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Books on the topic 'Volatility estimation'

Author: Grafiati

Published: 4 June 2021

Last updated: 4 March 2023

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1

Mancino, Maria Elvira, Maria Cristina Recchioni, and Simona Sanfelici. Fourier-Malliavin Volatility Estimation. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50969-3.

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2

Bishwal, Jaya P. N. Parameter Estimation in Stochastic Volatility Models. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-03861-7.

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3

Sandmann, G. Maximum likelihood estimation of stochastic volatility models. London: London School of Economics, Financial Markets Group, 1996.

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4

Aït-Sahalia, Yacine. Maximum likelihood estimation of stochastic volatility models. Cambridge, MA: National Bureau of Economic Research, 2004.

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5

Andersen, Torben G. Jump-robust volatility estimation using nearest neighbor truncation. Cambridge, MA: National Bureau of Economic Research, 2009.

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6

Andersen, Torben G. Jump-robust volatility estimation using nearest neighbor truncation. Cambridge, MA: National Bureau of Economic Research, 2009.

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7

Alizadeh, Sassan. High- and low-frequency exchange rate volatility dynamics: Range-based estimation of stochastic volatility models. Cambridge, MA: National Bureau of Economic Research, 2001.

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8

Aït-Sahalia, Yacine. Ultra high frequency volatility estimation with dependent microstructure noise. Cambridge, Mass: National Bureau of Economic Research, 2005.

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9

Zaffaroni, Paolo. Gaussian estimation of long-rangedependent volatility in asset prices. London: Suntory Centre, 1997.

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10

Aït-Sahalia, Yacine. Ultra high frequency volatility estimation with dependent microstructure noise. Cambridge, MA: National Bureau of Economic Research, 2005.

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11

Krichene, Noureddine. Modeling stochastic volatility with application to stock returns. [Washington, D.C.]: International Monetary Fund, African Department, 2003.

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12

Wright, Jonathan H. Log-periodogram estimation of long memory volatility dependencies with conditionally heavy tailed returns. Washington, D.C: Federal Reserve Board, 2000.

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13

P, Chaboud Alain, and Bank for International Settlements. Monetary and Economic Dept., eds. Frequency of observation and the estimation of integrated volatility in deep and liquid financial markets. Basel, Switzerland: Bank for International Settlements, 2008.

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14

Chaboud, Alain P. Frequency of observation and the estimation of integrated volatility in deep and liquid financial markets. Washington, D.C: Federal Reserve Board, 2007.

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15

Bollerslev, Tim. Dynamic estimation of volatility risk premia and investor risk aversion from option-implied and realized volatilities. Washington, D.C: Federal Reserve Board, 2004.

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16

Fornari, Fabio. A simple approach to the estimation of continuous time CEV stochastic volatility models of the short-term rate. [Roma]: Banca d'Italia, 2001.

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17

Bishwal, Jaya P. N. Parameter Estimation in Stochastic Volatility Models. Springer International Publishing AG, 2022.

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18

Mancino, Maria Elvira, Maria Cristina Recchioni, and Simona Sanfelici. Fourier-Malliavin Volatility Estimation: Theory and Practice. Springer International Publishing AG, 2017.

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19

Fengler, Matthias R. Semiparametric Modeling of Implied Volatility. Springer, 2008.

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20

Semiparametric Modeling of Implied Volatility. Springer, 2005.

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21

Krichene, Noureddine. Modeling Stochastic Volatility with Application to Stock Returns. International Monetary Fund, 2003.

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22

Krichene, Noureddine. Modeling Stochastic Volatility with Application to Stock Returns. International Monetary Fund, 2003.

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23

Krichene, Noureddine. Modeling Stochastic Volatility with Application to Stock Returns. International Monetary Fund, 2003.

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24

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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25

Lux, Thomas, and Mawuli Segnon. Multifractal Models in Finance. Edited by Shu-Heng Chen, Mak Kaboudan, and Ye-Rong Du. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199844371.013.8.

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This chapter provides an overview over the recently developed so-called multifractal (MF) approach for modeling and forecasting volatility. For analysts and policy makers, volatility is a key variable for understanding market fluctuations. Analysts need accurate forecasts of volatility for tasks such as risk management, as well as option and futures pricing. In addition, asset market volatility plays an important role in monetary policy. The chapter, then, outlines the genesis of the multifractal approach from similar models of turbulent flows in statistical physics and provides details about different specifications of multifractal time series models in finance, available methods for their estimation, and the current state of their empirical applications.

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26

Makatjane, Katleho, and Roscoe van Wyk. Identifying structural changes in the exchange rates of South Africa as a regime-switching process. UNU-WIDER, 2020. http://dx.doi.org/10.35188/unu-wider/2020/919-8.

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Exchange rate volatility is said to exemplify the economic health of a country. Exchange rate break points (known as structural breaks) have a momentous impact on the macroeconomy of a country. Nonetheless, this country study makes use of both unsupervised and supervised machine learning algorithms to classify structural changes as regime shifts in real exchange rates in South Africa. Weekly data for the period January 2003–June 2020 are used. To these data we apply both non-linear principal component analysis and Markov-switching generalized autoregressive conditional heteroscedasticity. The former approach is used to reduce the dimensionality of the data using an orthogonal linear transformation by preserving the statistical variance of the data, with the proviso that a new trait is non-linearly independent, and it identifies the number of regime switches that are to be used in the Markov-switching model. The latter is used to partition the variance in each regime by allowing an estimation of multiple break transitions. The transition breakpoints estimates derived from this machine learning approach produce results that are comparable to other methods on similar system sizes. Application of these methods shows that the machine learning approach can also be employed to identify structural changes as a regime-switching process. During times of financial crisis, the growing concern over exchange rate volatility, including its adverse effects on employment and growth, broadens the debates on exchange rate policies. Our results should help the South African monetary policy committee to anticipate when exchange rates will pick up and be prepared for the effects of periods of high exchange rates.

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27

Poel, Jeff D. A novel apparatus for estimating pesticide volatility from spray droplets. 1996.

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