Position sizing and portfolio allocation have not received much attention in the options trading community. Here, we are going to apply a simple position sizing rule and see how it performs within the context of volatility trading.

http://blog.harbourfronts.com/2017/04/30/use-market-timing-rule-size-option-position/

In the previous post, we presented a system for trading VXX, a volatility Exchange Traded Note. The trading system was built based on simple moving averages. In this post, we are going to examine the time series properties of VXX in more details.

http://tech.harbourfronts.com/trading/stationarity-autocorrelation-functions-vxx-time-series-analysis-python/

When trading options, we often use the volatility index, VIX, as a measure of volatility in order to enter and manage positions. This works most of the time. However, there exist some differences between the VIX index and at-the-money implied volatility. In this post, we are going to show such a difference through an example. Specifically, we study the relationship between the implied volatility and forward realized volatility of the SP500

http://blog.harbourfronts.com/2019/03/28/differences-vix-index-money-implied-volatility/

A lot of research has been devoted to answering the question: do options price in the volatility risks correctly? The most noteworthy phenomenon (or bias) is called the volatility risk premium, i.e. options implied volatilities tend to overestimate future realized volatilities. Much less attention is paid, however, to the underlying asset dynamics, i.e. to answering the question: do options price in the asset dynamics correctly?

http://blog.harbourfronts.com/2018/12/31/asset-dynamics-priced-correctly-black-scholes-merton-model/

Robert Merton published a seminal paper that laid the foundation for the development of structural credit risk models. We’re going to provide an example of how it can be used for managing credit risks. We are going to present a case study based on the Merton credit risk model.

http://tech.harbourfronts.com/risk-management/merton-credit-risk-model-case-study/

We have provided examples of pricing European and American options in Excel. For pricing the European option, we utilized the Black-Scholes formula, and for pricing the American option we utilized the binomial approach. Here, we are going to implement these methods in Python.

http://tech.harbourfronts.com/derivatives/valuation-european-american-options-derivative-pricing-python/

We are going to provide an example of interest rate swap pricing in Python. We are going to use the USD Libor swap curve as at December 31 2018. Note that we utilize the deposit and swap rates only and ignore the futures prices in the bootstrapping process. The values of the fixed, floating legs and the interest rate swap are calculated using a Python program.

http://tech.harbourfronts.com/derivatives/interest-rate-swap-derivative-pricing-python/

The weighted average cost of capital (WACC) is the rate that a company is expected to pay on average to all its security holders to finance its assets. The WACC is commonly referred to as the firm's cost of capital. For illustration purposes, we are going to calculate the WACC of Barrick Gold, a major Canadian mining company.

http://tech.harbourfronts.com/derivatives/weighted-average-cost-of-capital-wacc-business-valuation-calculator-in-excel/

A convertible bond (or preferred share) is a hybrid security, part debt and part equity. Its valuation is derived from both the level of interest rates and the price of the underlying equity. Several modeling approaches are available to value these complex hybrid securities such as Binomial Tree, Partial Differential Equation and Monte Carlo simulation. One of the earliest approaches was the Binomial Tree model originally developed by Goldman Sachs and this model allows for an efficient implementation with high accuracy.

http://tech.harbourfronts.com/derivatives/derivative-valuation-how-to-price-a-convertible-bond/

Debt instruments are an important part of the capital market. In this post, we are going to provide an example of pricing a fixed-rate bond. We are going to price a hypothetical bond as at October 31, 2018. We first build a zero coupon curve, then use it to price a hypothetical fixed rate bond.

http://tech.harbourfronts.com/derivatives/valuing-fixed-rate-bond-derivative-pricing-python/