Abstract. Portfolio managers, option traders and market makers are all interested in volatility forecasting in order to get higher profits or less risky positions. Based on the fact that volatility is time varying in high frequency data and that periods of high volatility tend to cluster, the most popular models in modelling volatility are GARCH type models because they can account excess kurtosis and asymmetric effects of financial time series. A standard GARCH(1,1) model usually indicates high persistence in the conditional variance, which may originate from structural changes. The first objective of this paper is to develop a parsimonious neural networks (NN) model, which can capture the nonlinear relationship between past return innovations and conditional variance. Therefore, the goal is to develop a neural network with an appropriate recurrent connection in the context of nonlinear ARMA models, i.e., the Jordan neural network (JNN). The second objective of this paper is to determine if JNN outperforms the standard GARCH model. Out-of-sample forecasts of the JNN and the GARCH model will be compared to determine their predictive accuracy. The data set consists of returns of the CROBEX index daily closing prices obtained from the Zagreb Stock Exchange. The results indicate that the selected JNN(1,1,1) model has superior performances compared to the standard GARCH(1,1) model. The contribution of this paper can be seen in determining the appropriate NN that is comparable to the standard GARCH(1,1) model and its application in forecasting conditional variance of stock returns. Moreover, from the econometric perspective, NN models are used as a semiparametric method that combines flexibility of nonparametric methods and the interpretability of parameters of parametric methods.

Background: Liquidity is, in practice of portfolio investment, an important attribute of stocks and measuring illiquidity presents a real challenge for researchers, primarily on developed stock markets. Moreover, there is a lack of research dealing with (il)liquidity on emerging markets. In the paper, the problem of applicability and validity of two well-known illiquidity measures, ILLIQ and TURN, on European emerging markets is observed. Objectives: The paper has two main purposes. The first is to test the relative performance of the two selected illiquidity measures in terms of their validity on European emerging stock markets. The second is to propose a new and improved illiquidity measure named Relative Change in Volume (RCV). Methods/Approach: Using daily returns and traded volumes for 12 stocks which are constituents of stock indices on seven observed markets, ILLIQ and TURN along with the new proposed measure are calculated and tested based on correlation with return. All measures are tested and proposed using the single stock approach. Results: It is shown that ILLIQ and TURN are not appropriate for seven observed markets. The measures do not follow the obligatory request that returns increase in illiquidity while RCV has the ability of taking into account the pressure of big differences in volume on return. RCV gives satisfactory results, making clear the distinction between liquid and illiquid stocks and between liquid and illiquid markets. Conclusions: The proposed measure potentially has important implications in illiquidity measurement in general, and not only for investors on emerging stock markets.

This paper proposes the PROMETHEE II based multicriteria approach for cryptocurrency portfolio selection. Such an approach allows considering a number of variables important for cryptocurrencies rather than limiting them to the commonly employed return and risk. The proposed multiobjective decision making model gives the best cryptocurrency portfolio considering the daily return, standard deviation, value-at-risk, conditional value-at-risk, volume, market capitalization and attractiveness of nine cryptocurrencies from January 2017 to February 2020. The optimal portfolios are calculated at the first of each month by taking the previous 6 months of daily data for the calculations yielding with 32 optimal portfolios in 32 successive months. The out-of-sample performances of the proposed model are compared with five commonly used optimal portfolio models, i.e., naïve portfolio, two mean-variance models (in the middle and at the end of the efficient frontier), maximum Sharpe ratio and the middle of the mean-CVaR (conditional value-at-risk) efficient frontier, based on the average return, standard deviation and VaR (value-at-risk) of the returns in the next 30 days and the return in the next trading day for all portfolios on 32 dates. The proposed model wins against all other models according to all observed indicators, with the winnings spanning from 50% up to 94%, proving the benefits of employing more criteria and the appropriate multicriteria approach in the cryptocurrency portfolio selection process.

No abstract

The purpose of this paper is to investigate which of the proposed parametric models for extracting risk-neutral