Mathematical Models for Quantitative Finance: Market Microstructure, Networks, and Systemic Risk

Scuola Normale Superiore
LEVEL
PhD
TYPE
Course
MODES
-
LANGUAGE
-
ECTS
60
PERIOD
18/01/2022 to 28/05/2022

Course Description

Market Microstructure. Electronic markets and limit order book. High frequency data. Statistical and structural models (Roll and its generalizations). Asymmetric information models (Glosten-Milgrom, Kyle). Information share. Inventory management models. Market making. Statistical limit order book models. Trading models: Market impact and order flow. Trading costs. Optimal execution. High Frequency Trading. High Frequency Econometrics: Realized volatility and covariance, Microstructure noise. Point processes in finance (Hawkes processes and ACD models). Financial networks. Basic elements of graph theory. Random walks on graphs. Centrality measures. Scale free networks and small world graphs. Models of random graphs: Erdos Renyi graphs, Exponential random graphs, Stochastic block model, configuration model. Maximum entropy principle and networks. Networks from time series. Systemic risk. Mechanisms for systemic risk and models: Bank runs, leverage cycles, Interbank networks, Fire sales spillovers. Econometric approaches to systemic risk: CoVar, MES,SRISK, Granger causality networks. High frequency systemic risk: flash crashes, liquidity crises, systemic cojumps.

Subject area

Mathematics

Educational-info

Competences

The first goal of the course is to introduce the fundamental notions of market microstructure, network modeling, and financial systemic risk. The second goal is to present some recent contributions of the scientific literature and open problems. The third goal is to provide the student with the tool for the empirical and computational analysis of high frequency data and data relevant for systemic risk.

Prerequisites

at least a Bachelor

Duration

40h

Day of the weeks

Tuesday morning,Thursday morning

ECTS

60

Validation mode

Oral Examination,Written report

Maximum number of students

7

Organizer

Partner

Scuola Normale Superiore

Faculty

Scuola Normale Superiore

Department

Classe di Scienze

Contact or registration links