PRACE NAUKOWE Uniwersytetu Ekonomicznego we Wrocławiu

PRACE NAUKOWE Uniwersytetu Ekonomicznego we Wrocławiu RESEARCH PAPERS of Wrocław University of Economics Nr 381 Financial Investments and Insurance Global Trends and the Polish Market edited by Krzysztof Jajuga Wanda Ronka-Chmielowiec Publishing House of Wrocław University of Economics Wrocław 2015

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Contents Introduction... 9 Roman Asyngier: The effect of reverse stock split on the Warsaw Stock Exchange... 11 Monika Banaszewska: Foreign investors on the Polish Treasury bond market in the years 2007-2013... 26 Katarzyna Byrka-Kita, Mateusz Czerwiński: Large block trades and private benefits of control on Polish capital market... 36 Ewa Dziwok: Value of skills in fixed income investments... 50 Łukasz Feldman: Household risk management techniques in an intertemporal consumption model... 59 Jerzy Gwizdała: Equity Release Schemes on selected housing loan markets across the world... 72 Magdalena Homa: Mathematical reserves in insurance with equity fund versus a real value of a reference portfolio... 86 Monika Kaczała, Dorota Wiśniewska: Risks in the farms in Poland and their financing research findings... 98 Yury Y. Karaleu: Slice-Of-Life customization of bankruptcy models: Belarusian experience and future development... 115 Patrycja Kowalczyk-Rólczyńska: Equity release products as a form of pension security... 132 Dominik Krężołek: Volatility and risk models on the metal market... 142 Bożena Kunz: The scope of disclosures of fair value measurement methods of financial instruments in financial statements of banks listed on the Warsaw Stock Exchange... 158 Szymon Kwiatkowski: Venture debt financial instruments and investment risk of an early stage fund... 177 Katarzyna Łęczycka: Accuracy evaluation of modeling the volatility of VIX using GARCH model... 185 Ewa Majerowska: Decision-making process: technical analysis versus financial modelling... 199 Agnieszka Majewska: The formula of exercise price in employee stock options testing of the proposed approach... 211 Sebastian Majewski: The efficiency of the football betting market in Poland. 222 Marta Małecka: Spectral density tests in VaR failure correlation analysis... 235

6 Contents Adam Marszk: Stock markets in BRIC: development levels and macroeconomic implications... 250 Aleksander R. Mercik: Counterparty credit risk in derivatives... 264 Josef Novotný: Possibilities for stock market investment using psychological analysis... 275 Krzysztof Piasecki: Discounting under impact of temporal risk aversion a case of discrete time... 289 Aleksandra Pieloch-Babiarz: Dividend initiation as a signal of subsequent earnings performance Warsaw trading floor evidence... 299 Radosław Pietrzyk, Paweł Rokita: On a concept of household financial plan optimization model... 314 Agnieszka Przybylska-Mazur: Selected methods of the determination of core inflation... 334 Andrzej Rutkowski: The profitability of acquiring companies listed on the Warsaw Stock Exchange... 346 Dorota Skała: Striving towards the mean? Income smoothing dynamics in small Polish banks... 364 Piotr Staszkiewicz, Lucia Staszkiewicz: HFT s potential of investment companies... 376 Dorota Szczygieł: Application of three-dimensional copula functions in the analysis of dependence structure between exchange rates... 390 Aleksandra Szpulak: A concept of an integrative working capital management in line with wealth maximization criterion... 405 Magdalena Walczak-Gańko: Comparative analysis of exchange traded products markets in the Czech Republic, Hungary and Poland... 426 Stanisław Wanat, Monika Papież, Sławomir Śmiech: Causality in distribution between European stock markets and commodity prices: using independence test based on the empirical copula... 439 Krystyna Waszak: The key success factors of investing in shopping malls on the example of Polish commercial real estate market... 455 Ewa Widz: Single stock futures quotations as a forecasting tool for stock prices... 469 Tadeusz Winkler-Drews: Contrarian strategy risks on the Warsaw Stock Exchange... 483 Marta Wiśniewska: EUR/USD high frequency trading: investment performance... 496 Agnieszka Wojtasiak-Terech: Risk identification and assessment guidelines for public sector in Poland... 510 Ewa Wycinka: Time to default analysis in personal credit scoring... 527 Justyna Zabawa, Magdalena Bywalec: Analysis of the financial position of the banking sector of the European Union member states in the period 2007 2013... 537

Contents 7 Streszczenia Roman Asyngier: Efekt resplitu na Giełdzie Papierów Wartościowych w Warszawie... 25 Monika Banaszewska: Inwestorzy zagraniczni na polskim rynku obligacji skarbowych w latach 2007 2013... 35 Katarzyna Byrka-Kita, Mateusz Czerwiński: Transakcje dotyczące znaczących pakietów akcji a prywatne korzyści z tytułu kontroli na polskim rynku kapitałowym... 49 Ewa Dziwok: Ocena umiejętności inwestycyjnych dla portfela o stałym dochodzie... 58 Łukasz Feldman: Zarządzanie ryzykiem w gospodarstwach domowych z wykorzystaniem międzyokresowego modelu konsumpcji... 71 Jerzy Gwizdała: Odwrócony kredyt hipoteczny na wybranych światowych rynkach kredytów mieszkaniowych... 85 Magdalena Homa: Rezerwy matematyczne składek UFK a rzeczywista wartość portfela referencyjnego... 97 Monika Kaczała, Dorota Wiśniewska: Zagrożenia w gospodarstwach rolnych w Polsce i finansowanie ich skutków wyniki badań... 114 Yury Y. Karaleu: Podejście Slice-Of-Life do dostosowania modeli upadłościowych na Białorusi... 131 Patrycja Kowalczyk-Rólczyńska: Produkty typu equity release jako forma zabezpieczenia emerytalnego... 140 Dominik Krężołek: Wybrane modele zmienności i ryzyka na przykładzie rynku metali... 156 Bożena Kunz: Zakres ujawnianych informacji w ramach metod wyceny wartości godziwej instrumentów finansowych w sprawozdaniach finansowych banków notowanych na GPW... 175 Szymon Kwiatkowski: Venture debt instrumenty finansowe i ryzyko inwestycyjne funduszy finansujących wczesną fazę rozwoju przedsiębiorstw... 184 Katarzyna Łęczycka: Ocena dokładności modelowania zmienności indeksu VIX z zastosowaniem modelu GARCH... 198 Ewa Majerowska: Podejmowanie decyzji inwestycyjnych: analiza techniczna a modelowanie procesów finansowych... 209 Agnieszka Majewska: Formuła ceny wykonania w opcjach menedżerskich testowanie proponowanego podejścia... 221 Sebastian Majewski: Efektywność informacyjna piłkarskiego rynku bukmacherskiego w Polsce... 234 Marta Małecka: Testy gęstości spektralnej w analizie korelacji przekroczeń VaR... 249 Adam Marszk: Rynki akcji krajów BRIC: poziom rozwoju i znaczenie makroekonomiczne... 263

8 Contents Aleksander R. Mercik: Ryzyko niewypłacalności kontrahenta na rynku instrumentów pochodnych... 274 Josef Novotný: Wykorzystanie analizy psychologicznej w inwestycjach na rynku akcji... 288 Krzysztof Piasecki: Dyskontowanie pod wpływem awersji do ryzyka terminu przypadek czasu dyskretnego... 298 Aleksandra Pieloch-Babiarz: Inicjacja wypłaty dywidend jako sygnał przyszłych dochodów spółek notowanych na warszawskim parkiecie... 313 Radosław Pietrzyk, Paweł Rokita: Koncepcja modelu optymalizacji planu finansowego gospodarstwa domowego... 333 Agnieszka Przybylska-Mazur: Wybrane metody wyznaczania inflacji bazowej... 345 Andrzej Rutkowski: Rentowność spółek przejmujących notowanych na Giełdzie Papierów Wartościowych w Warszawie... 363 Dorota Skała: Wyrównywanie do średniej? Dynamika wygładzania dochodów w małych polskich bankach... 375 Piotr Staszkiewicz, Lucia Staszkiewicz: Potencjał handlu algorytmicznego firm inwestycyjnych... 389 Dorota Szczygieł: Zastosowanie trójwymiarowych funkcji copula w analizie zależności między kursami walutowymi... 404 Aleksandra Szpulak: Koncepcja zintegrowanego zarządzania operacyjnym kapitałem pracującym w warunkach maksymalizacji bogactwa inwestorów. 425 Magdalena Walczak-Gańko: Giełdowe produkty strukturyzowane analiza porównawcza rynków w Czechach, Polsce i na Węgrzech... 438 Stanisław Wanat, Monika Papież, Sławomir Śmiech: Analiza przyczynowości w rozkładzie między europejskimi rynkami akcji a cenami surowców z wykorzystaniem testu niezależności opartym na kopule empirycznej... 454 Krystyna Waszak: Czynniki sukcesu inwestycji w centra handlowe na przykładzie polskiego rynku nieruchomości komercyjnych... 468 Ewa Widz: Notowania kontraktów futures na akcje jako prognoza przyszłych cen akcji... 482 Tadeusz Winkler-Drews: Ryzyko strategii contrarian na GPW w Warszawie... 495 Marta Wiśniewska: EUR/USD transakcje wysokiej częstotliwości: wyniki inwestycyjne... 509 Agnieszka Wojtasiak-Terech: Identyfikacja i ocena ryzyka wytyczne dla sektora publicznego w Polsce... 526 Ewa Wycinka: Zastosowanie analizy historii zdarzeń w skoringu kredytów udzielanych osobom fizycznym... 536 Justyna Zabawa, Magdalena Bywalec: Analiza sytuacji finansowej sektora bankowego krajów Unii Europejskiej w latach 2007 2013... 552

PRACE NAUKOWE UNIWERSYTETU EKONOMICZNEGO WE WROCŁAWIU nr 207 RESEARCH PAPERS OF WROCŁAW UNIVERSITY OF ECONOMICS nr 381 2015 Financial Investment and Insurance ISSN 1899-3192 Global Trends and the Polish Market e-issn 2392-0041 Ewa Wycinka University of Gdańsk e-mail: ewa.wycinka@ug.edu.pl TIME TO DEFAULT ANALYSIS IN PERSONAL CREDIT SCORING Summary: Credit scoring is a technique mainly used in making consumer credit decisions. Traditional credit scoring systems aim to estimate the probability that an applicant will default. However, for the financial institution, not only if but also when the creditor defaults is important. The aim of this paper is to analyse the usefulness of survival methods, especially the Kaplan-Meier estimator, in the process of making decisions of credit granting, as well as in monitoring credit portfolios. The focus is put on the differences in the influence of particular characteristics used in scoring models on the probability of default and the probability of early repayment during credit life-span. The survival analysis approaches to credit scoring were tested on 60-month personal loan data from one of the Polish financial institutions. All loans were observed until 24 months after inception, or until default or earlier repayment if one of these happened earlier. In the first part of analysis, early full repayments qualified as censored data, and in the second as failures. Keywords: Credit scoring, survival analysis, Kaplan-Meier estimator. DOI: 10.15611/pn.2015.381.38` 1. Introduction Credit scoring is a technique mainly used in making consumer credit decisions. Traditional credit scoring systems aim to estimate the probability that an applicant will default. However, for the financial institution it is important to consider not only if but also when the creditor defaults. With an increasing number of repaid instalments the loss of the lender decreases. If the time of default is long, the acquired interest will compensate or even exceed the value of credit [Stepanova, Thomas 2002]. Most creditors fully repay the loan, so default will never occur for them. Such an approach to credit risk has a lot in common with survival analysis. In survival analysis, interest centres on a group of individuals for each of whom there is a defined point, an event called failure, occurring after a length of time, which is called the failure time. Failure can occur once at most for any individual [Cox, Oakes 1984]. Narain [1992] first introduced survival analysis methods to credit scoring. He applied a proportional hazard model to loan data. Since then, several research studies

528 Ewa Wycinka have been carried out on this topic. Banasik, Crook and Thomas [1999] applied three types of proportional hazard models and accelerated life models to loan data and compared the results with regression scorecard approaches. They also considered competing risk approaches to loans, assuming that there are two reasons for repayment schedule interruption: defaults and early repayments. Stepanova and Thomas [2001] explored different applications of proportional hazard models to behavioural scoring. They used survival probability profiles of customers to calculate the expected profit from a loan. In an article published in 2002, these authors focused on competing risk issues. They proposed the method of coarse classifying variables with the use of Cox s proportional hazard models with binary variables. They proved that this method is more appropriate when using survival analysis modelling than traditional approaches. They also underlined the necessity of separate splits for every type of failure considered [Stepanova, Thomas 2002]. Mavri et al. [2008] suggested a two-stage dynamic credit scoring model; while Cao, Vilar and Devia [2009] applied GLM under censoring and a nonparametric kernel method to estimate the default probability. A review of the improvements in credit modelling techniques was offered in [Thomas, Oliver, Hand 2005] and [Marques, Garcia, Sanchez 2013]. The aim of this paper is to analyse the usefulness of the survival method, especially the Kaplan-Meier estimator of survival function, in the process of making decisions about credit granting, as well as in monitoring of credit portfolios. The focus is on the differences in the influence of particular characteristics used in scoring models on the probability of default and early repayment during credit life-span. 2. Theory of analysis of lifetime data Let T be the random variable representing time until failure. The cause of failure could be default or early repayment. In the first part of the analysis, time is estimated until default, assuming time until early repayment is censored. In the second part, time until early repayment is a failure, while time until default is censored. Therefore, survival analysis will be performed separately on both types of failures. Time-to-event is described by a survival function, the probability of an entity surviving beyond time t. It is defined as S ( t) = P( T > t). When T is a continuous random variable, the survival function is a continuous, strictly decreasing function [Klein, Moeschberger 1997, p. 22]. The standard estimator of the survival function is the product limit estimator proposed by Kaplan and Meier [1958]. It is defined as 1 t < t1 S ˆ( t) = di (1 ) ti t li, t > t 1

Time to default analysis in personal credit scoring 529 where: d i a number of events at time t i ; l i a number at risk at time t i [Klein, Moeschberger 1997, p. 84]. The graphical presentation of the KM estimator is a step curve that starts with a horizontal line at a survival probability of 1, and then steps down to the other survival probabilities to follow ordered failure times. The KM estimator is based on an assumption of non-informative censoring, which means that knowledge of a censoring time for an individual provides no further information about this entity s survival at a future time, should the individual continue the study [Klein, Moeschberger 1997, p. 91]. To determine whether there is a significant difference between two or more survival curves, one can test the hypothesis. The hypothesis tests on the equality of Kaplan-Meier curves can be conducted using one of the several available statistical tests designed for this purpose, but the most commonly used is a log-rank test [Suciu, Lemeshow, Moeschberger 2004, p. 252]. The null hypothesis is that all survival curves are the same. If the number of groups (k) being compared is more than two, the log-rank statistic has asymptotic chi-square distribution with k 1 degrees of freedom. The mathematical formula of the test statistic can be found in [Kleinbaum, Klein 2005, p. 82]. The computer package used in empirical analysis uses Mantel s procedure to calculate test statistics. First, a score is assigned to each survival time; next, a chi-square value is computed based on the sums (for each group) of this score. If only two groups are specified, then this test is equivalent to Gehan s generalized Wilcoxon test. The characteristics of this test can be found inter alia in [Jurkiewicz, Wycinka 2011, p. 107 114]. 3. Data set The survival analysis approaches to credit scoring were tested on 60-month personal loan data from one of the Polish financial institutions. The data consisted of application information for 5000 loans accepted in the period of the following six months. All loans were observed for 24 months or until failure. The failure was default or earlier repayment. The default was defined as 90 days lateness in payment of instalments. The initial characteristics included information about the creditor such as, age, marital status and residency type, as well as about the loan: the purpose of the loan and its capacity. Altogether, 15 characteristics have been taken into account. They have been coded by letters: X 1,, X 15. 1 In the first step, the application 1 Due to the know-how of the financial institution that shared data for the purposes of this research, detailed information about the creditors and characteristics used in analysis was encrypted.

530 Ewa Wycinka characteristics were split into attributes. 2 The continuous variables were subdivided into fractiles. Then K-M curves were plotted for each fractile. Groups with the most similar curves were connected. In categorical variables, attributes with few units were joined to similar ones. Then, on the basis of the shape of survival curves, the number of groups was reduced by putting together those with similar curves. An example of such a procedure for variable X 12 is presented in Figures 1 and 2. Figure 1 shows K-M curves for 10 groups of values created after connecting small attributes with similar ones. Figure 1. Kaplan-Meier curves for initial groups of X 12 variable Source: own elaboration. On the basis of the shape of the survival curves in Figure 1, groups 1, 9 and 10 were connected into one new group, and the rest of the groups were connected to the second group. Finally, there were two groups with significantly different survival curves (Figure 2). The second group is at high risk of default and the first group is at low risk. 2 Classic methods of coarse classification of the characteristics are described in [Thomas, Edelman, Crook 2002]. In this article, the author proposed their own method of classification based on K-M estimators.

Time to default analysis in personal credit scoring 531 Figure 2. Kaplan-Meier curves for ultimate groups of X 12 variable Source: own elaboration. 4. Default and early repayment as failures In the data set, there were 2274 creditors (45.5%) who repaid all 24 instalments and were active creditors; 297 creditors (5.9%) who defaulted during the first 24 months; and 2429 creditors (48.58%) who repaid the credit earlier during this time. The default and early repayment were treated as failures and analysed separately. In the first part of the analysis, the default was treated as a failure and the early repayments as censored data. In the second part, the early repayments were considered failures and the defaults were considered censored observations. Time to default was treated as a non-negative continuous variable. Its distribution was described by a survival function. The product limit estimator (Kaplan-Meier estimator) was used to estimate the probability of not defaulting until a particular time (Figure 3). Figure 3 shows that the risk of default increases in the last months of observed time (increasing steps of the curve). The survival function of the early repayment risk is shown in Figure 4.

532 Ewa Wycinka Figure 3. Kaplan-Meier curves for the risk of default Source: own elaboration. Figure 4. Kaplan-Meier curves for risk of early repayment Source: own elaboration. The risk of the repayment seems to be higher in the first months of the credit span.

Time to default analysis in personal credit scoring 533 K-M curves were also drawn for creditors with different characteristics. 3 With the use of significance tests for survival curves, the differences in the influence of these characteristics on the probability of default in the following months of the credit span can be shown (Table 1). Table 1. The application characteristics and the significance of their predictive power on the risk of default and early repayment A Default as a failure Early repayment as a failure number Vari A A A A of able group group group group atribute chisquare/z chi- s of the of the square/z * p-value of the of the highest lowest highest lowest risk risk risk risk X 1 4 11.897 0.0078 1 3 4.413 0.2202 1, 2, 3 4 X 2 4 19.230 0.0003 1 3 11.509 0.0093 1 4 X 3 4 21.057 0.0001 4 3 20.355 0.0001 3 4 X 4 6 159.650 < 0.0001 2 6 29.221 < 0.0001 4 1 X 5 5 48.700 < 0.0001 1 4 14.325 0.0063 2 1 X 6 3 40.460 < 0.0001 2 1 15.157 0.0005 3 1 X 7 3 5.870 0.0531 1 2 6.311 0.0426 3 1 X 8 2 1.855 0.0636 1 2 0.685 0.4930 2 1 X 9 4 18.343 0.0004 2 4 13.873 0.0031 4 2 X 10 4 17.476 0.0002 1 3 7.885 0.0485 1 3 X 11 2 2.940 0.0033 1 2 3.477 0.0005 2 1 X 12 2 5.560 < 0.0001 1 2 0.691 0.4897 1 2 X 13 2 4.258 < 0.0001 1 2 0.534 0.5937 2 1 X 14 2 2.156 0.0311 1 2 0.289 0.7792 2 1 X 15 2 1.639 0.1012 1 2 3.018 0.0025 1 2 * Log-rank test (chi-square) for variables with more than two attributes and Gehan test (z) for dichotomous variables. Source: own elaboration. Twelve of the variables were significant predictors of default (at significance level 0.05). The numbers of attributes with the lowest and highest risks are given in Table 1. The right-hand part of the table includes significance tests for survival curves drawn for early repayment as a failure. The attributes of the variables were the same as in the first part of the analysis, which allowed the comparison of the results. Only 10 of the variables proved to be significant predictors of early repayment. It is worth mentioning that not all of these variables were significant predictors of default. For variables X 10 and X 12, the same groups of attributes indicate, respectively, the highest and the lowest risk of default and early repayment. However, for the 3 Due to limited space in this article, only selected curves were included. The values of significance tests for all of the characteristics are presented in Table 1.

534 Ewa Wycinka variables X 3, X 5, X 7, X 8, X 9, X 11, X 13, X 14, the attributes that were the highest risk of default proved to be the lowest risk of early repayment. Moreover, as shown in Figure 5, variables should be split in different ways for predicting the risk of default and early repayment. Figure 5. Kaplan-Meier curves for attributes of X 3 variable showing probability of not defaulting or repaying early, respectively Source: own elaboration. On the left-hand side of the graph, there are four groups with different curves showing the probability of survival until the time of default. For the risk of early repayment (the right-hand side of the graph), groups 1 3 do not differ significantly and should be aggregated. Finally, for the risk of early repayment, there should be only two different groups (attributes). 5. Conclusions and further research Survival analysis techniques as approaches to credit scoring have many advantages in comparison with a classic approach. First of all, in the classic approach, units that cannot be interchangeably classified to the group of good or bad creditors, e.g., early repayments, have to be removed from the sample. In the survival methods, the information about such creditors is included in the analysis. Moreover, it is possible to evaluate the probability of early repayments in the following months and to identify the characteristics of the creditors more likely to repay credit earlier. In some

Time to default analysis in personal credit scoring 535 credit portfolios, the share of such creditors is high, so it has an influence on the profit of the lender. In the empirical analysis of loan data, it was shown that the group of early repaying creditors differs, according to the application characteristics, from the group of creditors who defaulted. The same conclusion was drawn by Stepanova and Thomas [2002] using Cox models in the coarse classification of the creditors. The Kaplan-Meier estimator proved to be an effective tool both in classification of variables and in predicting the probability of not defaulting and repaying early. With the use of the K-M estimator, a lender can evaluate the number of defaults, and with knowledge of characteristics of new clients, he or she can evaluate the number of defaults and early repayments in the consecutive months of a credit span. It is, therefore, an important contribution to profit scoring. Further analysis of these data should include the usage of regression models such as the Cox semi-parametric model and accelerated model. The specific character of the default modelling approach is that there is a high rate of censored data. In the analysed sample, only 5.9% of observations were completed. The robustness of the proposed techniques for such a structure of data should be analysed. Another important issue is the assumption of non-informative censoring. If this assumption is violated, the K-M estimator and abovementioned regression models can be biased. In such a situation, the techniques devoted to competing risks should be applied. The cumulative incidence of an event can better predict the probability of competing risks than the K-M estimator. These problems will be analysed in the author s further research. References Banasik J., Crook J.N., Thomas L.C., 1999, Not If but When Borrowers Default, The Journal of the Operational Research Society, vol. 50, no. 12, p. 1185 1190. Cao R., Vilar M.V., Devia A., 2009, Modelling Consumer Credit Risk via Survival Analysis, Statistics and Operations Research Transactions, vol. 33, no. 1, p. 3 30. Cox D.R., Oakes D., 1984, Analysis of Survival Data, Chapman and Hall, London. Jurkiewicz T., Wycinka E., 2011, Significance tests of differences between two crossing survival curves for small samples, [in:] C. Domański, K. Zielińska-Sitkiewicz (eds)], Methodological Aspects of Multivariate Statistical Analysis, Statistical Models and Applications, Wydawnictwo Uniwersytetu Łódzkiego, Łódź, p. 107 114. Kaplan E.L., Meier P., 1958, Nonparametric Estimation from Incomplete Observations, Journal of the American Statistical Association, vol. 53, no. 282, p. 457 481. Klein J.P., Moeschberger M.L., 1997, Survival Analysis. Techniques for Censored and Truncated Data, Springer-Verlag, New York. Kleinbaum D.G., Klein M., 2005, Survival Analysis: A Self-learning Text, Springer, New York. Mavri M., Angelis V., Ioannou G., Gaki E., Koufodonti I., 2008, A Two-stage Dynamic Credit Scoring Model, Based on Customers Profile and Time Horizon, Journal of Financial Services Marketing, vol. 13, no. 1, p. 17 27. Marques A.I., Garcia V., Sanchez J.S., 2013, A Literature Review on the Application of Evolutionary Computing to Credit Scoring, The Journal of the Operational Research Society, vol. 64, no. 9, pp. 1384 1399.

536 Ewa Wycinka Narain B., 1992, Survival Analysis and the Credit Granting Decision, [in:] L.C. Thomas, J.N. Crook, D.B. Edelman (eds.), Credit Scoring and Credit Control, Oxford University Press, Oxford, p. 109 121. Stepanova M., Thomas L.C., 2001, PHAB Scores: Proportional Hazards Analysis Behavioural Scores, The Journal of the Operational Research Society, vol. 59, no. 9, p. 1007 1016. Stepanova M., Thomas L.C., 2002, Survival Analysis Methods for Personal Loan Data, The Journal of the Operational Research Society, vol. 50, no. 2, p. 277 289. Suciu G., Lemeshow S., Moeschberger M., 2004, Statistical Tests of the Equality of Survival Curves: Reconsidering the Options, [in:] N. Balakrishnan, C.R. Rao (eds.), Advances in Survival Analysis, Elsevier, Amsterdam, p. 251 262. Thomas L.C., Edelman D.B., Crook J.N., 2002, Credit Scoring and Its Applications, SIAM, Philadelphia. Thomas L.C., Oliver R.W., Hand D.J., 2005, A Survey of the Issues in Consumer Credit Modelling Research, The Journal of the Operational Research Society, vol. 56, no. 9, p. 1006 1015. ZASTOSOWANIE ANALIZY HISTORII ZDARZEŃ W SKORINGU KREDYTÓW UDZIELANYCH OSOBOM FIZYCZNYM Streszczenie: Skoring kredytowy jest metodą wykorzystywaną w procesie podejmowania decyzji o udzielenie kredytu. W tradycyjnym podejściu celem modelu skoringowego jest określenie prawdopodobieństwa, że kredytobiorca zaprzestanie spłat kredytu. Dla instytucji kredytowej ważna jest jednak nie tylko informacja, czy kredyt nie zostanie spłacony w całości, ale również to, ile rat zostanie wcześniej zapłaconych. Im później nastąpi przerwanie spłat, tym mniejszą stratę poniesie kredytodawca. Celem artykułu jest zbadanie użyteczności metod analizy przeżycia przy podejmowaniu decyzji o udzielaniu kredytu oraz przy monitorowaniu szkodowości portfela kredytów. Część empiryczną badania przeprowadzono na próbie pięciu tysięcy 60-miesięcznych pożyczek udzielonych przez jedną z polskich instytucji finansowych w ciągu kolejnych sześciu miesięcy. Każda pożyczka była obserwowana przez okres 24 miesięcy od jej udzielenia, chyba że jej spłaty zostały wcześniej przerwane. Jako zaprzestanie spłat przyjęto co najmniej 90-dniowe opóźnienie w spłacie wymagalnej raty. Całkowita wcześniejsza spłata była traktowana jako obserwacja cenzurowana. Czas do zaprzestania spłat kredytu jest nieujemną ciągłą zmienną losową, a jej rozkład został opisany za pomocą funkcji przeżycia. Do oszacowania tej funkcji wykorzystano estymator Kaplana-Meiera. Krzywe estymatorów zostały wyznaczone dla grup pożyczkobiorców wyodrębnionych na podstawie analizowanych charakterystyk. Wykorzystując testy zgodności krzywych przeżycia zidentyfikowano charakterystyki pożyczkobiorców, które istotnie różnicują prawdopodobieństwo spłaty pożyczki. Słowa kluczowe: modelowanie ryzyka kredytowego, analiza przeżycia (analiza historii zdarzeń), czas do zaprzestania spłat kredytu.