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1

ANALYSIS OF CALENDAR EFFECTS IN MARKETS OF PRECIOUS METALS

100%

Krawiec M., Górska A.

Metody Ilościowe w Badaniach Ekonomicznych

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2014

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vol. 15

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issue 2

392-402

EN

Calendar effects are anomalies in the behavior of asset prices that may disprove the efficient market hypothesis. The well recognized are: day-of-the-week effect, month-of-the-year effect, holidays effect and turn-of-the-month effect. These anomalies are observed in many financial markets, most often on stock exchanges, thus studies on calendar effects usually focus on stock markets. However, the aim of the paper is searching for the anomalies in precious metals markets (the empirical data covers London daily spot prices from 2008 through 2013). This is the continuation of authors’ prior research aimed at testing weak market efficiency hypothesis for precious metals markets.

2

Can Google Trends affect sentiment of individual investors? The case of the United States

100%

Rutkowska A., Kliber A.

Mathematical Economics

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2018

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issue 14(21)

51-70

EN

As the empirical studies show, investor sentiment is a significant factor in financial markets. The large-scale development of the technology has led to widespread access to information in real time (also to individual investors), which in turn has also led to the inflow of Big Data to market analysis. One of the sources of such data is the ability to track the phrases searched for in the web search engines. In our research we verify whether investor sentiment is affected by, among others, a daily Google keyword search called “Google Trends”. We consider measures of US investors’ sentiment calculated from survey studies – the AAII index. We investigate changes of sentiment and its volatility, which can be interpreted as nervousness of the market participants. We estimate a set of GARCH models with explanatory variables in conditional mean and variance. We confirm that negative keyword searches are connected with the decline of the investor confidence. The overall effect of a negative search is stronger than positive. Older searches have a weaker influence on investor sentiment than new ones – no lagged search proved to be significant.

3

BOOSTING UNDER QUANTILE REGRESSION – CAN WE USE IT FOR MARKET RISK EVALUATION?

100%

Bień-Barkowska K.

Metody Ilościowe w Badaniach Ekonomicznych

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2014

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vol. 15

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issue 1

7-17

XX

We consider boosting, i.e. one of popular statistical machine-learning meta-algorithms, as a possible tool for combining individual volatility estimates under a quantile regression (QR) framework. Short empirical exercise is carried out for the S&P500 daily return series in the period of 2004-2009. Our initial findings show that this novel approach is very promising and the in-sample goodness-of-fit of the QR model is very good. However much further research should be conducted as far as the out-of-sample quality of conditional quantile predictions is concerned.

4

On detection of homogeneous segments of observations in financial time series

100%

Sarnowski W., Wrocław University of Technology I. o. M. a. I.

Acta Universitatis Lodziensis. Folia Oeconomica

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2008

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vol. 216

EN

The aim of this article is to present financial data modelling in presence of stochastic disorders. Change-point analysis is applied. We adapt universal method of change-point detection for disorder in parameters of GARCH processes. A comparison of the model fitted to whole sample with models built on homogenous data subset is made.

PL

Praca podejmuje zagadnienie modelowania finansowych szeregów czasowych w obecności rozregulowań struktury probabilistycznej. Zmiany wykrywane są za pomocą uniwersalnej metody detekcji zaadaptowanej do wykrywania rozregulowań w parametrach procesów typu GARCH. Przeprowadzona została statystyczna analiza jakości modeli uwzględniających wykryte zaburzenia z modelami, które zakładają iż ciąg danych ma jednorodną strukturę probabilistyczną.

5

An Empirical Evaluation of GARCH Models in Value-at-Risk Estimation: Evidence from the Macedonian Stock Exchange

88%

Bucevska V.

Business Systems Research Journal

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2013

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vol. 4

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issue 1

49-64

EN

Background: In light of the latest global financial crisis and the ongoing sovereign debt crisis, accurate measuring of market losses has become a very current issue. One of the most popular risk measures is Value-at-Risk (VaR). Objectives: Our paper has two main purposes. The first is to test the relative performance of selected GARCH-type models in terms of their ability of delivering volatility estimates. The second one is to contribute to extend the very scarce empirical research on VaR estimation in emerging financial markets. Methods/Approach: Using the daily returns of the Macedonian stock exchange index-MBI 10, we have tested the performance of the symmetric GARCH (1,1) and the GARCH-M model as well as of the asymmetric EGARCH (1,1) model, the GARCH-GJR model and the APARCH (1,1) model with different residual distributions. Results: The most adequate GARCH family models for estimating volatility in the Macedonian stock market are the asymmetric EGARCH model with Student’s t-distribution, the EGARCH model with normal distribution and the GARCH-GJR model. Conclusion: The econometric estimation of VaR is related to the chosen GARCH model. The obtained findings bear important implications regarding VaR estimation in turbulent times that have to be addressed by investors in emerging capital markets

6

Arch Effects in Multifactor Market-Timing Models of Polish Mutual Funds

75%

Olbryś J.

Folia Oeconomica Stetinensia

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2012

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vol. 10

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issue 2

60-80

EN

Performance measurement of investment managers is a topic of interest to practitioners and academics alike. The traditional performance evaluation literature has attempted to distinguish stock-picking ability (selectivity) from the ability to predict overall market returns (market-timing). However, the literature finds that it is not easy to separate ability into two such dichotomous categories. To overcome these problems multifactor alternative market-timing models have been proposed. The author's recent research provides evidence of strong ARCH effects in the market-timing models of Polish equity open-end mutual funds. For this reason, the main goal of this paper is to present the regression results of the new GARCH(p, q) versions of market-timing models of these funds. We estimate multifactor extensions of classical market-timing models with Fama & French's spread variables SMB and HML, and Carhart's momentum factor WML. We also include lagged values of the market factor as an additional independent variable in the regressions of the models because of the pronounced "Fisher effect" in the case of the main Warsaw Stock Exchange indexes. The market-timing and selectivity abilities of fund managers are evaluated for the period January 2003-December 2010. Our findings suggest that the GARCH(p, q) model is suitable for such applications.

7

Income tax: a comparison of the forecasting methodologies

75%

Pavlik M.

Econometrics. Ekonometria. Advances in Applied Data Analytics

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2013

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issue 3(41)

104-112

EN

The author focuses on income tax forecasting. He compares the different forecasting methodologies. He also compares his solution to the official annual forecasts in the Slovak Republic. He chose the quarters of years as the time units.

8

Can Lognormal, Weibull or Gamma Distributions Improve the EWS-GARCH Value-at-Risk Forecasts?

63%

Chlebus M.

Przegląd Statystyczny

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2016

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vol. 63

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issue 3

329-350

PL

W badaniu analizie poddane zostały dwustopniowe modele EWS-GARCH służące do prognozowania wartości narażonej na ryzyko. W ramach analizy rozpatrywane były modele EWS-GARCH zakładające rozkłady lognormalny, Weibulla oraz Gamma w stanie turbulencji oraz modele GARCH(1,1) i GARCH(1,1) z poprawką na rozkład empiryczny w stanie spokoju. Ocena jakości prognoz Value-at-Risk uzyskanych na podstawie wspomnianych modeli została przeprowadzona na podstawie miar adekwatności (wskaźnik przekroczeń, test Kupca, test Christoffersena, test asymptotyczny bezwarunkowego pokrycia oraz kryteria backtestingu określone przez Komitet Bazylejski) oraz analizy funkcji strat (kwadratowa funkcja straty Lopeza, absolutna funkcja straty Abad i Benito, 3 wersja funkcji straty Caporina oraz funkcja nadmiernych kosztów). Uzyskane wyniki wskazują, że modele EWS-GARCH z rozkładem lognormalnym, Weibulla lub Gamma mogą konkurować z modelami EWS-GARCH z rozkładem wykładniczym lub empirycznym. Modele EWS-GARCH z rozkładem lognormalnym, Weibulla lub Gamma są nieco mniej konserwatywne, jednocześnie jednak koszt ich stosowania jest mniejszy niż modeli EWS-GARCH z rozkładem wykładniczym lub empirycznym.

EN

In the study, two-step EWS-GARCH models to forecast Value-at-Risk are analysed. The following models were considered: the EWS-GARCH models with lognormal, Weibull or Gamma distributions as a distributions in a state of turbulence, and with GARCH(1,1) or GARCH(1,1) with the amendment to empirical distribution of random error models as models used in a state of tranquillity. The evaluation of the quality of the Value-at-Risk forecasts was based on the Value-at-Risk forecasts adequacy (the excess ratio, the Kupiec test, the Christoffersen test, the asymptotic test of unconditional coverage and the backtesting criteria defined by the Basel Committee) and the analysis of loss functions (the Lopez quadratic loss function, the Abad & Benito absolute loss function, the 3rd version of Caporin loss function and the function of excessive costs). Obtained results show that the EWSGARCH models with lognormal, Weibull or Gamma distributions may compete with EWS-GARCH models with exponential and empirical distributions. The EWS-GARCH model with lognormal, Weibull or Gamma distributions are relatively less conservative, but using them is less expensive than using the other EWS-GARCH models.

9

Zależności pomiędzy kursami walut środkowoeuropejskich w okresie kryzysu 2008

63%

Kliber A., Kliber P.

Przegląd Statystyczny

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2010

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vol. 57

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issue 1

3-16

EN

In the paper we try to analyze the interrelations between currencies in Central Europe during the financial crisis in 2008. In order to find out the transition mechanism of the crisis we estimate the jumps (i.e. sudden changes) in exchange rates of four currencies of the region: Polish Zloty, Hungarian Forint, Czech Crown and Slovakian Crown. We use the obtained moments of jumps as dummy variables in GARCH models for exchange rates. Then we also estimate co-jumps for pairs of analyzed currencies to check how much of the volatility is due to the common jumps. The results suggest that sudden jumps in any currency causes the changes in levels of other currencies (although not in volatility of other currencies) and that the common jumps in Polish zloty and Hungarian forint had the greatest influence.

PL

W artykule zajmujemy się analizą powiązań walut Europy Środkowej w okresie kryzysu z końca roku 2008. Staramy się odpowiedzieć na pytanie o mechanizmy przenoszenia tego kryzysu. W tym celu wyznaczamy skoki – gwałtowne zmiany kursów – dla czterech walut regionu: polskiego złotego, węgierskiego forinta, czeskiej korony i korony słowackiej. Otrzymane momenty skoków wykorzystujemy przy opisie zmienności kursów modelami GARCH. Następnie estymujemy wspólne skoki dla par walut i sprawdzamy, jaka część zmienności kursów jest przez nie spowodowana. Otrzymane wyniki sugerują, że gwałtowne zmiany kursu jednej waluty miały wpływ na poziom kursów innych walut (ale już nie zawsze na ich zmienność) oraz, że największy wpływ miały tu wspólne skoki polskiego złotego i węgierskiego forinta.

10

An approach to measuring the relation between risk and return. Bayesian analysis for WIG data

63%

Pipień M.

Folia Oeconomica Cracoviensia

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2007

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vol. 48

95-117

EN

The main goal of this paper is an application of Bayesian inference in testing the relation between risk and return of the financial time series. On the basis of the Intertemporal CAl’M model, proposed by Merton (1973), we built a general sampling model suitable in analysing such relationship. The most important feature of our model assumptions is that the possible skewness of conditional distribution of returns is used as an alternative source of relation between risk and return. Thus, pure statistical feature of the sampling model is equipped with economic interpretation. This general specification relates to GARCH-In-Mean model proposed by Osiewalski and Pipień (2000). In order to make conditional distribution of financial returns skewed we considered a constructive approach based on the inverse probability integral transformation. In particular, we apply the hidden truncation mechanism, two approaches based on the inverse scale factors in the positive and the negative orthant, order statistics concept, Beta distribution transformation, Bernstein density transformation and the method recently proposed by Ferreira and Steel (2006). Based on the daily excess returns of WIG index we checked the total impact of conditional skewness assumption on the relation between return and risk on the Warsaw Stock Market. Posterior inference about skewness mechanisms confirmed positive and decisively significant relationship between expected return and risk. The greatest data support, as measured by the posterior probability value, receives model with conditional skewness based on the Beta distribution transform ation with two free parameters.

11

Wpływ instrumentu quantitative easing Rezerwy Federalnej na wybrane kursy walutowe

54%

Kołodziejczyk H.

Ruch Prawniczy, Ekonomiczny i Socjologiczny

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2014

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vol. 76

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issue 4

261-277

EN

This paper examines the impact of implementing Large Scale Asset Purchases by the Federal Reserve on selected exchange rates, using statistical and econometrical methods, including GARCH models. There is limited statistical evidence suggesting that the increase of asset purchases is a significant factor in explaining exchange rate returns of Australian dollar, Brazilian real, Canadian dollar, Indian rupee and Japanese yen. Evidence also suggests that during the first and second phase of quantitative easing foreign currencies have strengthened due to Federal Reserves’ asset purchase program.

PL

Przedstawiona w niniejszym opracowaniu analiza ma na celu zbadanie wpływu zastosowania przez Rezerwę Federalną (FED) niestandardowych instrumentów z zakresu quantitative easing (QE) na kursy wybranych walut. W szczególności poszczególne fazy działań Rezerwy Federalnej, znane powszechnie jako QE1, QE2 i QE3, porównano pod kątem wywoływania efektu prowadzącego do osłabienia wartości dolara amerykańskiego i aprecjacji walut obcych w wyniku zastosowanego na dużą skalę zasilenia rynków finansowych. Przeprowadzona analiza umożliwi prowadzenie dalszych badań nad efektami podejmowanych przez banki centralne działań antykryzysowych. Na podstawie dokonanych analiz autorka wysuwa wniosek o statystycznie istotnym wpływie działań podjętych przez Rezerwę Federalną na kursy walut, w szczególności Australii i Brazylii. Zmiany stanu bilansowego aktywów stanowiły bowiem przyczynę w sensie Grangera dla kształtowania się zmian kursu dolara australijskiego i reala brazylijskiego w okresie od momentu wdrożenia programu zakupu papierów wartościowych. Sformułowane na podstawie tej obserwacji modele VAR pozwalają wyciągnąć wniosek o spadku wartości dolara amerykańskiego w wyniku działań z zakresu quantitative easing. Zależność ta nie została potwierdzona w odniesieniu do pozostałych walut.

Refine search results

ANALYSIS OF CALENDAR EFFECTS IN MARKETS OF PRECIOUS METALS

Can Google Trends affect sentiment of individual investors? The case of the United States

BOOSTING UNDER QUANTILE REGRESSION – CAN WE USE IT FOR MARKET RISK EVALUATION?

On detection of homogeneous segments of observations in financial time series

An Empirical Evaluation of GARCH Models in Value-at-Risk Estimation: Evidence from the Macedonian Stock Exchange

Arch Effects in Multifactor Market-Timing Models of Polish Mutual Funds

Income tax: a comparison of the forecasting methodologies

Can Lognormal, Weibull or Gamma Distributions Improve the EWS-GARCH Value-at-Risk Forecasts?

Zależności pomiędzy kursami walut środkowoeuropejskich w okresie kryzysu 2008

An approach to measuring the relation between risk and return. Bayesian analysis for WIG data

Wpływ instrumentu quantitative easing Rezerwy Federalnej na wybrane kursy walutowe