C53 - Forecasting Models; Simulation MethodsReturn

Although it is notoriously difficult to utilize financial ratios to forecast the crude oil market prices, our study challenges this perception and reveals that the second risk premium principal component analysis (RP-PCA) factor may contain statistically significant information for both in-sample and out-of-sample forecasts of future crude oil prices. Our evidence illustrates that the second RP-PCA factor substantially outperforms many other popular predictors (approximately 30 conventional predictors) in forecasting crude oil prices and generating adequate higher values of economic profits. We conduct a range of informative tests, including bootstrap simulation, success ratio tests, alternative out-of-sample evaluation periods, and structure break tests. Furthermore, we illustrate that the forecasting ability of the second RP-PCA factor may stem from its ability to forecast oil market sentiment. Our study presents a novel and indicatable financial instrument for policymakers to predict crude oil prices robustly. The theoretical motivation of this study links to Cochrane's (2005) framework for general candidate factors in asset pricing.

The manufacturing sector has been regarded as a key factor in the history of economic devel-opment and growth. However, economic fluctuations affect manufacturing seriously. This study examines the impact of the 2008 global economic crisis on Turkish manufacturing sector profit-ability. This paper uses micro-econometric difference in differences methods in conjunction with the macroeconomic forecasting method to investigate how profit levels in the Turkish manufacturing industry are affected by the crisis. The results indicate that the profit levels changed significantly after the crisis with a one-year lag and actual profits exceeded the estimated profits in the later years. Economic impacts of crises have long been investigated; however, this paper differs from the literature in using a new analytical framework for the issue. The suggested method can be expanded to other areas, which can spark new future studies.

When there is bad news hoarding from managers, returns of stocks are no longer efficient. We hypothesize that a proxy for efficient returns predicts stock price bubbles, crashes and crash risk. We find evidence in support of our hypotheses. Lagged price efficiency significantly predicts bubbles, crashes and crash risk in multivariate linear regressions and logit regressions, as predicted by our hypotheses. We also find that the lagged probability of bubbles is only correlated with future returns. In contrast, the lagged probability of crashes is correlated with both future returns and fundamental values of stocks. This result validates our explanation for the formation of bubbles and crashes. Finally, the out-of-sample accuracy ratio of our bubble and the crash prediction model is higher than in previous studies. Our results provide alternative explanations of the mechanics of stock price bubbles and crashes and are helpful for academicians, investors and policymakers.

The article contributes to the knowledge of customer lifetime value (CLV) models, where extensive empirical analyses on large datasets from online stores are missing. Based on this knowledge, practitioners can decide about the deployment of a particular model in their business and academics can design or enhance CLV models. The article presents predictive performance of selected CLV models: the extended Pareto/NBD model, the Markov chain model, the vector autoregressive model and the status quo model. Six large datasets of medium and large‑sized online stores in the Czech Republic and Slovakia are used for a comparison of the predictive performance of the models. Online stores have annual revenues in the order of tens of millions of euros and more than one million customers. The comparison of CLV models is based on selected evaluation metrics. The results of some of the models which use additional non‑financial data on customer behaviour - the Markov chain model and the vector autoregressive model - do not justify the effort which is needed to collect such data. The advantages and disadvantages of the selected CLV models are discussed in the context of their deployment.

Business failure prediction is an important issue in corporate finance. Different prediction models are proposed by financial theory and are often used in practice. Their application is effortless, selecting only few key inputs with the greatest informative power from the large list of possible indicators. Our paper identifies the financial distress predictors for 5 post-communist countries (Bulgaria, Croatia, the Czech Republic, Hungary and Romania) based on information collected from the Amadeus database for the period 2011-2013 using CHAID decision trees and neural networks. We propose a short list of indicators, which can offer a synthetic perspective on corporate distress risk, adapted for these countries. The best prediction models are substantially different from country to country: in the Czech Republic, Hungary and Romania the flow-approach indicators perform better, while in Bulgaria and Croatia - the stock-approach indicators. The results suggest that the extrapolation of such models from one country to another should be made cautiously. One interesting finding is the presence of the ratios per employee as predictors of financial distress.

In this paper the authors examine the determinants of NPL ratio using a cross-county analysis from the sample of 25 emerging countries. Using the panel data approach, determinants of NPL are analysed for the period from 2000 to 2011. The main aim of this paper is to draw a relevant econometric model, to demonstrate the impact of independent variables on the dependent variable by using static and dynamic model estimation techniques. The results show that NPLs rate can be mainly explained by crucial macroeconomic factors, such as the GDP and inflation rate, and bank-specific factors, such as ROA, CAP and lagged NPLs rate.

Connection between macroeconomic variables and foreign exchange (FX) rates evaluated in the context of out-of-sample forecasting is a well-known problem in economics. We propose a method that utilizes stochastic models based on jump processes (namely the normal inverse Gaussian and Meixner models), combines them with macroeconomic fundamentals, and using a moving (rolling or recursive) regularized estimation procedure produces forecasts of FX rates. These are compared to benchmark models, namely the direct forecast and the Gauss model forecast. Empirical out-of-sample experiments are performed on EUR/USD and USD/DKK currencies.

This research is related to the assessment of alternative unemployment rate predictions for the Romanian economy, the forecasts being provided by three anonymous forecasters: F1, F2 and F3. F3 provided the most accurate forecasts for the horizon 2001-2014, while F2 predictions are the less accurate according to U1 Theil's statistic and according to a new method that has not been used before in literature in this context. The multi-criteria ranking was applied to make a hierarchy of the forecasters regarding the accuracy and five important accuracy measures were taken into account at the same time: mean errors, mean squared error, root mean squared error, U1 and U2 statistics of Theil. The combined forecasts of forecasters' predictions are the best strategy to improve the forecasts accuracy. The filtered and smoothed original predictions based on Hodrick-Prescott filter, respectively Holt-Winters technique, are a good strategy of improving the accuracy only for F2 expectations. The assessment and improvement of forecasts accuracy have an important contribution in growing the quality of decision-making process.

Electricity forward contracts can normally be traded in two ways in the Czech Republic: OTC forwards, which means bilaterally or bilaterally through a broker, and futures through the Power Exchange Central Europe. Each way has its own economic pros and cons. As the most crucial point, a counterparty risk and costs of funding are usually mentioned. Contracts traded on the power exchange bear less or no credit risk, as every deal is paired via central counterparty. On the other hand, the power exchange requires a margin deposit and daily profit and loss settlement which might increase funding costs. The fact that the counterparty risk is lower for exchange contracts with higher funding costs is well-known, but rarely quantified. We use the so-called Credit Value Adjustment concept in order to quantify the market value of the credit risk. We compare this value with potential funding costs. The aim of this paper is to compare both the OTC and exchange ways of trading using risk-adjusted economic characteristics.

At present the majority of advanced countries are dealing with the problem of the ageing of the population. The Czech Republic is no exception. Demographic ageing is caused by the fact that mortality is dropping, especially infant mortality, and this expectation of life at birth. At the same time the birth rate is declining and subsequently total fertility rate drops below the preservation level of simple reproduction, which means that there are less children and more persons in particular in the older and oldest age-groups. It is very important to realise that the changes in the level of mortality bring with them positive impacts in lengthening of life expectancy on the one hand, but on the other hand, there is significant demographic ageing of the population. In this contribution we would like to show how the life expectancy has developed in the Czech Republic in a historical context and how it might develop in the coming years. For professionals the application of the Lee-Carter method will certainly be interesting - this is a method commonly used in the world by demographers and actuaries for modelling the future development of mortality and it is also the basic method used for stochastic demographic projections.

This paper aims to analyze the cost factors that influence the export of the Czech Republic, and to estimate models suitable for quantitative analysis of export and its prediction. According to the macroeconomic theory, the fundamental export factors include foreign demand, domestic and foreign price level and exchange rate. Foreign demand reflects the business cycle of foreign economy, price levels and exchange rate characterize the competitiveness of the exported goods, and the exchange rate determines, among others, the production costs through the prices of imported crucial inputs. Several models are applied to set of these variables, and their impact on the export dynamics of the Czech Republic is evaluated.

In this paper, we focus on thorough yield curve modelling. We build on extended classical NelsonSiegel model, which we further develop to accommodate unobserved regional common factors. We centre our discussion on Central European currencies' yield curves: CZK, HUF, PLN and SKK. We propose a model to capture regional dynamics purely based on state space formulation. The contribution of this paper is twofold: we examine regional yield curve dynamics and we quantify regional interdependencies amongst considered currencies' yield curves. We conclude that the CZK yield curve possesses its own dynamics corresponding to country specific features, whereas other currencies' yield curves are strongly influenced by the regional level, the regional slope factor or both.

In this paper we discuss the estimation and methodology of the real equilibrium exchange rate partial equilibrium models and analyse to what extent the resulting estimates are applicable for setting the central parity prior to ERM II entry in the New EU Member States. Given the uncertainty surrounding the estimates, we argue that they are informative in the sign rather than the size of the misalignment of the exchange rate, but may still serve as useful consistency checks for the decision on the setting of the central parity. We argue that policy makers should consider the estimates in their decision-making only if the real exchange rate is substantially misaligned.