32

Many of them are on my website at emanuelderman.com. Others I probably have anyway. Feel free to email me


23

In fact you have three papers available to go further: The Avellaneda-Stoikov one, with proper model and an approximate solution The Bayraktar-Ludvkosli one, with a solution for the linear utility function The L-Guéant-Fernandez one, with a full solution for a generic utility function I prefer the last one ;{)}


23

Hah! There is no such thing as the “rigorous mathematical underpinning” of high frequency trading - because HFT, like all trading, is not primarily a mathematical endeavour. It’s true that many people who work in HFT have a mathematical background, but that’s because the tools of applied math and statistics are useful when analysing the large amounts of ...


22

The lead paper in the January 2011 Journal of Finance (Hendershott, Jones, and Menkveld) addresses algorithmic trading (AT). In short, they find that AT improves liquidity as measured by bid-offer spreads. Taking the econometrics as correct (it is in the Journal of Finance) the next question is if bid-offer spreads are a sufficient statistic for measuring ...


20

Ledoit and Wolf shrinkage methods ("Honey I shrunk the sample covariance matrix") Ceria and Stubbs - Robust optimization literature (2006) Stock & Watson (2002ab) - papers on large N small P estimation Rockafellar & Uryasev (2000) - "Optimization of CVaR and coherent risk measures" Sorensen, Qian, Hua - "Quantitative Portfolio Management" Ang ...


18

I did some digging and found the following papers - most of them offering quite a distinct perspective compared to classical option pricing theory! Stock Options as Lotteries by Brian H. Boyer et al. (2011) The Efficiency of the Buy-Write Strategy: Evidence from Australia by Tafadzwa Mugwagwa et al. (2010) The following is my favorite: You could do some ...


17

Nick Higham's specialty is algorithms to find the nearest correlation matrix. His older work involved increased performance (in order-of-convergence terms) of techniques that successively projected a nearly-positive-semi-definite matrix onto the positive semidefinite space. Perhaps even more interesting, from the practitioner point of view, is his ...


16

A very conservative stand is to distinguish between anomalies and arbitrage opportunities. Roughly speaking, while an arbitrage opportunity is risk-free by definition, an anomaly allows for unaccounted risk factors. It is the magnitude of these unidentified risk factors that might determine the long term persistence of certain anomalies. A good starting ...


15

Eric Zivot's Introduction to Computational Finance and Financial Econometrics on Coursera.


15

Very good question! I think part of the answer lies in the structure of the financial industry. Some anomalies have a certain kind of structure which cannot be exploited by the players that are big enough to let the anomaly disappear. I would put e.g. the Turn-of-the-month effect (TOTM) into this category since big funds just can't turn their whole ...


14

ArXiv is the standard resource of preprints in the field of physics. Almost all papers in physics are uploaded here before they are submitted to a journal. They also have a quantitative finance part: http://arxiv.org/archive/q-fin This section is not nearly as active as the physics-part of ArXiv though. Hopefully this will change in the future. There is ...


13

This answer is my ongoing attempt to consolidate some recent commentary on this hot topic. A good place to start for anyone thinking about this question is the Economists's Buttonwood: Not So Fast, which mentions recent research by Biais and Woolley (2011) and Dichev, Huang, and Zhou (2011). Does Algorithmic Trading Improve Liquidity? This paper claims yes....


11

I find this one very helpful: Re-Examining the Hidden Costs of the Stop-Loss by Wilson Ma, Guy Morita, Kira Detko Abstract: In this paper, we present general implications of the impact of stop-losses to future returns. The use of stop-losses change return distributions, but not in the way that one would typically expect. We find that while stop-...


11

Here are couple references. Especially the first link to Andy Lo's paper contains a list of Sharpe ratios of popular mutual and hedge funds: The Statistics of Sharpe Ratios Dow Jones Credit Suisse Hedge Fund Index Generalized Sharpe Ratios and Portfolio Performance Evaluation I would go with the first paper.


11

I had read some of them; actually, it does not exist an on-line library that collected them (or, better, it existed here, but it seems the website does not work anymore). I reported here below some of them that you did not find: More Than You Ever Wanted To Know* About Volatility Swaps Model Risk The Volatility Smile And Its implied Tree Enhanced Numerical ...


10

Sell Side Macquarie Quant - Venkat Eleswarapu Bernstein Research - Vadim Zlotnikov Nomura - Joe Mezrich JPMorgan Investment Strategies series Societe Generale - Alain Bokobza Independent CXO Advisory Empirical Finance Blog Russell Indexes: Research and Insights MSCI Research Papers Axioma Research Papers


10

Yes Strategic Asset Allocation: Determining the Optimal Portfolio with Ten Asset Classes Strategic Asset Allocation and Commodities The Case for Commodities An Asset Class for All Seasons: The Benefits of a Strategic Allocation to Commodities No Should Investors Include Commodities in Their Portfolios After All? New Evidence My Take Although there seems ...


10

Joel Hasbrouck (imho, a leading expert in market microstructure) has a paper on this: http://people.stern.nyu.edu/jhasbrou/Research/Working%20Papers/HS10-11-10.pdf From the abstract: Our conclusion is that increased low-latency activity improves traditional market quality measures such as short-term volatility, spreads, and displayed depth in the limit ...


10

The answer your are looking for might be the story in "Benchmarking Measures of Investment Performance with Perfect-Foresight and Bankrupt Asset Allocation Strategies", by Grauer (Journal of Portfolio Management). While this work main concerns are the differential ranking of various performance measures and with negative betas for market timing strategies, ...


9

I have been learning more about speech recognition motivated by its application to financial forecasting. I have identified a couple connect points. Turns out each of these tools can and are regularly used in financial modeling as well. Use of Markov Models Use of Fourier transforms (sine/cosine decompositions) Use of component analysis


9

Since I, too, have been very interested in this question, I will share some of my findings in the dual hope of encouraging comments on the papers and eliciting more activity on this question. Ammann, Skovmand, and Verhofen (2008): Implied and Realized Volatility in the Cross-Section of Equity Options Ang, Bali, and Cakici (2010): The Joint Cross Section of ...


9

Quantivity's "People of Quant Research"(mirror) has quite an exhaustive list (albeit not exclusively equity-orientated).


9

At strikes distant from the forward value, pretending that options have some meaningful implied volatility gets kind of silly. Options really have prices (both bids and offers), and we all just translate that to volatility because doing so provides a convenient normalization. Just to take one example, discrete price quoting completely obfuscates the ...


9

Indeed, algorithmic trading is a very hidden subject. All I can help you with are some industry-specific terms which might speed up your search for relevant papers and information: Risk of ruin tables (Peak-to-valley) drawdown (maximum drawdown, duration of drawdown etc.) Number of consecutive losses Confidence intervals Empirical distributions (for risk ...


9

If you want to address interesting problems that are interesting for financial mathematics, I do not believe you have the good list. Pricing. For instance, most of explicit formulas for pricing that are not available yet will never be. In this direction, you should have a look at simulation techniques. See for instance Nonlinear Option Pricing. Interesting ...


9

I would say that most ML methods risk overfitting and it depends very much on the asset class. The only area where more sophisticated ML methods such as deep learning appear to make a major difference is in cash equities, where the feature space is very rich (NLP, news and announcements, corporate earnings, other financials) and the data is relatively good, ...


9

I would argue, taking a note from John von Neumman, that quantitative finance lacks rigorous underpinnings. Von Neumann warned in 1953 that many things that look like proofs in economics and finance depended on problems that were yet to be solved in mathematics, and where economists were assuming solutions into existence. As the problems were solved in math,...


8

While not strictly quantitative finance, for the first year in the PhD I found this Youtube-Channel extremely helpful: http://www.youtube.com/user/mathematicalmonk I covers almost only math, but does a very good job at explaining the basics of probability theory. Most people will already have mastered that stuff, but it will surely help those unfamiliar ...


8

Deutsche Bank's Quantitative Strategy (US) team put together the following piece on this topic (note: their research is available for clients, but I found that somebody uploaded the piece to a sketchy web site). In case the link dies, some of the academic papers they site are: Akbras, F., E. Kocatulum, and S. Sorescu, 2008, “Mispricing following public ...


8

Grinold and Kahn (2000) remains the bible for people just starting to get into quantitative portfolio management. Some readers may prefer the treatment in Litterman (2003). Both of these, however, are thorough books covering all the foundational material. Most of the recent work in portfolio management has built upon the research covered in those books. ...


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