerical
10. HHI and other methods to compute monopoly in economics; sum of squared % of market share of top 50 firm. 10,000 for monopoly, above 1800 indicate market is not competitive (HHI).......
11. 4 firm ratio of over 60 depicts oligopoly  
12. Money supply * velocity = price level * real output; money multiplier = (1+c)/(c+d)
13. Days of sale outstanding = 365 / receivable turnover ......  Inventory in-hand  = 365 / inventory turnover ..... Number of days payable = 365 / payable turnover ratio
14. Equity Minority interest,  current assets placement in USGAP VS IFRS
15. DOL = % change in EBIT / % change in sales ; DFL = % change in EPS / % change in EBIT 
16. DOL = (S - TVC)/(S-TVC-F)
17. DFL = (EBIT/(EBIT-Intrest))
18. Impairment of inventory in USGAP (lower of NPV, historical, replaceable)... CV > UFCF ? long lived assets?
19. Derivative formula  for forward rate agreement. i.e. formula to long the settlement for the notional amount 
20. NI ---> CFO (indirect conversion)
21. Cost of trade credit = {1 + PD/(1-PD)}^[365/DAYS post discount]
22. BEQ into BDY (R_MM = (360* _RBD)/(360 - (t* R_BD) ) this has been imp
23. HPY as a function of Bdy
24. ebit=operating income
25. Interest burden = EBI/EBIT; used in DuP (FRA)
26. Operating cycle vs Cash convergence cycle  (Corporate Fin)
27. 9 major sections of GIPS 
28. Target FFR= 2% + actual inflation + 0.5(actual inflation - 2%)+ 0.5(output gap) 
29. beta in terms of sd of market and port (portfolio management)
30. Margin for equity(maintained margin) vs future (replenish) 
31. Renewable source economy demand elastics or inelastic? 
32. Eco elasticities  (0-1, greater than 1)
33. hypothesis of paired tests, chia quare, others complex ones
34. Tax loss carry forward, details about loss carry forward 
35. Do comparative advantage questions in economics
36. Quality of earning ans scandals
37. different type of markets: again
38. types of data: time series, longitudinal, cross sectional
39. long question of cash convergence cycle
40. long quest of equity 2 methods, p/e forward done by the person in the class
41. maintenance variation initial
42. step-up inverse floater inflation
43. types of unemployment
44. Still dof doo leverages giving problems
45. fundamental weighting techniques
46. Embryonic, mature, shakeout, phases in equity
47. price to earning ratio = dividend payut ratio/ (req ROE rate - growth rate)
48. net income for common or both in diluted earning questions
49. breaking points and operating break even point
50. financial leverage, fin multiplier, d/e,
51. hpy to rmm, rmm to bdy
52. rm to bdy formula 360rb/ (360-trb)
53. Escalation bias, confirmation bias different bias in cfa
54. Classical Keynesian and Monetarists
55. Pension list of all elements
56. technical analysis of all element
57. hypothesis entire revision
58. percentage of completion and all methods of revenue collection
59. what to do about loss in held till maturity, does it comes in OCI
60. money weighted is irr, time weighted returns in quant
61. ordinal interval nominal
62. minute details about proxy statement
63. ifrs usgap differences
64. sale type lease vs operating lease vs direct financing lease  

Fixed income (different types of bonds like inflation adjusted bond, tax issues, etc)
Corporate Finance (like Corporate Governance which is theoretical)
From the experience of students who are giving the exam, accuracy is the most important thing, but the questions are also predictable especially of equity.




Conclusion: You need the very right balance in your approach, depending on time resources, energy and partners, you must dedicate time in understanding and memorizing.