Tag Archives: Financial engineering

Generalized Regression

Linear regression is one of the most useful applications in the financial engineer’s tool-kit, but it suffers from a rather restrictive set of assumptions that limit its applicability in areas of research that are characterized by their focus on highly … Continue reading

Posted in Financial Engineering, Regression | Tagged , | Comments Off

Master’s in High Frequency Finance

I have been discussing with some potential academic partners the concept for a new graduate program in High Frequency Finance.¬† The idea is to take the concept of the Computational Finance program developed in the 1990s and update it to … Continue reading

Posted in Algorithmic Trading, Econometrics, Education, Financial Engineering, Graduate Programs, High Frequency Finance, High Frequency Trading, Market Microstructure | Tagged , , , , , , | Comments Off

On Testing Direction Prediction Accuracy

As regards the question of forecasting accuracy discussed in the paper on Forecasting Volatility in the S&P 500 Index, there are two possible misunderstandings here that need to be cleared up.¬† These arise from remarks by one commentator¬† as follows: … Continue reading

Posted in Direction Prediction, Forecasting, Modeling, Options, S&P500 Index, Volatility Modeling, volatility sign prediction forecasting Engle | Tagged , , , , , , , | Comments Off

Yield Curve Construction Models – Tools & Techniques

Yield curve models are used to price a wide variety of interest rate-contingent claims. The purpose of this review is to gain a thorough understanding of current methodologies, to validate their theoretical frameworks and implementation, identify any weaknesses in the current modeling methodologies, and to suggest improvements or alternative approaches that may enhance the accuracy, generality and robustness of modeling procedures. Continue reading

Posted in Financial Engineering, Interest Rate Models, Spline Methods, Yield Curve Modeling | Tagged , , , | Comments Off

The Lognormal Mixture Variance Model

The LNVM model is a mixture of lognormal models and the model density is a linear combination of the underlying densities, for instance, log-normal densities. The resulting density of this mixture is no longer log-normal and the model can thereby better fit skew and smile observed in the market. The model is becoming increasingly widely used for interest rate/commodity hybrids.

In this review of the model, Iexamine the mathematical framework of the model in order to gain an understanding of its key features and characteristics. Continue reading

Posted in Derivatives, Financial Engineering, Hybrid Products, Model Review | Tagged , , , | Comments Off