Retirement planning attempts to build sufficient funds to retire at a fixed, predefined horizon. In that the only guaranteed way to hit a target wealth at a fixed horizon is by appropriate risk free investments, these strategies either have a large risk of falling short of the goal or require...

Valuation of mortgage backed securities (MBSs) and collateralized mortgage obligations (CMOs) is the big science of the financial world. There are many moving parts, each one drawing on expertise in a different field. Prepayment modeling draws on statistical modeling of economic behavior. Data...

Exotic interest rate derivatives are hard to value. Care must be taken to make sure that sources of volatility that impact the contingent claim are properly modeled, and that appropriate relationships are maintained between the underlying rates involved.In this presentation, we outline the...

The most widely used option pricing model is the Black-Scholes model.We motivate an alternative option pricing model called the Variance Gamma (VG) model and demonstrate its implementation in the Bloomberg system

We review and detail the causes of errors in numerical differentiation, including roundoff error, convexity error, cancellation error and correlated errors. We discuss methods for improving accuracy, including step size selection and smoothing techniques, as well as a number of approaches...

Lecture notes for a short course on FX option valuation. Includes: - Mathematical framework for FX valuation - Handling the smile and term structure for vanilla options (calls and puts): --- Interpolation issues and techniques --- Handling business time --- Handling market conventions - Pricing...

Tutorial on valuation of mortgage backed securities and collateralized mortgage obligations, including: - Structure of the mortgage market - Prepayment modeling - OAS analysis - Interest rate modeling - Numerical methods - Parallelization

The dichotomy between risk analytics and pricing is well known amongst financial practitioners and researchers. For risk analysis, such as computing value at risk and credit exposures, expectations of future values must be computed under the real world measure. For pricing, expectations are...

Amidst the avalanche of articles on big data and machine learning, the phrase "after cleaning the data" is often found. Here we focus on the work hidden behind this phrase. We analyze the types of dirty data found in financial time series, the problems caused by dirty data, and the performance...