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İAFL Türkiye 206. sı olarak Samsun 3. sü olarak kazandım.

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Matematik Olimpiyatları sınavına katıldım.

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YTÜ Endüstri Mühendiliği Mezunu oldum.

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Bogaziçi Ünv. Endüstri Mühendisliği Master yaptım.

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ECB'nin o günkü toplantısında faizi neden indirmeyecegini kimseden yardım almadan acıkladım. İki saat sonra ECB faizi sabit tuttugunu acıkladı. Sonraki kısa zamanda ECB'nin Alman başkanı ayrılarak yerine İtalyan başkan seçildi ve faizler dedigim sekilde aşağıya çekildi.

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FED'in faizi neden 0-0,25 bandına indiridiğini kimseden yardım almadan acıkladım.

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USD cinsi Swap fiyatının EUR/USD paritesine baglı olarak nasıl degistigini kimseden yardım almadan acıkladım.

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Browse through the details below on our FX swap rates. Find out what they are and how and when they are calculated with examples.

What are swap rates?

Swap rates are the interest rate differentials embedded in currency trades.

To put it more simply, consider how a forex trade works: you borrow one currency to buy another. For instance, if you are buying EUR/USD, you are borrowing US dollars and buying euros with the proceeds. In doing so, you are paying interest on the US dollars you borrow, but earning interest on the euros you bought.

Where can I find your swap rates?

Our swap rates are visible on all our platforms, by following these simple steps:

MT4

1) Click 'View' along the top menu

2) Select 'Symbols'

3) Select which currency pair you want to see the swaps for, and click 'Properties'

4) You can now see the long and short swap rates in the pop-up window

How do you calculate a swap rate?

Swap debits/credits are calculated as follows:

Current long/short rate * number of lots = swap debit/credit in second currency

Your account currency is AUD and you hold one lot long on AUD/USD on 5 February 2015:

+4.96 * 1 = $.4.96, converted to your account currency = A$6.35

Account currency is USD, you hold 2.5 lots short of EUR/GBP on 5 February 2015:

-1.08 * 2.5 = -£2.70, converted to USD = -$4.13

When do you calculate your swap rates?

Our swap rates are calculated each day at 4.59pm New York time/11.59pm MT4 platform time (GMT+2). Trades that have been opened before 4.59pm and held open past this time will be subject to swap rates. Swap rates are tripled on Wednesday at 4.59pm to account for weekends. Please note that this is the standard structure of swaps - however, on weeks where there are holidays, the swap rate structure may be modified to account for the holiday.

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Browse through the details below on our FX swap rates. Find out what they are and how and when they are calculated with examples. Swap rates are the interest rate differentials embedded in currency trades. To put it more simply, consider how a forex trade works: you borrow one currency to buy another.

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Oxford Üniversitesi tarafından
-Test of Bounded Lognormal Process with IS- isimli makalem yayınlandı.

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Volume 3, Issue 4: ... free online access to latest articlesACRN Journal of Finance and Risk Perspectives

Vol. 3, Issue 4, Dec 2014, ISSN 2305-7394

EDITORIAL BOARD TABLE OF CONTENTS

THE PREDICTIVE POWER OF THE J-CURVE
Cyril Demaria
University of Sankt-Gallen, Switzerland.

Abstract. Dealing with a recurring low level of data quality, we approach the behavior of Private Equity Funds (PEFs) by using illiquidity as a factor of analysis. PEF cash-flows ("J-Curves") are the basis of the research. After identifying aggregated PEF return categories ("ideal-types"), individual J-Curves are compared with the ideal-types. The resulting model acts as a predictor of future performance of PEF, excluding first return categories; and then attributes a fund to a specific category with a certain level of confidence. This model could help reduce solvency costs associated with investing by PEFs, and support the on-going assessment of active PEFs.

Keywords: private equity (PE), venture capital (VC), leveraged buy-out (LBO), cash-flow, solvency ratio, J-curve

A LITERATURE REVIEW ABOUT THE LANDSCAPE OF SOCIAL FINANCE
Christa Hangl
University of Applied Science, Upper Austria

Abstract: The financial and economic crisis, from 2007 on, adversely affected the whole world. Approximately seven million Americans and two million Europeans lost their jobs, and nearly ten million were pushed below the poverty line (Benedikter, 2011). The rebound in the economy from the effects of the crisis will take some time. Consequentially, public welfare spending has been stretched to the limits and more and more social services are on the verge of discontinuation. Besides the public sector, more and more traditional NGOs stemming from the third sector, and an increasing number of social entrepreneurs as hybrid organizations are emerging, tackling these new societal challenges. As a result, social banking and social finance providing means to start and support such initiatives have become important activities in Europe, despite a seemingly under-developed set of regulations and instruments for rational portfolio building.
The efficient allocation of financial resources for primarily social and environmental returns, as well as in some cases, a financial return, is the main focus of social finance. Research on social finance has primarily concentrated on the new institutions, mechanisms and instruments that allow financial resources to be created and directed towards sustainable ideas, initiatives, programs or products. The object is to create social and environmental value (Moore et al, 2012).

Keywords: Social Finance, Social Entrepreneurship, Social Impact Investing, Social Impact Bonds, Social Finance, Microfinance, GIIN

AMBIGUITY IN MARKETS: A TEST IN AN AUSTRALIAN EMISSIONS MARKET
Deborah Cotton 1, Professor David Michayluk 2
1 University of Technology, Sydney; corresponding author
2 University of Technology, Sydney

Abstract: Research suggests that ambiguity not only reduces the desirability to trade but also the overall effectiveness of financial markets. This paper tests the hypothesis that information related to climate change mitigation in Australia reduces the ambiguity surrounding investor participation in Australia's largest emissions trading scheme. This market was chosen due to the high level of ambiguity surrounding government policy and the ability to determine the factors likely to reduce ambiguity. We use government announcements and international and locally significant events as sources of information. From this we find that information does reduce the level of ambiguity, as shown by reduced bid-ask spreads and increased relative trading volume.

Keywords: Ambiguity, Market efficiency, Emissions Trading

TEST OF BOUNDED LOG-NORMAL PROCESS FOR OPTIONS PRICING
Semih Yön 1, Cafer Erhan Bozdağ 1
1 Department of Industrial Engineering, Istanbul Technical University,

Abstract: Bounded stochastic processes may be occupied for modelling the underlying price process in options pricing. Bounds can be included in some shallow markets by ceiling-floor price rule. It is useful to apply such a rule when an increase in the liquidity is needed. Log-normal process brings some bias on the premium of options. It is possible to reduce the bias by adding more parameters like jump diffusion, stochastic volatility or regime switching. As a result closed form solutions and numerical approximations suffer from increased dimension. Monte Carlo integration then appears to be unique solution for high dimensional calculations. However variance of the output of interest should be decreased in order to have confident results. The method of Importance Sampling can be used in an attempt to reduce error term. We test the bounded log-normal process with Importance Sampling Monte Carlo Simulation. Our analysis is based on the theory of variance reduction. Numerical results indicate that the risk neutral density should be substituted in the range of moneyness.

Keywords: Options pricing, bounded lognormal process, importance sampling, moneyness.

THE DOWNSIDE TO INVESTING WITH ONE HAND BEHIND YOUR BACK - THE QUANTITATIVE - QUALITATIVE DIVIDE
Merav Ozair 1 and Carol Royal 2
1 Department of Finance and Risk Engineering, Polytechnic Institute at NYU, USA
2 School of management, Australian Business School, University of New South Wales

Abstract: This paper attempts to provide an answer to the Quantitative versus Qualitative investment methods debate. It starts with a discussion on each method individually and how each method perceives the opposing method. The Qualitative method sees the flaws in the Quantitative method and its inability to include in the analysis valuable qualitative information, in particular Human Capital (HC) practices, which can lead to misleading evaluations such as in the case of the Lehman bankruptcy in 2008. The Quantitative methods, however, are very powerful in their ability to retrieve and analyse data, especially in this era of - Big Data. Data is growing exponentially and only the use of technology and quantitative methods will be able to tap into this information. Yet, it includes only information that can be quantified and it neglects what cannot be quantified, such as HC practices. The paper suggests (1) new approaches to quantify HC and integrate it into the investment process; (2) a new quantitative which synergizes all investment methodologies; and (3) an integrated investment process which combines both Quantitative and Qualitative methods to achieve a holistic picture of equity investments and the estimation of asset values.

Keywords: Quantitative Methods, qualitative Methods, Human Capital Analysis, Quantifiable Data, Quantifiable Information, Investment Risk, Investment Edge

THE ROLE OF RISK PERCEPTION IN THE SYSTEMIC RISK GENERATION AND AMPLIFICATION: AGENT-BASED APPROACH
Jagoda Kaszowska1 Juan Luis Santos2
1 Cracow University of Economics, Poland
2 Institute for Economic and Social Analysis (IAES), University of Alcalá, Madrid, Spain

Abstract: In the paper we study how systemic risk, and in result stability of financial system, depends on the market participants' perception of risk and perception of risk attitudes of the remaining market participants. In our analysis we use both the general equilibrium approach and the complex systems approach to economic dynamics. Namely, we use insights from the social amplification of risk framework to build an agent-based model of financial system. The perception of risk has been widely studied, using both qualitative and quantitative methods, in psychology, sociology, communications theory, behavioral economics and finance. However, addressing the central problem in managing and mitigating systemic risk requires not only understanding of how and why people and institutions perceive risk but also how their perception of risk attitudes of the other market participants affects the distribution of risks in the financial system. We model dependence of the financial market risk distribution on the agents' perception of risk. We show that the perception of risk attitudes increases the vulnerability of the financial system to external shocks. Furthermore, the perception of risk attitudes can fasten the self-organization of the system and lead to emergence of new kinds of risks that would generate the systemic effects. As a result, the notion of systemic risk endogeneity seems to be redefined.

Keywords: systemic risk, non-equilibrium theory, complex systems, self-organized criticality, behavioral finance, social amplification of risk framework

Q GAUSSIAN MODEL OF DEFAULT
Yuri A. Katz
S&P Capital IQ, New York, USA

Abstract: We present the qGaussian generalization of the Merton framework, which takes into account slow fluctuations of the volatility of the firm's market value of financial assets. The minimal version of the model depends on the Tsallis entropic parameter q and the generalized "distance to default". The empirical foundation and implications of the model are illustrated by the study of 645 North American industrial firms during the financial crisis, 2006 - 2012. All defaulters in the sample have exceptionally large q > 3/2, corresponding to unusually fat-tailed unconditional distributions of log-asset-returns. Using Receiver Operating Characteristic curves, we demonstrate the high forecasting power of the model in prediction of 1-year defaults. Our study suggests that the level of complexity of the realized time series, quantified by q, should be taken into account to improve valuations of default risk.

Keywords: Default risk, stochastic volatility, qGauss, time-series, fat-tails.

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Volume 3, Issue 4: ... free online access to latest articlesACRN Journal of Finance and Risk Perspectives Vol. 3, Issue 4, Dec 2014, ISSN 2305-7394 EDITORIAL BOARD TABLE OF CONTENTS THE PREDICTIVE POWER OF THE J-CURVE Cyril DemariaUniversity of Sankt-Gallen, Switzerland. Abstract.

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ITU Endüstri Mühendisliği doktaradan mezun oldum.