Simulation Monte Carlo methods in extended stochastic volatility models

A new technique for nonlinear state and parameter estimation of discrete time stochastic volatility models is developed. Algorithms of Gibbs sampler and simulation filters are used to construct a simulation tool that reflects both inherent model variability and parameter uncertainty. The proposed chain converges to equilibrium enabling the estimation of unobserved volatilities and unknown model parameter distributions. The estimation algorithm is illustrated using numerical examples. Copyright © 2002 John Wiley & Sons, Ltd.     

Prevalence of key developments in trade of agro-food producers in the new member states of the European Union

The ten new member states (NMS-10) of the European Union (EU) have contributed to an enhancement in the agro-food trade among the EU-25 members. Intra-industry trade (IIT) represents only a minor part of NMS-10 trade in agro-food products. Their trade in agro-food products is specialized to different countries in terms of economic distance, factor endowments and/or degree of competitiveness of their agro-food sectors. The borderless EU markets are likely to influence the increase of IIT with implications for the EU trade policy, since more than one-third of agro-food trade in the EU-25 members is represented by NMS-10.     

Gender in banking and mortgage behavior

Compared to men, women, even financial professionals, exhibit higher risk aversion. We exploit random assignment of clients to banking advisors (‘private bankers’) in a large Czech bank to study the effects of advisor gender on the probability of mortgage issuance and on the probability that a newly issued mortgage is insured, which we interpret as corresponding to risk averse mortgage behaviour. Male advisors do not substantially affect the chances that their clients will take a new mortgage. However, the mortgages that they issue are dramatically less likely to be insured, particularly so for female clients who never had an insured loan with the bank.     

Development of spatial thinking abilities in engineering 3D modeling course aimed at lower secondary students

International Journal of Technology and Design Education - Spatial visualization ability is an important factor in a child’s cognitive development. Its development is affected by numerous...     

Markov decision processes under model uncertainty

We introduce a general framework for Markov decision problems under model uncertainty in a discrete-time infinite horizon setting. By providing a dynamic programming principle, we obtain a local-to-global paradigm, namely solving a local, that is, a one time-step robust optimization problem leads to an optimizer of the global (i.e., infinite time-steps) robust stochastic optimal control problem, as well as to a corresponding worst-case measure. Moreover, we apply this framework to portfolio optimization involving data of the . We present two different types of ambiguity sets; one is fully data-driven given by a Wasserstein-ball around the empirical measure, the second one is described by a parametric set of multivariate normal distributions, where the corresponding uncertainty sets of the parameters are estimated from the data. It turns out that in scenarios where the market is volatile or bearish, the optimal portfolio strategies from the corresponding robust optimization problem outperforms the ones without model uncertainty, showcasing the importance of taking model uncertainty into account.     

Forecasting high-frequency excess stock returns via data analytics and machine learning

Borsa Istanbul introduced data analytics to present additional information about its market conditions. We examine whether this product can be utilized via various machine learning methods to predict intraday excess returns. Accordingly, these analytics provide significant prediction ratios above 50% with ideal profit ratios that can reach up to 33%. Among all the methods considered, XGBoost (logistic regression) performs better in predicting excess returns in the long-term analysis (short-term analysis). Results provide evidence for the benefits of both the analytics and the machine learning methods and raise further discussion on the semistrong market efficiency.     

Gradual stimulation of respondents' reflexivity: A research design for studying unreflexive consumers

In interpretive research accounts, reflexivity has been understood primarily in terms of the researcher's reflexivity, while the respondent's reflexivity has been considered only marginally. We regard this gap as critical and therefore introduce a research design for the gradual stimulation of respondents' reflexivity (GSRR), which we propose as a methodological tool for strengthening research validity. First, to frame our argumentation in the contemporary socio-technological context, we emphasize the need to focus on the respondent's reflexivity in the onto-epistemological conditions of the hybrid space and posthuman consumer culture, which unprecedentedly eliminate actors' abilities to monitor their actions reflexively. Second, we present and methodologically examine the GSRR as a 3-phase sequential mixed-method research design for stimulating respondents' reflexivity. GSRR's logic is as follows: the questionnaire captures what respondents think they are doing; the digital self-tracking diary captures what they are doing and what they often do not acknowledge (unreflexivity); the interviews use the previous phases' data to elicit respondents' reflexivity. Third, we present examples from our research practice to demonstrate the strengthening of data validity elicited from respondents by stimulating their reflexivity. We conclude by outlining the GSRR's possible future applications and directions.