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Journal articles on the topic 'Finance Finance Stochastic analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

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1

Carmona, René, Martin Schweizer, and Nizar Touzi. "Stochastic Analysis in Finance and Insurance." Oberwolfach Reports 11, no. 2 (2014): 1279–312. http://dx.doi.org/10.4171/owr/2014/23.

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2

Rheinlander, Thorsten. "Introductory Stochastic Analysis for Finance and Insurance." Journal of the American Statistical Association 102, no. 478 (2007): 765–66. http://dx.doi.org/10.1198/jasa.2007.s195.

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3

Kao, Erin H., Chuan-Hao Hsu, Yunlin Lu, and Hung-Gay Fung. "Ranking of finance journals: a stochastic dominance analysis." Managerial Finance 42, no. 4 (2016): 312–23. http://dx.doi.org/10.1108/mf-04-2015-0125.

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Purpose – Prior studies in citation-based journal rankings tend to be static to compare across journals. One journal may be judged better in citations than other journals at some points in time but not at the others. The assumption that the citation distribution is normally distributed and that the citation observations are independent and identically distributed (i.i.d.) may not be appropriate. The paper aims to discuss these issues. Design/methodology/approach – This study uses a stochastic dominance (SD) analysis, which overcomes the dynamic nature of changes in citation over time. The SD m

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4

Sadalia, Isfenti, Muhammad Haikal Kautsar, Nisrul Irawati, and Iskandar Muda. "Analysis of the efficiency performance of Sharia and conventional banks using stochastic frontier analysis." Banks and Bank Systems 13, no. 2 (2018): 27–38. http://dx.doi.org/10.21511/bbs.13(2).2018.03.

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There are two sectors of banks operating in Indonesia, namely Sharia banks and conventional banks. Improving performance is important in maintaining public confidence in the bank. Efficiency is one of the parameters to measure the performance of Sharia banks. This study measures the comparative level of technical efficiency of Sharia commercial banks and conventional banks by Stochastic Frontier Analysis method during 2011–2015 period by using 10 samples of Sharia commercial banks and conventional banks. Input variables in this study are total deposits, operational costs, and other operational

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5

Janssen, Jacques. "Applied stochastic models and data analysis. Special Issue on Finance." Applied Stochastic Models and Data Analysis 8, no. 3 (1992): i. http://dx.doi.org/10.1002/asm.3150080302.

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6

Sallam, Sahar Shawky. "Determinants of private investment in Egypt: an empirical analysis." Review of Economics and Political Science 4, no. 3 (2019): 257–66. http://dx.doi.org/10.1108/reps-12-2018-0043.

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Purpose This paper aims to study the determinants of private investment in Egypt while accounting for uncertainty associated with financing decisions of the firm using time series analysis over the period 1982-2015. The analysis is based on Tobin’s (1969) Q-theory of investment. The variables used in the empirical model are investment rate, average q index, prices of capital goods, internal finance and external finance. Design/methodology/approach This research is concerned with the model specification of a dynamic Average Q model. In that respect, the current research describes the data, pres

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7

Stoyanov, Jordan. "Introductory Stochastic Analysis for Finance and Insurance by X. S. Lin." Journal of the Royal Statistical Society: Series A (Statistics in Society) 170, no. 2 (2007): 508–9. http://dx.doi.org/10.1111/j.1467-985x.2007.00473_11.x.

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8

Sasikumar, R. "An enhanced applications of brownian motion to mathematical finance in stochastic modeling." Journal of Interdisciplinary Mathematics 14, no. 4 (2011): 471–81. http://dx.doi.org/10.1080/09720502.2011.10700765.

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9

Conte, Andrea. "Is Italian R&D spending becoming more efficient?" ECONOMIA E POLITICA INDUSTRIALE, no. 2 (June 2009): 187–98. http://dx.doi.org/10.3280/poli2009-002009.

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- The recent economic downturn is putting increasing pressure on governments to improve the quality of their public finances. Using macro-economic data on R&D expenditures and patents, this paper aims to determine whether business and government R&D spending has become more efficient over time and in comparison to other EU countries. Descriptive evidence is coupled with empirical estimates of cross-country efficiency of R&D expenditure calculated by the Stochastic Frontier Analysis. . Keywords: R&D, patents, efficiency, public finance Parole chiave: R&S, brevetti, efficienz

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10

Hayre, Lakhbir S., Charles Huang, and Vincent Pica. "Stochastic Horizon Analysis." Journal of Fixed Income 3, no. 1 (1993): 48–53. http://dx.doi.org/10.3905/jfi.1993.408074.

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11

Stein, Jerome L. "Applications of stochastic optimal control/dynamic programming to international finance and debt crises." Nonlinear Analysis: Theory, Methods & Applications 63, no. 5-7 (2005): e2033-e2041. http://dx.doi.org/10.1016/j.na.2005.02.106.

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12

Li, Pei Ze. "Research on Stock Analysis Based on Stochastic Process." Advanced Materials Research 433-440 (January 2012): 5967–74. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.5967.

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In stock market, the stock prices directly reflects market condition, therefore, the research on stock price process is one of the research contents of mathematical finance. In this paper by using the election model of statistical physics model to study the stock price fluctuation . This paper first applying stochastic process theory to establish election model, then the election model and stopping time theory are applied to establish stock profit process, we get the stock price process.

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13

Banker, Rajiv D., Srikant M. Datar, and Madhav V. Rajan. "Measurement of Productivity Improvements: An Empirical Analysis." Journal of Accounting, Auditing & Finance 2, no. 4 (1987): 319–47. http://dx.doi.org/10.1177/0148558x8700200401.

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In this paper, we test for productivity gains resulting from the introduction of a productivity-based incentive program in a large manufacturing plant of a Fortune 500 corporation. We develop a methodology based on a stochastic nonparametric frontier estimation technique to evaluate productivity in the postincentive plan period relative to the pre-incentive plan period. We also test for productivity gains using stochastic parametric frontier approaches. The results of both the nonparametric and parametric stochastic frontier analyses indicate that the incentive program has a positive effect on

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14

Tippett, Mark. "ESTIMATING RETURNS ON FINANCIAL INSTRUMENTS - STOCHASTIC ANALYSIS." Accounting & Finance 30, no. 2 (1990): 87–98. http://dx.doi.org/10.1111/j.1467-629x.1990.tb00245.x.

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15

Dshalalow, Jewgeni H., Kizza Nandyose, and Ryan T. White. "Time Sensitive Analysis of Antagonistic Stochastic Processes and Applications to Finance and Queueing." Mathematics and Statistics 9, no. 4 (2021): 481–500. http://dx.doi.org/10.13189/ms.2021.090408.

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16

Fields, Gary S., Jesse B. Leary, and Efe A. Ok. "Stochastic dominance in mobility analysis." Economics Letters 75, no. 3 (2002): 333–39. http://dx.doi.org/10.1016/s0165-1765(02)00007-1.

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17

Yang, Jie, and Weidong Zhao. "Convergence of Recent Multistep Schemes for a Forward-Backward Stochastic Differential Equation." East Asian Journal on Applied Mathematics 5, no. 4 (2015): 387–404. http://dx.doi.org/10.4208/eajam.280515.211015a.

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AbstractConvergence analysis is presented for recently proposed multistep schemes, when applied to a special type of forward-backward stochastic differential equations (FB-SDEs) that arises in finance and stochastic control. The corresponding k-step scheme admits a k-order convergence rate in time, when the exact solution of the forward stochastic differential equation (SDE) is given. Our analysis assumes that the terminal conditions and the FBSDE coefficients are sufficiently regular.

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18

CUTHBERTSON, CHARLES, GRIGORIOS PAVLIOTIS, AVRAAM RAFAILIDIS, and PETTER WIBERG. "ASYMPTOTIC ANALYSIS FOR FOREIGN EXCHANGE DERIVATIVES WITH STOCHASTIC VOLATILITY." International Journal of Theoretical and Applied Finance 13, no. 07 (2010): 1131–47. http://dx.doi.org/10.1142/s0219024910006145.

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We consider models for the valuation of derivative securities that depend on foreign exchange rates. We derive partial differential equations for option prices in an arbitrage-free market with stochastic volatility. By use of standard techniques, and under the assumption of fast mean reversion for the volatility, these equations can be solved asymptotically. The analysis goes further to consider specific examples for a number of options, and to a considerable degree of complexity.

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19

Wang, Guangchen, and Zhen Wu. "Mean-Variance Hedging and Forward-Backward Stochastic Differential Filtering Equations." Abstract and Applied Analysis 2011 (2011): 1–20. http://dx.doi.org/10.1155/2011/310910.

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This paper is concerned with a mean-variance hedging problem with partial information, where the initial endowment of an agent may be a decision and the contingent claim is a random variable. This problem is explicitly solved by studying a linear-quadratic optimal control problem with non-Markov control systems and partial information. Then, we use the result as well as filtering to solve some examples in stochastic control and finance. Also, we establishbackwardandforward-backwardstochastic differential filtering equations which aredifferentfrom the classical filtering theory introduced by Li

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20

Zhou, Yanli, Yonghong Wu, Xiangyu Ge, and B. Wiwatanapataphee. "A Robust Weak Taylor Approximation Scheme for Solutions of Jump-Diffusion Stochastic Delay Differential Equations." Abstract and Applied Analysis 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/750147.

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Stochastic delay differential equations with jumps have a wide range of applications, particularly, in mathematical finance. Solution of the underlying initial value problems is important for the understanding and control of many phenomena and systems in the real world. In this paper, we construct a robust Taylor approximation scheme and then examine the convergence of the method in a weak sense. A convergence theorem for the scheme is established and proved. Our analysis and numerical examples show that the proposed scheme of high order is effective and efficient for Monte Carlo simulations f

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21

Gasbarro, Dominic, Wing-Keung Wong, and J. Kenton Zumwalt. "Stochastic Dominance Analysis of iShares." European Journal of Finance 13, no. 1 (2007): 89–101. http://dx.doi.org/10.1080/13518470601025243.

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22

Jostova, Gergana, and Alexander Philipov. "Bayesian Analysis of Stochastic Betas." Journal of Financial and Quantitative Analysis 40, no. 4 (2005): 747–78. http://dx.doi.org/10.1017/s0022109000001964.

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AbstractWe propose a mean-reverting stochastic process for the market beta. In a simulation study, the proposed model generates significantly more precise beta estimates than GARCH betas, betas conditioned on aggregate or firm-level variables, and rolling regression betas, even when the true betas are generated based on these competing specifications. Our model significantly improves out-of-sample hedging effectiveness. In asset pricing tests, our model provides substantially stronger support for the conditional CAPM relative to competing beta models and helps resolve asset pricing anomalies s

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23

Gelain, Paolo. "The external finance premium in the Euro area: A dynamic stochastic general equilibrium analysis." North American Journal of Economics and Finance 21, no. 1 (2010): 49–71. http://dx.doi.org/10.1016/j.najef.2009.11.004.

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24

Ho, Tin H., Dat T. Nguyen, Thanh Ngo, and Tu D. Q. Le. "Efficiency in Vietnamese Banking: A Meta-Regression Analysis Approach." International Journal of Financial Studies 9, no. 3 (2021): 41. http://dx.doi.org/10.3390/ijfs9030041.

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This study explains the differences and variances in the efficiency scores of the Vietnamese banking sector retrieved from 27 studies published in refereed academic journals under the framework of meta-regression analysis. These scores are mainly based on frontier efficiency measurements, which essentially are Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) for Vietnamese banks over the period of 2007–2019. The meta-regression is estimated by using truncated regression to obtain bias-corrected scores. Our findings suggest that only the year of publication is positively c

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25

Zanzotto, Pio Andrea. "Some applications of stochastic analysis in financial economics: An outline." Rivista di Matematica per le Scienze Economiche e Sociali 18, no. 2 (1995): 181–98. http://dx.doi.org/10.1007/bf02096427.

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26

Ahlip, Rehez, Laurence A. F. Park, Ante Prodan, and Stephen Weissenhofer. "Forward start options under Heston affine jump-diffusions and stochastic interest rate." International Journal of Financial Engineering 08, no. 01 (2021): 2150005. http://dx.doi.org/10.1142/s2424786321500055.

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This paper presents a generalization of forward start options under jump diffusion framework of Duffie et al. [Duffie, D, J Pan and K Singleton (2000). Transform analysis and asset pricing for affine jump-diffusions, Econometrica 68, 1343–1376.]. We assume, in addition, the short-term rate is governed by the CIR dynamics introduced in Cox et al. [Cox, JC, JE Ingersoll and SA Ross (1985). A theory of term structure of interest rates, Econometrica 53, 385–408.]. The instantaneous volatilities are correlated with the dynamics of the stock price process, whereas the short-term rate is assumed to b

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27

Hurn, Stan, Kenneth A. Lindsay, and Lina Xu. "Revisiting the numerical solution of stochastic differential equations." China Finance Review International 9, no. 3 (2019): 312–23. http://dx.doi.org/10.1108/cfri-12-2018-0155.

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Purpose The purpose of this paper is to revisit the numerical solutions of stochastic differential equations (SDEs). An important drawback when integrating SDEs numerically is the number of steps required to attain acceptable accuracy of convergence to the true solution. Design/methodology/approach This paper develops a bias reducing method based loosely on extrapolation. Findings The method is seen to perform acceptably well and for realistic steps sizes provides improved accuracy at no significant additional computational cost. In addition, the optimal step size of the bias reduction methods

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28

CAPOBIANCO, ENRICO. "WAVELET TRANSFORMS FOR THE STATISTICAL ANALYSIS OF RETURNS GENERATING STOCHASTIC PROCESSES." International Journal of Theoretical and Applied Finance 04, no. 03 (2001): 511–34. http://dx.doi.org/10.1142/s0219024901001097.

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We study high frequency Nikkei stock index series and investigate what certain wavelet transforms suggest in terms of volatility features underlying the observed returns process. Several wavelet transforms are applied for exploratory data analysis. One of the scopes is to use wavelets as a pre-processing smoothing tool so to de-noise the data; we believe that this procedure may help in identifying, estimating and predicting the latent volatility. Evidence is shown on how a non-parametric statistical procedure such as wavelets may be useful for improving the generalization power of GARCH models

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29

Lee, Roger, and Dan Wang. "Displaced lognormal volatility skews: analysis and applications to stochastic volatility simulations." Annals of Finance 8, no. 2-3 (2009): 159–81. http://dx.doi.org/10.1007/s10436-009-0145-7.

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30

Hambly, Ben, and Nikolaos Kolliopoulos. "Erratum: Stochastic Evolution Equations for Large Portfolios of Stochastic Volatility Models." SIAM Journal on Financial Mathematics 10, no. 3 (2019): 857–76. http://dx.doi.org/10.1137/19m1260980.

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31

KAWAI, REIICHIRO. "SENSITIVITY ANALYSIS AND DENSITY ESTIMATION FOR THE HOBSON-ROGERS STOCHASTIC VOLATILITY MODEL." International Journal of Theoretical and Applied Finance 12, no. 03 (2009): 283–95. http://dx.doi.org/10.1142/s0219024909005294.

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Monte Carlo estimators of sensitivity indices and the marginal density of the price dynamics are derived for the Hobson-Rogers stochastic volatility model. Our approach is based mainly upon the Kolmogorov backward equation by making full use of the Markovian property of the dynamics given the past information. Some numerical examples are presented with a GARCH-like volatility function and its extension to illustrate the effectiveness of our formulae together with a clear exhibition of the skewness and the heavy tails of the price dynamics.

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32

Mutarindwa, Samuel, Ibrahim Siraj, and Andreas Stephan. "Ownership and bank efficiency in Africa: True fixed effects stochastic frontier analysis." Journal of Financial Stability 54 (June 2021): 100886. http://dx.doi.org/10.1016/j.jfs.2021.100886.

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33

Fong, Wai Mun. "A stochastic dominance analysis of yen carry trades." Journal of Banking & Finance 34, no. 6 (2010): 1237–46. http://dx.doi.org/10.1016/j.jbankfin.2009.11.017.

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34

VILELA MENDES, R., R. LIMA, and T. ARAÚJO. "A PROCESS-RECONSTRUCTION ANALYSIS OF MARKET FLUCTUATIONS." International Journal of Theoretical and Applied Finance 05, no. 08 (2002): 797–821. http://dx.doi.org/10.1142/s0219024902001730.

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The statistical properties of a stochastic process may be described (1) by the expectation values of the observables, (2) by the probability distribution functions or (3) by probability measures on path space. Here an analysis of level (3) is carried out for market fluctuation processes. Gibbs measures and chains with complete connections are considered. Some other topics are also discussed, in particular the asymptotic stationarity of the processes and the behavior of statistical indicators of level (1) and (2). We end up with some remarks concerning the nature and origin of the market fluctu

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35

DULOV, EUGENE V., HUMBERTO SARRIA ZAPATA, and NATALIA A. ANDRIANOVA. "GENERALIZED SINGULAR VALUE DECOMPOSITION AND ITS APPLICATIONS IN MODEL ANALYSIS." International Journal of Theoretical and Applied Finance 09, no. 02 (2006): 171–84. http://dx.doi.org/10.1142/s0219024906003500.

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For a variety of processes we can observe and register their characteristics, making up a sequence of measurement vectors or matrices (rectangular in general). Our goal is to extract some model dependent information using the available information. Such approaches are typical in technology (for a neat chemistry example, see [7,9]) and model analysis like parameter identification of linear stochastic dynamic systems. Since a stochastic nature of financial and economic data is evident, we can extend this data analysis technique to a number of new applications. If we are successful, some kind of

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36

Bohner, Martin, and Ivanka Stamova. "An impulsive delay discrete stochastic neural network fractional-order model and applications in finance." Filomat 32, no. 18 (2018): 6339–52. http://dx.doi.org/10.2298/fil1818339b.

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In this paper, we propose a new tool for modeling and analysis in finance, introducing an impulsive discrete stochastic neural network (NN) fractional-order model. The main advantages of the proposed approach are: (i) Using NNs which can be trained without the restriction of a model to derive parameters and discover relationships, driven and shaped solely by the nature of the data; (ii) using fractional-order differences, whose nonlocal property makes the fractional calculus a suitable tool for modeling actual financial systems; (iii) using impulsive perturbations, which give an opportunity to

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37

HAN, CHUAN-HSIANG, WEI-HAN LIU, and TZU-YING CHEN. "VaR/CVaR ESTIMATION UNDER STOCHASTIC VOLATILITY MODELS." International Journal of Theoretical and Applied Finance 17, no. 02 (2014): 1450009. http://dx.doi.org/10.1142/s0219024914500095.

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This paper proposes an improved procedure for stochastic volatility model estimation with an application to Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) estimation. This improved procedure is composed of the following instrumental components: Fourier transform method for volatility estimation, and importance sampling for extreme event probability estimation. The empirical analysis is based on several foreign exchange series and the S&P 500 index data. In comparison with empirical results by RiskMetrics, historical simulation, and the GARCH(1,1) model, our improved procedure out

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38

ZHANG, DI, and RODERICK V. N. MELNIK. "COMPUTATIONAL ASPECTS OF MONTE-CARLO SIMULATIONS OF THE FIRST PASSAGE TIME FOR MULTIVARIATE TRANSFORMED BROWNIAN MOTIONS WITH JUMPS." International Journal of Computational Methods 10, no. 05 (2013): 1350026. http://dx.doi.org/10.1142/s0219876213500266.

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Many problems in science, engineering, and finance require the information on the first passage time (FPT) of a stochastic process. Mathematically, such problems are often reduced to the evaluation of the probability density of the time for such a process to cross a certain level, a boundary, or to enter a certain region. While in other areas of applications the FPT problem can often be solved analytically, in finance we usually have to resort to the application of numerical procedures, in particular when we deal with jump-diffusion stochastic processes (JDP). In this paper, we propose a Monte

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39

Mallam, Hassane Abba, Natatou Dodo Moutari, Barro Diakarya, and Saley Bisso. "Extremal Copulas and Tail Dependence in Modeling Stochastic Financial Risk." European Journal of Pure and Applied Mathematics 14, no. 3 (2021): 1057–81. http://dx.doi.org/10.29020/nybg.ejpam.v14i3.3951.

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These last years the stochastic modeling became essential in financial risk management related to the ownership and valuation of financial products such as assets, options and bonds. This paper presents a contribution to the modeling of stochastic risks in finance by using both extensions of tail dependence coefficients and extremal dependance structures based on copulas. In particular, we show that when the stochastic behavior of a set of risks can be modeled by a multivariate extremal process a corresponding form of the underlying copula describing theirdependence is determined. Moreover a n

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40

Li, Shuang, Yanli Zhou, Xinfeng Ruan, and B. Wiwatanapataphee. "Pricing of American Put Option under a Jump Diffusion Process with Stochastic Volatility in an Incomplete Market." Abstract and Applied Analysis 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/236091.

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We study the pricing of American options in an incomplete market in which the dynamics of the underlying risky asset is driven by a jump diffusion process with stochastic volatility. By employing a risk-minimization criterion, we obtain the Radon-Nikodym derivative for the minimal martingale measure and consequently a linear complementarity problem (LCP) for American option price. An iterative method is then established to solve the LCP problem for American put option price. Our numerical results show that the model and numerical scheme are robust in capturing the feature of incomplete finance

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41

Wilmott, Paul, and Asli Oztukel. "Uncertain Parameters, an Empirical Stochastic Volatility Model and Confidence Limits." International Journal of Theoretical and Applied Finance 01, no. 01 (1998): 175–89. http://dx.doi.org/10.1142/s0219024998000096.

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In this paper we build upon the recently developed uncertain parameter framework for valuing derivatives in a worst-case scenario. We start by deriving a stochastic volatility model based on a simple analysis of time-series data. We use this stochastic model to examine the time evolution of volatility from an initial known value to a steady-state distribution in the long run. This empirical model is then incorporated into the uncertain parameter option valuation framework to provide "confidence limits" for the option value.

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42

KREMER, MARCEL, FRED ESPEN BENTH, BJÖRN FELTEN, and RÜDIGER KIESEL. "VOLATILITY AND LIQUIDITY ON HIGH-FREQUENCY ELECTRICITY FUTURES MARKETS: EMPIRICAL ANALYSIS AND STOCHASTIC MODELING." International Journal of Theoretical and Applied Finance 23, no. 04 (2020): 2050027. http://dx.doi.org/10.1142/s0219024920500272.

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This paper investigates the relationship between volatility and liquidity on the German electricity futures market based on high-frequency intraday prices. We estimate volatility by the time-weighted realized variance acknowledging that empirical intraday prices are not equally spaced in time. Empirical evidence suggests that volatility of electricity futures decreases as time approaches maturity, while coincidently liquidity increases. Established continuous-time stochastic models for electricity futures prices involve a growing volatility function in time and are thus not able to capture our

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43

Bodie, Zvi. "Robert C. Merton and the Science of Finance." Annual Review of Financial Economics 11, no. 1 (2019): 1–20. http://dx.doi.org/10.1146/annurev-financial-011019-040506.

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Starting with his 1970 doctoral dissertation and continuing to today, Robert C. Merton has revolutionized the theory and practice of finance. In 1997, Merton shared a Nobel Prize in Economics “for a new method to determine the value of derivatives.” His contributions to the science of finance, however, go far beyond that. In this article I describe Merton's main contributions. They include the following: 1. The introduction of continuous-time stochastic models (the Ito calculus) to the theory of household consumption and investment decisions. Merton's technique of dynamic hedging in continuous

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44

ÖNDER, A. ÖZLEM, ERTUG¬RUL DELIKTAS, and AYKUT LENGER. "Efficiency in the Manufacturing Industry of Selected Provinces in Turkey : A Stochastic Frontier Analysis." Emerging Markets Finance and Trade 39, no. 2 (2003): 98–113. http://dx.doi.org/10.1080/1540496x.2003.11052537.

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45

Fouque, Jean-Pierre, and Ning Ning. "Uncertain Volatility Models with Stochastic Bounds." SIAM Journal on Financial Mathematics 9, no. 4 (2018): 1175–207. http://dx.doi.org/10.1137/17m1116908.

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46

Sirignano, Justin, and Konstantinos Spiliopoulos. "Stochastic Gradient Descent in Continuous Time." SIAM Journal on Financial Mathematics 8, no. 1 (2017): 933–61. http://dx.doi.org/10.1137/17m1126825.

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47

Polala, Arun Kumar, and Giray Ökten. "Implementing de-biased estimators using mixed sequences." Monte Carlo Methods and Applications 26, no. 4 (2020): 293–301. http://dx.doi.org/10.1515/mcma-2020-2075.

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AbstractWe describe an implementation of the de-biased estimator using mixed sequences; these are sequences obtained from pseudorandom and low-discrepancy sequences. We use this implementation to numerically solve some stochastic differential equations from computational finance. The mixed sequences, when combined with Brownian bridge or principal component analysis constructions, offer convergence rates significantly better than the Monte Carlo implementation.

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48

Cozma, Andrei, Matthieu Mariapragassam, and Christoph Reisinger. "Calibration of a Hybrid Local-Stochastic Volatility Stochastic Rates Model with a Control Variate Particle Method." SIAM Journal on Financial Mathematics 10, no. 1 (2019): 181–213. http://dx.doi.org/10.1137/17m1114570.

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49

ALBEVERIO, SERGIO, ALEX POPOVICI, and VICTORIA STEBLOVSKAYA. "A NUMERICAL ANALYSIS OF THE EXTENDED BLACK–SCHOLES MODEL." International Journal of Theoretical and Applied Finance 09, no. 01 (2006): 69–89. http://dx.doi.org/10.1142/s0219024906003469.

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In this article some numerical results regarding the multidimensional extension of the Black–Scholes model introduced by Albeverio and Steblovskaya [1] (a multidimensional model with stochastic volatilities and correlations) are presented. The focus lies on aspects concerning the use of this model for the practice of financial derivatives. Two parameter estimation methods for the model using historical data from the market and an analysis of the corresponding numerical results are given. Practical advantages of pricing derivatives using this model compared to the original multidimensional Blac

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BOITOUT, NICOLAS, and LOREDANA URECHE-RANGAU. "TOWARDS A MULTIFRACTAL PARADIGM OF STOCHASTIC VOLATILITY?" International Journal of Theoretical and Applied Finance 07, no. 07 (2004): 823–51. http://dx.doi.org/10.1142/s0219024904002736.

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Abstract:

This paper examines the behavior of volatility and trading volume in an extended Mixture-of-Distribution Hypothesis framework. According to this Hypothesis, both volatility and volume are subordinated to the same latent, stochastic variable: the information flow. One way to enlarge this modeling in order to capture long range dependecies observed in these two variables is to use multi-component volatility models. However, traditional multi-component volatility models seem no longer enough to describe the more complex behavior of the volatility. This is why we introduce in this class of models,

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