Academic literature on the topic 'Finance – Mathematical models – Swaziland'

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Journal articles on the topic "Finance – Mathematical models – Swaziland"

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Byrne, Patrick, S. D. Howison, F. P. Kelly, and P. Wilmott. "Mathematical Models in Finance." Statistician 45, no. 3 (1996): 389. http://dx.doi.org/10.2307/2988481.

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Busika, Themba, and Muhammad Hoque. "An investigation into the best approach to the implementation of Basel II in Swaziland." Banks and Bank Systems 12, no. 4 (2017): 131–43. http://dx.doi.org/10.21511/bbs.12(4-1).2017.02.

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After the exposition of the Basel I Capital Accord weaknesses, the advent of the Basel II Capital Framework profoundly redefined global banking regulation and risk management practices. Many African countries had been lethargic on the migration to Basel for various reasons, amongst many being lack of skills and infrastructure. The purpose of this study was to investigate the prospect of migrating from the 1988 Basel I Capital Accord to the Basel II Capital Framework and to analyze the best approach to the implementation of the new framework in Swaziland. This was a qualitative study conducted
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CARMONA, RENÉ, and SERGEY NADTOCHIY. "TANGENT MODELS AS A MATHEMATICAL FRAMEWORK FOR DYNAMIC CALIBRATION." International Journal of Theoretical and Applied Finance 14, no. 01 (2011): 107–35. http://dx.doi.org/10.1142/s0219024911006280.

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Motivated by the desire to integrate repeated calibration procedures into a single dynamic market model, we introduce the notion of a "tangent model" in an abstract set up, and we show that this new mathematical paradigm accommodates all the recent attempts to study consistency and absence of arbitrage in market models. For the sake of illustration, we concentrate on the case when market quotes provide the prices of European call options for a specific set of strikes and maturities. While reviewing our recent results on dynamic local volatility and tangent Lévy models, we present a theory of t
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Fatone, Lorella, Francesca Mariani, Maria Cristina Recchioni, and Francesco Zirilli. "The Calibration of Some Stochastic Volatility Models Used in Mathematical Finance." Open Journal of Applied Sciences 04, no. 02 (2014): 23–33. http://dx.doi.org/10.4236/ojapps.2014.42004.

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Egozcue, Martín, Luis Fuentes García, Konstantinos Katsikopoulos, and Michael Smithson. "Simple models in finance: a mathematical analysis of the probabilistic recognition heuristic." Journal of Risk Model Validation 11, no. 2 (2017): 83–103. http://dx.doi.org/10.21314/jrmv.2017.175.

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Scrimnger-Christian, Charmaine, and Saratiel Wedzerai Musvoto. "Rethinking The Use Of Causal Theories In Social Sciences: A Focus On Accounting And Finance." International Business & Economics Research Journal (IBER) 10, no. 10 (2011): 115. http://dx.doi.org/10.19030/iber.v10i10.5991.

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This study highlights the problems associated with the use of deterministic models in social scientific disciplines such as accounting and finance. A deterministic theory connotes a self-defining set of physical relations and it yields a set of mathematical functions, parameterized by time, which describes how a set of ideal measure numbers changes with the time parameter while statistical models are used to describe the distribution of variations of concrete measured data from the ideal mathematical law. In this study, it is argued that in disciplines such as accounting and finance there are
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Ishimura, Naoyuki. "Research on Nonlinear Partial Differential Equations in Mathematical Finance." Impact 2020, no. 8 (2020): 48–50. http://dx.doi.org/10.21820/23987073.2020.8.48.

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Mathematical finance is a field of applied mathematics which focuses on crafting special mathematical models and computational methods which are used by the finance markets. The basis of mathematical finance lies in probability theory which focuses on analysing the behaviour of the markets to help the prediction of any random events. From this work financial companies and individuals interested in the markets can make informed choices based on a calculated risk level. Professor Naoyuki Ishimura has performed research in mathematical finance for many years, and is currently based at Chuo Univer
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Blanchet-Scalliet, Christophette, Awa Diop, Rajna Gibson, Denis Talay, and Etienne Tanré. "Technical analysis compared to mathematical models based methods under parameters mis-specification." Journal of Banking & Finance 31, no. 5 (2007): 1351–73. http://dx.doi.org/10.1016/j.jbankfin.2006.10.017.

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Rosenberger, Jay M., and H. W. Corley. "Mathematical programming models for some smallest-world problems." Nonlinear Analysis: Real World Applications 6, no. 5 (2005): 955–61. http://dx.doi.org/10.1016/j.nonrwa.2005.02.001.

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Bekri, Mahmoud, Young Shin (Aaron) Kim, and Svetlozar (Zari) T. Rachev. "Tempered stable models for Islamic finance asset management." International Journal of Islamic and Middle Eastern Finance and Management 7, no. 1 (2014): 37–60. http://dx.doi.org/10.1108/imefm-10-2012-0096.

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Purpose – In Islamic finance (IF), the safety-first rule of investing (hifdh al mal) is held to be of utmost importance. In view of the instability in the global financial markets, the IF portfolio manager (mudharib) is committed, according to Sharia, to make use of advanced models and reliable tools. This paper seeks to address these issues. Design/methodology/approach – In this paper, the limitations of the standard models used in the IF industry are reviewed. Then, a framework was set forth for a reliable modeling of the IF markets, especially in extreme events and highly volatile periods.
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Dissertations / Theses on the topic "Finance – Mathematical models – Swaziland"

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Pitsillis, Zachry Steven. "Estimating dynamic affine term structure models." Master's thesis, University of Cape Town, 2015. http://hdl.handle.net/11427/15731.

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Duffee and Stanton (2012) demonstrated some pointed problems in estimating affine term structure models when the price of risk is dynamic, that is, risk factor dependent. The risk neutral parameters are estimated with precision, while the price of risk parameters are not. For the Gaussian models they investigated, these problems are replicated and are shown to stem from a lack of curvature in the log-likelihood function. This geometric issue for identifying the maximum of an essentially horizontal log-likelihood has statistical meaning. The Fisher information for the price of risk parameters i
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Ziervogel, Graham. "Hedging performance of interest-rate models." Master's thesis, University of Cape Town, 2016. http://hdl.handle.net/11427/20482.

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This dissertation is a hedging back-study which assesses the effectiveness of interest- rate modelling and the hedging of interest-rate derivatives. Caps that trade in the Johannesburg swap market are hedged using two short-rate models, namely the Hull and White (1990) one-factor model and the subsequent Hull and White (1994) two-factor extension. This is achieved by using the equivalent Gaussian additive-factor models (G1++ and G2++) outlined by Brigo and Mercurio (2007). The hedges are constructed using different combinations of theoretical zero-coupon bonds. A flexible factor hedging method
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Putyatin, Vladislav Evgenievich. "Mathematical models for derivative securities markets." Thesis, University of Southampton, 1998. https://eprints.soton.ac.uk/50648/.

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The classical Black-Scholes analysis determines a unique, continuous, trading strategy which allows one to hedge a financial option perfectly and leads to a unique price for the option. It assumes, however, that there are no transaction costs involved in implementing this strategy, and the stock market is absolutely liquid. In this work some new results are obtained to accommodate costs of hedging, which occur in practice, and market imperfections into the option pricing framework. In Part One transaction charges are dealt with by means of the mean-variance technique, originally developed by M
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蕭德權 and Tak-kuen Siu. "Risk measures in finance and insurance." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31242297.

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Cullinan, Cian. "Implementation of Bivariate Unspanned Stochastic Volatility Models." Master's thesis, University of Cape Town, 2018. http://hdl.handle.net/11427/29266.

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Unspanned stochastic volatility term structure models have gained popularity in the literature. This dissertation focuses on the challenges of implementing the simplest case – bivariate unspanned stochastic volatility models, where there is one state variable controlling the term structure, and one scaling the volatility. Specifically, we consider the Log-Affine Double Quadratic (1,1) model of Backwell (2017). In the class of affine term structure models, state variables are virtually always spanned and can therefore be inferred from bond yields. When fitting unspanned models, it is necessary
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Wort, Joshua. "Pricing with Bivariate Unspanned Stochastic Volatility Models." Master's thesis, Faculty of Commerce, 2019. http://hdl.handle.net/11427/31323.

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Unspanned stochastic volatility (USV) models have gained popularity in the literature. USV models contain at least one source of volatility-related risk that cannot be hedged with bonds, referred to as the unspanned volatility factor(s). Bivariate USV models are the simplest case, comprising of one state variable controlling the term structure and the other controlling unspanned volatility. This dissertation focuses on pricing with two particular bivariate USV models: the Log-Affine Double Quadratic (1,1) – or LADQ(1,1) – model of Backwell (2017) and the LinearRational Square Root (1,1) – or L
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Endekovski, Jessica. "Pricing multi-asset options in exponential levy models." Master's thesis, Faculty of Commerce, 2019. http://hdl.handle.net/11427/31437.

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This dissertation looks at implementing exponential Levy models whereby the un- ´ derlyings are driven by Levy processes, which are able to account for stylised facts ´ that traditional models do not, in order to price basket options more efficiently. In particular, two exponential Levy models are implemented and tested: the multi- ´ variate Variance Gamma (VG) model and the multivariate normal inverse Gaussian (NIG) model. Both models are calibrated to real market data and then used to price basket options, where the underlyings are the constituents of the KBW Bank Index. Two pricing methods
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Oagile, Joel. "Sequential Calibration of Asset Pricing Models to Option Prices." Master's thesis, University of Cape Town, 2018. http://hdl.handle.net/11427/29840.

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This paper implements four calibration methods on stochastic volatility models. We estimate the latent state and parameters of the models using three non-linear filtering methods, namely the extended Kalman filter (EKF), iterated extended Kalman filter (IEKF) and the unscented Kalman filter (UKF). A simulation study is performed and the non-linear filtering methods are compared to the standard least square method (LSQ). The results show that both methods are capable of tracking the hidden state and time varying parameters with varying success. The non-linear filtering methods are faster and ge
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Sylvester, Matthew. "Calibrating Term Structure Models to an Initial Yield Curve." Master's thesis, Faculty of Commerce, 2021. http://hdl.handle.net/11427/33027.

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The modelling of the short rate offers many advantages, with the models explored in this dissertation all offering closed-form, analytic formulae for bond prices and for options on bonds. Often, a vital primary condition is for a model to be calibrated to the initial term structure and to recover the bond prices observed in the market – that is, to be calibrated to the initial yield curve. Under the two exogenous models explored in this dissertation, the Hull-White and the CIR++, the effect of increasing the volatility parameter of the SDE increases the mean of the short rate. Increasing volat
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Guedes, Maria do Carmo Vaz de Miranda. "Mathematical models in capital investment appraisal." Thesis, University of Warwick, 1988. http://wrap.warwick.ac.uk/107492/.

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Books on the topic "Finance – Mathematical models – Swaziland"

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Mathematical finance. Wiley, 2011.

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Numerical techniques in finance. MIT Press, 1989.

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Experiments in quantitative finance. Transaction Publishers, 2011.

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Knight, John L. Linear factor models in finance. Elsevier/Butterworth-Heinemann, 2005.

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Mathematical finance: Theory, modeling, implementation. John Wiley & Sons, Inc., 2007.

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Iftekhar, Hasan, ed. Quantitative methods for finance and investments. Blackwell Publishers, 2002.

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1963-, Waldron Patrick, ed. Mathematics for economics and finance. Routledge, 2010.

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Handbook of computational finance. Springer, 2012.

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Norman, Biggs, ed. Mathematics for economics and finance: Methods and modelling. Cambridge University Press, 1996.

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Introduction to mathematical finance: Discrete time models. Blackwell, 1997.

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Book chapters on the topic "Finance – Mathematical models – Swaziland"

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Fleming, Wendell H. "Optimal Investment Models and Risk Sensitive Stochastic Control." In Mathematical Finance. Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4757-2435-6_6.

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Biagini, Francesca. "A Quadratic Approach To Interest Rates Models In Incomplete Markets." In Mathematical Finance. Birkhäuser Basel, 2001. http://dx.doi.org/10.1007/978-3-0348-8291-0_8.

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Ingersoll, J. E. "General One-Period Models." In Mathematical Finance and Probability. Birkhäuser Basel, 2003. http://dx.doi.org/10.1007/978-3-0348-8041-1_6.

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Dikta, Gerhard. "Semi-parametric Random Censorship Models." In From Statistics to Mathematical Finance. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50986-0_3.

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Taniguchi, Setsuo. "Stochastic Analytical Models in Mathematical Finance." In Mathematics for Industry. Springer Japan, 2014. http://dx.doi.org/10.1007/978-4-431-55060-0_20.

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McLeish, D. L., and A. W. Kolkiewicz. "Fitting Diffusion Models in Finance." In Institute of Mathematical Statistics Lecture Notes - Monograph Series. Institute of Mathematical Statistics, 1997. http://dx.doi.org/10.1214/lnms/1215455054.

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Gökay, Selim, Alexandre F. Roch, and H. Mete Soner. "Liquidity Models in Continuous and Discrete Time." In Advanced Mathematical Methods for Finance. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18412-3_13.

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Bender, Christian, Tommi Sottinen, and Esko Valkeila. "Fractional Processes as Models in Stochastic Finance." In Advanced Mathematical Methods for Finance. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18412-3_3.

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González-Manteiga, Wenceslao, Jorge Passamani Zubelli, Abelardo Monsalve-Cobis, and Manuel Febrero-Bande. "Goodness–of–Fit Test for Stochastic Volatility Models." In From Statistics to Mathematical Finance. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50986-0_6.

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Mijatović, Aleksandar, and Martijn Pistorius. "Exotic Derivatives under Stochastic Volatility Models with Jumps." In Advanced Mathematical Methods for Finance. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18412-3_17.

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Conference papers on the topic "Finance – Mathematical models – Swaziland"

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Chiarella, Carl, Sara Pasquali, and Wolfgang J. Runggaldier. "On Filtering in Markovian Term Structure Models." In Proceedings of the International Conference on Mathematical Finance. WORLD SCIENTIFIC, 2001. http://dx.doi.org/10.1142/9789812799579_0012.

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Ma, Jin, and Xiaodong Sun. "Sharp Estimates of Ruin Probabilities for Insurance Models Involving Investments." In Proceedings of the International Conference on Mathematical Finance. WORLD SCIENTIFIC, 2001. http://dx.doi.org/10.1142/9789812799579_0007.

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Gyulov, Tihomir B., Radoslav L. Valkov, George Venkov, Ralitza Kovacheva, and Vesela Pasheva. "Classical and Weak Solutions for Two Models in Mathematical Finance." In APPLICATIONS OF MATHEMATICS IN ENGINEERING AND ECONOMICS (AMEE '11): Proceedings of the 37th International Conference. AIP, 2011. http://dx.doi.org/10.1063/1.3664370.

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Perrotta, Adamaria. "A learner-centered approach to design a Computational Finance module in higher education." In Seventh International Conference on Higher Education Advances. Universitat Politècnica de València, 2021. http://dx.doi.org/10.4995/head21.2021.12955.

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In this paper, we describe our design of ACM30070 “Computational Finance”, a core module in the BSc in Financial Mathematics in the School of Mathematics and Statistics. The over-arching purpose of this module is to help students to develop mathematical, statistical and coding skills, along with significant knowledge and critical thinking, that allows them to effectively construct, manipulate and visualize financial datasets and to build financial mathematical models. The use of computation and a FinTech software (FinCad Analytics) are pointed out as essential to facilitate sensemaking in comp
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