Statistical & Financial Consulting by Stanford PhD

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I am a professional offering tutoring services in the fields of actuarial science, statistics and quantitatve finance. I hold a PhD in Statistics and a PhD Minor in Finance from Stanford University. During my five years at Stanford I did research on multiple life models and related pricing issues. Also, I taught more than ten undergraduate, master's level and PhD level statistics and probability courses. After graduation I moved to New York and, for more than a decade, I have been working in the industry focusing on projects pertaining to data mining, credit risk, derivatives pricing and overall risk management. Equally importantly, I have tutored students and business professionals in actuarial mathematics, statistics and finance for the last eight years. I have consulted researchers in industry and academia on modeling, estimation and programming aspects of their research for the last ten years.

I help with exams, projects, presentations, dissertations, advanced research, analytics development and data analysis in R, Matlab, Stata, SAS, JMP, SPSS, Minitab, EViews and Excel. As my experience shows, I work with all types of clients, from undergraduate students to managers at insurance companies. I meet in Manhattan on selected days or tutor via videoconferencing and screen sharing in Skype. In the past I have worked with clients in New York, Boston, Chicago, Seattle, San Francisco, San Jose, Los Angeles, San Diego, Phoenix, Denver, Dallas, Austin, Houston, Miami, Orlando, Washington, Baltimore, Philadelphia, Pittsburgh, Columbus, Montreal, Toronto, Vancouver, Brisbane, Sydney, Melbourne, Adelaide, Perth, Hong Kong, Frankfurt, Zurich, London, Edinburgh, and so on.

**TYPES OF SERVICE**

**1] Tutoring on the Hourly Basis **

This includes preparation for exams, work on assignments and business related projects, sessions to improve general knowledge, and so on. The minimum duration of a session is 2 hours.

2] Doing a Project for a Fixed Fee

This includes solutions to specific problems, data analysis, model validation, novel research, and so on. Please make sure to check out examples of the projects I have done in the past.

3] Designing A Course in a Given Area

I prepare study materials, exercises and data-driven projects for you. You read the materials and do the projects on your own timing. After that, we meet in Skype, I check the results of your work and answer the questions you have prepared. I explain how to approach several selected tasks. After that, I give you a new block of study materials and exercises. The cycle repeats itself... Structuring our interaction as a "course" allows you to enhance your knowledge relatively cheaply, since you are not paying for the time when I am not around.

4] Dissertation Assistance

This is a mixture of services 1 and 2 spanning months (or even years, sometimes). I help you rephrase research objectives and choose appropriate methodology. I perform the data analysis in the software of your choice: Matlab, R, SAS, Stata, SPSS, JMP, Minitab or EViews. Alternatively, I guide you through performing the analysis yourself and help you interpret the results. I navigate your e-mail correspondence with the scientific adviser to make sure that he understands what you have done, you understand what he wants and he gives you only the tasks that you can handle. I prepare you for each public appearance, including the dissertation defense... Make sure to read the dissertation tips.

5] Infrastructure Development

I develop infrastructure necessary for real-time modeling, estimating and forecasting cashflows coming from pools of correlated insurance policies. I build systems for pattern recognition, reinsurance derivatives pricing, risk management or any other type of quantitative analysis.

The rate starts at

Also, some of you may choose to minimize the costs by engaging in long periods of independent study before working with me on "high tech" topics. This is completely fine. In fact, to learn a big area well you should approach the task as a marathon and not a sprint. And this is only possible if the process does not put much strain on your emotions and finances. For those of you who would like to embark on a lonely journey, I am

The independent study program assumes that the student comes with fundamental training in mathematics (calculus, geometry, matrix algebra) and scrappy knowledge of probability and statistics. Again... the plan is designed without knowing you. So please take it with a grain of salt and contact me if at any point in the future you feel the need for a customized solution.

Start with browsing the study materials for standardized actuarial exams (the first group of links below). This will give you an idea of important areas and will boost your stamina in subsequent educational exploits. Next, go through my descriptions of various statistical and financial methods (the second group of links below). Do not read too much into it and do not spend much time on it. You will not understand 50% of the descriptions but they will start painting the big picture of relevant human thought. Finally, do it right and start reading the books I am listing below. Do practice problems in most of them. It is best to read the books in the order presented, perhaps with slight variations based on interests. Feel free to skip the material you already know. Having that said, it is important to preserve the order in the first half of the sequence, where fundamental results are introduced. If for a specific book I indicate the chapters to read, read only those chapters. If I am not saying much, I am implying that either the whole book or a big part of it might be useful. If I am saying something like "read the whole book" or "the whole book is important", try reading it all and in the worst case scenario settle for 80%. In fact, going through a key reference twice might be more important than going through something else for the first time.

- Study Materials for Actuarial Exams, Michigan State University
- Study Materials for Actuarial Exams, University of Chicago
- Study Materials for Actuarial Exams, ActuarialZone
- Study Guide, University of California, Santa Barbara

Ross, S. M. (2012). A First Course in Probability (9th ed). Pearson Education Limited.

Freedman, D., Pisani, R., & Purves, R. (2007). Statistics (4th ed). New York: W. W. Norton & Company. -

Ross, S. M. (2009). Introduction to Probability Models (10th ed). Academic Press. -

Williams, D. (1991). Probability with Martingales. Cambridge University Press. -

Oksendal, B. K. (2002). Stochastic Differential Equations: An Introduction with Applications (5th ed). Springer-Verlag Berlin Heidelberg. -

Lehmann, E. L., & Casella, G. (1998). Theory of Point Estimation (2nd ed). New York: Springer. -

Lehmann, E. L., & Romano, J. P. (2006). Testing Statistical Hypotheses (corrected 2nd printing of the 3rd ed). New York: Springer. -

Greene, W. H. (2011). Econometric Analysis (7th ed). Upper Saddle River, NJ: Prentice Hall. -

Agresti, A. (2002). Categorical Data Analysis. New York: Wiley-Interscience. -

Lee, E. T., & Wang, J. W. (2003). Statistical Methods for Survival Data Analysis (3rd ed). Wiley-Interscience, Hoboken, New Jersey.

Duffie, D. (2001). Dynamic Asset Pricing Theory (3rd ed). Princeton University Press. -

Brigo, D., & Mercurio, F. (2006). Interest Rate Models - Theory and Practice (2nd ed). Springer-Verlag Berlin Heidelberg. -

Duffie, D., & Singleton, K. (2003). Credit Risk: Pricing, Measurement, and Management. Princeton University Press. -

Bowers, N. L., Gerber, H. U., Hickman, J. C., Jones, D. A., & Nesbitt, C. J. (1997). Actuarial Mathematics (2nd ed). Society of Actuaries. -

Shang, H. (2006). Actuarial Science: Theory and Methodology. World Scientific Publishing, Singapore. -

Embrechts, P., Klüppelberg, C., & Mikosch, T. (2011). Modelling Extremal Events for Insurance and Finance (Corr. ed). Springer-Verlag Berlin Heidelberg.

Rausand, M. & Høyland, A. (2004). System Reliability Theory: Models, Statistical Methods, and Applications. Wiley-Interscience, Hoboken, New Jersey. -

Weerahandi, S. (2004). Generalized Inference in Repeated Measures: Exact Methods in MANOVA and Mixed Models. Wiley-Interscience, Hoboken, New Jersey. -

Gibbons, J. D., & Chakraborti, S. (2003). Nonparametric Statistical Inference (4th ed). New York: Marcel Dekker.

Brockwell, P. J., & Davis, R. A. (1991). Time Series: Theory and Methods (2nd ed). New York: Springer. -

Ross, S. M. (2012). Simulation (5th ed). Academic Press.

Hastie, T., Tibshirani, R., & Friedman, J. H. (2008). The Elements of Statistical Learning: Data mining, Inference, and Prediction. New York: Springer. -

Bishop, C. M. (2006). Pattern Recognition and Machine Learning. New York: Springer. -

Boyd, S., & Vandenberghe, L. (2004). Convex Optimization. Cambridge University Press.

Spall, J. C. (2003). Introduction to Stochastic Search and Optimization. Wiley-Interscience, Hoboken, New Jersey. -

- Detailed profile: experience, case studies and payment options