Central banks in independent nations drive monetary policy, thereby shaping the economic landscape. Their impact is steadfast, but the way in which they are utilized varies greatly. It’s been debated whether or not operational independence for a central bank leads to greater economic prosperity for the host nation. Existing literature surrounding the topic suggests greater CBI (Central Bank Independence) may not create growth itself, but rather lay the foundation by implementing a stable monetary environment for prosperity. Additionally, the “Time Inconsistency Problem” presents the crucial argument that true CBI eliminates the political temptations (election success) of short term shocks which are ultimately harmful for economies in the long run. To evaluate this complicated relationship between CBI and growth, the Romelli CBIE Index will be used. The index annually scores the countries and their central bank independence from 0-1, using a combination of over 40 different criteria regarding institutional design across 6 dimensions: Governor and central bank board, monetary policy and conflict resolution, objectives, lending limitations to government, financial independence, and reporting/disclosure. Using a difference in difference analysis, points of treatment (when a country’s CBI increases or decreases) will be identified and cross analyzed with indicators of economic health and well being. From there, a hypothesized and comprehensive link between the increasing of CBI and economic growth will be laid out for final evaluation. If there is a proven relationship between the two, it could have larger implications in how countries choose to operate their central banks in the future.

When prompting six different LLMs to estimate the intrinsic value of BTC, what do they produce and what sort of implicit assumptions about risk and liquidity are embedded in those estimates. It takes a unique approach to different valuation outputs formed by AI synthesized logic, seeing if they converge or have diverged financial narratives, and using that to conform to a tailored valuation model to reach a final conclusion. We are trying to create an interview protocol for the LLMs and learn from them. 

The recent decoupling of Gold and BTC is a divergence that exposed the fragility of the narrative that was confident amongst market participants in 2022-2024. Fast forward to early 2026, gold has soared around $5000/ounce, majorly because of its role as a geopolitical bunker amid fiscal anxieties and central bank accumulation, while BTC stayed stagnant in the $80,000- $90,000 corridor, acting more like a high-beta technology stock hedge tethered to liquidity cycle and M2 expansions. In a qualitative outlook, the difference between Gold and BTC is evident in its reactive elements. Gold responds to sovereign fear and deficits while the latter reacts to network constraints or technology dislocations like the recent hash rate drawdowns. Since the BTC/Gold ratio is at an all time low, this begets the question of identity. It challenges whether BTC is a store of value, or a speculative growth asset. It could also be a new monetary instrument awaiting a proper pricing model. This tension is exactly why it is relevant to assess BTC’s economic worth. 

Classical hedging methods are based on strong generalized assumptions, such as frictionless markets and continuous trading. However, real-world markets are incomplete and are characterized by discrete time trading, transaction costs, and model uncertainty. These frictions render perfect replication or the derivation of the optimal hedging strategy infeasible. Recent advances propose utilizing the flexibility of deep hedging, which involves the use of reinforcement learning to incorporate these constraints and produce an approximate optimal hedging strategy.

The existing deep hedging literature discusses various components, including market dynamics, training methods, objective functions, market frictions, and risk measures. This study identifies key trade-offs in robustness, computational efficiency, and sensitivity to model misspecification.

By forming a rigorous comparison of these approaches within a representative experimental setting, we provide a structured assessment of existing approaches. The results contribute to the literature by clarifying how the design choices of a deep hedging framework affect hedging performance.