Simulation models

Image: Anna Nekrashevich


Using simulation models helps explore how different variables and events interact and influence future outcomes in a complex and dynamic environment

Changes in short-term interest rates affect future interest rates on long-term bonds already issued in several ways. First, they meet investors' expectations regarding inflation.

If short-term interest rates rise due to concerns about inflation, investors demand higher interest rates on long-term bonds to offset the impact of expected inflation on the future value of interest payments.

Changes in short-term interest rates reflect changes in the monetary policy discretion of the Central Bank. If they increase, due to a more restrictive monetary policy, investors anticipate an economic slowdown in the future and, consequently, demand higher interest rates on long-term bonds to compensate for the increase in credit risk. Now, don't public debt securities offer sovereign risk in national currency?!

Speculators claim these changes affect the liquidity premium required to hold long-term bonds. If short-term interest rates rise, investors would opt for more liquid short-term investments over less liquid long-term bonds.

This results in reduced demand for long-term bonds. It would lead to an increase in future interest rates on long-term bonds already issued, due to the necessary drop in their prices on the secondary market to attract investors.

As a result, changes in short-term interest rates influence future interest rates on long-term bonds already issued through their effects on inflation expectations, monetary policy expectations and the liquidity premium demanded by investors.

Making predictions about the future in an environment where decisions are decentralized, uncoordinated and uninformed about each other involves analyzing complexity and uncertainty. There are, however, some approaches capable of helping financial market operators make predictions.

Analyzing historical trends, based on past evolution, provides insights about patterns of behavior that may be repeated in the future, although there is no guarantee that these patterns will continue. Growth trend analysis helps identify seasonal patterns, economic cycles, and other regularities. They are distinguished from random cyclical fluctuations.

When using statistical and time series models, operators rely on quantifications of historical patterns and estimates of future values ​​based on patterns identified in the data. They need to consider a variety of possible scenarios and conduct sensitivity analyzes in order to understand how different events and conditions affect future results. This allows them to prepare for a variety of possible outcomes.

Likewise, using simulation models helps to explore how different variables and events interact and influence future results in a complex and dynamic environment, that is, variable over time. Recently, advanced Artificial Intelligence and Machine Learning techniques have been applied to analyze large volumes of data and identify patterns that are not obvious to human intelligence and can help predict the future.

Although it is impossible to predict the future with absolute certainty, these approaches help create more reasonable and better informed predictions about what might happen, allowing market analysts and traders to prepare and make decisions based on these predictions. Being prepared for the uncertainty inherent in the forecasting process makes it possible to mitigate the associated risks.

Conducting sensitivity analyzes involves examining how variations in the inputs to a model or system affect the outputs or results. First, it is necessary to identify the variables in the model or system that have the potential to affect the results. These variables will be those subject to sensitivity analysis.

Then, it is necessary to determine the ranges or ranges of values ​​for each input variable to be tested. It involves considering different scenarios, such as best (optimistic) and worst (pessimistic) cases, or constructing ranges based on historical data or future estimates.

You must run the model or system using each combination of input variable values ​​within the defined ranges. This was previously done manually, now it is done using specialized.

The results obtained for each set of values ​​of the input variables are then analyzed. It identifies how changes in them affect the outputs or results of the model. It involves identifying trends, cause and effect relationships, inflection points or specific sensitivities.

Then, the input variables with the greatest impact on the model results are identified. This makes it possible to identify which ones are most critical or uncertain and deserve more attention or consideration.

Finally, it is important to communicate the results of the sensitivity analysis in a clear and understandable way, highlighting the main insights and implications for decision makers and relevant stakeholders. It is a powerful tool for understanding the reliability of models and systems in the face of uncertainty in inputs. By examining how variations in inputs affect outputs, they make more informed decisions and better prepare for a variety of possible scenarios.

Financial markets are influenced by a range of macroeconomic and political factors. They affect investor confidence, economic conditions and asset price trends.

For example, monetary policy decisions, such as interest rate changes by the Central Bank, affect borrowing costs, market liquidity and investment preferences. Fiscal measures, such as changes in income taxes, government spending, and economic stimulus policies, influence economic growth and corporate prospects.

Situational indicators such as GDP growth, inflation, unemployment, industrial production and retail sales provide insights on the current (and future) state of the economy and affect investor expectations. Even geopolitical conflicts, trade tensions, diplomatic crises and events such as elections create uncertainty and volatility in financial markets.

All events related to the financial system, such as banking crises, collapses of financial institutions or sovereign debt problems, evidently affect the stability of the market average and investor confidence. Therefore, changes in financial regulations and government policies related to the financial market influence investor activity, capital allocation and transparency in behavior.

Because these factors interact and a complex and dynamic configuration emerges from these interactions, they have both immediate and long-term effects on financial markets. They lead investors to adjust their investment strategies and make decisions based on prevailing economic and political conditions, although they may be based only on rumors or misguided narratives.

The human brain resorts to narratives, including those without commitment to the truth, to rationalize behavior. We are storytellers.

The Narrative of the Foolish Man is known as the investor's belief, when he buys a certain supposedly valued asset, that he will be able to sell it in the future with even greater appreciation. He hopes to find an even “sillier” investor capable of buying it.

Deduction from the narrative: an asset is not purchased because the price corresponds to the justified fair value, but rather because of the expectation of reselling it for a higher value. The Market, although revered with capital letters as a supernatural, omnipotent, omniscient and omnipresent being, is “what it is”, that is, the result of multiple decentralized, uncoordinated, uninformed and conflicting decisions. The Market is not “what it should be” rationally…

*Fernando Nogueira da Costa He is a full professor at the Institute of Economics at Unicamp. Author, among other books, of Brazil of banks (EDUSP). []

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