Trading Frequency and Volatility Clustering

Volatility clustering, with autocorrelations of the hyperbolic decay rate, is unquestionably one of the most important stylized facts of financial time series. This paper presents a market microstructure model, that is able to generate volatility clustering with hyperbolic autocorrelations through traders with multiple trading frequencies using Bayesian information updating in an incomplete market. The model illustrates that signal extraction, which is induced by multiple trading frequency, can increase the persistence of the volatility of returns. Furthermore, we show that the local temporal memory of the underlying time series of returns and their volatility varies greatly varies with the number of traders in the marketTrading frequency, Volatility clustering, Signal extraction, Hyperbolic decay

Artificial intelligence and systemic risk

Artificial intelligence (AI) is rapidly changing how the financial system is operated, taking over core functions for both cost savings and operational efficiency reasons. AI will assist both risk managers and the financial authorities. However, it can destabilize the financial system, creating new tail risks and amplifying existing ones due to procyclicality, unknown-unknowns, the need for trust, and optimization against the system

Agent-Based Models and Human Subject Experiments

This paper considers the relationship between agent-based modeling and economic decision-making experiments with human subjects. Both approaches exploit controlled ``laboratory'' conditions as a means of isolating the sources of aggregate phenomena. Research findings from laboratory studies of human subject behavior have inspired studies using artificial agents in ``computational laboratories'' and vice versa. In certain cases, both methods have been used to examine the same phenomenon. The focus of this paper is on the empirical validity of agent-based modeling approaches in terms of explaining data from human subject experiments. We also point out synergies between the two methodologies that have been exploited as well as promising new possibilities.agent-based models, human subject experiments, zero- intelligence agents, learning, evolutionary algorithms

Indenture as a Commitment Device in Self-Enforced Contracts: An Experimental Test

How can a principal (an agent) ensure that an agent (a principal) will work (pay up), if payment (work) precedes work (payment)? When a banknote is torn in two, each part is by itself worthless. A principal can pre-commit to payment-on-delivery, by tearing a banknote and giving the agent the first half as "prepayment"; the agent receives the completing half upon delivery of the service. This contract design is known as "indenture". It is selfenforcing and incentive-compatible. This paper experimentally tests the efficacy of the "indenture game" and its implications for cooperation in one-shot environments. We find that cooperation rates are high and stable over time. Its efficacy is moderated by expected losses due to the existence of uncooperative types.Cooperation, Experiment, Contracts, Indenture, Reciprocity