Results 1  10
of
49
A microsimulation of traders activity in the stock market: the role of heterogeneity, agents' interaction and . . .
, 2000
"... ..."
A learning marketmaker in the GlostenMilgrom model
 Quantitative Finance
, 2005
"... This paper develops a model of a learning marketmaker by extending the GlostenMilgrom model of dealer markets. The marketmaker tracks the changing true value of a stock in settings with informed traders (with noisy signals) and liquidity traders, and sets bid and ask prices based on its estimate ..."
Abstract

Cited by 17 (5 self)
 Add to MetaCart
(Show Context)
This paper develops a model of a learning marketmaker by extending the GlostenMilgrom model of dealer markets. The marketmaker tracks the changing true value of a stock in settings with informed traders (with noisy signals) and liquidity traders, and sets bid and ask prices based on its estimate of the true value. We empirically evaluate the performance of the marketmaker in markets with different parameter values to demonstrate the effectiveness of the algorithm, and then use the algorithm to derive properties of price processes in simulated markets. When the true value is governed by a jump process, there is a two regime behavior marked by significant heterogeneity of information and large spreads immediately following a price jump, which is quickly resolved by the marketmaker, leading to a rapid return to homogeneity of information and small spreads. We also discuss the similarities and differences between our model and real stock market data in terms of distributional and time series properties of returns. Submitted to: Quantitative Finance 1.
The Effect of the U.S
 Welfare System on Marital Status,” Journal of Public Economics
, 1990
"... of interactions among wave aberrations on optical image quality ..."
Abstract

Cited by 12 (0 self)
 Add to MetaCart
(Show Context)
of interactions among wave aberrations on optical image quality
Intelligent MarketMaking in Artificial Financial Markets
, 2003
"... This thesis describes and evaluates a marketmaking algorithm for setting prices in financial markets with asymmetric information, and analyzes the properties of artificial markets in which the algorithm is used. The core of our algorithm is a technique for maintaining an online probability density ..."
Abstract

Cited by 6 (3 self)
 Add to MetaCart
This thesis describes and evaluates a marketmaking algorithm for setting prices in financial markets with asymmetric information, and analyzes the properties of artificial markets in which the algorithm is used. The core of our algorithm is a technique for maintaining an online probability density estimate of the underlying value of a stock. Previous theoretical work on marketmaking has led to pricesetting equations for which solutions cannot be achieved in practice, whereas empirical work on algorithms for marketmaking has focused on sets of heuristics and rules that lack theoretical justification. The algorithm presented in this thesis is theoretically justified by results in finance, and at the same time flexible enough to be easily extended by incorporating modules for dealing with considerations like portfolio risk and competition from other marketmakers. We analyze the performance of our algorithm experimentally in artificial markets with different parameter settings and find that many reasonable realworld properties emerge. For example, the spread increases in response to uncertainty about the true value of a stock, average spreads tend to be higher in more volatile markets, and marketmakers with lower average spreads perform better in environments with multiple competitive marketmakers. In addition, the time series data generated by simple markets populated with marketmakers using our algorithm replicate properties of realworld financial time series, such as volatility clustering and the fattailed nature of return distributions, without the need to specify explicit models for opinion propagation and herd behavior in the trading crowd.
FORECASTING VOLATILITY WITH THE MULTIFRACTAL RANDOM WALK MODEL
, 801
"... Abstract. We study the problem of forecasting volatility for the multifractal random walk model. In order to avoid the ill posed problem of estimating the correlation length T of the model, we introduce a limiting object defined in a quotient space; formally, this object is an infinite range logvola ..."
Abstract

Cited by 5 (2 self)
 Add to MetaCart
(Show Context)
Abstract. We study the problem of forecasting volatility for the multifractal random walk model. In order to avoid the ill posed problem of estimating the correlation length T of the model, we introduce a limiting object defined in a quotient space; formally, this object is an infinite range logvolatility. For this object and the non limiting object, we obtain precise prediction formulas and we apply them to the problem of forecasting volatility and pricing options with the MRW model in the absence of a reliable estimate of σ and T.
An agentbased model of dealership markets
 University of Oxford, UK
"... This paper describes an agentbased model of financial markets with monopolistic or competitive marketmakers and analyzes some of the emergent properties of these markets, including time series properties. The artificial markets we discuss utilize models of “informed ” trading agents who decide to ..."
Abstract

Cited by 5 (0 self)
 Add to MetaCart
(Show Context)
This paper describes an agentbased model of financial markets with monopolistic or competitive marketmakers and analyzes some of the emergent properties of these markets, including time series properties. The artificial markets we discuss utilize models of “informed ” trading agents who decide to trade based on received signals of the true or fundamental value of the stock, and “uninformed ” trading agents (sometimes called liquidity traders) who trade for exogenous reasons and are modeled as buying and selling stock randomly. These simple models of traders, combined with more complex marketmaking agents who function as pricesetters and inventoryholders in the market, lead to a rich array of market properties, many of which qualitatively replicate properties observed in real financial markets. For example, the bidask spread increases in response to uncertainty about the true value of a stock, average spreads tend to be higher in more volatile markets, and marketmakers with lower average spreads perform better in competitive environments. The time series data generated by our market models demonstrate phenomena like volatility clustering and the fattailed nature of return
GAUSSIAN MULTIPLICATIVE CHAOS REVISITED
, 807
"... Abstract. In this article, we extend the theory of multiplicative chaos for positive definite functions in Rd of the form f(x) = λ2 + T ln x + g(x) where g is a continuous and bounded function. The construction is simpler and more general than the one defined by Kahane in 1985. As main applicatio ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
(Show Context)
Abstract. In this article, we extend the theory of multiplicative chaos for positive definite functions in Rd of the form f(x) = λ2 + T ln x + g(x) where g is a continuous and bounded function. The construction is simpler and more general than the one defined by Kahane in 1985. As main application, we give a rigorous mathematical meaning to the KolmogorovObukhov model of energy dissipation in a turbulent flow.
Correlation and volatility in an Indian stock market: A random matrix approach”, The European
 Physical Journal B
, 2007
"... We examine volatility of an Indian stock market in terms of aspects like participation, synchronization of stocks and quantification of volatility using the random matrix approach. Volatility pattern of the market is found using the BSE index for the threeyear period 20002002. Random matrix analys ..."
Abstract

Cited by 3 (1 self)
 Add to MetaCart
(Show Context)
We examine volatility of an Indian stock market in terms of aspects like participation, synchronization of stocks and quantification of volatility using the random matrix approach. Volatility pattern of the market is found using the BSE index for the threeyear period 20002002. Random matrix analysis is carried out using daily returns of 70 stocks for several time windows of 85 days in 2001 to (i) do a brief comparative analysis with statistics of eigenvalues and eigenvectors of the matrix C of correlations between price fluctuations, in time regimes of different volatilities. While a bulk of eigenvalues falls within RMT bounds in all the time periods, we see that the largest (deviating) eigenvalue correlates well with the volatility of the index, the corresponding eigenvector clearly shows a shift in the distribution of its components from volatile to less volatile periods and verifies the qualitative association between participation and volatility (ii) observe that the Inverse participation ratio for the last eigenvector is sensitive to market fluctuations (the two quantities are observed to anti correlate significantly) (iii) set up a variability index, V whose temporal evolution is found to be significantly correlated with the volatility of the overall market index. 1 1