## Dettagli progetto

### Description

The research unit's objective is to design an artificial market environment to study how software agents endowed with learning abilities interact and co-evolve over

time. We are particularly interested in investigating the dynamics created by agents with heterogeneous beliefs interacting through an automated trading setting. We

will model a financial market where several risky assets can be exchanged to analyze the interactions between asset allocation decisions and the dynamics of prices.

We will focus our attention on the market volatility generated by the co-evolution of prices and beliefs.

The research program will involve the following steps.

First, we will model the learning process. We will assume that agents have no structural information about the economy, but they have some beliefs about the

evolution of the economy. We will model the diversity of beliefs as the consequence of different interpretations of the available common information. Agents will

adjust their beliefs using time series observations on relevant economic variables. In particular, we will allow for the existence of regimes in the joint distribution of

asset returns and we will assume that agents do not observe the state variable, but can use time series data to learn about the state. We will analyze the asset

allocation implications of the assumed unobservable regime dynamics.

Second, we plan to investigate how the endogenous component of market volatility is affected by the asset allocation dynamics induced by the objective function that

investors utilize to determine the optimal portfolio allocations. We plan to compare two cases. One where, as in standard asset pricing models, agents' objective is to

maximize the expected utility of final wealth, and another where we assign to the agents a prospect-type utility function defined in term of upward and downward

movements of the agent's wealth with respect to a target level of wealth.

Third, we will analyse the interactions between the institutional trading mechanism and the dynamics of asset prices. Automated systems may offer advantages in

terms of operational and trading costs, but they depend on public limit orders for the provision of liquidity. The time variation in liquidity can affect the evolution of

prices, and a complex dynamics may arise between measures of market trading activity and measures of market volatility.

Finally, we plan to model the formation and the evolution of groups of agents with correlated beliefs (communication networks). We will assume that agents,

cognizant that they possess limited information, understand that alternative interpretations of public information made by other traders may be of value. Under this

setting, agents are not isolated but are connected to other agents (neighbors) and can communicate before updating their beliefs. We plan to structure the agents'

population in terms of communities or groups of nodes characterized by having more internal than external connections between them. Using a topological design

with weighted links where the weights are generated dynamically, we will be able to obtain a model where communities emerge endogenously

time. We are particularly interested in investigating the dynamics created by agents with heterogeneous beliefs interacting through an automated trading setting. We

will model a financial market where several risky assets can be exchanged to analyze the interactions between asset allocation decisions and the dynamics of prices.

We will focus our attention on the market volatility generated by the co-evolution of prices and beliefs.

The research program will involve the following steps.

First, we will model the learning process. We will assume that agents have no structural information about the economy, but they have some beliefs about the

evolution of the economy. We will model the diversity of beliefs as the consequence of different interpretations of the available common information. Agents will

adjust their beliefs using time series observations on relevant economic variables. In particular, we will allow for the existence of regimes in the joint distribution of

asset returns and we will assume that agents do not observe the state variable, but can use time series data to learn about the state. We will analyze the asset

allocation implications of the assumed unobservable regime dynamics.

Second, we plan to investigate how the endogenous component of market volatility is affected by the asset allocation dynamics induced by the objective function that

investors utilize to determine the optimal portfolio allocations. We plan to compare two cases. One where, as in standard asset pricing models, agents' objective is to

maximize the expected utility of final wealth, and another where we assign to the agents a prospect-type utility function defined in term of upward and downward

movements of the agent's wealth with respect to a target level of wealth.

Third, we will analyse the interactions between the institutional trading mechanism and the dynamics of asset prices. Automated systems may offer advantages in

terms of operational and trading costs, but they depend on public limit orders for the provision of liquidity. The time variation in liquidity can affect the evolution of

prices, and a complex dynamics may arise between measures of market trading activity and measures of market volatility.

Finally, we plan to model the formation and the evolution of groups of agents with correlated beliefs (communication networks). We will assume that agents,

cognizant that they possess limited information, understand that alternative interpretations of public information made by other traders may be of value. Under this

setting, agents are not isolated but are connected to other agents (neighbors) and can communicate before updating their beliefs. We plan to structure the agents'

population in terms of communities or groups of nodes characterized by having more internal than external connections between them. Using a topological design

with weighted links where the weights are generated dynamically, we will be able to obtain a model where communities emerge endogenously

Stato | Finito |
---|---|

Data di inizio/fine effettiva | 9/22/08 → 9/22/10 |

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