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Quantitative Finance Collector is a blog on Quantitative finance analysis, financial engineering methods in mathematical finance focusing on derivative pricing, quantitative trading and quantitative risk management. Random thoughts on financial markets and personal staff are posted at the sub personal blog.

Dec 7
Pawel wrote a great article on predicting heavy and extreme losses in real-time for portfolio holders, the goal is to calculate the probability of a very rare event (e.g. a heavy and/or extreme loss) in the trading market (e.g. of a stock plummeting 5% or much more) in a specified time-horizon (e.g. on the next day, in one week, in one month, etc.). The probability. Not the certainty of that event.

In this Part 1, first, we look at the tail of an asset return distribution and compress our knowledge on Value-at-Risk (VaR) to extract the essence required to understand why VaR-stuff is not the best card in our deck. Next, we move to a classical Bayes’ theorem which helps us to derive a conditional probability of a rare event given… yep, another event that (hypothetically) will take place. Eventually, in Part 2, we will hit the bull between its eyes with an advanced concept taken from the Bayesian approach to statistics and map, in real-time, for any return-series its loss probabilities. Again, the probabilities, not certainties.

Read this excellent post and accompanying Pathon codes at http://www.quantatrisk.com/2015/06/14/predicting-heavy-extreme-losses-portfolio-1/
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May 1
A paper published in The Journal of Portfolio Management, 2013, 39 (3), pp 102-111, by James X. Xiong, Rodney N. Sullivan, and Peng Wang.

We propose a model of portfolio selection that adjusts an investors’ portfolio allocation in accordance with changing market liquidity environments and market conditions. We found that market liquidity provides a useful “leading indicator” in dynamic asset allocation. Specifically, market liquidity risk premium cycles anticipate economic and market cycles. Investors can therefore act to avoid markets with low liquidity premiums, waiting to extract liquidity risk premiums when the likelihood of extracting a liquidity premium improves. The result, meaningfully enhanced portfolio performance through economic and market cycles, and is robust to transactions costs and alternate specifications.

Basically this article examines a portfolio strategy that buys stocks and sells bonds when the market is less liquid, thus enjoying a higher liquidity premium, this strategy outperforms a benchmark with equal weights on stocks and bonds by generating a higher sharpe ratio and positive alpha.

Journal paper Working paper
Jan 31
A paper published in the Journal of Portfolio Management, 2013, Vol. 39, No. 2: pp. 28-40, by Alexandre Hocquard, Sunny Ng, and Nicolas Papageorgiou.

Since Lehman Brothers collapsed in 2008, tail-risk hedging has become an increasingly important concern for investors. Traditional approaches, such as purchasing options or variance swaps as insurance, are often expensive, illiquid, and result in a substantial drag on performance. A more prudent, cost-effective way to maintain a constant risk exposure is to actively manage portfolio exposure according to the prevailing volatility level within underlying assets. The authors implement a robust methodology based on Dybvig’s payoff distribution model to target a constant level of volatility and normalize monthly returns. This approach to portfolio and risk management can help investors obtain their desired risk exposures over both short and longer time frames, reduce exposure to tail risk, and in general increase portfolios’ risk-adjusted performance.

The idea is simple, easy to implement, has a good performance based on the authors' results.
constant volatility tail risk

Journal paper, Working paper.
Aug 20
CVA (credit value adjustment) is a hot topic, thanks to the financial crisis.  It is the difference between the risk-free portfolio value and the true portfolio value that takes into account the possibility of a counterparty’s default. In other words, CVA is the market value of counterparty credit risk. Check Wikipedia for its detail definition.

A paper "CVA and Wrong-Way Risk" by John Hull and Alan White published in the Financial Analysts Journal uses Monte Carlo simulation to demonstrate the CVA calculation via a simple model.
This paper proposes a simple model for incorporating wrong-way and right-way risk into CVA (credit value adjustment) calculations. These are the calculations, involving Monte Carlo simulation, made by a dealer to determine the reduction in the value of its derivatives portfolio because of the possibility of a counterparty default. The model assumes a relationship between the hazard rate of the counterparty and variables whose values can be generated as part of the Monte Carlo simulation. Numerical results for portfolios of 25 instruments dependent on five underlying market variables are presented. The paper finds that wrong-way and right-way risk have a significant effect on the Greek letters of CVA as well as on CVA itself. It also finds that the percentage effect depends on the collateral arrangements.

Article, Working paper.
Jun 12
General publication strategies: advice on paper publication, especially for early stage researchers.

New Book Fore­cast­ing: prin­ci­ples and practice: a free online book on forecasting with a fore­cast pack­age for R by Rob J Hyn­d­man and George Athana­sopou­los.

It’s All in the Timing: Simple Active Portfolio Strategies that Outperform Naïve Diversification: we develop 2 new methods of mean-variance portfolio selection (volatility timing and reward-to-risk timing) that deliver portfolios characterized by low turnover. These timing strategies outperform naïve diversification even in the presence of high transaction costs.

Option pricing models implemented in AirXCell: an online R application framework currently supporting a programmable spreadsheet, an R development environment and various financial calculation forms.

A Dynamic Copula Approach to Recovering the Index Implied Volatility Skew: We show that moderate tail dependence coupled with asymmetric correlation response to negative news is essential to explain the index implied volatility skew. Standard dynamic correlation models with zero tail dependence fail to generate a sufficiently steep implied volatility skew.
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