A Modeling Approach of Return and Volatility of Structured Investment Products with Caps and Floors
Popular investment structured products in Puerto Rico are stock market tied Individual Retirement Accounts (IRA), which offer some stock market growth while protecting the principal. The performance of these retirement strategies has not been studied. This work examines the expected return and risk of Puerto Rico stock market IRA (PRIRAs) and compares their statistical properties with other investment instruments before and after tax. We propose a parametric modeling approach for structured products and apply it to PRIRAs. Our method first estimates the conditional expected return (and variance) of PRIRA assets from which we extract marginal moments through the Law of Iterated Expectation. Our results indicate that PRIRAs underperform against investing directly in the stock market while still carrying substantial risk. The expected return of the stock market IRA from Popular Bank (PRIRA1) after tax is slightly greater than that of investing in U.S. bonds, while PRIRA1 has almost two times the risk. The stock market IRA from Universal (PRIRA2) performs similarly to PRIRA1, while PRIRA2 has a lower risk than PRIRA1. PRIRAs may be reasonable for some risk-averse investors due to their principal protection and tax deferral.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Information Geometry of Risks and Returns
We reveal a geometric structure underlying both hedging and investment products. The structure follows from a simple formula expressing investment risks in terms of returns. This informs optimal product designs. Optimal …
The Limits of Leverage
When trading incurs proportional costs, leverage can scale an asset's return only up to a maximum multiple, which is sensitive to its volatility and liquidity. In a model with one safe and one risky asset, with constant …
The Zero-Coupon Rate Model for Derivatives Pricing
The aim of this paper is to present a dual-term structure model of interest rate derivatives in order to solve the two hardest problems in financial modeling: the exact volatility calibration of the entire swaption matri…
modelDynamic Investment Strategies Through Market Classification and Volatility: A Machine Learning Approach
This study introduces a dynamic investment framework to enhance portfolio management in volatile markets, offering clear advantages over traditional static strategies. Evaluates four conventional approaches : equal weigh…
ManagementThe VIX as Stochastic Volatility for Corporate Bonds
Classic stochastic volatility models assume volatility is unobservable. We use the Volatility Index: S&P 500 VIX to observe it, to easier fit the model. We apply it to corporate bonds. We fit autoregression for corporate…
Time Series