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Books on the topic 'Asset Volatility'

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Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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1

Attanasio, Orazio P. Asset holding and consumption volatility. Cambridge, MA: National Bureau of Economic Research, 1998.

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2

Bernanke, Ben. Monetary policy and asset price volatility. Cambridge, MA: National Bureau of Economic Research, 2000.

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3

Asset price dynamics, volatility, and prediction. Princeton, N.J: Princeton University Press, 2005.

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4

Fund, International Monetary, ed. Discretionary trading and asset price volatility. Washington, D.C: International Monetary Fund, 1995.

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5

Adrian, Tobias. Inference, arbitrage, and asset price volatility. [New York, N.Y.]: Federal Reserve Bank of New York, 2004.

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6

Bodenstein, Martin. International asset markets and real exchange rate volatility. Washington, D.C: Federal Reserve Board, 2006.

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7

Allen, Franklin. Limited market participation and volatility of asset prices. London: London School of Economics, Financial Markets Group, 1993.

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8

Aït-Sahalia, Yacine. Dynamic equilibrium and volatility in financial asset markets. Cambridge, MA: National Bureau of Economic Research, 1996.

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9

Anderson, Nicola. UK asset price volatility over the last 50 years. London: Bank of England, 1996.

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10

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

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11

Anderson, Nicola. UK asset price volatility over the last 50 years. London: Bank of England, 1996.

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12

Volatility as an asset class: Obvious benefits and hidden risks. New York: Lang, 2015.

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13

Christoffersen, Peter F. Financial asset returns, direction-of-change forecasting, and volatility dynamics. Cambridge, Mass: National Bureau of Economic Research, 2003.

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14

Byers, J. D. Volatility persistence in asset markets: Long memory in high/low prices. Cardiff: Cardiff Business School, Financial and Banking Economics Research Group, 1996.

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15

Bartolini, Leonardo. Excess volatility and the asset-pricing exchange rate model with unobservable fundamentals. [Washington, D.C.]: International Monetary Fund, Research Department and Asia and Pacific Department, 1999.

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16

Graham, John R. Market timing ability and volatility implied in investment newsletters' asset allocation recommendations. Cambridge, MA: National Bureau of Economic Research, 1994.

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17

Diebold, Francis X. Measuring financial asset return and volatility spillovers, with application to global equity markets. Cambridge, MA: National Bureau of Economic Research, 2008.

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18

Dickens, R. R. The arch model as applied to the study of international asset market volatility. London: Economics Division, Bank of England, 1986.

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19

King, Mervyn. A heteroscedastic factor model of asset returns and risk premia with time-varying volatility. London: LSE Financial Markets Group, 1990.

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20

Gayioglu, S. Influences of imperfect capital mobility and asset substitutability on exchange rate volatility: Money market and demandshocks. Aberdeen: University of Aberdeen. Department of Economics, 1990.

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21

Gazioglu, S. Influence of fiscal shocks and varying marginal propensity to consume (MPC) on exchange rate volatility under imperfect capital mobility and asset substitutability. Aberdeen: University of Aberdeen, Dept. of Economics, 1991.

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22

Andersen, Torben G. Construction and interpretation of model-free implied volatility. Cambridge, Mass: National Bureau of Economic Research, 2007.

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23

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

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24

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

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25

Guo, Hui. Does stock market volatility forecast returns: The international evidence. [St. Louis, Mo.]: Federal Reserve Bank of St. Louis, 2003.

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26

Christoffersen, Peter F. How relevant is volatility forecasting for financial risk management? Cambridge, MA: National Bureau of Economic Research, 1998.

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27

Rachev, S. T. Financial models with Lévy processes and volatility clustering. Hoboken, N.J: John Wiley, 2011.

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28

Andersen, Torben G. Answering the critics: Yes, ARCH models do provide good volatility forecasts. Cambridge, MA: National Bureau of Economic Research, 1997.

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29

Financial models with Levy processes and volatility clustering. Hoboken, N.J: Wiley, 2011.

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30

Chacko, George. Dynamic consumption and portfolio choice with stochastic volatility in incomplete markets. Cambridge, MA: National Bureau of Economic Research, 1999.

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31

Andersen, Torben G. Heterogeneous information arrivals and return volatility dynamics: Uncovering the long-run in high frequency returns. Cambridge, MA: National Bureau of Economic Research, 1996.

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32

Nelken, Israel. Volatility As An Asset Class. Risk Publications, 2007.

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33

Taylor, Stephen J. Asset Price Dynamics, Volatility, and Prediction. Princeton University Press, 2011.

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34

Taylor, Stephen J. Asset Price Dynamics, Volatility, and Prediction. Princeton University Press, 2011.

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35

Taylor, Stephen J. Asset Price Dynamics, Volatility, and Prediction. Princeton University Press, 2007.

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36

Cova, Pietro, Alessandro Rebucci, and Akito Matsumoto. New Shocks and Asset Price Volatility in General Equilibrium. International Monetary Fund, 2011.

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37

Cova, Pietro, Massimiliano Pisani, Alessandro Rebucci, and Akito Matsumoto. New Shocks and Asset Price Volatility in General Equilibrium. International Monetary Fund, 2011.

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38

Cova, Pietro, Massimiliano Pisani, Alessandro Rebucci, and Akito Matsumoto. New Shocks and Asset Price Volatility in General Equilibrium. International Monetary Fund, 2011.

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39

Cohen, Benjamin H. Derivatives and asset price volatility: A test using variance ratios. Basle, 1996.

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40

Kokoszczynski, Ryszard, Piotr Wójcik, Juliusz Jablecki, Pawel Sakowski, and Robert Slepaczuk. Volatility As an Asset Class: Obvious Benefits and Hidden Risks. Lang GmbH, Internationaler Verlag der Wissenschaften, Peter, 2015.

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41

Kokoszczynski, Ryszard, Piotr Wójcik, Juliusz Jablecki, Pawel Sakowski, and Robert Slepaczuk. Volatility As an Asset Class: Obvious Benefits and Hidden Risks. Lang GmbH, Internationaler Verlag der Wissenschaften, Peter, 2015.

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42

Kokoszczynski, Ryszard, Piotr Wójcik, Juliusz Jablecki, Pawel Sakowski, and Robert Slepaczuk. Volatility As an Asset Class: Obvious Benefits and Hidden Risks. Lang GmbH, Internationaler Verlag der Wissenschaften, Peter, 2015.

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43

Bartolini, Leonardo, and Lorenzo Giorgianni. Excess Volatility and the Asset-Pricing Exchange Rate Model with Unobservable Fundamentals. International Monetary Fund, 1999.

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44

Bartolini, Leonardo, and Lorenzo Giorgianni. Excess Volatility and the Asset-Pricing Exchange Rate Model with Unobservable Fundamentals. International Monetary Fund, 1999.

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45

Bartolini, Leonardo, and Lorenzo Giorgianni. Excess Volatility and the Asset-Pricing Exchange Rate Model with Unobservable Fundamentals. International Monetary Fund, 1999.

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46

Larson, Donald F. Food Prices and Food Price Volatility. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190656010.003.0022.

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This chapter examines food prices from 1900 to 2015. Despite growing populations, rising incomes, new technologies, globalization, and the emergence of commodities as an asset class, no trends are evident in food price levels or volatility. Still, food prices have averaged higher since 2010, harming the poor and raising fears that agricultural productivity growth has slowed. Consistently since 1900, food prices have been more volatile than the prices of manufactured goods and most other commodity groups. This relation drives terms-of-trade volatility, which slows economic growth. At the farm level, price volatility impedes investment and technology adoption, and encourages low-income livelihood strategies. Past policies to manage food prices have not worked and governments have shifted to policies aimed at mitigating the consequences of high and volatile food prices. Extending the reach of risk markets, warehouse receipt systems, index insurance, and contract farming can be useful policy components.
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47

Min, Hong G., and Jong-goo Park. How the Republic of Korea's Financial Structure Affects the Volatility of Four Asset Prices. The World Bank, 1999. http://dx.doi.org/10.1596/1813-9450-2327.

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48

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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49

Back, Kerry E. Learning. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190241148.003.0023.

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Continuous‐time filtering is explained, including the Kalman filter and filtering for a Markov chain with hidden states. Filtering theory is applied to analyze portfolio choice and equilibrium asset prices. When the expected return of an asset is unknown and is estimated from past returns, the myopic demand is a momentum strategy. When investors learn expected consumption growth from realized consumption growth, equilibrium prices are more sensitive to consumption shocks and the equity premium is higher. When the consumption growth rate follows a Markov chain with hidden states, return volatility tends to be higher when investors are less certain about which state the economy is in.
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50

Spagna, Irene. Becoming the World’s Biggest Market. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190864576.003.0002.

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This chapter analyzes the growth of OTC derivatives before the global financial crisis of 2008 and the role of credit default swaps, in particular, in the near collapse of the global economy. It begins by exploring the basic characteristics of derivatives used as risk management instruments by investors to hedge against or exploit the volatility of asset prices. The analysis further reveals that the pre-crisis period was characterized by a broad-based consensus favoring deregulated markets and globally designed private rules. While not always unanimously supported, permissive public regulatory choices were often encouraged by interest group lobbying, the market-friendly views of many domestic authorities, and concerns about regulatory uncertainty and international competitiveness.
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