A5048259466- Financial Risk and Volatility Modeling
- Market Dynamics and Volatility
- Monetary Policy and Economic Impact
- Complex Systems and Time Series Analysis
- Finance, Markets, and Regulation
- Stochastic processes and financial applications
- finance, banking, and market dynamics
- Accounting Theory and Financial Reporting
- Polish socio-economic development
- Forecasting Techniques and Applications
- Financial Reporting and Valuation Research
- Consumer Market Behavior and Pricing
- Stock Market Forecasting Methods
- Renewable energy and sustainable power systems
- Digital Platforms and Economics
- Consumer Retail Behavior Studies
- Energy Load and Power Forecasting
- Financial Markets and Investment Strategies
- Globalization, Economics, and Policies
- Banking Systems and Strategies
- Insurance and Financial Risk Management
- Global Financial Crisis and Policies
- Intellectual Property Rights and Media
- Environmental and Biological Research in Conflict Zones
- European Monetary and Fiscal Policies
Nicolaus Copernicus University
2012-2023
Prague University of Economics and Business
2020-2023
Instytut Nauk Ekonomicznych
2019
Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds future is both exciting challenging, with individuals organisations seeking to minimise risks maximise utilities. large number forecasting applications calls for a diverse set methods tackle real-life challenges. This article provides non-systematic review theory practice forecasting. We provide an overview wide range theoretical, state-of-the-art models, methods, principles,...
Forecasting cryptocurrency volatility can help investors make better-informed investment decisions in order to minimize risks and maximize potential profits. Accurate forecasting of price fluctuations is crucial for effective portfolio management contributes the stability financial system by identifying threats developing risk strategies. The objective this paper provide a comprehensive study statistical machine learning methods predicting daily weekly following four cryptocurrencies:...
The dynamic conditional correlation (DCC) model by Engle (2002) is one of the most popular multivariate volatility models. This based solely on closing prices. It has been documented in literature that high and low prices a given day can be used to obtain an efficient estimation. We therefore suggest incorporates into DCC framework. conduct empirical evaluation this three datasets: currencies, stocks, commodity exchange traded funds. Regardless whether we consider in-sample fit, covariance...
Research background: The Russian invasion on Ukraine of February 24, 2022 sharply raised the volatility in commodity and financial markets. This had adverse effect accuracy forecasts. scale negative effects war was, however, market-specific some markets exhibited a strong tendency to return usual levels short time. Purpose article: We study shocks caused by war. Our focus is highly exposed this conflict: stock, currency, cryptocurrency, gold, wheat crude oil evaluate forecasting models...
This paper studies the impact of investor attention to oil prices on returns, volatility, and covariances three exchange traded funds representing oil, gold, stock market. For this purpose, we suggest a new multivariate volatility model based open, high, low, closing that incorporates covariances. We find model, which Google searches for "oil prices" as an exogeneous variable, outperforms other considered models, demonstrates can explain forecast between returns
We compare the forecasting performance of generalized autoregressive conditional heteroscedasticity (GARCH) -type models with support vector regression (SVR) for futures contracts selected energy commodities: Crude oil, natural gas, heating gasoil and gasoline. The GARCH are commonly used in volatility analysis, while SVR is one machine learning methods, which have gained attention interest recent years. show that accuracy forecasts depends substantially on applied proxy volatility. Our...
Abstract We introduce a new specification of the dynamic conditional correlation (DCC) model, where its parameters are estimated with use closing and additionally low high prices. Such prices often commonly available for many financial series contain more information about variation returns. construct model range-based estimator variance but importantly also covariance. The latter as consequence proposed DCC require, however, that range portfolio return is given. compare three other...
Abstract 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 can describe the dynamic characteristics of financial processes make more accurate forecasts than other machine learning techniques. The first main contribution this paper to propose methodology modelling forecasting covariance matrices based on support using Cholesky decomposition. procedure applied range-based...
Volatility models based on the daily high-low range have become increasingly popular. The high and low prices are easily available, yet contains very useful information about volatility. It has been established in literature that range-based volatility outperform standard closing prices. However, little is known which model performs best. We therefore evaluate two models, i.e. CARR Range-GARCH with GARCH asymmetric i.e., GJR EGARCH, Monte Carlo experiments a wide sample of currencies stock...
The joint distribution of low, high and closing prices the arithmetic Brownian motion is used to evaluate properties most popular estimators variance constructed on basis high, low prices. expected values mean square errors Parkinson, Garman–Klass Rogers–Satchell for process with a zero drift non‐zero are derived. Moreover, new volatility estimators, more efficient in majority financial applications than estimator, proposed. considered applied estimation Polish stock index WIG20.
Linear and nonlinear Granger causality between three grains: corn, soybean, wheat two livestock commodities: live cattle lean hogs, was verified. Weak evidence of linear causal relationships found, supporting the results published in other studies. However, strong grain returns were which had not yet been documented literature on this subject. The revealed have different patterns features, some cases, they arise from second moment dependencies, but nonlinearities a type also found. Most...
Models for variances and covariances of asset returns are crucial in risk management allocation. Traditionally, these models were based on daily returns. Daily opening, high, low closing (OHLC) prices have been sometimes used multivariate volatility variances, but not correlations. We therefore suggest a new version the Dynamic Conditional Correlation (DCC) model wherein information from OHLC is utilized both variance correlation equations. The evaluated two datasets: five exchange traded...
The paper deals with an analysis of factors influencing the acceptance seven major payment methods (i.e. cash on delivery, bank transfer, online integrator, in person, pay-by-link, card and virtual provider) by Polish shops. Our research was based empirical data obtained from survey interviews conducted managers univariate logit models describing were constructed. A total 45 explanatory variables divided into five categories taken account. results study demonstrated that a shop's strategy...
Abstract In this paper we introduce a new specification of the BEKK model, where its parameters are estimated with use closing and additionally low high prices. an empirical application, show that additional information related to prices in formulation model improved estimation covariance matrix returns increased accuracy variance forecasts based on compared using only. This analysis was performed for following three most heavily traded currency pairs Forex market: EUR/USD, USD/JPY, GBP/USD....
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We combine machine learning tree-based algorithms with the usage of low and high prices suggest a new approach to forecasting currency covariances. apply three algorithms: Random Forest Regression, Gradient Boosting Regression Trees Extreme tree learner. conduct an empirical evaluation this procedure on most heavily traded pairs in Forex market: EUR/USD, USD/JPY GBP/USD. The forecasts covariances formulated applied are predominantly more accurate than Dynamic Conditional Correlation model...
The effectiveness of the monetary policies European Central Bank (ECB) and Narodowy Polski (NBP) is compared directly in terms influencing spread between interbank overnight rate main rates central banks during periods different economic conditions, i.e. global financial crisis 2008, sovereign debt period relative stability. Three categories determinants Euro Overnight Index Average/Polish Average (EONIA/POLONIA) spreads are considered: (1) policy instruments such as open market operations,...
The work deals with an analysis of factors determining the intensity usage payment methods by Polish customers. study covers 3 main used in physical Points-of-Sale: cash, debit cards and credit cards. In order to describe number payments count data models: Poisson regression, negative binomial hurdle model, ZIP ZINB were applied. results obtained revealed a significant effect many demographic factors, as well use financial telecommunication services. A substitution between has been shown....