FG Wirtschaftsstatistik und Ökonometrie
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- Forecasting (5)
- Limit Order Book (4)
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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.
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.
Ovaj priručnik daje vrijednosne informacije kupcima prirodnoga plina. Priručnik je rezultat višemjesečnoga rada autora. Namijenjen je prije svega svim potrošačima prirodnoga plina kao energenta. Također, priručnik može biti teorijska podloga koja se može primijeniti na uvodnim satima seminara, ali i kod prikaza područja u kojima se spominje opskrba i prodaja prirodnoga plina. Također, može poslužiti u strukovnim školama kao dopunska literatura studentima veleučilišta i visokih škola na kolegijima s područja poslovanja u unutarnjoj trgovini, poslovnog upravljanja u trgovini, usluga u trgovini, trgovina i trgovinske politike, međunarodnog i domaćeg tržišta roba i usluga, ali i nastavnicima te predavačima. Sveučilišni fakulteti mogu se također koristiti ovim priručnikom kao dopunskom literaturom iz navedenih područja. Također, njime se mogu koristiti čitatelji željni stjecanja novih znanja iz trgovine i trgovačkog poslovanja. Čitatelji mogu u njemu pronaći vrijedne informacije o tržištu plina i opskrbi potrošača. Osim toga, može poslužiti kao dodatna literatura u nastavi ekonomske skupine predmeta. Budući studenti i nastavnici ekonomije mogu je primijeniti u različitim područjima prirodnih, tehničkih i društvenih znanosti.
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.
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.
Knjiga sadržava osnovne pojmove kontrolinga (govori se o potrebi za kontrolingom), poglavlje o istinitosti informacija, poglavlje o informacijskim sustavima za potporu funkcije kontrolinga i interne revizije, poglavlje o informacijskim sustavima za potporu procesnom kontrolingu, poglavlje o značenju revizije informacijskih sustava, poglavlje o financijskom kontrolingu unutar energetskih tvrtki, poglavlje o uređajima mobilne tehnologije sa softverom za potporu kontrolingu itd. Knjiga govori o zadatcima uprave i suštini kontrolinga.
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.
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.
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.