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Dec 19
Missing data imputation is a common technique many researchers have to apply for some certain situations, especially when we do some portfolio analysis that requires an equal length of historical returns of assets in the portfolio. Typically we assume a distribution of the underlying data and simulate missing data based on the assumption, MLE or EM algorithm is used for simulation. For example, a great R package I have introduced for missing data imputation was at here.

"How to Combine Long and Short Return Histories Efficiently" is a good paper forthcoming in Financial Analysts Journal by Sébastien Page, as introduced
A common challenge in portfolio risk analysis is that certain assets have shorter return histories than others. Unfortunately, many standard portfolio risk analysis techniques—including historical tail risk measurement, regime-dependent risk analysis, and bootstrapping simulations—require full return histories for all assets or risk factors. The author presents easy instructions on how to efficiently combine data for investments whose histories differ in length and offers a new model to better account for non-normal distributions.

An important feature of this paper is instead of assuming that the uncertainty around the backfilled returns is normally distributed, the model samples empirical residuals from the short sample. Evidence shows this method is efficient. The author also provides Matlab code in the Appendix for us to play around.

Dec 17
Everyone wishes to be sucessful, when it comes to find a road to wealth, there are over 100 Ways to get out of debt and on the road to wealth, each way tells a different story and the one suitable for others may not be good for you. Below is the list of 10 students becoming today's top billionaires under the age of 60, how did they make the fortune? perhaps we can learn something from the infographics.

Via: Grown Up Me
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Dec 12
A guest post from Anton Kwaijtaal: A CRO guide to deal with financial amnesia.

1. Don’t fear the risk of falling behind
Whether it is the risk of falling behind, peer group pressure or ill-defined incentive schemes, there exists a tendency to choose direction based on the illusion of control when there is actually too much uncertainty. Instead, questions should be asked as to whether decisions based on more or less unfounded assumptions should be made at all. Unfounded and inappropriate assumptions are dangerous because of at least two well-known biases. First, we tend to be over-confident in our ability to make financial and economic probability models. The second bias is our tendency to favour information that confirms our beliefs or hypotheses. This is called the confirmation bias. Moreover, by using hyperbolic discounting we reveal a strong tendency to make choices that are inconsistent over time. In other words, we make choices today that our future self would prefer not to make, despite using the same reasoning. Therefore, CRO’s and all other professionals should minimize their bold assumptions about how the economy works. We know much less than we think we know. Warren Buffet, the highly successful investor, sets strict restraints on using assumptions. He nevertheless makes above average profits.

2. Use real risk indicators
The volatility is wrong when you really need it. When reading this sentence most risk managers immediately think about skewness, kurtosis or perhaps about extreme losses. However, it is necessary to take it one step further. Most of the risk indicators, also in a regulatory context, are based on statistics. In most circumstances this is a second moment, named "variance" or "volatility". The volatility is however an affect heuristic driven indicator. It has no real correlation with the actual risk. The affect heuristic leads people to have a low perception of risk when we feel positive about the economy (and the other way around). However, during long periods of bull markets – driven by debt accumulation – actual risk (e.g. the probability of a deep debt crisis) increases, but our perception of risk reduces.

What you are really interested in is the consequence of market shocks when it actually goes terribly wrong. In this way you correlate risk with the probability of survival of your firm. The use of volatility is a good example of attribute substitution. A complex problem (what are the consequences of a serious meltdown) is replaced with a less complex problem (what is the observed volatility of the market over the last few months/years), at which point the answer to the less complex problem is seen as the solution to the original problem. Risk indicators should be correlated with actual risk, not with indicators such as (implied) volatility. A better risk indicator is the price to profit ratio of stocks, which reveals – in combination with debt levels – a lot about instability accumulating in an economy.
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Nov 22
Time flies, another thanksgiving day of a year.

I would like to express utmost thanks to my supervisor, Professor David Newton, for his continued encouragement, support and guidance throughout the course of my PhD research. I am grateful for his patience, interest and willingness to accept my PhD research topics. Not only does he provide me with research guidance but also his advice for my career drives the whole course of research and makes the three-year PhD study in Nottingham much more interesting.

I thank my parents for their unconditional love and understanding. My life wouldn’t be as it is now without their selfless support. I also want to thank Ms. Haoyu Ma, who has always been at my side supporting me throughout this whole research. Your love and support make every mission possible.

I also take this opportunity to show my thanks to my PhD colleagues and friends at the Nottingham University Business School for their encouragement and help. Spending three fantastic years with you is memorable for the rest of my life. In particular, I would like to thank Dr. Huainan Zhao, Dr. Kai Dai, Ms. Ting Qiu and Mr. Ding Chen, who have always provided me with invaluable advice and suggestions, and helped me in the many ways they can.

Importantly, I thank my co-authors, Dr. Qian Han, Dr. Doojin Ryu, Dr. SongTao Wang, and Prof. David Newton. Our publications and working papers would not be so great without your collaboration. I also appreciate the fly-out opportunities given by University of Otago (New Zealand), Renmin University (China), and KAIST (Korea Advanced Institute of Science and Technology), I had very good time and the experience is memorable no matter an offer will be given or not.

Finally, thanks for your continue reading my blog despite my infrequent posts this year. A photo taken few weeks ago when I visit a famous temple in HangZhou, China, wish you all healthy and successful in the coming year.
temple China
Nov 21
The Basel Committee on Banking Supervision has received a number of interpretation questions related to the December 2010 publication of the Basel III regulatory frameworks for capital and liquidity and the 13 January 2011 press release on the loss absorbency of capital at the point of non-viability.
basel banking
Below are three sets of frequently asked questions (FAQs) that relate to counterparty credit risk, including the default counterparty credit risk charge, the credit valuation adjustment (CVA) capital charge and asset value correlations. More sets may be forthcoming, stay tuned.

First set
Second set
Third set
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