The major task of a financial market is to intermediate risk and to ensure an optimal allocation of resources. An essential feature of a well functioning trading system is the continuous provision of liquidity. This is particularly important in periods when markets are volatile and fundamental information is processed. A lack of liquidity confronts traders with high transaction costs or the risk of non-execution. For instance, illiquidity was one of the central reasons for the collapse of the Long Term Capital hedge fund in 1999.
In periods of low liquidity, the investor can reduce trading costs by posting a limit order or by successively splitting the trading volume over time. However, changing market conditions imply stochastic transaction costs and consequently generate trading risks. Similarly to the trade-off between risk and return a trade-off between trading costs and trading risks arises.
In this project, we will further develop and apply modern multivariate time series techniques to monitor intraday trading, to quantify liquidity risks, and to model and predict transaction costs. The project will provide a methodological basis for the management of trading costs and liquidity risks as well as for the empirical validation of liquidity orientated market microstructure theory. A fundamental econometric contribution will be the further development of multivariate multiplicative error models, parametric and semi-parametric dynamic factor models, regime switching models as well as multivariate intensity based models. Two major challenges will be the consideration of time-varying higher moments in trading variables and the specification of computationally tractable models with time-varying parameters. A central empirical contribution will be the prediction of trading costs, the analysis of the relation between volatility, liquidity and the probability of informed trading as well as the evaluation of the underlying cost-risk tradeoff of trading strategies.