<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>16463</id>
    <completedYear/>
    <publishedYear>2015</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>529</pageFirst>
    <pageLast>550</pageLast>
    <pageNumber/>
    <edition/>
    <issue>4</issue>
    <volume>30</volume>
    <type>articler</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2016-06-06</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Local Adaptive Multiplicative Error Models for High-Frequency Forecasts</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Journal of Applied Econometrics</parentTitle>
    <identifier type="doi">10.1002/jae.2376</identifier>
    <identifier type="url">http://onlinelibrary.wiley.com/doi/10.1002/jae.2376/abstract</identifier>
    <identifier type="issn">1099-1255</identifier>
    <enrichment key="BTU">nicht an der BTU erstellt / not created at BTU</enrichment>
    <author>
      <firstName>Wolfgang Karl</firstName>
      <lastName>Härdle</lastName>
    </author>
    <submitter>
      <firstName>Andrija</firstName>
      <lastName>Mihoci</lastName>
    </submitter>
    <author>
      <firstName>Nikolaus</firstName>
      <lastName>Hautsch</lastName>
    </author>
    <author>
      <firstName>Andrija</firstName>
      <lastName>Mihoci</lastName>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Multiplicative Error Model</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Local Adaptive Modelling</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>High-Frequency Processes</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Trading Volume</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Forecasting</value>
    </subject>
    <collection role="institutes" number="5309H01">FG Wirtschaftsstatistik und Ökonometrie</collection>
  </doc>
  <doc>
    <id>16449</id>
    <completedYear/>
    <publishedYear>2012</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>610</pageFirst>
    <pageLast>625</pageLast>
    <pageNumber/>
    <edition/>
    <issue>4</issue>
    <volume>19</volume>
    <type>articler</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2016-05-31</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Modelling and Forecasting Liquidity Supply Using Semiparametric Factor Dynamics</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="deu">Journal of Empirical Finance</parentTitle>
    <identifier type="doi">10.1016/j.jempfin.2012.04.002</identifier>
    <identifier type="issn">0927-5398</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <author>
      <firstName>Wolfgang Karl</firstName>
      <lastName>Härdle</lastName>
    </author>
    <submitter>
      <firstName>Andrija</firstName>
      <lastName>Mihoci</lastName>
    </submitter>
    <author>
      <firstName>Nikolaus</firstName>
      <lastName>Hautsch</lastName>
    </author>
    <author>
      <firstName>Andrija</firstName>
      <lastName>Mihoci</lastName>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Limit Order Book</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Liquidity Risk</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Semiparametric Modelling</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Factor Structure</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Prediction</value>
    </subject>
    <collection role="institutes" number="5309H01">FG Wirtschaftsstatistik und Ökonometrie</collection>
  </doc>
  <doc>
    <id>22475</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>198</pageFirst>
    <pageLast>220</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>48</volume>
    <type>articler</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-11-07</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">lCARE - localizing Conditional AutoRegressive Expectiles</title>
    <abstract language="eng">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&amp;P 500 portfolios. The tail risk measure implied by our model finally provides valuable insights for asset allocation and portfolio insurance.</abstract>
    <parentTitle language="eng">Journal of Empirical Finance</parentTitle>
    <identifier type="doi">10.1016/j.jempfin.2018.06.006</identifier>
    <identifier type="url">https://www.sciencedirect.com/science/article/pii/S0927539818300446</identifier>
    <identifier type="issn">0927-5398</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <submitter>
      <firstName>Andrija</firstName>
      <lastName>Mihoci</lastName>
    </submitter>
    <author>
      <firstName>Xiu</firstName>
      <lastName>Xu</lastName>
    </author>
    <author>
      <firstName>Andrija</firstName>
      <lastName>Mihoci</lastName>
    </author>
    <author>
      <firstName>Wolfgang Karl</firstName>
      <lastName>Härdle</lastName>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Expectile</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Tail Risk</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Local Parametric Approach</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Risk Management</value>
    </subject>
    <collection role="institutes" number="5309H01">FG Wirtschaftsstatistik und Ökonometrie</collection>
  </doc>
  <doc>
    <id>29408</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>13</pageLast>
    <pageNumber/>
    <edition/>
    <issue>4</issue>
    <volume>12</volume>
    <type>articler</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-10-20</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Employee Performance Measures appraised by Training and Labour Market: Evidence from the Banking Sector of Germany</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Administrative Science</parentTitle>
    <identifier type="url">https://www.mdpi.com/2076-3387/12/4/143</identifier>
    <identifier type="doi">10.3390/admsci12040143</identifier>
    <identifier type="issn">2076-3387</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="Artikelnummer">143</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="Fprofil">1 Energiewende und Dekarbonisierung / Energy Transition and Decarbonisation</enrichment>
    <enrichment key="Fprofil">3 Globaler Wandel und Transformationsprozesse / Global Change and Transformation Processes</enrichment>
    <author>
      <firstName>Naveed Ahmad</firstName>
      <lastName>Khan</lastName>
    </author>
    <submitter>
      <firstName>Kirill</firstName>
      <lastName>Sarachuk</lastName>
    </submitter>
    <author>
      <firstName>Andrija</firstName>
      <lastName>Mihoci</lastName>
    </author>
    <author>
      <firstName>Silke</firstName>
      <lastName>Michalk</lastName>
    </author>
    <author>
      <firstName>Kirill</firstName>
      <lastName>Sarachuk</lastName>
    </author>
    <author>
      <firstName>Hafiz Ali</firstName>
      <lastName>Javed</lastName>
    </author>
    <collection role="institutes" number="5302">FG ABWL, insbesondere Planung, Innovation und Gründung</collection>
    <collection role="institutes" number="5384">FG ABWL mit den Schwerpunkten Personalwesen und Managementlehre</collection>
  </doc>
</export-example>
