MARKET FORCES AND DYNAMIC ASSET PRICING

We study a dynamic model of asset pricing which is driven by two characteristic market features: the law of investor demand (e.g., “buy low, sell high”) and the law of the market institution (which codifies the trading rules under which the market operates). We demonstrate in a simple investor–specialist trading market that these features are sufficient to guarantee an equilibrium where investors' trading strategies and the specialist's rule of price adjustments are best responses to each other. The drift term appearing in the resulting equation of the asset price process may be interpreted using Newtonian mechanics as the acceleration of a “market force.” If either of the market participants is risk-neutral, the result leads to risk-neutral asset pricing (e.g., the Black and Scholes option pricing formula).     

Chance and necessity in sociological theory

This paper proposes a mathematical model of financial markets as networks. The model examines the effect of network structure on market behavior (price volatility and trading volume). In the model, investors are arrayed in various network configurations through which they gather information to make trading decisions. The basic network considered is a chain graph with two parameters, number of investors (n) and the length of time in which information is transmitted (k). Closed‐form expressions for price volatility and expected trading volume are provided. The model is generalized to more complex networks, focusing on the hub‐and‐spoke network. The network configurations analyzed do not represent the real (and unknown) communication network among investors, but predictions from the model are consistent with price and volume patterns observed in sociological and economic research on financial markets. The main result is that network structure alone influences price volatility and expected trading volume even though investors are homogeneous and the information introduced into the system is unbiased and random. This result suggests that the structure of the real communication network among investors may influence market behavior.     

Incorporating order-flow into optimal execution

We provide an explicit closed-form strategy for an investor who executes a large order when market order-flow from all agents, including the investor’s own trades, has a permanent price impact. The strategy is found in closed-form when the permanent and temporary price impacts are linear in the market’s and investor’s rates of trading. We do this under very general assumptions about the stochastic process followed by the order-flow of the market. The optimal strategy consists of an Almgren–Chriss execution strategy adjusted by a weighted-average of the future expected net order-flow (given by the difference of the market’s rate of buy and sell market orders) over the execution trading horizon and proportional to the ratio of permanent to temporary linear impacts. We use historical data to calibrate the model to Nasdaq traded stocks and use simulations to show how the strategy performs.     

Optimal Market Making in the Foreign Exchange Market

Abstract

This paper is concerned with optimal market making in the foreign exchange market. The market maker's holdings in the different currencies are modelled as stochastic processes that are influenced by both the stochastic exchange rates and the stochastic customer buy and sell orders. The market maker can control their own bid and ask price quotes and, additionally, can buy and sell at other market participants' quotes. The resulting stochastic control problem consists of a controlled diffusion problem for the optimal quotes and a singular control problem for optimal trades at other market participants' quotes. A Markov chain approximation is used to derive optimal strategies.     

Concentrated Equilibrium and Intraday Patterns in Financial Markets

Abstract

We introduce endogenous participation of market makers into a Kyle-type model with long-lived asymmetric information. In our model with plausible parameter values, the trading volume and price volatility show a U-shaped intraday pattern, often observed in actual financial markets. It will be shown that the pattern is caused not only by the trading behaviour of liquidity traders but also by that of market makers. Our findings shed new light on the stylized fact of the trade concentration at the opening and closing periods.     

Two-level periodic marketing networks without market news

A model is constructed for a two-level periodic marketing network wherein traders buy goods from farmers in a number of spatially separated markets and transport them to urban centers for sale to consumers. It is assumed that there is no market news dissemination. As a result, market disturbances are transmitted throughout the network at a rate limited by the trading activity. Moreover, distant markets may even be isolated from local disturbances. This model possesses one and only one equilibrium state. That equilibrium state is asymptotically stable if the slopes of the various supply and demand functions are sufficiently restricted in the vicinity of the equilibrium state.     

Optimal market making

Market makers provide liquidity to other market participants: they propose prices at which they stand ready to buy and sell a wide variety of assets. They face a complex optimization problem with both static and dynamic components. They need indeed to propose bid and offer/ask prices in an optimal way for making money out of the difference between these two prices (their bid–ask spread). Since they seldom buy and sell simultaneously, and therefore hold long and/or short inventories, they also need to mitigate the risk associated with price changes and subsequently skew their quotes dynamically. In this paper, (i) we propose a general modelling framework which generalizes (and reconciles) the various modelling approaches proposed in the literature since the publication of the seminal paper ‘High-frequency trading in a limit order book’ by Avellaneda and Stoikov, (ii) we prove new general results on the existence and the characterization of optimal market making strategies, (iii) we obtain new closed-form approximations for the optimal quotes, (iv) we extend the modelling framework to the case of multi-asset market making and we obtain general closed-form approximations for the optimal quotes of a multi-asset market maker, and (v) we show how the model can be used in practice in the specific (and original) case of two credit indices.     

Models for bundle trading in financial markets

Bundle trading is a new trend in financial markets that allows traders to submit consolidated orders to sell and buy packages of assets. We propose a new bundle-based market-clearing formulation for portfolio balancing that extends the previous models in the literature through a more detailed representation of portfolios and the formulation of new bidding requirements. We also present post-optimality tie-breaking procedures intended to discriminate between equivalent orders on the basis of submission times. Numerical results evaluate the “bundle” effect as well as the bidding flexibility and the computational complexity of the formulation.     

Continuous time trading of a small investor in a limit order market

We provide a mathematical framework to model continuous time trading of a small investor in limit order markets. We show how elementary strategies can be extended in a suitable way to general continuous time strategies containing orders with infinitely many different limit prices. The general limit buy order strategies are predictable processes with values in the set of nonincreasing demand functions. It turns out that our strategy set of limit and market orders is closed, but limit orders can turn into market orders when passing to the limit, and any element can be approximated by a sequence of elementary strategies.     

Accounting for risk aversion in derivatives purchase timing

We study the problem of optimal timing to buy/sell derivatives by a risk-averse agent in incomplete markets. Adopting the exponential utility indifference valuation, we investigate this timing flexibility and the associated delayed purchase premium. This leads to a stochastic control and optimal stopping problem that combines the observed market price dynamics and the agent??s risk preferences. Our results extend recent work on indifference valuation of American options, as well as the authors?? first paper (Leung and Ludkovski, SIAM J Finan Math 2(1) 768?C793, 2011). In the case of Markovian models of contracts on non-traded assets, we provide analytical characterizations and numerical studies of the optimal purchase strategies, with applications to both equity and credit derivatives.     

Heuristic option pricing with neural networks and the neuro-computer synapse 3

Today’s option and warrant pricing is based on models developed by Black, Scholes and Merton in 1973 and Cox, Ross and Rubinstein in 1979. The price movement of the underlying asset is modeled by continuous-time or discrete-time stochastic processes. Unfortunately these models are based on severely unrealistic assumptions. Permanently an unsatisfactory and quite artificial adaption to the true market conditions is necessary (future volatility of the underlying price). Here, an alternative heuristic approach with a highly accurate neural network approximation is presented. Market prices of options and warrants and the values of the influence variables form the usually very large output/ input data set. Thousands of multi-layer perceptrons with various topologies and with different weight initializations are trained with a fast sequential quadratic programming (SQP) method. The best networks are combined to an expert council network to synthesize market prices accurately. All options and warrants can be compared to single out overpriced and underpriced ones for each trading day. For each option and warrant overpriced and underpriced trading days can be used to ascertain a better buy and sell timing. Furthermore the neural model gains deep insight into the market price sen-sitivities (option Greeks), e.g., ?, Г, Θ and Ω. As an illustrative example we inves-tigate BASF stock call warrants. Time series from the beginning of 1996 to mid 1997 of 74 BASF call warrant prices at the Frankfurter Wertpapierborse (Frankfurt Stock Exchange) form the data basis. Finally a possible speed up of the training with the neuro-computer SYNAPSE 3 is briefly discussed     

Noncooperative exchange using money and broker-dealers

This paper presents a model of exchange where a single commodity serves as a means of payment and trade must pass through designated brokers. Broker buy and sell prices, trader allocations, and broker profits depend on the buy and sell decisions of all the market participants, and the exchange problem is described as a noncooperative game. The existence of an equilibrium is established and bounds are placed on the price spread on each commodity. Finally, the properties of the noncooperative equilibria under replication are examined.     

A kinetic thermodynamics approach to the psychology of fluctuations in financial markets

We derive a set of equations which are a simple model for investor behavior in a theoretical financial market. The model incorporates the emotional aspect of investor sentiment with memory of price history which decays exponentially in time. Within this model, the emotional reaction of the body of investors is to buy when the recent price has been increasing and sell when it has been decreasing. The rational motivations are based on capitalizing on the difference between the price and intrinsic value, with the possibility of some inertia in taking action. These two competing effects provide the basis for fluctuations and instability.     

Analysis of asymptotic distributions of some profitability characteristics of trade algorithms

The asymptotic behavior of a model sequence of price increments and some probability characteristics of a trade algorithm are investigated. It is proved that characteristics such as the number of buy and sell transactions and the value of total profitability are asymptotically normal for a stationary m-dependent sequence of price increments. Approximate formulas are derived to calculate their mathematical expectations and variances as functions of parameters of the price series and the trade algorithm. Some results of numerical experiments are presented.     

Stylized facts of price gaps in limit order books

Price gap, defined as the logarithmic price difference between the first two occupied price levels on the same side of a limit order book (LOB), is a key determinant of market depth, which is one of the dimensions of liquidity. However, the properties of price gaps have not been thoroughly studied due to the less availability of ultrahigh frequency data. In the paper, we rebuild the LOB dynamics based on the order flow data of 26 A-share stocks traded on the Shenzhen Stock Exchange in 2003. Three key empirical statistical properties of price gaps are investigated. We find that the distribution of price gaps has a power-law tail for all stocks with an average tail exponent close to 3.2. Applying modern statistical methods, we confirm that the gap time series are long-range correlated and possess multifractal nature. These three features appear to be different in the measures across stocks, but they are similar for the buy and sell LOBs within each stock. Furthermore, we also unveil buy–sell asymmetry phenomena in the properties of price gaps on the buy and sell sides of the LOBs for individual stocks. These findings deepen our understanding of the dynamics of liquidity of common stocks and can be used to calibrate agent-based computational financial models.     

An extension of the Clark–Haussmann formula and applications

ABSTRACT

This work considers a financial market stochastic model where the uncertainty is driven by a multidimensional Brownian motion. The market price of the risk process makes the transition between real world probability measure and risk neutral probability measure. Traditionally, the martingale representation formulas under the risk neutral probability measure require the market price of risk process to be bounded. However, in several financial models the boundedness assumption of the market price of risk fails; for example a financial market model with the market price of risk following an Ornstein–Uhlenbeck process. This work extends the Clark–Haussmann representation formula to underlying stochastic processes which fail to satisfy the standard requirements. Our methodology is classical, and it uses a sequence of mollifiers. Our result can be applied to hedging and optimal investment in financial markets with unbounded market price of risk. In particular, the mean variance optimization problem can be addressed within our framework.     

Optimal portfolio execution under cointegrated vector autoregressive systems

Abstract

In this paper, an optimal portfolio execution problem under price model which exhibits cointegration behaviour is proposed. The proposed problem is formulated as a quadratic programming problem. With different statistical procedures and parameter estimation methods, employed on real market financial data, the four portfolios are constructed with which, computational study is performed. It is shown that the trading strategies constructed out of portfolios with cointegrated price dynamics show significant reduction in execution cost.     

Spatial oligopolistic equilibria with arbitrage,shared resources,and price function conjectures

This paper considers equilibria among multiple firms that are competing non-cooperatively against each other to sell electric power and buy resources needed to produce that power. Examples of such resources include fuels, power plant sites, and emissions allowances. The electric power market is a spatial market on a network in which flows are constrained by Kirchhoffs current and voltage laws. Arbitragers in the power market erase spatial price differences that are non-cost based. Power producers can compete in power markets à la Cournot (game in quantities), or in a generalization of the Cournot game (termed the conjectured supply function game) in which they anticipate that rivals will respond to price changes. In input markets, producers either compete à la Bertrand (price-taking behavior) or they can conjecture that price will increase with consumption of the resource. The simultaneous competition in power and input markets presents opportunities for strategic price behavior that cannot be analyzed using models of power markets alone. Depending on whether the producers treat the arbitrager endogenously or exogenously, we derive two mixed nonlinear complementarity formulations of the oligopolistic problem. We establish the existence and uniqueness of solutions as well as connections among the solutions to the model formulations. A numerical example is provided for illustrative purposes.Support was provided by the National Science Foundation under grant ECS-0080577.This authors research was partially supported by the National Science Foundation under grants ECS-0080577 and CCR-0098013.The authors thank Chris Day, Fieke Riekers, and Adrian Wals for their collaboration. They are particularly indebted to Grant Roch for writing AMPL and MATLAB codes for solving the numerical example reported in the paper.     

Modeling carbon spot and futures price returns with GARCH and Markov switching GARCH models

This paper makes use of spot and futures market data to carry out a thorough analysis of the dynamics of carbon price returns in the European Union Emission Trading Scheme for the whole first commitment period from 2008 to 2012. Understanding the properties of carbon price returns is especially crucial for industries which have to comply with an emission trading system and other market participants such as risk managers and speculators. We therefore seek to develop accurate models which capture the behavior of carbon price returns comprehensively. We apply a broad spectrum of GARCH model specifications, using different distributions for model innovations. As both time series, spot and futures price returns, exhibit asymmetric behavior in their variance, we additionally take Markov regime switching models for the variance equation into consideration. Empirical results demonstrate that AGARCH, NARCH and GJR fit the data best. We further show that, in the error term of any model, fat-tailed distributions—in particular the generalized error distribution—significantly improve the fit. Additionally, as futures returns seem to carry informational content concerning subsequent spot returns, we propose a sound, yet parsimonious, spot returns model, well-suited to capturing the dynamics. Finally, the most appropriate models for spot and futures price returns are tested in an out-of-sample environment, and further checked for robustness in data subsets. Subsequently a model for each market is proposed.     

连续交易制度与价格发现功能——基于我国黄金期货市场的实证研究

连续交易制度是提升我国黄金期货市场国际竞争力的重要举措。采用2011年1月至2014年9月中美黄金期货市场日收盘价数据,利用VEC模型、信息份额模型、VEC-BEKK-MGARCH模型、DCC-MGARCH模型,研究了该制度对上海黄金期货市场价格发现功能的影响。结果表明:制度推出后,上海黄金期货市场的价格发现功能得到提升,不过仍弱于美国市场,美国市场对上海市场的收益率传递效应减弱,两市场之间的波动溢出效应有所增强,时变动态相关系数振动幅度明显降低。