Statistical & Financial Consulting by Stanford PhD
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I am a Wall Street professional offering tutoring services in the fields of asset pricing, empirical finance, financial economics, MBA-level finance, econometrics, data mining, optimization, systematic trading, risk management and insurance modeling. I hold a PhD in Statistics from the Stanford School of Arts & Sciences and a PhD Minor in Finance from the Stanford Business School. During my five years at Stanford I did research in stochastic processes, derivative pricing and distress contagion. 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 financial industry focusing on projects in time series analysis, data mining, volatility modeling, derivatives pricing, quantitative trading and risk management. Equally importantly, I have tutored students and business professionals in statistics and finance for the last eight years. I have consulted researchers and companies on modeling, programming and execution aspects of their work for the last ten years.

I help with projects, class exams, quantitative parts of CFA and Series 7 exams, presentations, dissertations, advanced academic and industry-oriented research, infrastructure development and general skill enhancement. Data analysis and programming is performed in R, Matlab, Stata, SAS, Excel, EViews, SPSS, Minitab, JMP or Python. As my experience shows, I work with all types of clients, from undergraduate students to senior portfolio managers. I meet in Manhattan on selected days or tutor via videoconferencing and desktop sharing in Skype. In the past I have worked with clients in New York, Long Island, Stamford, Boston, Chicago, Columbus, Pittsburgh, Philadelphia, Baltimore, Washington, Orlando, Miami, Houston, Austin, Dallas, Phoenix, Denver, San Diego, Los Angeles, San Jose, San Francisco, Seattle, Vancouver, Montreal, Toronto, Edinburgh, Cambridge, London, Bergen, Berlin, Frankfurt, Zurich, Madrid, Dubai, Tokyo, Hong Kong, Singapore, Perth, Adelaide, Melbourne, Brisbane, Sydney, and so on.


1] Tutoring on the Hourly Basis

This includes preparation for exams, work on assignments and business related projects, preparation for presentations and interviews, real-time training in one of the software packages, general mentoring and skill enhancement, and so on. The minimum duration of one session is 2 hours.

2] Doing a Project for a Fixed Fee

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

3] Designing A Course in a Given Area

I prepare learning materials, exercises and cases for you. You read the materials and do the cases on your own timing. After that, we meet in Skype, I check the results of your work and answer the questions you have accumulated. I explain how to approach several selected tasks. After that, I give you a new block of learning materials and exercises. The cycle repeats itself... Structuring our interaction as a semi-independent study allows you to improve 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 years sometimes. I help you rephrase research questions and choose appropriate methodology. I perform the data analysis in the software of your choice: Matlab, R / R Studio, Stata, SPSS, SAS, EViews, Minitab, JMP or Python. Alternatively, I guide you through performing the analysis and interpreting the results yourself. I navigate your e-mail correspondence with the dissertation committee to make sure they understand what you have done, you understand what they want and they give you only the tasks that you can handle. I coach you for each public appearance, including the dissertation defense... Make sure to read the dissertation tips.

5] Infrastructure Development

I develop infrastructure necessary for pattern recognition, systematic trading, derivatives pricing, risk management or any other type of quantitative analysis. I train you to the level of being able to update / modify selected parts of the infrastructure.

The rate starts at $80 per hour and increases with the complexity of material. For example, the minimum rate applies to introductory, "Hull-like" option pricing but for any industry-level analytics the rate is substantially higher. The best course of action is to contact me with a list of topics or project description and I will provide you with a quote.

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. Having this situation in mind, I am sketching a study program designed to provide a logical and comfortable path through quantitative finance. The program assumes knowledge of calculus, linear algebra and elementary probability. It 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 various financial topics on this web-site. Do not spend too much time on them. Their purpose is to provide an overview. Next, read the books I am listing below. It is best to follow the books in the order presented. However, feel free to skip the material you already know. The "Introductory Probability and Finance" and "Prerequisite Statistics" modules are essential for understanding subsequent literature on asset pricing, empirical finance and data mining. The "Asset Pricing and Risk Management" module is highly mathematical. The order is especially important here, since the mathematics in one book relies on the mathematics in the previous book, for the most part.



Introductory Probability and Finance

Ross, S. M. (2009). Introduction to Probability Models (10th ed). Academic Press. - Chapters 1 - 6 and 10 - 11.

Hull, J. (2011). Options, Futures, and Other Derivatives (8th ed). Pearson / Prentice Hall. - A friendly introduction for MBAs and anybody with imperfect quantitative training. Includes many institutional details.

Madura, J. (2014). International Financial Management (12th ed). Cengage Learning, Stamford, Connecticut.

Prerequisite Statistics

Agresti, A., & Franklin, C. (2013). Statistics: The Art and Science of Learning from Data. Pearson Education.

Efron, B., & Hastie, T. (2017). Computer Age Statistical Inference: Algorithms, Evidence, and Data Science. Cambridge University Press. - Focus on parts I and II. After reading the data mining literature later in this list, you can come back to Part III.

Tsay, R. S. (2005). Analysis of Financial Time Series. New Jersey: Wiley-Interscience.

Asset Pricing and Risk Management [highly mathematical block]

Williams, D. (1991). Probability with Martingales. Cambridge University Press. - Read part A. It is an introduction to measure-theoretic probability theory, which is a prerequisite for any rigorous pricing literature.

Oksendal, B. K. (2002). Stochastic Differential Equations: An Introduction with Applications (5th ed). Springer-Verlag Berlin Heidelberg. - A clean and rigorous introduction to stochastic calculus, which is accessible only after picking up measure theory in Williams or a similar reference. Read chapters 3 - 5 and 7 - 8 only.

Duffie, D. (2001). Dynamic Asset Pricing Theory (3rd ed). Princeton University Press. - Read chapters 1 - 2 and 5 - 11. The book is a coherent and logical exposition of asset pricing designed for a reader with the knowledge of stochastic processes. Not for pure MBA background.

Brigo, D., & Mercurio, F. (2006). Interest Rate Models - Theory and Practice (2nd ed). Springer-Verlag Berlin Heidelberg. - Continuation of Duffie with emphasis on the change of numeraire and term structure models.

Lipton, A. (2001). Mathematical Methods for Foreign Exchange: A Financial Engineer's Approach. World Scientific. - Continuation of Duffie with emphasis on exotic options.

Taleb, N. (1997). Dynamic Hedging: Managing Vanilla and Exotic Options. Wiley Finance, New York. - Standard reference for traders and risk managers.

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

Skoglund, J., & Chen, W. (2015). Financial Risk Management: Applications in Market, Credit, Asset and Liability Management and Firmwide Risk. Wiley, Hoboken, New Jersey.

Financial Economics and Portfolio Optimization

Grinold, R. C., & Kahn, R. N. (1999). Active Portfolio Management: A Quantitative Approach for Producing Superior Returns and Controlling Risk (2nd ed). McGraw-Hill. - Factor models for trading with long horizon.

Empirical Finance

Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1996). The Econometrics of Financial Markets (2nd ed). Princeton University Press.

Singleton, K. (2006). Empirical Dynamic Asset Pricing: Model Specification and Econometric Assessment. Princeton University Press.

Fundamental Analysis

Damodaran, A. (2006). Damodaran on Valuation: Security Analysis for Investment and Corporate Finance (2nd ed). Wiley, Hoboken, New Jersey.

Koller, T., Goedhart, M., & Wessels, D. (2015). Valuation: Measuring and Managing the Value of Companies (6th ed). McKinsey & Company Inc.

Systematic Trading

Archer, M. D. (2010). Getting Started in Currency Trading - Winning in Today's Forex Market (3rd ed). Wiley, Hoboken, New Jersey.

Kritzer, A. (2012). Forex for Beginners: A Comprehensive Guide to Profiting from the Global Currency Markets. Apress and Springer-Verlag, New York.

Machine Learning, Optimization and Analytics

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2017). An Introduction to Statistical Learning: with Applications in R (Corr. 7th printing). Springer New York.

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

Weiming, J. M. (2015). Mastering Python for Finance: Understand, Design, and Implement State-of-the-art Mathematical and Statistical Applications Used in Finance with Python. Packt Publishing.