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Nov 9
Do not know whether you visited it before or not, I came across this site "Run My Code" today when I searched a paper. It is interesting and useful so I have no hesitation to share with you immediately.

Basically RunMyCode is a novel cloud-based platform that enables scientists to openly share the code and data that underlie their research publications. It has many files accompanying those published papers so you can easily replicate the results, which dramatically decreases your research efforts. You can choose to download the coding files directly, or upload your data and run it via the site's cloud platform. (I tried twice but failed for unknown reasons, so I recommend you to download the file and run on your own computer.)

The site is a newly established and is expanding, at the moment it includes 64 files under the following categories
run my code

A sample search in Finance returns you the codes.

It is free to use, quite nice, isn't it?
Oct 29
An excellent and practical paper by Attilio Meucci, "A Fully Integrated Liquidity and Market Risk Model" forthcoming in Financial Analysts Journal.

Going beyond the simple bid–ask spread overlay for a particular Value at Risk, the author introduces an innovative framework that integrates liquidity risk, funding risk, and market risk. He overlaid a whole distribution of liquidity uncertainty on future market risk scenarios and allowed the liquidity uncertainty to vary from one scenario to another, depending on the liquidation or funding policy implemented. The result is one easy-to-interpret, easy-to-implement formula for the total liquidity-plus-market-risk profit and loss distribution.

Journal paper, Working paper
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Oct 11
A paper forthcoming in The Econometrics Journal by Qu and Perron, worth to read carefully.

This paper proposes a framework for the modeling, inference and forecasting of volatility in the presence of level shifts of unknown timing, magnitude and frequency. First, we consider a stochastic volatility model comprising both a level shift and a short-memory component, with the former modeled as a compound binomial process and the latter as an AR(1). Next, we adopt a Bayesian approach for inference and develop algorithms to obtain posterior distributions of the parameters and the two latent components. Then, we apply the model to daily S&P 500 and NASDAQ returns over the period 1980.1–2010.12. The results show that although the occurrence of a level shift is rare, about once every two years, this component clearly contributes most to the variation in the volatility. The half-life of a typical shock from the AR(1) component is short, on average between 9 and 15 days. Interestingly, isolating the level shift component from the overall volatility reveals a stronger relationship between volatility and business cycle movements. Although the paper focuses on daily index returns, the methods developed can potentially be used to study the low frequency variation in realized volatility or the volatility of other financial or macroeconomic variables.

Journal paper, Working paper in PDF
Sep 29
A great paper by Cartea, Álvaro and Karyampas, Dimitrios, published in Applied Mathematical Finance, Volume 19, Number 6, 1 December 2012 , pp. 535-552(18).

We test the performance of different volatility estimators that have recently been proposed in the literature and have been designed to deal with problems arising when ultra high-frequency data are employed: microstructure noise and price discontinuities. Our goal is to provide an extensive simulation analysis for different levels of noise and frequency of jumps to compare the performance of the proposed volatility estimators. We conclude that the maximum likelihood estimator filter (MLE-F), a two-step parametric volatility estimator proposed by Cartea and Karyampas (2011a; The relationship between the volatility returns and the number of jumps in financial markets, SSRN eLibrary, Working Paper Series, SSRN), outperforms most of the well-known high-frequency volatility estimators when different assumptions about the path properties of stock dynamics are used.

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.
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