@techreport{HaerdleHautschMihoci, author = {H{\"a}rdle, Wolfgang Karl and Hautsch, Nikolaus and Mihoci, Andrija}, title = {Modelling and Forecasting Liquidity Supply Using Semiparametric Factor Dynamics}, publisher = {Center for Financial Studies (CFS)}, address = {Frankfurt}, abstract = {We model the dynamics of ask and bid curves in a limit order book market using a dynamic semiparametric factor model. The shape of the curves is captured by a factor structure which is estimated nonparametrically. Corresponding factor loadings are assumed to follow multivariate dynamics and are modelled using a vector autoregressive model. Applying the framework to four stocks traded at the Australian Stock Exchange (ASX) in 2002, we show that the suggested model captures the spatial and temporal dependencies of the limit order book. Relating the shape of the curves to variables reflecting the current state of the market, we show that the recent liquidity demand has the strongest impact. In an extensive forecasting analysis we show that the model is successful in forecasting the liquidity supply over various time horizons during a trading day. Moreover, it is shown that the model's forecasting power can be used to improve optimal order execution strategies.}, language = {en} } @inproceedings{HaerdleMihociHianAnnTing, author = {H{\"a}rdle, Wolfgang Karl and Mihoci, Andrija and Hian-Ann Ting, Christopher}, title = {Adaptive Order Flow Forecasting with Multiplicative Error Models}, series = {Book of abstracts of the ISCCRO - international statistical conference in Croatia}, booktitle = {Book of abstracts of the ISCCRO - international statistical conference in Croatia}, publisher = {Croatian Statistical Association}, address = {Zagreb}, abstract = {A flexible statistical approach for the analysis of time-varying dynamics of transaction data on financial markets is here applied to intra-day trading strategies. A local adaptive technique is used to successfully predict financial time series, i.e., the buyer and the seller-initiated trading volumes and the order flow dynamics. Analysing order flow series and its information content of mini Nikkei 225 index futures traded at the Osaka Securities Exchange in 2012 and 2013, a data-driven optimal length of local windows up to approximately 1-2 hours is reasonable to capture parameter variations and is suitable for short-term prediction. Our proposed trading strategies achieve statistical arbitrage opportunities and are therefore beneficial for quantitative finance practice.}, language = {en} } @misc{HaerdleHautschMihoci, author = {H{\"a}rdle, Wolfgang Karl and Hautsch, Nikolaus and Mihoci, Andrija}, title = {Modelling and Forecasting Liquidity Supply Using Semiparametric Factor Dynamics}, series = {Journal of Empirical Finance}, volume = {19}, journal = {Journal of Empirical Finance}, number = {4}, issn = {0927-5398}, doi = {10.1016/j.jempfin.2012.04.002}, pages = {610 -- 625}, abstract = {We model the dynamics of ask and bid curves in a limit order book market using a dynamic semiparametric factor model. The shape of the curves is captured by a factor structure which is estimated nonparametrically. Corresponding factor loadings are modelled jointly with best bid and best ask quotes using a vector error correction specification. Applying the framework to four stocks traded at the Australian Stock Exchange (ASX) in 2002, we show that the suggested model captures the spatial and temporal dependencies of the limit order book. We find spill-over effects between both sides of the market and provide evidence for short-term quote predictability. Relating the shape of the curves to variables reflecting the current state of the market, we show that the recent liquidity demand has the strongest impact. In an extensive forecasting analysis we show that the model is successful in forecasting the liquidity supply over various time horizons during a trading day. Moreover, it is shown that the model's forecasting power can be used to improve optimal order execution strategies.}, language = {en} } @inproceedings{KlinkeMihociHaerdle, author = {Klinke, Sigbert and Mihoci, Andrija and H{\"a}rdle, Wolfgang Karl}, title = {Exploratory Factor Analysis in Mplus, R and SPSS}, series = {8th International Conference on Teaching Statistics, Ljubljana, 2010}, booktitle = {8th International Conference on Teaching Statistics, Ljubljana, 2010}, isbn = {978-90-77713-54-9}, pages = {6}, abstract = {In teaching, factor analysis and principal component analysis are often used together, although they are quite different methods. We first summarise the similarities and differences between both approaches. From submitted theses it appears that student have difficulties seeing the differences. Although books and online resources mention some of the differences they are incomplete. A view either oriented on the similarities or the differences is reflected in software implementations. We therefore look at the implementations of factor analysis in Mplus, R and SPSS and finish with some conclusions for the teaching of Multivariate Statistics.}, language = {en} } @incollection{Mihoci, author = {Mihoci, Andrija}, title = {Modelling Limit Order Book Volume Covariance Structures}, series = {Advances in Statistical Methodologies and Their Application to Real Problems}, booktitle = {Advances in Statistical Methodologies and Their Application to Real Problems}, editor = {Hokimoto, Tsukasa}, publisher = {InTech}, address = {Rijeka}, isbn = {978-953-51-3102-1}, pages = {187 -- 199}, abstract = {Limit order volume data have been here analysed using key multivariate techniques: principal components, factor and discriminant analysis. The focus lies on understanding of the covariance structure of posted quantities of the asset to be potentially sold or bought at the market. Employing the methods to data of 20 blue chip companies traded at the NASDAQ stock market in June 2016, one observes that two principal components account for approximately 85-95\% of order book variation. The most important factor related to order book data variation has furthermore been the demand side (variability). The order book data variation, moreover, successfully classifies stock price movements. Potential applications include improving order execution strategies, designing trading algorithms and understanding price formation.}, language = {en} } @misc{XuMihociHaerdle, author = {Xu, Xiu and Mihoci, Andrija and H{\"a}rdle, Wolfgang Karl}, title = {lCARE - localizing Conditional AutoRegressive Expectiles}, series = {Journal of Empirical Finance}, volume = {48}, journal = {Journal of Empirical Finance}, issn = {0927-5398}, doi = {10.1016/j.jempfin.2018.06.006}, pages = {198 -- 220}, abstract = {We account for time-varying parameters in the conditional expectile-based value at risk (EVaR) model. The EVaR downside risk is more sensitive to the magnitude of portfolio losses compared to the quantile-based value at risk (QVaR). Rather than fitting the expectile models over ad-hoc fixed data windows, this study focuses on parameter instability of tail risk dynamics by utilizing a local parametric approach. Our framework yields a data-driven optimal interval length at each time point by a sequential test. Empirical evidence at three stock markets from 2005-2016 shows that the selected lengths account for approximately 4-6 months of daily observations. This method performs favourable compared to the models with one-year fixed intervals, as well as quantile based candidates while employing a time invariant portfolio protection (TIPP) strategy for the DAX, FTSE 100 and S\&P 500 portfolios. The tail risk measure implied by our model finally provides valuable insights for asset allocation and portfolio insurance.}, language = {en} } @misc{KhanMihociMichalketal., author = {Khan, Naveed Ahmad and Mihoci, Andrija and Michalk, Silke and Sarachuk, Kirill and Javed, Hafiz Ali}, title = {Employee Performance Measures appraised by Training and Labour Market: Evidence from the Banking Sector of Germany}, series = {Administrative Science}, volume = {12}, journal = {Administrative Science}, number = {4}, issn = {2076-3387}, doi = {10.3390/admsci12040143}, pages = {1 -- 13}, abstract = {Our paper examines the impact of training outputs and employment factors on several facets of employee performance while supporting managerial decision-making in the banking sector. First, we introduce four performance measures in individual productivity assessment. Second, three identified groups of covariates are associated with these measures, namely, the training method success, delivery of knowledge, and labor market performance feedback. Based on our empirical results from Germany, we suggest that response-related decisions are accompanying bank employees' profiles and appraisal. In particular, we form decision-making functions and finally show that the banking industry successfully balances between internal and external factors in optimizing employees' performance.}, language = {en} }