Blog Posts

Lattice models have widespread use in the valuation of financial derivatives, risk management, and trading/investment strategy development. They provide a structured, discrete framework for modeling the evolution of financial variables over time. This makes them most useful in scenarios where continuous models are either too complex or inappropriate.   Key Takeaways – Lattice Models Flexibility […]

The Stochastic Alpha, Beta, Rho (SABR) model is widely used in financial engineering in the context of interest rate derivatives pricing. It’s designed to capture the volatility smile in derivatives markets. The SABR model is a type of stochastic volatility model and was developed by Patrick Hagan, Deep Kumar, Andrew Lesniewski, and Diana Woodward in […]

Dynamic Stochastic General Equilibrium (DSGE) models are a class of macroeconomic models that are widely used in economic research and policy analysis.   Key Takeaways – Dynamic Stochastic General Equilibrium (DSGE) Models Macro-to-Micro Insights DSGE models integrate macroeconomic theories with microeconomic foundations. When done well, offer a comprehensive view of economic dynamics and policy impacts. […]

Structured product design in finance involves the creation of financial instruments that are tailored to meet specific investment objectives or risk-return profiles. These products are often combinations of traditional assets like stocks and bonds with derivatives such as options, futures, or swaps.   Key Takeaways – Structured Product Design Customization to Meet Specific Needs Structured […]

Positive and negative feedback loops in financial markets are concepts that can contribute to systemic risk. They can lead to market volatility and even crises in extreme circumstances. These loops are mechanisms through which the dynamics of financial markets can either self-reinforce (positive feedback) or self-correct (negative feedback).   Key Takeaways – Positive & Negative […]

Designing an institutional trading system with both strategic and tactical asset allocation components, along with sophisticated forms of analysis and a comprehensive risk management system, requires a multi-layered approach. Each component ensures the system can adapt to market conditions, manage risk effectively, and strive for optimal returns.   Key Takeaways – How to Design an […]

Numerical methods in finance are computational techniques used to solve mathematical problems that arise in financial modeling. These methods are important because many financial models lead to equations that: can’t be solved analytically, or require simulation for prediction and risk assessment. These methods are used in various areas such as option pricing, risk management, portfolio […]

Complex analysis is a branch of mathematics focusing on functions of complex numbers. Complex numbers, blending real and imaginary parts, are important because they allow us to solve equations (x^2 = -1) that can’t be solved with just real numbers, which expands our understanding and capabilities in various fields (e.g., finance, machine learning, quant trading). […]

Artificial General Intelligence (AGI) in the context of finance, markets, and trading refers to the development of AI systems that possess the ability to understand, learn, and apply intelligence across a wide range of financial activities in a manner similar or better to human intelligence. AGI would differ significantly from the current state of AI, […]

Setting up a trading algorithm in C++ and connecting it to a broker for live trading involves several steps and considerations. In the first part of the article, we’ll include a high-level overview of the process. In the second part, we’ll go into more specifics on where to write the algorithm/trading system and how to […]