Journal articles: '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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Anwar, Saiful. "The Influence of Ownership Structure, Asset Structure, and Earning Volatility on Debt Policy in Indonesia." Journal of Accounting and Strategic Finance 2, no. 1 (June 30, 2019): 93–106. http://dx.doi.org/10.33005/jasf.v2i1.54.

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The higher the proportion of debt, the higher the stock price, but at a certain point, the increase in debt will reduce the value of the company because the benefits obtained from the use of debt are smaller than the costs incurred. The purpose of this study is to analyze the influence of ownership structure – managerial ownership and institutional ownership, asset structure, and earning volatility on debt policy. The data used in this study are secondary data in the form of managerial ownership data, institutional ownership, asset structure, earning volatility and debt policy in pharmaceutical companies that go public on the Indonesia Stock Exchange 2013-2017. The statistical method used is the Stepwise Regression, because there is high multicollinearity in managerial ownership variables, institutional ownership, assets structure, earning volatility. Based on the results of the Stepwise Regression shows that the variables entered into the regression model are earning volatility. Other variables such as managerial ownership, institutional ownership, asset structures are not included in the Stepwise Regression so that conclusion is only earning volatility variable that influences the debt policy earning volatility variables that affect debt policy.

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Rosenbaum, Mathieu, and Mehdi Tomas. "From microscopic price dynamics to multidimensional rough volatility models." Advances in Applied Probability 53, no. 2 (June 2021): 425–62. http://dx.doi.org/10.1017/apr.2020.60.

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AbstractRough volatility is a well-established statistical stylized fact of financial assets. This property has led to the design and analysis of various new rough stochastic volatility models. However, most of these developments have been carried out in the mono-asset case. In this work, we show that some specific multivariate rough volatility models arise naturally from microstructural properties of the joint dynamics of asset prices. To do so, we use Hawkes processes to build microscopic models that accurately reproduce high-frequency cross-asset interactions and investigate their long-term scaling limits. We emphasize the relevance of our approach by providing insights on the role of microscopic features such as momentum and mean-reversion in the multidimensional price formation process. In particular, we recover classical properties of high-dimensional stock correlation matrices.

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Chen, Andrew Y. "Precautionary Volatility and Asset Prices." Finance and Economics Discussion Series 2014, no. 59 (August 2014): 1–62. http://dx.doi.org/10.17016/feds.2014.59.

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Gauger, Jean, and Harold A. Black. "Asset Substitution and Monetary Volatility." Journal of Money, Credit and Banking 23, no. 4 (November 1991): 677. http://dx.doi.org/10.2307/1992703.

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최시열, 안성필, and Gwangheon Hong. "Risk Shifting and Asset Volatility." KOREAN JOURNAL OF FINANCIAL MANAGEMENT 32, no. 4 (December 2015): 177–202. http://dx.doi.org/10.22510/kjofm.2015.32.4.007.

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Attanasio, Orazio P., James Banks, and Sarah Tanner. "Asset Holding and Consumption Volatility." Journal of Political Economy 110, no. 4 (August 2002): 771–92. http://dx.doi.org/10.1086/340774.

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Kallianpur, G. "Asset Pricing with Stochastic Volatility." Applied Mathematics and Optimization 43, no. 1 (January 1, 2001): 47–62. http://dx.doi.org/10.1007/s002450010018.

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Zhang, Yu. "Asset price volatility and banks." Journal of Mathematical Economics 71 (August 2017): 96–103. http://dx.doi.org/10.1016/j.jmateco.2017.05.001.

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Hu, Xiao, Xinming Tian, and Kuitai Wang. "Volatility morphology of asset value and credit spread puzzle." International Journal of Financial Engineering 08, no. 03 (July 7, 2021): 2142007. http://dx.doi.org/10.1142/s242478632142007x.

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Merton model has provided a classic theoretical framework for explaining credit spreads. This paper extends Merton model by introducing morphology factor of asset value volatility in the model, and conducts empirical studies on the effect of asset volatility morphology on credit spreads in China’s bond market. The results show that asset volatility morphology is economically important and can explain credit spreads well. Furthermore, this paper analyzes the asymmetric influences of monetary policy on credit spreads and asset volatility morphology. This paper points out that the responses of credit spreads and asset volatility morphology to monetary policy are consistent in the tight liquidity environments. To this end, monetary policy and liquidity, which are two factors that have been ignored by classic Merton model but proved to have significant influences on credit spreads, play roles in influencing credit spreads by changing volatility morphology of asset value. Since asset volatility morphology can reflect the change of investors’ expectation on the default probability of asset, the argument mentioned in the credit spread puzzle that the fundamentals related to bond default probability cannot explain credit spreads needs to be reexamined.

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Ma, Chaoqun, Shengjie Yue, and Yishuai Ren. "Pricing Vulnerable European Options under Lévy Process with Stochastic Volatility." Discrete Dynamics in Nature and Society 2018 (October 23, 2018): 1–16. http://dx.doi.org/10.1155/2018/3402703.

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This paper considers the pricing issue of vulnerable European option when the dynamics of the underlying asset value and counterparty’s asset value follow two correlated exponential Lévy processes with stochastic volatility, and the stochastic volatility is divided into the long-term and short-term volatility. A mean-reverting process is introduced to describe the common long-term volatility risk in underlying asset price and counterparty’s asset value. The short-term fluctuation of stochastic volatility is governed by a mean-reverting process. Based on the proposed model, the joint moment generating function of underlying log-asset price and counterparty’s log-asset value is explicitly derived. We derive a closed-form solution for the vulnerable European option price by using the Fourier inversion formula for distribution functions. Finally, numerical simulations are provided to illustrate the effects of stochastic volatility, jump risk, and counterparty credit risk on the vulnerable option price.

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Lisnawati, Lisna, and Vinola Herawati. "Ukuran Perusahaan Memoderasi Pengaruh Penilaian Aset Biologi dan Income Smoothing terhadap Volatilitas Laba." Akuntabilitas 15, no. 1 (June 1, 2022): 113–24. http://dx.doi.org/10.15408/akt.v15i1.25019.

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PSAK 69 effective January 1, 2016 has provided direction regarding the valuation of biological assets from historical value to Fair value which uses the basis of market prices which always fluctuate in line with the demand and supply of biological assets.The purpose of this study was to examine the effect of valuation of biological assets on earnings volatility by moderating firm size in manufacturing companies in the agricultural sector in Indonesia and Malaysia for the period 2016 to 2020. This study uses quantitative methods with secondary data sources with samples used through purposive sampling where the total data is 220 company year data. The results of this study are that Biological Asset Valuation and Income Smoothing have a positive effect on earnings volatility, then for the moderating variable Firm size cannot strengthen the effect of biological asset valuation on Earning Volatility and can strengthen the effect of Income Smoothing on Profit Volatility.

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BARUCCI, EMILIO, and MARIA ELVIRA MANCINO. "COMPUTATION OF VOLATILITY IN STOCHASTIC VOLATILITY MODELS WITH HIGH FREQUENCY DATA." International Journal of Theoretical and Applied Finance 13, no. 05 (August 2010): 767–87. http://dx.doi.org/10.1142/s0219024910005991.

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We consider general stochastic volatility models driven by continuous Brownian semimartingales, we show that the volatility of the variance and the leverage component (covariance between the asset price and the variance) can be reconstructed pathwise by exploiting Fourier analysis from the observation of the asset price. Specifying parametrically the asset price model we show that the method allows us to compute the parameters of the model. We provide a Monte Carlo experiment to recover the volatility and correlation parameters of the Heston model.

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Polato, Maurizio, and Giulio Velliscig. "Operational risk, market risk and value of the asset managers." Risk Governance and Control: Financial Markets and Institutions 12, no. 4 (2022): 46–54. http://dx.doi.org/10.22495/rgcv12i4p3.

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Asset management has been one of the fastest-growing industries in the financial industry for a long time (Bigelli & Manuzzi, 2019). Moreover, after the eruption of the financial turmoil in 2008, financial intermediation has been characterized by a rapid increase in the role of the asset management industry. This paper aims to analyse the determinants of asset manager value and, in particular, it is focused on the value implicit in the assets under management. Starting from the works by Huberman (2005) and Joenväärä and Scherer (2017) the paper proposes a model for determining the enterprise value (EV) of asset managers by assessing the role of the contribution margin and the degree of risk (operational and market risk). As noted by Scherer (2008), following the financial crisis, asset management companies suffered a decline in profits, also due to the exposure of their revenues to the market risk. Although, as it’s known, the asset management firms are not directly subject to the market (and credit) risk, their revenues are exposed to the market risk, not only to the operational risk that had been thought of as the main risk factor (Hull, 2007). Management companies, in fact, operate in a cyclical context closely linked to the performance of the financial markets, which contributes to determining the size and volatility of the assets under management (AuM). Starting from a discounted cash flow (DCF) asset side model, a simple stochastic Monte Carlo simulation is provided in order to capture the relevance of the asset under management return and volatility and, therefore, the volatility of the benchmark return and management style. In this theoretical framework, the key point is that the enterprise value depends on the specific asset class the firm is involved with. Given the asset class, the enterprise value depends on the management style also.

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Posedel Šimović, Petra, and Azra Tafro. "Pricing the Volatility Risk Premium with a Discrete Stochastic Volatility Model." Mathematics 9, no. 17 (August 25, 2021): 2038. http://dx.doi.org/10.3390/math9172038.

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Investors’ decisions on capital markets depend on their anticipation and preferences about risk, and volatility is one of the most common measures of risk. This paper proposes a method of estimating the market price of volatility risk by incorporating both conditional heteroscedasticity and nonlinear effects in market returns, while accounting for asymmetric shocks. We develop a model that allows dynamic risk premiums for the underlying asset and for the volatility of the asset under the physical measure. Specifically, a nonlinear in mean time series model combining the asymmetric autoregressive conditional heteroscedastic model with leverage (NGARCH) is adapted for modeling return dynamics. The local risk-neutral valuation relationship is used to model investors’ preferences of volatility risk. The transition probabilities governing the evolution of the price of the underlying asset are adjusted for investors’ attitude towards risk, presenting the asset returns as a function of the risk premium. Numerical studies on asset return data show the significance of market shocks and levels of asymmetry in pricing the volatility risk. Estimated premiums could be used in option pricing models, turning options markets into volatility trading markets, and in measuring reactions to market shocks.

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Leite, André Luís, Antonio Carlos Figueiredo Pinto, and Marcelo Cabus Klotzle. "Effects of Idiosyncratic Volatility in Asset Pricing." Revista Contabilidade & Finanças 27, no. 70 (April 2016): 98–112. http://dx.doi.org/10.1590/1808-057x201501940.

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This paper aims to evaluate the effects of the aggregate market volatility components - average volatility and average correlation - on the pricing of portfolios sorted by idiosyncratic volatility, using Brazilian data. The study investigates whether portfolios with high and low idiosyncratic volatility - in relation to the Fama and French model (1996) - have different exposures to innovations in average market volatility, and consequently, different expectations for return. The results are in line with those found for US data, although they portray the Brazilian reality. Decomposition of volatility allows the average volatility component, without the disturbance generated by the average correlation component, to better price the effects of a worsening or an improvement in the investment environment. This result is also identical to that found for US data. Average variance should thus command a risk premium. For US data, this premium is negative. According to Chen and Petkova (2012), the main reason for this negative sign is the high level of investment in research and development recorded by companies with high idiosyncratic volatility. As in Brazil this type of investment is significantly lower than in the US, it was expected that a result with the opposite sign would be found, which is in fact what occurred.

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Zhu, Qing, Shuyu Bai, and Jia Wang. "Liquidity, Asset Price Volatility, and Monetary Policy Choices: Empirical Evidence from China." Complexity 2022 (May 26, 2022): 1–19. http://dx.doi.org/10.1155/2022/4710234.

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This article effectively identifies the high and low volatility state of asset prices in China by constructing the MS-AR model, and further investigates the relationship between different dimensions of liquidity and asset price volatility. Moreover, we try to incorporate liquidity into the analytical framework and adopt the TVP-SV-VAR model to study the time-varying characteristics between monetary policy, liquidity, asset price volatility and macroeconomy. The results are as follows: firstly, it shows that the high or low volatility state of China’s stock market and real estate market can be clearly divided, and display the consistency with the trend of asset price volatility. Secondly, liquidity has a strong ability to explain the high and low volatility state of asset prices, but it shows some hysteresis effects. Thirdly, the time-varying results reveal that monetary policy has a regulating effect on liquidity, and the response cycle of quantitative monetary policy is relatively short, which reflects the effects of macroeconomy precisely. However, price-based monetary policy has a longer response cycle and plays a vital role in the anticipatory adjustment and fine-tuning of asset price volatility. These conclusions can provide an explanation for the attention to asset price bubbles and potential financial risks, and offer decision-making references for the central bank to implement differentiated and dynamic monetary policy choices.

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Khil, Jaeuk, Song Hee Kim, and Eun Jung Lee. "The Determinants of Idiosyncratic Volatility." Journal of Derivatives and Quantitative Studies 25, no. 4 (November 30, 2017): 509–45. http://dx.doi.org/10.1108/jdqs-04-2017-b0002.

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We investigate the cross-sectional and time-series determinants of idiosyncratic volatility in the Korean market. In particular, we focus on the empirical relation between firms’ asset growth rate and idiosyncratic stock return volatility. We find that, in the cross-section, companies with high idiosyncratic volatility tend to be small and highly leveraged, have high variance of ROE and Market to Book ratio, high turnover rate, and pay no dividends. Furthermore, firms with extreme (either high positive or negative) asset growth rates have high idiosyncratic return volatility than firms with moderate growth rates, suggesting the V-shaped relation between asset growth rate and idiosyncratic return volatility. We find that the V-shaped relation is robust even after controlling for other factors. In time-series, we find that firm-level idiosyncratic volatility is positively related to the dispersion of the cross-sectional asset growth rates. As a result, this study is contributed to show that the asset growth is the most important predictor of firm-level idiosyncratic return volatility in both the cross-section and the time-series in the Korean stock market. In addition, we show how the effect of risk factors varies with industries.

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Huang, Kun, Qiuge Yao, and Chong Li. "Impacts of Financial Market Shock on Bank Asset Allocation from the Perspective of Financial Characteristics of Banks." International Journal of Financial Studies 7, no. 2 (June 12, 2019): 29. http://dx.doi.org/10.3390/ijfs7020029.

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Given ongoing financial disintermediation and the need for central banks to establish interest rate corridors, commercial banks have increasingly enriched their asset allocation choices, forming an allocation pattern that combines traditional credit assets (loans) and financial assets (interbank and securities investment). Due to the long-standing dual interest rate system in China, the yields of credit assets and financial assets have differed, which means the latter has greater volatility. Using the quarterly panel data of 23 listed commercial banks in China from 2002 to 2017, the empirical results of this paper show that the fluctuation of the return rate of the two types of assets will affect the asset allocation of banks. Specifically, on the one hand, when the price of financial assets falls, which leads to the narrowing of the credit spread between the two types of assets, banks reduce transaction demand to prevent loss and reduce their holdings of financial assets, thus increasing the ratio of their credit assets to financial assets. On the other hand, rising benchmark lending rates leads to the increase in the credit financing cost of demanders, reducing the willingness of demanders to lend, forcing the demander to obtain funds through other channels. This results in the decrease in the ratio of credit assets to financial assets. Furthermore, the financial characteristics of banks also influence the dynamic adjustment range of asset allocation. That is, the lower the reserve ratio and capital adequacy ratio, the smaller the impact of financial asset yield volatility on bank asset allocation.

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Li, Yu. "A mean bound financial model and options pricing." International Journal of Financial Engineering 04, no. 04 (December 2017): 1750047. http://dx.doi.org/10.1142/s2424786317500475.

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Most of financial models, including the famous Black–Scholes–Merton options pricing model, rely upon the assumption that asset returns follow a normal distribution. However, this assumption is not justified by empirical data. To be more concrete, the empirical observations exhibit fat tails or heavy tails and implied volatilities against the strike prices demonstrate U-shaped curve resembling a smile, which is the famous volatility smile. In this paper we present a mean bound financial model and show that asset returns per time unit are Pareto distributed and assets are log Gamma distributed under this model. Based on this we study the sensitivity of the options prices to a change in underlying parameters, which are commonly called the Greeks, and derive options pricing formulas. Finally, we reveal the relation between correct volatility and implied volatility in Black–Scholes model and provide a mathematical explanation of volatility smile.

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Vardar, Gulin, and Berna Aydogan. "Return and volatility spillovers between Bitcoin and other asset classes in Turkey." EuroMed Journal of Business 14, no. 3 (October 7, 2019): 209–20. http://dx.doi.org/10.1108/emjb-10-2018-0066.

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Purpose With a substantial return and volatility characteristic of Bitcoin, which may be seen as a new category of investment assets, better understanding of the nature of return and volatility spillover can help investors and regulators in achieving the potential goal from portfolio diversification. The paper aims to discuss these issues. Design/methodology/approach This paper explores the return and volatility transmission between the Bitcoin, as the largest cryptocurrency, and other traditional asset classes, namely stock, bond and currencies from the standpoint of Turkey over the period July, 2010–June, 2018 using the newly developed multivariate econometric technique, VAR–GARCH, in mean framework with the BEKK representation. Findings The empirical results reveal the existence of the positive unilateral return spillovers from the bond market to Bitcoin market. Regarding the results of shock and volatility spillovers, there exists strong evidence of bidirectional cross-market shock and volatility spillover effects between Bitcoin and all other financial asset classes, except US Dollar exchange rate. Originality/value The important extention is the adoption of a newly developed multivariate econometric technique, VAR–GARCH, in mean framework with the BEKK representation, proposed by Engle and Kroner (1995), which is employed for the first time specifically to examine the extent of integration in terms of volatility and return between Bitcoin and key asset classes. Second, Bitcoin has experienced a rapid growth since around a decade and a number of investors are showing interest in its potential as an integrative part of portfolio diversification. The information provided by empirical results gives empirical bases from which to address topics concerning hedging purposes and optimal portfolio allocation. It is also increasingly important to analyze the current behavior of Bitcoin in relation to other assets to provide policy makers and regulatory bodies with guidance on the role of the Bitcoin as an investment asset in Turkey. Thus, this is the first serious attempt at exploring the potential for Bitcoin to offer diversification opportunities in the context of Turkey.

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Alòs, Elisa, Jorge A. León, Monique Pontier, and Josep Vives. "A Hull and White Formula for a General Stochastic Volatility Jump-Diffusion Model with Applications to the Study of the Short-Time Behavior of the Implied Volatility." Journal of Applied Mathematics and Stochastic Analysis 2008 (February 10, 2008): 1–17. http://dx.doi.org/10.1155/2008/359142.

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We obtain a Hull and White type formula for a general jump-diffusion stochastic volatility model, where the involved stochastic volatility process is correlated not only with the Brownian motion driving the asset price but also with the asset price jumps. Towards this end, we establish an anticipative Itô's formula, using Malliavin calculus techniques for Lévy processes on the canonical space. As an application, we show that the dependence of the volatility process on the asset price jumps has no effect on the short-time behavior of the at-the-money implied volatility skew.

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32

Ochoa, Marcelo. "Volatility, Labor Heterogeneity and Asset Prices." Finance and Economics Discussion Series 2013, no. 71 (October 2013): 1–48. http://dx.doi.org/10.17016/feds.2013.71.

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Caginalp, Carey, and Gunduz Caginalp. "Asset price volatility and price extrema." Discrete & Continuous Dynamical Systems - B 25, no. 5 (2020): 1935–58. http://dx.doi.org/10.3934/dcdsb.2020010.

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Brière, Marie, Alexandre Burgues, and Ombretta Signori. "Volatility Exposure for Strategic Asset Allocation." Journal of Portfolio Management 36, no. 3 (April 30, 2010): 105–16. http://dx.doi.org/10.3905/jpm.2010.36.3.105.

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35

Bekierman, Jeremias. "Asset volatility with prospect theory investors." Quantitative Finance 19, no. 4 (December 13, 2018): 533–43. http://dx.doi.org/10.1080/14697688.2018.1520393.

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36

Gyles, Anthony F. "Asset Price Dynamics, Volatility, and Prediction." Journal of the Royal Statistical Society: Series A (Statistics in Society) 170, no. 4 (October 2007): 1187–89. http://dx.doi.org/10.1111/j.1467-985x.2007.00506_18.x.

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37

Jacquier, Eric, and Alan J. Marcus. "Asset Allocation Models and Market Volatility." Financial Analysts Journal 57, no. 2 (March 2001): 16–30. http://dx.doi.org/10.2469/faj.v57.n2.2430.

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38

Kubler, Felix, and Karl Schmedders. "Financial Innovation and Asset Price Volatility." American Economic Review 102, no. 3 (May 1, 2012): 147–51. http://dx.doi.org/10.1257/aer.102.3.147.

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We compare asset prices in an overlapping generations model for incomplete and complete markets. Individuals within a generational cohort have heterogeneous beliefs about future states of the economy and thus would like to make bets against each other. In the incomplete-markets economy, agents cannot make such bets. Asset price volatility is very small. The situation changes dramatically when markets are completed through financial innovations as the set of available securities now allows agents with different beliefs to place bets against each other. Wealth shifts across agents and generations. Such changes in the wealth distribution lead to substantial asset price volatility.

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39

BANSAL, RAVI, DANA KIKU, IVAN SHALIASTOVICH, and AMIR YARON. "Volatility, the Macroeconomy, and Asset Prices." Journal of Finance 69, no. 6 (November 10, 2014): 2471–511. http://dx.doi.org/10.1111/jofi.12110.

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40

Drees, Burkhard, and Bernhard Eckwert. "Intrinsic bubbles and asset price volatility." Economic Theory 9, no. 3 (October 1997): 499–510. http://dx.doi.org/10.1007/bf01213851.

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NEELY, CHRISTOPHER J., and BRETT W. FAWLEY. "CAPITAL FLOWS AND JAPANESE ASSET VOLATILITY." Pacific Economic Review 17, no. 3 (August 2012): 391–414. http://dx.doi.org/10.1111/j.1468-0106.2012.00590.x.

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42

Adrian, Tobias. "Inference, arbitrage, and asset price volatility." Journal of Financial Intermediation 18, no. 1 (January 2009): 49–64. http://dx.doi.org/10.1016/j.jfi.2008.06.001.

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43

Aziz, Jahangir. "Discretionary Trading and Asset Price Volatility." IMF Working Papers 95, no. 104 (1995): 1. http://dx.doi.org/10.5089/9781451947922.001.

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Li, Yan, and Liyan Yang. "Asset-Pricing Implications of Dividend Volatility." Management Science 59, no. 9 (September 2013): 2036–55. http://dx.doi.org/10.1287/mnsc.1120.1676.

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Song, Zhongzhi. "Asset Growth and Idiosyncratic Return Volatility *." Review of Finance 20, no. 3 (July 25, 2015): 1235–58. http://dx.doi.org/10.1093/rof/rfv033.

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Tagkalakis, Athanasios. "Asset price volatility and government revenue." Economic Modelling 28, no. 6 (November 2011): 2532–43. http://dx.doi.org/10.1016/j.econmod.2011.07.015.

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Tagkalakis, Athanasios. "Fiscal policy and asset price volatility." Empirica 39, no. 1 (January 29, 2011): 123–56. http://dx.doi.org/10.1007/s10663-011-9167-2.

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48

Drees, Burkhard, and Bernhard Eckwert. "Intrinsic bubbles and asset price volatility." Economic Theory 9, no. 3 (April 4, 1997): 499–510. http://dx.doi.org/10.1007/s001990050138.

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Attanasio, Orazio P. "Asset price volatility and information structures." Economics Letters 33, no. 2 (June 1990): 159–64. http://dx.doi.org/10.1016/0165-1765(90)90162-t.

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Tarhan, Vefa. "Policy and volatility of asset returns." Journal of Economics and Business 45, no. 3-4 (August 1993): 269–83. http://dx.doi.org/10.1016/0148-6195(93)90017-i.

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Journal articles on the topic 'Asset Volatility'

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