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Books on the topic 'Financial market modelling'

Author: Grafiati

Published: 19 February 2023

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1

Market practice in financial modelling. Singapore: World Scientific Pub., 2012.

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2

Modelling international financial markets: An empirical study. Amsterdam: Thesis Publishers, 1994.

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3

Chevallier, Julien, Stéphane Goutte, David Guerreiro, Sophie Saglio, and Bilel Sanhaji, eds. Financial Mathematics, Volatility And Covariance Modelling: Volume 2. Milton, Cambridge, UK: Routledge, 2019.

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4

Watson, Iain David. An investigation of the use of market and industry data in financial distress modelling: Basedon data derived from the unlisted securities market and offical list. [s.l: The Author], 1995.

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5

Goutte, Stéphane, David Guerreiro, Sophie Saglio, and Bilel Sanhaji, eds. International Financial Markets: Volume 1. London, UK: Routledge, 2019.

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6

Cockerline, Jon P. Multicountry modelling of financial markets. Cambridge, MA: National Bureau of Economic Research, 1988.

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7

Marida, Bertocchi, Cavalli Enrico, Komlósi S. 1947-, EURO Working Group on Financial Modelling. Meeting., and EURO Working Group on Financial Modelling. Meeting., eds. Modelling techniques for financial markets and bank management. Heidelberg: Physica-Verlag, 1996.

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8

Bertocchi, Marida, Enrico Cavalli, and Sándor Komlósi, eds. Modelling Techniques for Financial Markets and Bank Management. Heidelberg: Physica-Verlag HD, 1996. http://dx.doi.org/10.1007/978-3-642-51730-3.

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9

Prices in financial markets. New York: Oxford University Press, 1990.

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10

Chaos and order in the capital markets: A new view of cycles, prices, and market volatility. 2nd ed. New York: Wiley, 1996.

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11

Chaos and order in the capital markets: A new view of cycles, prices, and market volatility. New York: Wiley, 1991.

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12

Walker, Martin, 1953 Jan. 17-, ed. Information and capital markets. Oxford, UK: B. Blackwell, 1987.

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13

Strong, Norman. Information and capital markets. Oxford: Basil Blackwell, 1987.

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14

Strong, Norman. Information and capital markets. Oxford: Basil Blackwell, 1989.

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15

Markowitz, H. Mean-variance analysis in portfolio choice and capital markets. Oxford, OX, UK: B. Blackwell, 1987.

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16

Markowitz, H. Mean-variance analysis in portfolio choice and capital markets. Oxford: Blackwell, 1990.

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17

Markowitz, H. Mean-variance analysis in portfolio choice and capital markets. New Hope: Frank J. Fabozzi Associates, 1987.

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18

Finance theory and asset pricing. Oxford: Clarendon Press, 1995.

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19

Guerreiro, David, Sophie Saglio, Julien Chevallier, Stéphane Goutte, and Bilel Sanhaji. Financial Mathematics Volatility and Covariance Modelling. Taylor & Francis Group, 2021.

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20

Guerreiro, David, Sophie Saglio, Julien Chevallier, Stéphane Goutte, and Bilel Sanhaji. Financial Mathematics, Volatility and Covariance Modelling: Volume 2. Taylor & Francis Group, 2019.

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21

Guerreiro, David, Sophie Saglio, Julien Chevallier, Stéphane Goutte, and Bilel Sanhaji. Financial Mathematics, Volatility and Covariance Modelling: Volume 2. Taylor & Francis Group, 2019.

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22

Guerreiro, David, Sophie Saglio, Julien Chevallier, Stéphane Goutte, and Bilel Sanhaji. Financial Mathematics, Volatility and Covariance Modelling: Volume 2. Taylor & Francis Group, 2019.

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23

Jia, Daniel Lukui. Dynamic Macroeconomic Models in Emerging Market Economies: DSGE Modelling with Financial and Housing Sectors. Springer Singapore Pte. Limited, 2021.

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24

Jia, Daniel Lukui. Dynamic Macroeconomic Models in Emerging Market Economies: DSGE Modelling with Financial and Housing Sectors. Springer Singapore Pte. Limited, 2020.

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25

Watanapalachaikul, Sethapong, and Sardar M. N. Islam. Empirical Finance: Modelling and Analysis of Emerging Financial and Stock Markets. Physica-Verlag, 2012.

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26

Islam, Sardar M. N., and Sethapong Watanapalachaikul. Empirical Finance: Modelling and Analysis of Emerging Financial and Stock Markets (Contributions to Economics). Physica-Verlag Heidelberg, 2004.

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27

Moix, Pierre-Yves. Measurement of Market Risk: Modelling of Risk Factors, Asset Pricing, and Approximation of Portfolio Distributions. Springer London, Limited, 2012.

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28

International Financial Markets. Taylor & Francis Group, 2021.

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29

The Measurement of Market Risk: Modelling of Risk Factors, Asset Pricing, and Approximation of Portfolio Distributions (Lecture Notes in Economics and Mathematical Systems). Springer, 2001.

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30

Guerreiro, David, Sophie Saglio, Julien Chevallier, Stéphane Goutte, and Bilel Sanhaji. International Financial Markets: Volume 1. Taylor & Francis Group, 2019.

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31

Guerreiro, David, Sophie Saglio, Julien Chevallier, Stéphane Goutte, and Bilel Sanhaji. International Financial Markets: Volume 1. Taylor & Francis Group, 2019.

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32

Guerreiro, David, Sophie Saglio, Julien Chevallier, Stéphane Goutte, and Bilel Sanhaji. International Financial Markets: Volume 1. Taylor & Francis Group, 2019.

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33

Guerreiro, David, Sophie Saglio, Julien Chevallier, Stéphane Goutte, and Bilel Sanhaji. International Financial Markets: Volume 1. Taylor & Francis Group, 2019.

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34

Financial Modelling in Commodity Markets. Taylor & Francis Group, 2020.

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35

Fanelli, Viviana. Financial Modelling in Commodity Markets. Taylor & Francis Group, 2020.

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36

Fanelli, Viviana. Financial Modelling in Commodity Markets. Taylor & Francis Group, 2019.

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37

Fanelli, Viviana. Financial Modelling in Commodity Markets. Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781315184371.

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38

Fanelli, Viviana. Financial Modelling in Commodity Markets. Taylor & Francis Group, 2019.

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39

Fanelli, Viviana. Financial Modelling in Commodity Markets. Taylor & Francis Group, 2020.

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40

Fanelli, Viviana. Financial Modelling in Commodity Markets. Taylor & Francis Group, 2019.

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41

Ugolini, Andrea. Modelling systemic risk in financial markets. Editorial de la Universidad de Cantabria, 2017. http://dx.doi.org/10.22429/euc2020.007.

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42

Tunaru, Radu S. Financial Modelling for Mortgages. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198742920.003.0003.

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The purpose of this chapter is to familiarize the reader with the concept of mortgages and to describe the behavioural factors behind decisions to prepay and default on mortgages that ultimately influence the evolution of real-estate markets. The other scope of this chapter is to highlight the complexity of the financial modelling related to the real-estate area, and to mortgages in general. Mortgage loans are contracts carrying real-estate risk. They represent a large part of the assets in the banking systemsworldwide and banks find it difficult to hedge this asset class against real-estate risk. Prepayments and default models are essential to any risk management enterprise looking to contain this type of risk, and they are reviewed in this chapter. There are many models proposed in the academic and practitioner literature, but there is not a single model that is widely accepted.

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43

Benes, Jaromir, Andrew Berg, Rafael Portillo, and David Vavra. Modelling Sterilized Interventions and Balance Sheet Effects of Monetary Policy in a New Keynesian Framework. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198785811.003.0013.

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The authors study a wide range of hybrid inflation-targeting (IT) and managed exchange rate regimes, analysing their implications for inflation, output and the exchange rate in the presence of various domestic and external shocks. To this end, the chapter presents an open economy New Keynesian model featuring sterilized interventions in the foreign exchange (FX) market as an additional central bank instrument operating alongside the Taylor rule, and affecting the economy through portfolio balance sheet effects in the financial sector. The chapter shows that there can be advantages to combining IT with some degree of exchange rate management via FX interventions. Unlike ‘pure’ IT or exchange rate management via interest rates, FX interventions can help insulate the economy against certain shocks, especially shocks to international financial conditions. However, managing the exchange rate through FX interventions may also hinder necessary exchange rate adjustments, e.g., in the presence of terms of trade shocks.

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44

Quintana, José Mario, Carlos Carvalho, James Scott, and Thomas Costigliola. Extracting S&P500 and NASDAQ Volatility: The Credit Crisis of 2007–2008. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.13.

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This article demonstrates the utility of Bayesian modelling and inference in financial market volatility analysis, using the 2007-2008 credit crisis as a case study. It first describes the applied problem and goal of the Bayesian analysis before introducing the sequential estimation models. It then discusses the simulation-based methodology for inference, including Markov chain Monte Carlo (MCMC) and particle filtering methods for filtering and parameter learning. In the study, Bayesian sequential model choice techniques are used to estimate volatility and volatility dynamics for daily data for the year 2007 for three market indices: the Standard and Poor’s S&P500, the NASDAQ NDX100 and the financial equity index called XLF. Three models of financial time series are estimated: a model with stochastic volatility, a model with stochastic volatility that also incorporates jumps in volatility, and a Garch model.

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45

Modelling Techniques for Financial Markets and Bank Management. Physica, 2012.

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46

Cavalli, Enrico, Marida Bertocchi, and Sandor Komlosi. Modelling Techniques for Financial Markets and Bank Management. Physica-Verlag, 2012.

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47

Fernández-Villaverde, Jesús, Pablo Guerrón-Quintana, and Juan Rubio-Ramírez. Futures markets, Bayesian forecasting and risk modelling. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.14.

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This article demonstrates the utility of the Bayesian approach in forecasting and risk modelling regarding speculative trading strategies in financial futures markets. It first provides an overview of subjective expectations that are motivated as fair prices of futures contracts before discussing the futures markets and a portfolio mean-variance efficiency generalization. In particular, it considers the critical role of hedging to ensue attractive risk-adjusted performance. It also describes general Bayesian dynamic models and specific Bayesian dynamic linear models for assessing risk models in terms of their hedging effectiveness in the context of the risk-adjusted performance of trading strategies. The article showcases applied Bayesian thinking in the context of financial investment management, highlighting the corresponding concepts of betting and investing, prices and expectations, and coherence and arbitrage-free pricing in futures markets over the period 1990–2008.

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48

Bialkowski, Jedrzej. Between Futures and Spot Markets- An Approach to Modelling Linkages among Financial Markets. VDM Verlag Dr. Mueller e.K., 2007.

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49

Ruttiens, Alain. Mathematics of the Financial Markets: Financial Instruments and Derivatives Modelling, Valuation and Risk Issues. Wiley & Sons, Incorporated, John, 2013.

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50

Ruttiens, Alain. Mathematics of the Financial Markets: Financial Instruments and Derivatives Modelling, Valuation and Risk Issues. Wiley & Sons, Incorporated, John, 2013.

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