Quantitative Finance Collector

Finally finished reading the book High Probability ETF Trading: 7 Professional Strategies To Improve Your ETF Trading bought few weeks ago, in the book the authors share 7 professional quantitative trading strategies to improve ETF trading, namely: 3-day high/low method, RSI 25/75, R3 strategy, the %b strategy, multiple days up and down, and RSI 10/6 & RSI 90/94 strategy. Since ETF is not easily accessed for individual investors due to large amount fund requirement, my first thought is: are these trading strategies suitable for stocks trading? At the end of the book the authors also said: "the strategies in this book are intended for ETFs. Many of the concepts are derived from high probability equity trading strategies, but stocks have very different risks than ETFs". In addition, the authors also note "ETFs tend to move from overbought to oversold better than individual stocks", considering all of the 7 strategies are based on buying on pullbacks, I wasn't optimistic about them on stocks.

I tested 5 strategies out of 7 for a randomly selected Chinese stock downloaded from Yahoo, since shorting selling is hard in Chinese market I exercise long strategy only (which might influence their performance, I admit). Starting with capital 12500, transaction cost 0.5% and running for one year data, the results are (pls bear with me, the graphs look ugly, just for preliminary research):
1, 3-day high/low method

Tomorrow is the last day of this lunar year, wish all of you and me happy Chinese lunar new year.
抬板凳看春晚

1, Modeling the Cross Section of Stock Returns: A Model Pooling Approach, "This paper illustrates the advantages of a model pooling approach in contrast to model selection. Model pools of several asset pricing models including the CAPM, the Fama-French (1993) three-factor model, and the Carhart (1997) four-factor model are considered for the purpose of forming expectations (i.e., predictions) of the one-step-ahead returns for a cross section of stock portfolios." http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1536050;
2, Option Pricing with Piecewise-Constant Parameters, Discrete Jumps and Regime-Switching, "In this paper, I address systematically how to enhance the most existing option models with piecewise-constant parameters, and how to derive the corresponding closed-form characteristic function under the risk-neutral measure. As long as the characteristic function with piecewise-constant parameters is analytical known, the pricing formula for a European call is then given by inverse transform of the derived characteristic function." http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1547036;
3, Comparison of Numerical and Analytical Approximations of the Early Exercise Boundary of the American Put Option, "In this paper we present qualitative and quantitative comparison of various analytical and numerical approximation methods for calculating a position of the early exercise boundary of the American put option paying zero dividends." http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1547783;
4, Multivariate GARCH Models with Correlation Clustering, "This paper proposes a new clustered correlation multivariate GARCH model (CC-MGARCH) that allows conditional correlations to form clusters. This model can generalize the time-varying correlation structure in Tse and Tsui (2002) by determining a natural grouping of the correlations among the series." http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1548408;
5, Efficient Derivative Pricing by the Extended Method of Moments, "The local conditional moment restrictions are of special relevance in derivative pricing for reconstructing the pricing operator at a given day, by using the information in a few cross-sections of observed traded derivative prices and a time series of underlying asset returns." http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1550135.

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I have been struggling to find a suitable research topic for my PhD in finance these days, initially I chose to research on convertible bond underpricing considering multiple features other paper might fail to do so, but later on I realized the potential margin contribution is small with more knowledge on this field, indeed a few latest paper dealing with this issue already.

Do you happen to have an interesting topic that you or your colleagues want to work on while without enough time and resources? then probably you are able to help me by telling me what the topic is about to "abiao @ mathfinance.cn" (remove space). The topic needs to be:
1, applicable as a thesis topic;
2, about derivative pricing (equity, fx or IR), trading strategy, portfolio construction or quantitative risk analysis.

The benefits for both of us:
1, I find a sexy research topic to kill my boring overseas doctor life;
2, you get an update about the progress of the topic also attracting you from period to period;
3, a problem is solved hopefully.

I know it is hard to find a topic in this way, but I do appreciate any comment or hint or suggestion, especially from industry side.

A follow up post of my previous entry Using Quadrature method for option valuation, where the accuracy and computational speed are demonstrated briefly with a simple European option based on the paper "universal option valuation using quadrature methods". Today I play the Quadrature method for a vanilla convertible bond, still, the results are promising, for example, below is price performance comparision under Quadrature and PDE (specifically, finite element method) numerical solutions, where the CB has time-to-maturity two years, call barrier 12, call price 110, strike 10, risk-free rate 2%.




The exact computational time depends on the time steps and asset steps, but generally speaking, since Quadrature has a higher order of convergency rate, it is several times faster than finite element, in my case, Quadrature costs me less than ten seconds but finite elements costs me around one minute.

PS: the y-axis should be relative error.