How to Get 10x+ Improvements in Your Pricing Simulations
RPU BOOST SERIES – PART I
Asset valuation is a critical component of any investment decision-making process. It involves assessing the value of an asset, such as stocks, bonds, real estate, or commodities, to determine its worth in the market. Valuation helps investors make informed decisions about buying or selling assets and can provide essential insights into market trends and risks.
A critical aspect of asset valuation is scenario generation, which involves creating a range of potential future scenarios that could affect the asset’s value. These scenarios may include changes in economic conditions, regulatory environments, consumer behavior, and other factors that could impact the asset’s performance. By generating and analyzing these scenarios, investors can better understand the potential risks and opportunities associated with an asset and make more informed investment decisions.
Scenario generation involves using random numbers or sequences to model a wide range of potential outcomes. Two standard methods for generating these random numbers or sequences are Pseudo-Random Number Generators (PRNGs) and Low-Discrepancy Sequences.
PRNGs use complex mathematical algorithms to generate random numbers that are statistically similar to true random numbers. They are often used in scenario generation because they can quickly generate large sets of random numbers that can be used to model a wide range of potential outcomes. However, PRNGs are not truly random and can exhibit patterns or biases that may impact the accuracy of the scenarios they generate. Besides, they can be computationally expensive.
On the other hand, Low-Discrepancy Sequences, also known as quasi-random sequences, are designed to produce a more uniform and evenly distributed set of numbers. They are often used in scenario generation because they can be more effective than PRNGs at covering the entire range of potential outcomes, especially in higher dimensions. However, Low-Discrepancy Sequences can be more computationally intensive than PRNGs and may require more time to generate.
Both PRNGs and Low-Discrepancy Sequences can be practical tools for generating scenarios in asset valuation. PRNGs are quick and easy to use but may exhibit patterns or biases that can impact accuracy. Low-Discrepancy Sequences can produce more uniform and accurate scenarios but may require more computational resources. Furthermore, they need to be adjusted to the dimensionality of the problem, or else the estimations made with these methods may be failure-prone.
Overcoming the limitations of both methods, Quside’s RPUs are an innovative solution for scenario generation in asset valuation. Quside’s RPUs are designed to generate high-quality random sequences, covering the same range of potential outcomes as Low-Discrepancy Sequences at an unprecedented speed. Quside’s RPUs can be applied in various areas of finance, such as estimating the value at risk (VaR), modeling the distribution of financial returns, and simulating complex stochastic models.
In collaboration with one of our customers, we found Quside’s RPUs to outperform traditional PRNGs and Low-Discrepancy Sequences in terms of accuracy, computational speed, and flexibility. We used RPUs to generate 1 million scenarios for a portfolio of 100 bonds, producing far more accurate results than PRNGs and similar to Low-Discrepancy Sequences while also being significantly faster and more energy-efficient.
In particular, compared to a traditional PRNG and a Sobol sequence generator, we can see the RPU delivers 32X and 9X faster randomness generation, respectively, while maintaining the same accuracy.
Using Quside’s RPUs for scenario generation in asset valuation can bring multiple business benefits to end-users. The fast computational speed of Quside’s RPUs can significantly reduce the time and costs associated with conducting scenario analyses, enabling more efficient and timely decision-making. Moreover, the flexibility and reprogrammability of Quside’s RPUs allow them to be tailored to the specific needs and constraints of the end-user, such as the type of asset being evaluated, the level of accuracy required, and the available computational resources.
Overall, Quside’s RPU irrupts as a ground-breaking and innovative approach to scenario generation in asset valuation and risk management, potentially transforming the way investors make investment decisions.