TY - GEN A1 - Härdle, Wolfgang Karl A1 - Hautsch, Nikolaus A1 - Mihoci, Andrija T1 - Local Adaptive Multiplicative Error Models for High-Frequency Forecasts T2 - Journal of Applied Econometrics N2 - 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. KW - Multiplicative Error Model KW - Local Adaptive Modelling KW - High-Frequency Processes KW - Trading Volume KW - Forecasting Y1 - 2015 UR - http://onlinelibrary.wiley.com/doi/10.1002/jae.2376/abstract U6 - https://doi.org/10.1002/jae.2376 SN - 1099-1255 VL - 30 IS - 4 SP - 529 EP - 550 ER - TY - RPRT A1 - Zharova, Alona A1 - Mihoci, Andrija A1 - Härdle, Wolfgang Karl T1 - Academic Ranking Scales in Economics: Prediction and Imputation N2 - 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. KW - Scientometrics KW - Ranking KW - Quantile Regression KW - Handelsblatt KW - RePEc KW - Google Scholar Y1 - 2016 UR - https://sfb649.wiwi.hu-berlin.de/papers/pdf/SFB649DP2016-021.pdf PB - SFB 649 CY - Berlin ER - TY - RPRT A1 - Xu, Xiu A1 - Mihoci, Andrija A1 - Härdle, Wolfgang Karl T1 - lCARE - localizing Conditional AutoRegressive Expectiles N2 - 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. KW - Expectiles KW - Tail Risk KW - Local Parametric Approach KW - Risk Management Y1 - 2015 UR - https://sfb649.wiwi.hu-berlin.de/papers/pdf/SFB649DP2015-052.pdf PB - SFB 649 CY - Berlin ER - TY - RPRT A1 - Gschöpf, Philipp A1 - Härdle, Wolfgang Karl A1 - Mihoci, Andrija T1 - TERES - Tail Event Risk Expectile based Shortfall N2 - 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. KW - Expected Shortfall KW - Expectiles KW - Tail Risk KW - Risk Management KW - Tail Events KW - Tail Moments Y1 - 2015 UR - https://sfb649.wiwi.hu-berlin.de/papers/pdf/SFB649DP2015-047.pdf PB - SFB 649 CY - Berlin ER - TY - RPRT A1 - Härdle, Wolfgang Karl A1 - Mihoci, Andrija A1 - Hian-Ann Ting, Christopher T1 - Adaptive Order Flow Forecasting with Multiplicative Error Models N2 - 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. KW - Multiplicative Error Models KW - Trading Volume KW - Order Flow KW - Forecasting Y1 - 2014 UR - https://sfb649.wiwi.hu-berlin.de/papers/pdf/SFB649DP2014-035.pdf PB - SFB 649 CY - Berlin ER - TY - RPRT A1 - Härdle, Wolfgang Karl A1 - Hautsch, Nikolaus A1 - Mihoci, Andrija T1 - Local Adaptive Multiplicative Error Models for High-Frequency Forecasts N2 - 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. KW - Multiplicative Error Model KW - Local Adaptive Modelling KW - High-Frequency Processes KW - Trading Volume KW - Forecasting Y1 - 2012 UR - https://sfb649.wiwi.hu-berlin.de/papers/pdf/SFB649DP2012-031.pdf PB - SFB 649 CY - Berlin ER - TY - RPRT A1 - Härdle, Wolfgang Karl A1 - Hautsch, Nikolaus A1 - Mihoci, Andrija T1 - Modelling and Forecasting Liquidity Supply Using Semiparametric Factor Dynamics N2 - 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. KW - Limit Order Book KW - Liquidity Risk KW - Semiparametric Modelling KW - Factor Structure KW - Prediction Y1 - 2009 UR - http://sfb649.wiwi.hu-berlin.de/papers/pdf/SFB649DP2009-044.pdf PB - SFB 649 CY - Berlin ER - TY - RPRT A1 - Härdle, Wolfgang Karl A1 - Hautsch, Nikolaus A1 - Mihoci, Andrija T1 - Modelling and Forecasting Liquidity Supply Using Semiparametric Factor Dynamics N2 - 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. KW - Limit Order Book KW - Liquidity Risk KW - Semiparametric Model KW - Factor Structure KW - Prediction Y1 - 2009 UR - https://www.ifk-cfs.de/fileadmin/downloads/publications/wp/09_18.pdf PB - Center for Financial Studies (CFS) CY - Frankfurt ER - TY - CHAP A1 - Härdle, Wolfgang Karl A1 - Mihoci, Andrija A1 - Hian-Ann Ting, Christopher T1 - Adaptive Order Flow Forecasting with Multiplicative Error Models T2 - Book of abstracts of the ISCCRO - international statistical conference in Croatia N2 - 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. KW - Forecasting KW - Multiplicative Error Models KW - Order Flow KW - Trading Volume Y1 - 2016 UR - http://www.hsd-stat.hr/en/isccro_en/publications/ N1 - ISSN 1849-9864 PB - Croatian Statistical Association CY - Zagreb ER - TY - GEN A1 - Härdle, Wolfgang Karl A1 - Hautsch, Nikolaus A1 - Mihoci, Andrija T1 - Modelling and Forecasting Liquidity Supply Using Semiparametric Factor Dynamics T2 - Journal of Empirical Finance N2 - 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. KW - Limit Order Book KW - Liquidity Risk KW - Semiparametric Modelling KW - Factor Structure KW - Prediction Y1 - 2012 U6 - https://doi.org/10.1016/j.jempfin.2012.04.002 SN - 0927-5398 VL - 19 IS - 4 SP - 610 EP - 625 ER - TY - CHAP A1 - Klinke, Sigbert A1 - Mihoci, Andrija A1 - Härdle, Wolfgang Karl T1 - Exploratory Factor Analysis in Mplus, R and SPSS T2 - 8th International Conference on Teaching Statistics, Ljubljana, 2010 N2 - 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. KW - Factor Analysis KW - Principal Components Analysis KW - Software KW - Multivariate Statistics Y1 - 2010 UR - http://icots.info/8/cd/pdfs/invited/ICOTS8_4F4_KLINKE.pdf SN - 978-90-77713-54-9 ER -