The COVID-19 pandemic seems to be the most important phenomenon observed from March 2020 in virtually all countries of the world. The necessity to prevent the spread of COVID-19 and keep health care systems efficient resulted in the forced, drastic limitation of economic activity. Many service sectors were hit particularly hard with this but industry and agriculture were also affected. In particular, the pandemic substantially influenced financial markets and we can observe that some markets or instruments vary in stability since they have been affected in the different degree. In the paper, we present the problem of stability of stock markets during the COVID-19 pandemic. Due to the low number of works related to CEE countries during the pandemic, we analyze the Warsaw Stock Exchange, which is one of the most important markets in the CEE. Our main goal was to find how various industries represented by stock market indices have reacted to the COVID-19 shock and consequently which sectors turned out to keep stability and remained resistant to the pandemic. In our investigation, we use two clustering methods: the K-means and the Ward techniques with the criterion of maximizing the silhouette coefficient and six indicators describing stability in terms of profitability, volume, overbought/oversold conditions and volatility. The results of the research present that during the pandemic it was possible to identify 5 clusters of sector indices in the short term and 4 in the medium term. We found that the composition of the clusters is quite stable over time and that none of the obtained clusters can be univocally considered the most or the least stable taking into account all the analyzed indicators. However, we showed that the obtained clusters have different stability origins, i.e. they vary from each other in terms of the investigated indicators of stability.
We compare the forecasting performance of the generalized autoregressive conditional heteroscedasticity (GARCH) -type models with support vector regression (SVR) for futures contracts of selected energy commodities: Crude oil, natural gas, heating oil, gasoil and gasoline. The GARCH models are commonly used in volatility analysis, while SVR is one of machine learning methods, which have gained attention and interest in recent years. We show that the accuracy of volatility forecasts depends substantially on the applied proxy of volatility. Our study confirms that SVR with properly determined hyperparameters can lead to lower forecasting errors than the GARCH models when the squared daily return is used as the proxy of volatility in an evaluation. Meanwhile, if we apply the Parkinson estimator which is a more accurate approximation of volatility, the results usually favor the GARCH models. Moreover, it is difficult to choose the best model among the GARCH models for all analyzed commodities, however, forecasts based on the asymmetric GARCH models are often the most accurate. While, in the class of the SVR models, the results indicate the forecasting superiority of the SVR model with the linear kernel and 15 lags, which has the lowest mean square error (MSE) and mean absolute error (MAE) among the SVR models in 92% cases.
Alekneviciene A., Stareviciute B., Alekneviciute E. (2018): Evaluation of the efficiency of European Union farms: a risk-adjusted return approach. Agric. Econ. -Czech, 64: 241-255. Abstract:The aim of this study was to assess the efficiency of EU member-state farms using a risk-adjusted return approach and to determine the impact of subsidies on the efficiency of EU farms. Farm efficiency was analysed by the member-state and by the type of farming and was based on the calculation of Sharpe and Treynor ratios. Systemic risk was expressed by standard deviation in order to estimate the share of systemic risk in the total risk. The change in Sharpe ratios was assessed to determine the impact of subsidies on EU farm efficiency. The results of the risk-adjusted return analysis reveal that farms in the EU-15 were more efficient than farms in the EU-12 in 2004-2013, possibly due to being more experienced in risk management. Nevertheless, the EU-15 did not undertake a bigger share of systemic risk when compared to the EU-12 farms. The impact of financial support on the efficiency of the EU-12 farms was also not stronger when compared to the EU-15 farms.
Support vector regression is a promising method for time-series prediction, as it has good generalisability and an overall stable behaviour. Recent studies have shown that it can describe the dynamic characteristics of financial processes and make more accurate forecasts than other machine learning techniques. The first main contribution of this paper is to propose a methodology for dynamic modelling and forecasting covariance matrices based on support vector regression using the Cholesky decomposition. The procedure is applied to range-based covariance matrices of returns, which are estimated on the basis of low and high prices. Such prices are most often available with closing prices for many financial series and contain more information about volatility and relationships between returns. The methodology guarantees the positive definiteness of the forecasted covariance matrices and is flexible, as it can be applied to different dependence patterns. The second contribution of the paper is to show with an example of the exchange rates from the forex market that the covariance matrix forecasts calculated using the proposed approach are more accurate than the forecasts from the benchmark dynamic conditional correlation model. The advantage of the suggested procedure is higher during turbulent periods, i.e., when forecasting is the most difficult and accurate forecasts matter most.
The relationships between crude oil prices and exchange rates have always been of interest to academics and policy analysts. There are theoretical transmission channels that justify such links; however, the empirical evidence is not clear. Most of the studies on causal relationships in this area have been restricted to a linear framework, which can omit important properties of the investigated dependencies that could be exploited for forecasting purposes. Based on the nonlinear Granger causality tests, we found strong bidirectional causal relations between crude oil prices and two currency pairs: EUR/USD, GBP/USD, and weaker between crude oil prices and JPY/USD. We showed that the significance of these relations has changed in recent years. We also made an attempt to find an effective strategy to forecast crude oil prices using the investigated exchange rates as regressors and vice versa. To this aim, we applied Support Vector Regression (SVR)—the machine learning method of time series modeling and forecasting.
WYMIAR FRAKTALNY SZEREGÓW CZASOWYCH A RYZYKO INWESTOWANIA * Z a r y s t r e ś c i. W artykule scharakteryzowano wymiar fraktalny jako miarę ryzyka inwestowania w papiery wartościowe. Przedstawiono dwie metody obliczania wymiaru fraktalnego szeregu czasowego-analizę R/S oraz metodę segmentowo-wariacyjną, które następnie zastosowano do indeksów Giełdy Papierów Wartościowych w Warszawie. S ł o w a k l u c z o w e : wymiar fraktalny, zmienność szeregu czasowego, ryzyko inwestowania, analiza R/S, metoda segmentowo-wariacyjna. * Badanie zostało sfi nansowane przez Uniwersytet Mikołaja Kopernika w Toruniu w ramach grantu UMK nr 397-E.
Nonparametric regression is an alternative to the parametric approach, which consists of applying parametric models, i.e models of the certain functional form with a fixed number of parameters. As opposed to the parametric approach, nonparametric models have a general form, which can be approximated increasingly precisely when the sample size grows. Hereby they do not impose such restricted assumptions about the form of the modelling dependencies and in consequence, they are more flexible and let the data speak for themselves. One of the most popular nonparametric regression method is kernel smoothing, dating back to Rosenblatt (1956) and Parzen (1962). Nowadays, there are a number of variations of the kernel smoothers. In the paper, the local-linear kernel regression is assessed using a Monte Carlo study. The study considered varied linear and nonlinear data generating processes, comprising chaotic systems and the well-known in econometrics stochastic processes with nonlinearity in the mean and in the variance.
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