1,721,238 research outputs found
Introduction to market microstructure and heterogeneity of investors
No full textIn this paper I review some of the recent advancements in the understanding of the market microstructure of financial markets and of the role of heterogenous investors in explaining the statistical regularities observed in market data. After introducing some of the main problems in microstructure and describing the most common structure of financial markets, the Limit Order Book (LOB), I will focus on the interplay between order flow, describing the intention of the agents, and the price dynamics, describing the outcome of their interactions. I will show that the quantity embedding this subtle interaction, termed market impact, is determined by the large heterogeneity in size of the investors in the financial market. I will also show the relevance of this problem for investors, by introducing the problem of optimal execution and describing the empirical evidences on the associated cost, focusing also on the role of herding behavior. Finally, I will describe some evidences of the role of heterogeneity of time scales in determining some properties of LOB
Modeling shock propagation and resilience in financial temporal networks
Full text linkModeling how a shock propagates in a temporal network and how the system relaxes back to equilibrium is challenging but important in many applications, such as financial systemic risk. Most studies, so far, have focused on shocks hitting a link of the network, while often it is the node and its propensity to be connected that are affected by a shock. Using the configuration model—a specific exponential random graph model—as a starting point, we propose a vector autoregressive (VAR) framework to analytically compute the Impulse Response Function (IRF) of a network metric conditional to a shock on a node. Unlike the standard VAR, the model is a nonlinear function of the shock size and the IRF depends on the state of the network at the shock time. We propose a novel econometric estimation method that combines the maximum likelihood estimation and Kalman filter to estimate the dynamics of the latent parameters and compute the IRF, and we apply the proposed methodology to the dynamical network describing the electronic market of interbank deposit
Reinforcement Learning for Optimal Execution When Liquidity Is Time-Varying
No full textOptimal execution is an important problem faced by any trader. Most solutions are based on the assumption of constant market impact, while liquidity is known to be dynamic. Moreover, models with time-varying liquidity typically assume that it is observable, despite the fact that, in reality, it is latent and hard to measure in real time. In this paper we show that the use of Double Deep Q-learning, a form of Reinforcement Learning based on neural networks, is able to learn optimal trading policies when liquidity is time-varying. Specifically, we consider an Almgren-Chriss framework with temporary and permanent impact parameters following several deterministic and stochastic dynamics. Using extensive numerical experiments, we show that the trained algorithm learns the optimal policy when the analytical solution is available, and overcomes benchmarks and approximated solutions when the solution is not available
Transient impact from the Nash equilibrium of a permanent market impact game
Full text linkA large body of empirical literature has shown that market impact of
financial prices is transient. However, from a theoretical standpoint, the
origin of this temporary nature is still unclear. We show that an implied
transient impact arises from the Nash equilibrium between a directional trader
and one arbitrageur in a market impact game with fixed and permanent impact.
The implied impact is the one that can be empirically inferred from the
directional trader's trading profile and price reaction to order flow.
Specifically, we propose two approaches to derive the functional form of the
decay kernel of the Transient Impact Model, one of the most popular empirical
models for transient impact, from the behaviour of the directional trader at
the Nash equilibrium. The first is based on the relationship between past order
flow and future price change, while in the second we solve an inverse optimal
execution problem. We show that in the first approach the implied kernel is
unique, while in the second case infinite solutions exist and a linear kernel
can always be inferred
The impact of systemic and illiquidity risk on financing with risky collateral
No full textRepurchase agreements (repos) are one of the most important sources of funding liquidity for many financial investors and intermediaries. In a repo, some assets are given by a borrower as collateral in exchange of funding. The capital given to the borrower is the market value of the collateral, reduced by an amount termed as haircut (or margin). The haircut protects the capital lender from loss of value of the collateral contingent on the borrower׳s default. For this reason, the haircut is typically calculated with a simple Value at Risk estimation of the collateral for the purpose of preventing the risk associated to volatility. However, other risk factors should be included in the haircut and a severe undervaluation of them could result in a significant loss of value of the collateral if the borrower defaults. In this paper we present a stylized model of the financial system, which allows us to compute the haircut incorporating the liquidity risk of the collateral and, most important, possible systemic effects. These are mainly due to the similarity of bank portfolios, excessive leverage of financial institutions, and illiquidity of assets. The model is analytically solvable under some simplifying assumptions and robust to the relaxation of these assumptions, as shown through Monte Carlo simulations. We also show which are the most critical model parameters for the determination of haircuts
Behind the price: on the role of agent’s reflexivity in financial market microstructure
No full textIn this chapter we review some recent results on the dynamics of price formation in financial markets and its relations with the efficient market hypothesis. Specifically, we present the limit order book mechanism for markets and we introduce the concepts of market impact and order flow, presenting their recently discovered empirical properties and discussing some possible interpretation in terms of agent’s strategies. Our analysis confirms that quantitative analysis of data is crucial to validate qualitative hypothesis on investors’ behavior in the regulated environment of order placement and to connect these micro-structural behaviors to the properties of the collective dynamics of the system as a whole, such for instance market efficiency. Finally we discuss the relation between some of the described properties and the theory of reflexivity proposing that in the process of price formation positive and negative feedback loops between the cognitive and manipulative function of agents are present
A continuous and efficient fundamental price on the discrete order book grid
No full textThis paper develops a model of liquidity provision in financial markets by adapting the Madhavan et al. (1997) price formation model to realistic order books with quote discretization and liquidity rebates. We postulate that liquidity providers observe a fundamental price which is continuous, efficient, and can assume values outside the interval spanned by the best quotes. We confirm the predictions of our price formation model with extensive empirical tests on large high-frequency datasets of 100 liquid Nasdaq stocks. Finally we use the model to propose an estimator of the fundamental price based on the rebate adjusted volume imbalance at the best quotes and we empirically show that it outperforms other simpler estimators
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