@misc{HaerdleHautschMihoci, author = {H{\"a}rdle, Wolfgang Karl and Hautsch, Nikolaus and Mihoci, Andrija}, title = {Local Adaptive Multiplicative Error Models for High-Frequency Forecasts}, series = {Journal of Applied Econometrics}, volume = {30}, journal = {Journal of Applied Econometrics}, number = {4}, issn = {1099-1255}, doi = {10.1002/jae.2376}, pages = {529 -- 550}, abstract = {We propose a local adaptive multiplicative error model (MEM) accommodating time-varying parameters. MEM parameters are adaptively estimated based on a sequential testing procedure. A data-driven optimal length of local windows is selected, yielding adaptive forecasts at each point in time. Analysing 1-minute cumulative trading volumes of five large NASDAQ stocks in 2008, we show that local windows of approximately 3 to 4 hours are reasonable to capture parameter variations while balancing modelling bias and estimation (in)efficiency. In forecasting, the proposed adaptive approach significantly outperforms a MEM where local estimation windows are fixed on an ad hoc basis.}, language = {en} } @techreport{ZharovaMihociHaerdle, author = {Zharova, Alona and Mihoci, Andrija and H{\"a}rdle, Wolfgang Karl}, title = {Academic Ranking Scales in Economics: Prediction and Imputation}, publisher = {SFB 649}, address = {Berlin}, pages = {26}, abstract = {Publications are a vital element of any scientist's career. It is not only the number of media outlets but aslo the quality of published research that enters decisions on jobs, salary, tenure, etc. Academic ranking scales in economics and other disciplines are, therefore, widely used in classification, judgment and scientific depth of individual research. These ranking systems are competing, allow for different disciplinary gravity and sometimes give orthogonal results. Here a statistical analysis of the interconnection between Handelsblatt (HB), Research Papers in Economics (RePEc, here RP) and Google Scholar (GS) systems is presented. Quantile regression allows us to successfully predict missing ranking data and to obtain a so-called HB Common Score and to carry out a cross-rankings analysis. Based on the merged ranking data from different data providers, we discuss the ranking systems dependence, analyze the age effect and study the relationship between the research expertise areas and the ranking performance.}, language = {en} } @techreport{XuMihociHaerdle, author = {Xu, Xiu and Mihoci, Andrija and H{\"a}rdle, Wolfgang Karl}, title = {lCARE - localizing Conditional AutoRegressive Expectiles}, publisher = {SFB 649}, address = {Berlin}, pages = {32}, abstract = {We account for time-varying parameters in the conditional expectile based value at risk (EVaR) model. EVaR appears more sensitive to the magnitude of portfolio losses compared to the quantile-based Value at Risk (QVaR), nevertheless, by fitting the models over relatively long ad-hoc fixed time intervals, research ignores the potential time-varying parameter properties. Our work focuses on this issue by exploiting the local parametric approach in quantifying tail risk dynamics. By achieving a balance between parameter variability and modelling bias, one can safely fit a parametric expectile model over a stable interval of homogeneity. Empirical evidence at three stock markets from 2005- 2014 shows that the parameter homogeneity interval lengths account for approximately 1-6 months of daily observations. Our method outperforms models with one-year fixed intervals, as well as quantile based candidates while employing a time invariant portfolio protection (TIPP) strategy for the DAX portfolio. The tail risk measure implied by our model finally provides valuable insights for asset allocation and portfolio insurance.}, language = {en} } @techreport{GschoepfHaerdleMihoci, author = {Gsch{\"o}pf, Philipp and H{\"a}rdle, Wolfgang Karl and Mihoci, Andrija}, title = {TERES - Tail Event Risk Expectile based Shortfall}, publisher = {SFB 649}, address = {Berlin}, pages = {26}, abstract = {A flexible framework for the analysis of tail events is proposed. The framework contains tail moment measures that allow for Expected Shortfall (ES) estimation. Connecting the implied tail thickness of a family of distributions with the quantile and expectile estimation, a platform for risk assessment is provided. ES and implications for tail events under different distributional scenarios are investigated, particularly we discuss the implications of increased tail risk for mixture distributions. Empirical results from the US, German and UK stock markets, as well as for the selected currencies indicate that ES can be successfully estimated on a daily basis using a one-year time horizon across different risk levels.}, language = {en} } @techreport{HaerdleMihociHianAnnTing, author = {H{\"a}rdle, Wolfgang Karl and Mihoci, Andrija and Hian-Ann Ting, Christopher}, title = {Adaptive Order Flow Forecasting with Multiplicative Error Models}, publisher = {SFB 649}, address = {Berlin}, pages = {25}, 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} } @techreport{HaerdleHautschMihoci, author = {H{\"a}rdle, Wolfgang Karl and Hautsch, Nikolaus and Mihoci, Andrija}, title = {Local Adaptive Multiplicative Error Models for High-Frequency Forecasts}, publisher = {SFB 649}, address = {Berlin}, pages = {30}, abstract = {We propose a local adaptive multiplicative error model (MEM) accommodating time varyingparameters. MEM parameters are adaptively estimated based on a sequential testing procedure. A data-driven optimal length of local windows is selected, yielding adaptive forecasts at each point in time. Analyzing one-minute cumulative trading volumes of five large NASDAQ stocks in 2008, we show that local windows of approximately 3 to 4 hours are reasonable to capture parameter variations while balancing modelling bias and estimation (in)efficiency. In forecasting, the proposed adaptive approach significantly outperforms a MEM where local estimation windows are fixed on an ad hoc basis.}, language = {en} } @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 = {SFB 649}, address = {Berlin}, pages = {31}, 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} } @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} } @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} }