Category: Quant Finance (Page 2 of 2)

Extracting Interest Rate Bounds from Option Prices

January 19, 2021 / Thijs van den Berg

In this post we describe a nice algorithm for computing implied interest rates upper- and lower-bounds from European option quotes. These bounds tell you what the highest and lowest effective interest rates are that you can get by depositing or borrowing risk-free money through combinations of option trades. Knowing these bounds allows you to do two things:

1. Compare implied interest rate levels in the option markets with other interest rate markets. If they don’t align then you do a combination of option trades to capture the difference.

2. Check if the best borrowing rate is higher than the lowest deposit rate. If this is not the case, then this means there is a tradable arbitrage opportunity in the market: you can trader a combination of options that effectively boils down to borrowing money at a certain rate, and at the same time depositing that money at a higher rate.

Recovering Accurate Implied Dividend and Interest Rate Term-Structures from Option Prices

January 4, 2021 / Thijs van den Berg

In this post we discuss the algorithms we use to accurately recover implied dividend and interest rates from option markets.

Implied dividends and interest rates show up in a wide variety of applications:

to link future-, call-, and put-prices together in a consistent market view
de-noise market (closing) prices of options and futures and stabilize PnL’s of option books
give tighter true bid-ask spreads based on parity and arbitrage relationships
compute accurate implied volatility smiles and surfaces
provide predictive models and trading strategies with signals based on implied dividends, and implied interest rate information

Validating Trading Backtests with Surrogate Time-Series

December 29, 2020 / Thijs van den Berg

Back-testing trading strategies is a dangerous business because there is a high risk you will keep tweaking your trading strategy model to make the back-test results better. When you do so, you’ll find out that after tweaking you have actually worsened the ‘live’ performance later on. The reason is that you’ve been overfitting your trading model to your back-test data through selection bias.

In this post we will use two techniques that help quantify and monitor the statistical significance of backtesting and tweaking:

First, we analyze the performance of backtest results by comparing them against random trading strategies that similar trading characteristics (time period, number of trades, long/short ratio). This quantifies specifically how “special” the timing of the trading strategy is while keeping all other things equal (like the trends, volatility, return distribution, and patterns in the traded asset).
Second, we analyse the impact and cost of tweaking strategies by comparing it against doing the same thing with random strategies. This allows us to see if improvements are significant, or simply what one would expect when picking the best strategy from a set of multiple variants.