Relevant bibliographies by topics / Asset Volatility

Academic literature on the topic 'Asset Volatility'

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

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Journal articles on the topic "Asset Volatility"

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Correia, Maria, Johnny Kang, and Scott Richardson. "Asset volatility." Review of Accounting Studies 23, no. 1 (December 22, 2017): 37–94. http://dx.doi.org/10.1007/s11142-017-9431-1.

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Nagel, Stefan, and Amiyatosh Purnanandam. "Banks’ Risk Dynamics and Distance to Default." Review of Financial Studies 33, no. 6 (October 17, 2019): 2421–67. http://dx.doi.org/10.1093/rfs/hhz125.

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Abstract We adapt structural models of default risk to take into account the special nature of bank assets. The usual assumption of lognormally distributed asset values is not appropriate for banks. Typical bank assets are risky debt claims with concave payoffs. Because of the payoff nonlinearity, bank asset volatility rises following negative shocks to borrower asset values. As a result, standard structural models with constant asset volatility can severely understate banks’ default risk in good times when asset values are high. Additionally, bank equity return volatility is much more sensitive to negative shocks to asset values than in standard structural models.
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Khuzwayo, Bhekinkosi, and Eben Mare. "Aspects of volatility targeting for South African equity investors." South African Journal of Economic and Management Sciences 17, no. 5 (November 28, 2014): 691–99. http://dx.doi.org/10.4102/sajems.v17i5.662.

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We consider so-called volatility targeting strategies in the South African equity market. These strategies are aimed at keeping the volatility of a portfolio consisting of a risky asset, typically an equity index, and cash fixed. This is done by changing the allocation of the assets based on an indicator of the future volatility of the risky asset. We use the three month rolling implied volatility as an indicator of future volatility to influence our asset allocation. We compare investments based on different volatility targets to the performance of bonds, equities, property as well as the Absolute Return peer mean. We examine risk and return characteristics of the volatility targeting strategy as compared to different asset classes.
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Ayuning Putri, Anisa Ferata. "FAKTOR-FAKTOR PENENTU VOLATILITAS HARGA SAHAM SEKTOR PERUSAHAAN PROPERTI, REAL ESTATE DAN BUILDING CONSTRUCTION." Jurnal Akuntansi dan Keuangan 8, no. 2 (September 2, 2020): 109. http://dx.doi.org/10.29103/jak.v8i2.2563.

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Abstrak : Tujuan dari riset ini adalah untuk melihat pengaruh Devidend Payout Ratio, Devidend Yield, Earning Volatility, Pertumbuhan Aset, Leverage, Ukuran Perusahaan dan Blockholders terhadap Volatilitas Harga Saham. Pada penelitian ini akan menggunakan data laporan Perusahaan Lembaga Keuangan dari Bursa Efek Indonesia (BEI) selama periode 2016-2018 dengan populasi sebanyak 201 perusahaan metode purposive sampling digunakan untuk memperoleh sampel selama 3 tahun sebayak 36 perusahaan. Data penelitian ini di analisis menggunaka analisis regresi linier berganda. Hasil analisis ditemukan bahwa Dividen Payout Ratio, Earning Volatility, Pertumbuhan Aset, Ukuran Perusahaan, Blockholdres tidak berpengaruh terhadap Volatilitas Harga Saham Sedangkan untuk variabel Dividen Yield berpengaruh terhadap Volatilitas Harga Saham.Kata kunci : Volatilitas Harga Saham, Dividen Payout Ratio, Dividen Yield, Earning Volatility, Pertumbuhan Aset, Ukuran Perusahaan, Blockholdres Abstrack : The purpose of this study is to examine the effect of Dividend Payment Ratio, Dividend Results, Productive Volatility, Estimated Assets, Leverage, Firm Size and Blockholders on Stock Price Volatility. In this study will use the report data of Financial Institution Companies from the Indonesia Stock Exchange (BEI) during the 2016-2018 period with a population of 201 companies using a purposive sampling method for a 3-year sample of 36 companies. The data of this study were analyzed using multiple linear regression analysis. The results of the analysis found that Dividend Payment Ratios, Income Volatility, Assets, Company Size, Blockholdres are not in conflict with Stock Price Volatility While for the Result Dividend variable produced on Stock Price Volatility. Keywords: Stock Price Volatility, Dividend Payout Ratio, Yield Dividend, Income Volatility, Asset Inventory, Firm Size, Blockholding
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Fernholz, Ricardo T., and Christoffer Koch. "Big Banks, Idiosyncratic Volatility, and Systemic Risk." American Economic Review 107, no. 5 (May 1, 2017): 603–7. http://dx.doi.org/10.1257/aer.p20171007.

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Starting in the 1990s, US bank assets grew more concentrated among a few large institutions. We explore the changing role of idiosyncratic volatility as a shaping force of the bank asset power law distribution. Our results reveal that idiosyncratic asset volatilities for bank-holding companies declined since the 1990s. To the extent that firm-specific shocks can have significant macroeconomic consequences, this result implies that even as one obvious source of aggregate risk and contagion--bank asset concentration--has increased, another important source--idiosyncratic volatility--has diminished.
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Ezzat, Heba M. "Disposition effect and multi-asset market dynamics." Review of Behavioral Finance 11, no. 2 (June 28, 2019): 144–64. http://dx.doi.org/10.1108/rbf-01-2018-0003.

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Purpose Asset pricing dynamics in a multi-asset framework when investors’ trading exhibits the disposition effect is studied. The purpose of this paper is to explore asset pricing dynamics and the switching behavior among multiple assets. Design/methodology/approach The dynamics of complex financial markets can be best explored by following agent-based modeling approach. The artificial financial market is populated with traders following two heterogeneous trading strategies: the technical and the fundamental trading rules. By simulation, the switching behavior among multiple assets is investigated. Findings The proposed framework can explain important stylized facts in financial time series, such as random walk price dynamics, bubbles and crashes, fat-tailed return distributions, absence of autocorrelation in raw returns, persistent long memory of volatility, excess volatility, volatility clustering and power-law tails. In addition, asset returns possess fractal structure and self-similarity features; though the switching behavior is only allowed among the asset markets. Practical implications The model demonstrates stylized facts of most real financial markets. Thereafter, the proposed model can serve as a testbed for policy makers, scholars and investors. Originality/value To the best of knowledge, no research has been conducted to introduce the disposition effect to a multi-asset agent-based model.
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Paule-Vianez, Jessica, Camilo Prado-Román, and Raúl Gómez-Martínez. "Economic policy uncertainty and Bitcoin. Is Bitcoin a safe-haven asset?" European Journal of Management and Business Economics 29, no. 3 (March 11, 2020): 347–63. http://dx.doi.org/10.1108/ejmbe-07-2019-0116.

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PurposeThe goal of this work is to determine whether Bitcoin behaves as a safe-haven asset. In order to do so, the influence of Economic Policy Uncertainty (EPU) on Bitcoin returns and volatility was studied.Design/methodology/approachIt is evaluated whether, when compared with the evolution of EPU, Bitcoin's returns and volatility show behaviours typical of safe havens or rather, those of conventional speculative assets. When faced with an increase in EPU, safe havens – such as gold – can be expected to increase their returns and volatility, while conventional speculative assets will increase their volatility and reduce their returns. This study uses simple linear regression and quantile regression models on a daily data sample from 19 July 2010 to 11 April 2019, to analyse the influence of EPU on the returns and volatility of Bitcoin and gold.FindingsBitcoin's returns and volatility increase during more uncertain times, just like gold, showing that Bitcoin acts not only as a means of exchange but also shows characteristics of investment assets, specifically of safe havens. These findings provide useful information to investors by allowing Bitcoin to be considered as a tool to protect savings in times of economic uncertainty and to diversify portfolios.Originality/valueThis study complements and expands current research by aiming to answer the question of whether Bitcoin is a simple speculative asset or a safe haven. The most significant contribution is to show that Bitcoin is not a mere speculative asset but behaves like a safe haven.
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Blanco, Belen. "Capturing the volatility smile: parametric volatility models versus stochastic volatility models." Public and Municipal Finance 5, no. 4 (December 26, 2016): 15–22. http://dx.doi.org/10.21511/pmf.05(4).2016.02.

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Black-Scholes option pricing model (1973) assumes that all option prices on the same underlying asset with the same expiration date, but different exercise prices should have the same implied volatility. However, instead of a flat implied volatility structure, implied volatility (inverting the Black-Scholes formula) shows a smile shape across strikes and time to maturity. This paper compares parametric volatility models with stochastic volatility models in capturing this volatility smile. Results show empirical evidence in favor of parametric volatility models. Keywords: smile volatility, parametric, stochastic, Black-Scholes. JEL Classification: C14 C68 G12 G13
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Cirelli, Simone, Sebastiano Vitali, Sergio Ortobelli Lozza, and Vittorio Moriggia. "A conservative discontinuous target volatility strategy." Investment Management and Financial Innovations 14, no. 2 (July 12, 2017): 176–90. http://dx.doi.org/10.21511/imfi.14(2-1).2017.03.

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The asset management sector is constantly looking for a reliable investment strategy, which is able to keep its promises. One of the most used approaches is the target volatility strategy that combines a risky asset with a risk-free trying to maintain the portfolio volatility constant over time. Several analyses highlight that such target is fulfilled on average, but in periods of crisis, the portfolio still suffers market’s turmoils. In this paper, the authors introduce an innovative target volatility strategy: the discontinuous target volatility. Such approach turns out to be more conservative in high volatility periods. Moreover, the authors compare the adoption of the VIX Index as a risk measure instead of the classical standard deviation and show whether the former is better than the latter. In the last section, the authors also extend the analysis to remove the risk-free assumption and to include the correlation structure between two risky assets. Empirical results on a wide time span show the capability of the new proposed strategy to enhance the portfolio performance in terms of standard measures and according to stochastic dominance theory.
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La Bua, Gaetano, and Daniele Marazzina. "On the application of Wishart process to the pricing of equity derivatives: the multi-asset case." Computational Management Science 18, no. 2 (March 9, 2021): 149–76. http://dx.doi.org/10.1007/s10287-021-00388-7.

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AbstractGiven the inherent complexity of financial markets, a wide area of research in the field of mathematical finance is devoted to develop accurate models for the pricing of contingent claims. Focusing on the stochastic volatility approach (i.e. we assume to describe asset volatility as an additional stochastic process), it appears desirable to introduce reliable dynamics in order to take into account the presence of several assets involved in the definition of multi-asset payoffs. In this article we deal with the multi asset Wishart Affine Stochastic Correlation model, that makes use of Wishart process to describe the stochastic variance covariance matrix of assets return. The resulting parametrization turns out to be a genuine multi-asset extension of the Heston model: each asset is exactly described by a single instance of the Heston dynamics while the joint behaviour is enriched by cross-assets and cross-variances stochastic correlation, all wrapped in an affine modeling. In this framework, we propose a fast and accurate calibration procedure, and two Monte Carlo simulation schemes.
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Dissertations / Theses on the topic "Asset Volatility"

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Murara, Jean-Paul. "Asset Pricing Models with Stochastic Volatility." Licentiate thesis, Mälardalens högskola, Utbildningsvetenskap och Matematik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-31576.

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Asset pricing modeling is a wide range area of research in Financial Engineering. In this thesis, which consists of an introduction, three papers and appendices; we deal with asset pricing models with stochastic volatility. Here stochastic volatility modeling includes diffusion models and regime-switching models. Stochastic volatility models appear as a response to the weakness of the constant volatility models. In Paper A , we present a survey on popular diffusion models where the volatility is itself a random process and we present the techniques of pricing European options under each model. Comparing single factor stochastic volatility models to constant factor volatility models it seems evident that the stochastic volatility models represent nicely the movement of the asset price and its relations with changes in the risk. However, these models fail to explain the large independent fluctuations in the volatility levels and slope. We consider Chiarella and Ziveyi model, which is a subclass of the model presented in Christoffersen and in paper A, we also explain a multi-factor stochastic volatility model presented in Chiarella and Ziveyi. We review the first-order asymptotic expansion method for determining European option price in such model. Multiscale stochastic volatilities models can capture the smile and skew of volatilities and therefore describe more accurately the movements of the trading prices. In paper B, we provide experimental and numerical studies on investigating the accuracy of the approximation formulae given by this asymptotic expansion. We present also a procedure for calibrating the parameters produced by our first-order asymptotic approximation formulae. Our approximated option prices will be compared to the approximation obtained by Chiarella and Ziveyi. In paper C, we implement and analyze the Regime-Switching GARCH model using real NordPool Electricity spot data. We allow the model parameters to switch between a regular regime and a non-regular regime, which is justified by the so-called structural break behaviour of electricity price series. In splitting the two regimes we consider three criteria, namely the intercountry price di_erence criterion, the capacity/flow difference criterion and the spikes-in-Finland criterion. We study the correlation relationships among these criteria using the mean-square contingency coe_cient and the co-occurrence measure. We also estimate our model parameters and present empirical validity of the model.
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Zhou, Peilan. "Essays on financial asset return volatility." Diss., Restricted to subscribing institutions, 2007. http://proquest.umi.com/pqdweb?did=1432786781&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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Kim, Young Il. "Essays on Volatility Risk, Asset Returns and Consumption-Based Asset Pricing." The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1211912340.

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Brunetti, Celso. "Comovement and volatility in international asset markets." Thesis, Queen Mary, University of London, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.322235.

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Watt, Wing Hong. "Essays on conditional volatility in asset returns." Thesis, University of Strathclyde, 1994. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=21340.

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This dissertation consists of four papers that examine various aspects of the temporal patterns in the volatility of asset returns. The first paper compares the predictive performance of various parametric ARCH models. We find that ARCH models are generally good descriptions of the timevarying volatility of UK stock returns. There appears to be asymmetry in the conditional volatility, although no single model outperforms the rest in all instances. In the second paper, we uncover evidence of asymmetric predictability in the conditional variance of firms of different size. Large firms shocks affect the future volatility of small firms, but not vice versa. We also find that trading period shocks have a significant impact on future volatility, but not nontrading period shocks. In the third paper, we document a contemporaneous volatility-volume relationship. We find that volatility is related to change in trading volume, and we propose a conditional volatility model that incorporate this contemporaneous volatility-volume relationship. In the final paper, we examine the various method of adjusting for nontrading effects in ARCH models, and we propose a new diagnostic test to detect the validity of such adjustments. We also uncover evidence that conditional volatility increases prior to market closure, but declines after market opening.
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Animante, David. "Macroeconomic volatility and sovereign asset-liability management." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/24133.

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For most developing countries, the predominant source of sovereign wealth is commodity related export income. However, over-reliance on commodity related income exposes countries to significant terms of trade shocks due to excessive price volatility. The spillovers are pro-cyclical fiscal policies and macroeconomic volatility problems that if not adequately managed, could have catastrophic economic consequences including sovereign bankruptcy. The aim of this study is to explore new ways of solving the problem in an asset-liability management framework for an exporting country like Ghana. Firstly, I develop an unconditional commodity investment strategy in the tactical mean-variance setting for deterministic returns. Secondly, in continuous time, shocks to return moments induce additional hedging demands warranting an extension of the analysis to a dynamic stochastic setting whereby, the optimal commodity investment and fiscal consumption policies are conditioned on the stochastic realisations of commodity prices. Thirdly, I incorporate jumps and stochastic volatility in an incomplete market extension of the conditional model. Finally, I account for partial autocorrelation, significant heteroskedastic disturbances, cointegration and non-linear dependence in the sample data by adopting GARCH-Error Correction and dynamic Copula-GARCH models to enhance the forecasting accuracy of the optimal hedge ratios used for the state-contingent dynamic overlay hedging strategies that guarantee Pareto efficient allocation. The unconditional model increases the Sharpe ratio by a significant margin and noticeably improves the portfolio value-at-risk and maximum drawdown. Meanwhile, the optimal commodities investment decisions are superior in in-sample performance and robust to extreme interest rate changes by up to 10 times the current rate. In the dynamic setting, I show that momentum strategies are outperformed by contrarian policies, fiscal consumption must account for less than 40% of sovereign wealth, while risky investments must not exceed 50% of the residual wealth. Moreover, hedging costs are reduced by as much as 55% while numerically generating state-dependent dynamic futures hedging policies that reveal a predominant portfolio strategy analogous to the unconditional model. The results suggest buying commodity futures contracts when the country's current exposure in a particular asset is less than the model implied optimal quantity and selling futures contracts when the actual quantity exported exceeds the benchmark.
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Wang, Zhiguang. "Three Essays on Asset Pricing." FIU Digital Commons, 2009. http://digitalcommons.fiu.edu/etd/91.

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In this dissertation, I investigate three related topics on asset pricing: the consumption-based asset pricing under long-run risks and fat tails, the pricing of VIX (CBOE Volatility Index) options and the market price of risk embedded in stock returns and stock options. These three topics are fully explored in Chapter II through IV. Chapter V summarizes the main conclusions. In Chapter II, I explore the effects of fat tails on the equilibrium implications of the long run risks model of asset pricing by introducing innovations with dampened power law to consumption and dividends growth processes. I estimate the structural parameters of the proposed model by maximum likelihood. I find that the stochastic volatility model with fat tails can, without resorting to high risk aversion, generate implied risk premium, expected risk free rate and their volatilities comparable to the magnitudes observed in data. In Chapter III, I examine the pricing performance of VIX option models. The contention that simpler-is-better is supported by the empirical evidence using actual VIX option market data. I find that no model has small pricing errors over the entire range of strike prices and times to expiration. In general, Whaley’s Black-like option model produces the best overall results, supporting the simpler-is-better contention. However, the Whaley model does under/overprice out-of-the-money call/put VIX options, which is contrary to the behavior of stock index option pricing models. In Chapter IV, I explore risk pricing through a model of time-changed Lévy processes based on the joint evidence from individual stock options and underlying stocks. I specify a pricing kernel that prices idiosyncratic and systematic risks. This approach to examining risk premia on stocks deviates from existing studies. The empirical results show that the market pays positive premia for idiosyncratic and market jump-diffusion risk, and idiosyncratic volatility risk. However, there is no consensus on the premium for market volatility risk. It can be positive or negative. The positive premium on idiosyncratic risk runs contrary to the implications of traditional capital asset pricing theory.
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Näsström, Jens. "Volatility Modelling of Asset Prices using GARCH Models." Thesis, Linköping University, Department of Electrical Engineering, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1625.

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The objective for this master thesis is to investigate the possibility to predict the risk of stocks in financial markets. The data used for model estimation has been gathered from different branches and different European countries. The four data series that are used in the estimation are price series from: Münchner Rück, Suez-Lyonnaise des Eaux, Volkswagen and OMX, a Swedish stock index. The risk prediction is done with univariate GARCH models. GARCH models are estimated and validated for these four data series.

Conclusions are drawn regarding different GARCH models, their numbers of lags and distributions. The model that performs best, out-of-sample, is the APARCH model but the standard GARCH is also a good choice. The use of non-normal distributions is not clearly supported. The result from this master thesis could be used in option pricing, hedging strategies and portfolio selection.

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Gaunersdorfer, Andrea. "Adaptive beliefs and the volatility of asset prices." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 2000. http://epub.wu.ac.at/1250/1/document.pdf.

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I present a simple model of an evolutionary financial market with heterogeneous agents, based on the concept of adaptive belief systems introduced by Brock and Hommes (1997a). Agents choose between different forecast rules based on past performance, resulting in an evolutionary dynamics across predictor choice coupled to the equilibrium dynamics. The model generates endogenous price fluctuations with similar statistical properties as those observed in real return data, such as fat tails and volatility clustering. These similarities are demonstrated for data from the British, German, and Austrian stock market. (author's abstract)
Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
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Boguth, Oliver. "Essays on volatility risk premia in asset pricing." Thesis, University of British Columbia, 2010. http://hdl.handle.net/2429/27487.

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This thesis contains two essays. In the first essay, we investigate the impact of time varying volatility of consumption growth on the cross-section and time-series of equity returns. While many papers test consumption-based pricing models using the first moment of consumption growth, less is known about how the time-variation of consumption growth volatility affects asset prices. In a model with recursive preferences and unobservable conditional mean and volatility of consumption growth, the representative agent's estimates of conditional moments of consumption growth affect excess returns. Empirically, we find that estimated consumption volatility is a priced source of risk, and exposure to it predicts future returns in the cross-section. Consumption volatility is also a strong predictor of aggregate quarterly excess returns in the time-series. The estimated negative price of risk together with the evidence on equity premium predictability suggest that the elasticity of intertemporal substitution of the representative agent is greater than unity, a finding that contributes to a long standing debate in the literature. In the second essay, I present a simple model to show that if agents face binding portfolio constraints, stocks with high volatility in states of low market returns demand a premium beyond the one implied by systematic risks. Assets whose volatility positively covaries with market volatility also have high expected returns. Both effects of this idiosyncratic volatility risk premium are strongest for assets that face more binding trading restrictions. Unlike the prior empirical literature that obtains mixed results when focusing on the level of idiosyncratic volatility, I investigate the dynamic behavior of idiosyncratic volatility and find strong support for my predictions. Comovement of innovations of idiosyncratic volatility with market returns negatively predicts returns for trading restricted stocks relative to unrestricted stocks, and comovement of idiosyncratic volatility with market volatility positively predicts returns for restricted assets.
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Books on the topic "Asset Volatility"

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

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

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

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

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

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

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

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

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

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

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Book chapters on the topic "Asset Volatility"

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Galloppo, Giuseppe. "Volatility." In Asset Allocation Strategies for Mutual Funds, 317–45. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76128-8_7.

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Hol, Eugenie M. J. H. "Asset Return Volatility Models." In Dynamic Modeling and Econometrics in Economics and Finance, 7–26. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4757-5129-1_2.

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Chen, James Ming. "The Low-Volatility Anomaly." In Econophysics and Capital Asset Pricing, 87–98. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63465-4_5.

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Kallianpur, Gopinath, and Rajeeva L. Karandikar. "Asset Pricing with Stochastic Volatility." In Introduction to Option Pricing Theory, 225–39. Boston, MA: Birkhäuser Boston, 2000. http://dx.doi.org/10.1007/978-1-4612-0511-1_13.

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Zaremba, Adam, and Jacob Shemer. "Is Risk Always Rewarded? Low-Volatility Anomalies." In Country Asset Allocation, 81–104. New York: Palgrave Macmillan US, 2016. http://dx.doi.org/10.1057/978-1-137-59191-3_5.

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Chen, James Ming. "Relative Volatility Versus Correlation Tightening." In Econophysics and Capital Asset Pricing, 49–64. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63465-4_3.

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Chen, James Ming. "Asymmetrical Volatility and Spillover Effects." In Econophysics and Capital Asset Pricing, 65–86. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63465-4_4.

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Wang, Song. "Options on One Asset with Stochastic Volatility." In The Fitted Finite Volume and Power Penalty Methods for Option Pricing, 55–83. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9558-5_3.

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Flôres, Renato G., and Bruno B. Roche. "Volatility Modelling in the Forex Market: An Empirical Evaluation." In Advances in Quantitative Asset Management, 275–94. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4389-3_12.

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Gannon, Gerard L. "Stochastic Volatility Structures and Intraday Asset Price Dynamics." In Handbook of Financial Econometrics and Statistics, 1249–75. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7750-1_44.

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Conference papers on the topic "Asset Volatility"

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Arendas, Peter. "VOLATILITY INDEXES AS AN INVESTMENT ASSET." In SGEM 2014 Scientific SubConference on POLITICAL SCIENCES, LAW, FINANCE, ECONOMICS AND TOURISM. Stef92 Technology, 2014. http://dx.doi.org/10.5593/sgemsocial2014/b22/s6.111.

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Luna, Ivette, and Rosangela Ballini. "Online estimation of stochastic volatility for asset returns." In 2012 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr). IEEE, 2012. http://dx.doi.org/10.1109/cifer.2012.6327788.

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Lin, Chung-Gee. "Dynamic Asset Allocation under Stochastic Volatility - Theory and Practice." In 9th Joint Conference on Information Sciences. Paris, France: Atlantis Press, 2006. http://dx.doi.org/10.2991/jcis.2006.93.

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Li, Gang. "Idiosyncratic Volatility and the Intertemporal Capital Asset Pricing Model." In 10th International Conference on Modern Research in Management, Economics and Accounting. Acavent, 2020. http://dx.doi.org/10.33422/10th.mea.2020.03.56.

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Yaşar, Aysu, and Kenan Terzioğlu. "Long Memory in Exchange Rate Volatility." In International Conference on Eurasian Economies. Eurasian Economists Association, 2021. http://dx.doi.org/10.36880/c13.02560.

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Considering rapidly evolving technology and effective markets, wherein information and news are quickly and effectively reflected in financial asset prices, the positions of investors trading in financial markets regarding financial asset prices vary according to the continuous stream of information coming to the market. However, markets are not fully efficient in terms of maintaining a long memory that enables future pricing estimates based on the past market price of the financial asset. Revealing the existence of a long memory structure is essential to the development of monetary policies since exchange rates that tend to return to average exert high resistance. In this study, the exchange rate’s long-range dependence is determined in the scope of the log-periodogram estimator and using a fractional model structure, the average model, and the variance model structure related to the exchange rate between February 22, 2001–March 16, 2020 are examined. In this context, the parameters in the model allow an examination of the long memory process. According to the fractionally integrated exponential generalized autoregressive conditional heteroskedasticity model, it is determined that the effects of shocks in the exchange rate market continue and persist for a long period. Policy suggestions within the scope of exchange rates are evaluated within model outputs.
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Rosania, Sam M. "Waste-to-Energy Facilities: A National Strategic Asset." In 10th Annual North American Waste-to-Energy Conference. ASMEDC, 2002. http://dx.doi.org/10.1115/nawtec10-1006.

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The importance of the topics illustrated to the audience and industry in this presentation will become self evident as to the industry’s future. In light of the events of September 11, 2001; the volatility of the middle eastern oil interests; and initiatives regarding national security and homeland defense, it would appear that any energy technology that can reduce America’s dependence on foreign oil should be considered a national strategic asset. As such, one could assert that today’s municipal waste combustors that provide electrical capacity and/or steam capacity (i.e. waste-to-energy facilities) are a strategic asset since they reduce our dependence on foreign oil and convert “garbage” into a resource.
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YING, Yi-rong, Wei-yi ZHANG, Meng-le GU, and Yuya WANG. "Time Variability of Bank Asset Volatility on Deposit Insurance Price." In Proceedings of the 2019 International Conference on Economic Management and Cultural Industry (ICEMCI 2019). Paris, France: Atlantis Press, 2019. http://dx.doi.org/10.2991/aebmr.k.191217.017.

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Duarte, Flávio Gabriel, and Leandro Nunes Castro. "Asset Allocation based on a Partitional Clustering Algorithm." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2021. http://dx.doi.org/10.21528/cbic2021-49.

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This paper proposes a method for asset allocation based on partitional clustering. This method is different from the approaches already proposed in the literature, which essentially use either an optimization-based approach or a hierarchical clustering algorithm to allocate resources in assets. After finding the clusters, the method uniformly allocates the resources over the clusters and then within the clusters, thus guaranteeing that all assets are allocated. The method was tested using data from the Brazilian Stock Exchange (B3) and the assets eligible to enter the allocation were those that were part of the Ibovespa Index at the time of portfolio rebalancing. The results were compared with the Ibovespa index for different metrics, such as volatility, return, sharpe ratio, turnover and drawdown. The proposed approach illustrates the potential of machine learning techniques in portfolio allocation.
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Creamer, German G., Yong Ren, and Jeffrey V. Nickerson. "Impact of Dynamic Corporate News Networks on Asset Return and Volatility." In 2013 International Conference on Social Computing (SocialCom). IEEE, 2013. http://dx.doi.org/10.1109/socialcom.2013.121.

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Hu, Wenxiu, Gang Liu, Weiguo Zhang, and Tingting Wu. "Study on random trading behavior, herd behavior and asset price volatility." In 2016 Chinese Control and Decision Conference (CCDC). IEEE, 2016. http://dx.doi.org/10.1109/ccdc.2016.7531526.

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Reports on the topic "Asset Volatility"

1

Attanasio, Orazio, James Banks, and Sarah Tanner. Asset Holding and Consumption Volatility. Cambridge, MA: National Bureau of Economic Research, May 1998. http://dx.doi.org/10.3386/w6567.

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Neely, Christopher J., and Brett W. Fawley. Capital Flows and Japanese Asset Volatility,. Federal Reserve Bank of St. Louis, 2011. http://dx.doi.org/10.20955/wp.2011.034.

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Bansal, Ravi, Dana Kiku, Ivan Shaliastovich, and Amir Yaron. Volatility, the Macroeconomy and Asset Prices. Cambridge, MA: National Bureau of Economic Research, May 2012. http://dx.doi.org/10.3386/w18104.

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Bernanke, Ben, and Mark Gertler. Monetary Policy and Asset Price Volatility. Cambridge, MA: National Bureau of Economic Research, February 2000. http://dx.doi.org/10.3386/w7559.

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Flood, Robert, and Robert Hodrick. Asset Price Volatility, Bubbles, and Process Switching. Cambridge, MA: National Bureau of Economic Research, March 1986. http://dx.doi.org/10.3386/w1867.

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Ait-Sahalia, Yacine. Dynamic Equilibrium and Volatility in Financial Asset Markets. Cambridge, MA: National Bureau of Economic Research, March 1996. http://dx.doi.org/10.3386/w5479.

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Guerrieri, Veronica, and Péter Kondor. Fund Managers, Career Concerns, and Asset Price Volatility. Cambridge, MA: National Bureau of Economic Research, April 2009. http://dx.doi.org/10.3386/w14898.

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Christoffersen, Peter, and Francis Diebold. Financial Asset Returns, Direction-of-Change Forecasting, and Volatility Dynamics. Cambridge, MA: National Bureau of Economic Research, October 2003. http://dx.doi.org/10.3386/w10009.

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Herskovic, Bernard, Bryan Kelly, Hanno Lustig, and Stijn Van Nieuwerburgh. The Common Factor in Idiosyncratic Volatility: Quantitative Asset Pricing Implications. Cambridge, MA: National Bureau of Economic Research, April 2014. http://dx.doi.org/10.3386/w20076.

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Graham, John, and Campbell Harvey. Market Timing Ability and Volatility Implied in Investment Newletters' Asset Allocation Recommendations. Cambridge, MA: National Bureau of Economic Research, October 1994. http://dx.doi.org/10.3386/w4890.

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