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Estudo comparativo dos modelos de value-at-risk para instrumentos pré-fixados. / A comparative study of value-at-risk models for fixed rate instruments.Paulo Kwok Shaw Sain 07 August 2001 (has links)
Nos últimos anos, o value-at-risk tem se tornado uma ferramenta amplamente utilizada nas principais instituições financeiras, inclusive no Brasil. Dentre suas vantagens, destaca-se a possibilidade de se resumir em um único número os riscos de mercado incorridos e incorporar neste valor tanto a exposição da instituição quanto a volatilidade do mercado. O objetivo principal deste estudo é verificar a eficácia dos modelos mais conhecidos de value-at-risk - RiskMetrics(TM) e Simulação Histórica - na mensuração dos riscos de mercado de carteiras de renda fixa compostas por instrumentos pré-fixados em reais. No âmbito da alocação de capital para atendimento aos órgãos de regulamentação, o estudo estende-se também ao modelo adotado pelo Banco Central do Brasil. No decorrer do estudo, discute-se ainda as vantagens e desvantagens apresentadas, bem como o impacto que as peculiaridades do mercado brasileiro exercem sobre as hipóteses assumidas em cada um dos modelos. / Value-at-Risk (VaR) has become the primary tool for the systematic measuring and monitoring of market risk in most financial institutions. VaR is a statistical measure that comprises not only the exposure but also the market volatility in a single number. The main purpose of this work is to evaluate the performance of the well-known value-at-risk models - RiskMetrics(TM) and Historical Simulation - in the Brazilian fixed-income market. In the scope of capital allocation related to banking regulation, this study also extends briefly to the model adopted by the Brazilian Central Bank. Additionally, the underlying assumptions of these models are analyzed in the Brazilian financial market context. Also, this study discusses the advantages and disadvantages presented by the RiskMetrics and the Historical Simulation models.
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Can Duration -- Interest Rate Risk -- and Convexity Explain the Fractional Price Change and Market Risk of Equities?Cheney, David L. 01 May 1993 (has links)
In the last two decades, duration analysis has been largely applied to fixed - income securities . However, since rising and falling interest rates have been determined to be a major cause of stock price movements, equity duration has received a great deal of attention.
The duration of an equity is a measure of its interest rate risk. Duration is the sensitivity of the price of an equity with respect to the interest rate. Convexity is the sensitivity of duration with respect to the interest rate.
The analysis revealed that the fractional price change and market risk of equities can be explained by duration and convexity.
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Exploring the Feasibility of Replicating SPAN-Model's Required Initial Margin Calculations using Machine Learning : A Master Thesis Project for Intraday Margin Call Investigation in the Commodities MarketBranestam, Clara, Sandgren, Amanda January 2023 (has links)
Machine learning is a rapidly growing field within artificial intelligence that an increasing number of individuals and corporations are beginning to utilize. In recent times, the financial sector has also started to recognize the potential of these techniques and methods. Nasdaq Clearing is responsible for managing the clearing business for the clearinghouse's members, and the objective of this thesis has been to explore the possibilities of using machine learning to replicate a subpart of the SPAN model's margin call calculations, known as initial margin, in the commodities market. The purpose of replicating SPAN's initial margin calculations is to open up for possibilities to create transparency and understanding in how the input variables affect the output. In the long run, we hope to broaden the insights on how one can use machine learning within the margin call processes. Various machine learning algorithms, primarily focused on regression tasks but also a few classification ones, have been employed to replicate the initial margin size. The primary objective of the methodology was to determine the algorithm that demonstrated the best performance in obtaining values that were as close as possible to the actual initial margin values. The findings revealed that a model composed of a combination of classification and regression, with non-parametric algorithms such as Random Forest and KNN, performed the best in both cases. Our conclusion is that the developed model possesses the ability to effectively compute the size of the initial margin and thus accomplishes its objective. / Maskininlärning är ett snabbt växande område inom artificiell intelligens som allt fler individer och företag börjar använda. Finanssektorn har nu också börjat undersöka hur dessa tekniker och metoder kan skapa värde. Nasdaq Clearing hanterar clearingverksamheten för clearinghusets medlemmar och syftet med denna uppsats har varit att undersöka möjligheterna att använda maskininlärning för att replikera en del av SPAN-modellens beräkningar av marginkravet som kallas Initial Marginal. Syftet med att replikera SPANs initiala marginberäkningar är att öppna upp för möjligheter att skapa transparens och förståelse för hur inputvariablernapåverkar outputen. På sikt hoppas vi kunna bredda insikterna hur maskininlärningslösningar skulle kunna användas inom "Margin Call"- processen. De metoder som användes för att replikera storleken på Initial Margin var olika maskininlärningsalgoritmer, främst fokuserade på regressionsuppgifter men några klassificeringsalgoritmer användes också. Fokus i metoden var att hitta vilken algoritm som presterade bäst, det vill säga den algoritm som predikterade närmst de faktiska värdena för Initial Margin. Resultatet visade sig vara en modell som kombinerade klassificering och regression, där icke-parametriska algoritmer såsom Random Forest och KNN var de som presterade bäst i båda fallen. Vår slutsats är att den utvecklade modellen har en god förmåga att beräkna storleken på Initial Margin och därmed uppfyller den sitt syfte.
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Impact of Corporate Governance Mechanisms on Total, Systematic, Market, and Insolvency Risk of FintechRandombage, Sandun, Ramesh, Sudharshani January 2023 (has links)
Corporate governance practices of fintech companies have caused to increase in risk or caused to decrease in the risks. This study is mainly focused to identify the impact of corporate governance mechanisms, especially board structure and ownership structure, on the market-based risk of fintech companies. We have employed several corporate governance mechanisms such as, board size, board independence, board expertise on fintech, CEO duality, risk committee functioning, institutional ownership, and managerial ownership of the fintech companies. Total risk, systematic risk, market risk,and insolvency risk are employed as our dependent variables to examine this phenomenon. We have selected 46 listed fintech companies that are listed in any stock market of the world. Data is collected through 2012-2022 period. We have conducted our analysis using 369 unbalanced panel datasets. Our purpose was to emphasize the importance of better corporate governance mechanisms to risk management in fintech companies. From the management point of view, investors’ point of view, or directors’ point of view, what changes should do to better risk management of the company and also their personal benefit? In the recent past, two bluechip fintech companies have bankrupt due to corporate governance mispractices and risk management issues. Our results show that, corporate governance is one of the key factors in determining risk of the fintech companies. We have identified that the best practices caused to decrease risk while mispractices caused to increase risk.
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Two Essays on Asset PricingHur, Jungshik 01 May 2007 (has links)
This dissertation consists of two chapters. The first chapter shows that the measurement errors in betas for stocks induce corresponding measurement errors in alphas and a spurious negative covariance between the estimated betas and alphas across stocks. This negative covariance between the estimated betas and alphas results in a violation of the independence assumption between the independent variable (betas) and error terms in the Fama-MacBeth regressions of tests of the CAPM, thereby creating a downward bias in the estimated market risk premiums. The procedure of using portfolio returns and betas does not necessarily eliminate this bias. Depending upon the grouping variable used to form portfolios, the negative covariance between estimated betas and alphas can be increased, decreased, and can even be made positive. This paper proposes two methods for correcting the downward bias in the estimated market risk premium. The estimated market risk premiums are consistent with the CAPM after the proposed corrections.
The second chapter provides evidence that when the ex-post market risk premium is positive (up markets), the relation between returns and betas is positive, significant, and consistent with the CAPM. However, when the ex-post market risk premium is negative (down markets), the negative relation between betas and returns is significant, but stronger than what is implied by the CAPM. This strong negative relation offsets the positive relation, resulting in an insignificant relation between returns and betas for the overall period. The negative relation between size and returns, after controlling for beta differences, is present only when the ex-post market risk premium is negative, and is responsible for the negative relation for the overall period. This paper decomposes the negative relation between size and returns after controlling for beta differences into the intercept size effect (relation between alphas of stocks and their size) and the residual size effect (relation between residuals of stocks and their size). The asymmetrical size effect between up and down market is being driven by the residual size effect. Long term mean reversion in returns explains, in part, the negative relation between size and returns during down markets. / Ph. D.
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Is Value-at-Risk (VaR) a Fair Proxy for Market Risk Under Conditions of Market Leverage?Lang, Todd M. 29 December 2000 (has links)
Ex-post intraday market-risk extrema are compared with ex-ante standard RiskMetrics parametric Value-at-Risk (VaR) limits for three foreign currency futures markets (British Pound, Japanese Yen, Swiss Frank) to determine whether forecasted volatility of market returns based on settlement price data provides a valid proxy for short-term market risk independent of market leverage.
Intraday violations of ex-ante one-day VaR limits at the 95% confidence level should occur for less than 5% of market days. Violation frequencies for each of the markets tested are shown to occur well in excess of this 5% tolerance level: 9.54% for the British Pound, 7.09% for the Japanese Yen, and 7.79% for the Swiss Franc futures markets.
Thus, it is empirically demonstrated that VaR is a poor proxy for short-term market risk under conditions of market leverage.
Implications for managing (measuring, monitoring, controlling), reporting, and regulating financial market risk are discussed. / Master of Arts
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Estimating Market Risk of Private Real Estate AssetsWidigsson, Eric, Wolf-Watz, Björn January 2024 (has links)
This study aims to estimate the market risk of private real estate assets, specifically examining Swedish real estate companies, and seeks to identify the best model for estimating the quarterly squared return. An important assumption in this study is that private real estate assets are assumed to have the same market risk as publicly traded assets, all else being equal. With this assumption, the studied methods can be applied to publicly traded companies and evaluated based on the realized stock returns of these traded companies. The study examines two primary techniques for estimating the risk of private real estate assets: desmoothing of appraisal based returns and supervised learning on listed peers. Desmoothing is a technique used to estimate new economic returns from smoothed real estate appraisal returns. The original desmoothing method outlined by Geltner (1991) introduces AR desmoothing and is examined along with the MA desmoothing model presented by Getmansky et al. (2004). Performing these desmoothing techniques yields a new time series of returns that can be utilized in an EWMA (Exponentially Weighted Moving Average) estimation for predicting the squared return of the next quarter. The supervised learning on listed peers, on the other hand, is performed by studying similar listed assets and training the ability to predict the squared return based on explanatory variables representing selected key figures of the companies’ financials. Five supervised learning models are examined: Linear Regression, Lasso Regression, Ridge Regression, Elastic Net Regularization, and Random Forest Regression. The results show that four out of the five supervised learning models are superior to the desmoothing models. In particular, Random Forest Regression, Ridge Regression, and Lasso Regression yield the best estimates of the quarterly squared return. However, since this study assesses risk over a quarterly time period, the lack of data is significant, affecting the statistical confidence of the results. Although the superiority of the supervised learning models in terms of predicting the squared return is evident, the results from the desmoothing reveal some interesting properties about the techniques. AR desmoothing reduces the disparity between the sample variance of the stock compared to the original NAV time series, whereas MA desmoothing drastically increases the correlation of the desmoothed returns with the stock returns.
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Development of value at risk measures : towards an extreme value approachGanief, Moegamad Shahiem 12 1900 (has links)
Thesis (MBA)--Stellenbosch University, 2001. / ENGLISH ABSTRACT: Commercial banks, investment banks, insurance companies, non-financial firms, and
pension funds hold portfolios of assets that may include stocks, bonds, currencies,
and derivatives. Each institution needs to quantify the amount of risk its portfolio is
exposed to in the course of a day, week, month, or year. Extreme events in financial
markets, such as the stock market crash of October 1987, are central issues in finance
and particularly in risk management and financial regulation.
A method called value at risk (VaR) can be used to estimate market risk. Value at risk
is a powerful measure of risk that is gaining wide acceptance amongst institutions for
the management of market risk. Value at Risk is an estimate of the largest lost that
a portfolio is likely to suffer during all but truly exceptional periods. More precisely,
the VaR is the maximum loss that an institution can be confident it would lose a
certain fraction of the time over a particular period.
The power of the concept is its generality. VaR measures are applicable to entire
portfolios - encompassing many asset categories and multiple sources of risk. As with
its power, the challenge of calculating VaR also stems from its generality. In order to
measure risk in a portfolio using VaR, some means must be found for determining a
return distribution for the portfolio.
There exists a wide range of literature on different methods of implementing VaR.
But, when one attempts to apply the results, several questions remain open. For
example, given a VaR measure, how can the risk manager test that the particular
measure at hand is appropriately specified? And secondly, given two different VaR
measures, how can the risk manager pick the best measure?
Despite the popularity of VaR for measuring market risk, no consensus has yet been reach as to the best method to implement this risk measure. The absence of consensus
is in part derived from the realization that each method currently in use has some
significant drawbacks.
The aim of this project is threefold: to introduce the reader to the concept of VaR;
present the theoretical basis for the general approaches to VaR computations; and to
introduce and apply Extreme Value Theory to VaR calculations.
The general approaches to VaR computation falls into three categories, namely, Analytic
(Parametric) Approach, Historical Simulation Approach, and Monte Carlo Simulation
Approach. Each of these approaches has its strengths and weaknesses, which
will study more closely.
The extreme value approach to VaR calculation is a relatively new approach. Since
most observed returns are central ones, traditional VaR methods tend to ignore extreme
events and focus on risk measures that accommodate the whole empirical distribution
of central returns. The danger of this approach is that these models are prone
to fail just when they are needed most - in large market moves, when institutions can
suffer very large losses.
The extreme value approach is a tool that attempts to provide the user with the best
possible estimate of the tail area of the distribution. Even in the absence of useful
historical data, extreme value theory provides guidance on the kind of distribution
that should be selected so that extreme risks are handled conservatively. As an
illustration, the extreme value method will be applied to a foreign exchange futures
contract. The validity of EVT to VaR calculations will be tested by examining the
data of the Rand/Dollar One Year Futures Contracts. An extended worked example
will be provided wherein which attempts to highlight the considerable strengths of
the methods as well as the pitfalls and limitations. These results will be compared to
VaR measures calculated using a GARCH(l,l) model. / AFRIKAANSE OPSOMMING: Handelsbanke, aksepbanke, assuransiemaatskappye, nie-finansiële instellings en pensioenfondse
beskik oor portefeuljes van finansiële bates soos aandele, effekte, geldeenhede
en afgeleides. Elke instelling moet die omvang kan bepaal van die risiko waaraan
die portefeulje blootgestel is in die loop van 'n dag, week, maand of jaar. Uitsonderlike
gebeure op finansiële markte, soos die ineenstorting van die aandelemark in Oktober
1987, is van besondere belang vir finansies en veral vir risikobestuur en finansiële
regulering.
'n Metode wat genoem word Waarde op Risiko (WoR), kan gebruik word om markverliese
te meet. WoR is 'n kragtige maatstaf vir risiko en word deur vele instellings gebruik
vir die bestuur van mark-risiko. Waarde op Risiko is 'n raming van die grootste
verlies wat 'n portefeulje moontlik kan ly gedurende enige tydperk, met uitsluiting
van werklik uitsonderlike tydperke. Van nader beskou, is WoR die maksimum verlies
wat 'n instelling kan verwag om gedurende 'n sekere tydperk binne 'n bepaalde
periode te ly.
Die waarde van die konsep lê in die algemene aard daarvan. WoR metings is van
toepassing op portefeuljes in dié geheel en dit omvat baie kategorieë bates en veelvuldige
bronne van risiko. Soos met die waarde van die konsep, hou die uitdaging om WoR
te bereken ook verband met die algemene aard van die konsep. Ten einde die risiko
te bepaal in 'n portefeulje waar WoR gebruik word, moet metodes gevind word waarvolgens
'n opbrengsverdeling vir die portefeulje vasgestel kan word.
Daar bestaan 'n groot verskeidenheid literatuur oor die verskillende metodes om WoR
te implementeer. Wanneer dit egter kom by die toepassing van die resultate, bly
verskeie vrae onbeantwoord. Byvoorbeeld, hoe kan die risikobestuurder aan die hand
van 'n gegewe WoR-maatstaf toets of die spesifieke maatstaf reg gespesifiseer is? Tweedens, hoe kan die risikobestuurder die beste maatstaf kies in die geval van twee
verskillende WoR-maatstawwe?
Ondanks die feit dat WoR algemeen gebruik word vir die meting van markrisiko, is
daar nog nie konsensus bereik oor die beste metode om hierdie benadering tot risikometing
te implementeer nie. Die feit dat daar nie konsensus bestaan nie, kan deels
daaraan toegeskryf word dat elkeen van die metodes wat tans gebruik word, ernstige
leemtes het.
Die doel van hierdie projek is om die konsep WoR bekend te stel, om die teoretiese
grondslag te lê vir die algemene benadering tot die berekening van WoR en om die
Ekstreme Waarde-teorie bekend te stel en toe te pas op WoR-berekenings.
Die algemene benadering tot die berekening van WoR word in drie kategorieë verdeel
naamlik die Analitiese (Parametriese) benadering, die Historiese simulasiebenadering
en die Monte Carlo-simulasiebenadering. Elkeen van die benaderings het sterk- en
swakpunte wat van nader ondersoek sal word.
Die Ekstreme Waarde-benadering tot WoR is 'n relatief nuwe benadering. Aangesien
die meeste opbrengste middelwaarde-gesentreer is, is tradisionele WoR-metodes
geneig om uitsonderlike gebeure buite rekening te laat en te fokus op risiko-maatstawwe
wat die hele empiriese verdeling van middelwaarde-gesentreerde opbrengste akkommodeer.
Die gevaar bestaan dan dat hierdie modelle geneig is om te faal juis wanneer
dit die meeste benodig word, byvoorbeeld in die geval van groot markverskuiwings
waartydens organisasies baie groot verliese kan ly.
Daar word beoog om met behulp van die Ekstreme Waarde-benadering aan die gebruiker
die beste moontlike skatting van die stert-area van die verdeling te gee. Selfs
in die afwesigheid van bruikbare historiese data verskaf die Ekstreme Waarde-teorie
riglyne ten opsigte van die aard van die verdeling wat gekies moet word, sodat uiterste
risiko's versigtig hanteer kan word. Ten einde hierdie metode te illustreer, word
dit in hierdie studie toegepas op 'n termynkontrak ten opsigte van buitelandse wisselkoerse.
Die geldigheid van die Ekstreme Waarde-teorie ten opsigte van WoR berekenings
word getoets deur die data van die Rand/Dollar Eenjaartermynkontrak
te bestudeer. 'n Volledig uitgewerkte voorbeeld word verskaf waarin die slaggate en
beperkings asook die talle sterkpunte van die model uitgewys word. Hierdie resultate
sal vergelyk word met 'n WoR-meting wat bereken is met die GARCH (1,1) model.
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A model to investigate the impact of flooding on the vulnerability of value of commercial propertiesBhattacharya, Namrata January 2014 (has links)
Flooding has the potential to have significant impact on the value of properties depending on the level of inherent vulnerability. Experts argue that it is not the actual risk but the perception of risk among property holders that influences vulnerability of value. The hypothesis that changing perception of flood risk could make property value vulnerable in the market is the main focus of the research. This dimension of research has received very low attention in commercial property literature. The existing knowledge base of flooding and property value reveals that focus has been largely associated with residential properties. Conceptual understanding of the extent and scale of the effect of flooding on the vulnerability of property value of commercial properties would be worthwhile for relevant stakeholders. The research methodology follows a quantitative approach with sequential application: of literature review, conceptual model generation, data collection from primary and secondary sources with remote questionnaire survey of selected study areas in the UK. The conceptual model was operationalised using analysis and interpretation of the collected data and finally cross validated with secondary data gained from commercial real estate experts . The strength of this research lies in the conceptualisation of the subject matter of property value in the context of flood vulnerability. This work provides innovative conceptual insight towards business vulnerability and vulnerability of value. The variables contributing towards vulnerability were hierarchically ranked using both collected data and deductive methods. The patterns of impact and recovery analysis emphasized that within the commercial sector indirect effects of flooding should be given equal importance with direct damages. The implication of perception on the vulnerability of property value showed a slightly different picture from business vulnerability in the chosen study areas when differentiated based on flood experience. In a nutshell the study reflected that the commercial property sector does not take flooding as one of their priorities. This is in part due to differential attitude towards risk of the population within the flood plain based on their knowledge and experience of flooding. The perception of stakeholders towards vulnerability of value can change with increasing magnitude and severity of floods and it is possible that the implications on market value of commercial properties will be visible in the future. Practitioners and researchers will find this study useful in developing an understanding of the vulnerability of commercial property value in the context of changing flood risk.
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Corporate Social Responsibility och riskpåverkan : En studie av det sociala ansvarstagandets effekt på risk i Svenska börsbolagElman, Beatrice, Pers, Sebastian January 2016 (has links)
This study uses a quantitative method that aims to investigate the relationship between corporate social responsibility (CSR) and firm risk within Swedish public companies. Despite previous research at Anglo-Saxon companies with similar results, authors found cause for further investigation. Authors identified differences in the Swedish context that could affect the earlier found negative relation between CSR and firm risk, thereby legitimizing further examination. The research is built on secondary data collected from Nasdaq, Morningstar, Orbis and the CSRhub database. Through theory of relevance and current research, it develops a hypothesis which states that as CSR increases, firm risk is reduced in accordance with previous research. Testing was done with Pearsons bivariate correlation table and a multivariate regression analysis, controlling for various firm characteristics. The study found no connection between market risk and CSR, but could not determine whether a relationship between CSR and total risk exists within the population, only partly rejecting the hypothesis. The study raises attention as to how the relation between CSR and risk could be different in a context outside the typical Anglo-Saxon population. It could also be used as a base to further research on the cause to the lack of relation between CSR and market risk, in this study’s particular population.
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