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Journal articles on the topic 'Beta hedging'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

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1

Levi, Yaron, and Ivo Welch. "Symmetric and Asymmetric Market Betas and Downside Risk." Review of Financial Studies 33, no. 6 (September 19, 2019): 2772–95. http://dx.doi.org/10.1093/rfs/hhz108.

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Abstract Our paper explores whether a symmetric plain or an asymmetric down-beta is a better hedging measure (Roy 1952; Markowitz 1959). Unlike Ang, Chen, and Xing (2006) and Lettau, Maggiori, and Weber (2014), we find that the prevailing plain market beta is the better predictor, even for crashes. It also predicts the subsequent down-beta (i.e., beta measured only on days when the stock market had declined) better than down-beta itself. Stocks with higher down-betas ex ante also do not earn higher average rates of return ex post. Thus, down-betas are useful for neither hedging nor risk-pricing purposes.
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2

de Boer, Sanne. "Smart Currency Hedging for Smart Beta Global Equities." Journal of Investing 25, no. 4 (November 30, 2016): 64–78. http://dx.doi.org/10.3905/joi.2016.25.4.064.

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3

Lee Adams, Courtney. "Practical Applications of Smart Currency Hedging for Smart Beta Global EquitiesSmart Currency Hedging for Smart Beta Global Equities Sanne de Boer." Practical Applications 4, no. 5 (July 2017): 1–4. http://dx.doi.org/10.3905/pa.2017.4.5.226.

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4

Foglia, Matteo, Maria Cristina Recchioni, and Gloria Polinesi. "Smart Beta Allocation and Macroeconomic Variables: The Impact of COVID-19." Risks 9, no. 2 (February 4, 2021): 34. http://dx.doi.org/10.3390/risks9020034.

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Smart beta strategies across economic regimes seek to address inefficiencies created by market-based indices, thereby enhancing portfolio returns above traditional benchmarks. Our goal is to develop a strategy for re-hedging smart beta portfolios that shows the connection between multi-factor strategies and macroeconomic variables. This is done, first, by analyzing finite correlations between the portfolio weights and macroeconomic variables and, more remarkably, by defining an investment tilting variable. The latter is analyzed with a discriminant analysis approach with a twofold application. The first is the selection of the crucial re-hedging thresholds which generate a strong connection between factors and macroeconomic variables. The second is forecasting portfolio dynamics (gain and loss). The capability of forecasting is even more evident in the COVID-19 period. Analysis is carried out on the iShares US exchange traded fund (ETF) market using monthly data in the period December 2013–May 2020, thereby highlighting the impact of COVID-19.
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Lee Adams, Courtney. "Practical Applications of Smart Currency Hedging for Smart Beta Global Equities." Practical Applications 5, no. 1 (July 31, 2017): 1.6–4. http://dx.doi.org/10.3905/pa.2017.5.1.226.

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6

Jostova, Gergana, and Alexander Philipov. "Bayesian Analysis of Stochastic Betas." Journal of Financial and Quantitative Analysis 40, no. 4 (December 2005): 747–78. http://dx.doi.org/10.1017/s0022109000001964.

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AbstractWe propose a mean-reverting stochastic process for the market beta. In a simulation study, the proposed model generates significantly more precise beta estimates than GARCH betas, betas conditioned on aggregate or firm-level variables, and rolling regression betas, even when the true betas are generated based on these competing specifications. Our model significantly improves out-of-sample hedging effectiveness. In asset pricing tests, our model provides substantially stronger support for the conditional CAPM relative to competing beta models and helps resolve asset pricing anomalies such as the size, book-to-market, and idiosyncratic volatility effects in the cross section of stock returns.
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Gallien, Florent, Serge Kassibrakis, and Semyon Malamud. "Hedge or Rebalance: Optimal Risk Management with Transaction Costs." Risks 6, no. 4 (October 8, 2018): 112. http://dx.doi.org/10.3390/risks6040112.

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We solve the problem of optimal risk management for an investor holding an illiquid, alpha-generating fund and hedging his/her position with a liquid futures contract. When the investor is subject to a lower bound on net return, he/she is forced to reduce the total risk of his/her portfolio after a loss. In this case, he/she faces a tradeoff of either paying the transaction costs and deleveraging or keeping his/her current position in the illiquid instrument and hedging away some of the risk while keeping the residual, unhedgeable risk on his/her balance sheet. We explicitly characterize this tradeoff and study its dependence on asset characteristics. In particular, we show that higher alpha and lower beta typically widen the no-trading zone, while the impact of volatility is ambiguous.
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8

Zhang, Mengfei, and Frank J. Fabozzi. "On the Estimation of the SABR Model’s Beta Parameter: The Role of Hedging in Determining the Beta Parameter." Journal of Derivatives 24, no. 1 (August 31, 2016): 48–57. http://dx.doi.org/10.3905/jod.2016.24.1.048.

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9

GULKO, LES. "THE ENTROPY THEORY OF BOND OPTION PRICING." International Journal of Theoretical and Applied Finance 05, no. 04 (June 2002): 355–83. http://dx.doi.org/10.1142/s021902490200147x.

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An informationally efficient price keeps investors as a group in the state of maximum uncertainty about the next price change. The Entropy Pricing Theory (EPT) captures this intuition and suggests that, in informationally efficient markets, perfectly uncertain market beliefs must prevail. When the entropy functional is used to index collective market uncertainty, then the entropy-maximizing consensus beliefs must prevail. The EPT resolves the ambiguity of arbitrage-free valuation in incomplete markets. The EPT produces a new bond option model that is similar to Black–Scholes' with the lognormal distribution replaced by a beta distribution. Unlike alternative models, the beta model is valid for arbitrary term structure dynamics and for arbitrary credit risk of the underlying bonds. Option replication and hedging under the beta model accounts for random changes in the underlying bond price, price volatility and short-term interest rates.
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10

Yang, Yifan, Frank J. Fabozzi, and Michele Leonardo Bianchi. "Stochastic Alpha-Beta-Rho Hedging for Foreign Exchange Options: Is It Worth the Effort?" Journal of Derivatives 23, no. 2 (November 30, 2015): 76–89. http://dx.doi.org/10.3905/jod.2015.23.2.076.

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11

Dor, Arik Ben, Stephan Florig, Jingling Guan, and Xiaming Zeng. "Beta Instability and Implications for Hedging Systematic Risk: Takeaways from the COVID-19 Crisis." Journal of Portfolio Management 47, no. 6 (March 12, 2021): 139–55. http://dx.doi.org/10.3905/jpm.2021.1.233.

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12

Ivanov, Stoyu I. "Study of REIT ETF beta." Journal of Risk Finance 17, no. 3 (May 16, 2016): 347–69. http://dx.doi.org/10.1108/jrf-12-2015-0120.

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Purpose The aim of this study is to examine real estate investment trust exchange-traded funds (REIT ETFs) and test for the existence of the “asymmetric beta puzzle” phenomenon in these financial instruments that are relatively new and are gaining popularity. The “asymmetric beta puzzle” phenomenon is used to identify the hedging and diversification benefits of a financial instrument. “Asymmetric beta puzzle” exists when betas in declining markets are higher than betas in advancing markets. Design/methodology/approach To study 14 REIT ETFs by using monthly and daily Center for Research in Security Prices (CRSP) data. Capital asset pricing model (CAPM) and Fama–French three-factor model were used to estimate betas in REIT ETFs and those in advancing and declining markets. Both the S&P 500 and the CRSP value-weighted indices were used in the beta estimation. Two hypotheses with regard to betas in both advancing and declining markets were defined and tested to test for the existence of the “asymmetric beta puzzle” phenomenon. Findings This study confirms the presence of the “asymmetric beta puzzle” in the data of monthly REIT ETFs as documented by Goldstein and Nelling (1999) and Chatrath et al. (2000) for REITs; however, this phenomenon was not found when using daily data, but quite the opposite – REIT ETF betas are higher in advancing markets than they are in declining markets – was found. Originality/value Goldstein and Nelling (1999) and Chatrath et al. (2000) identify the phenomenon of “the asymmetric REIT-beta puzzle” in monthly REIT’s returns. This study revisits the phenomenon identified in the aforementioned authors’ studies by using daily data and a relatively new real estate financial instrument – REIT ETFs. Therefore, this paper fills a void in the literature and would benefit both institutional and retail investors in their portfolio designs.
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13

BRANGER, NICOLE, and CHRISTIAN SCHLAG. "OPTION BETAS: RISK MEASURES FOR OPTIONS." International Journal of Theoretical and Applied Finance 10, no. 07 (November 2007): 1137–57. http://dx.doi.org/10.1142/s0219024907004585.

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This paper deals with the problem of determining the correct risk measure for options in a Black–Scholes (BS) framework when time is discrete. For the purposes of hedging or testing simple asset pricing relationships previous papers used the "local", i.e., the continuous-time, BS beta as the measure of option risk even over discrete time intervals. We derive a closed-form solution for option betas over discrete return periods where we distinguish between "covariance betas" and "asset pricing betas". Both types of betas involve only simple Black–Scholes option prices and are thus easy to compute. However, the theoretical properties of these discrete betas are fundamentally different from those of local betas. We also analyze the impact of the return interval on two performance measures, the Sharpe ratio and the Treynor measure. The dependence of both measures on the return interval is economically significant, especially for OTM options.
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14

Rømer, Sigurd Emil, and Rolf Poulsen. "How Does the Volatility of Volatility Depend on Volatility?" Risks 8, no. 2 (June 3, 2020): 59. http://dx.doi.org/10.3390/risks8020059.

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We investigate the state dependence of the variance of the instantaneous variance of the S&P 500 index empirically. Time-series analysis of realized variance over a 20-year period shows strong evidence of an elasticity of variance of the variance parameter close to that of a log-normal model, albeit with an empirical autocorrelation function that one-factor diffusion models fail to capture at horizons above a few weeks. When studying option market behavior (in-sample pricing as well as out-of-sample pricing and hedging over the period 2004–2019), messages are mixed, but systematic, model-wise. The log-normal but drift-free SABR (stochastic-alpha-beta-rho) model performs best for short-term options (times-to-expiry of three months and below), the Heston model—in which variance is stationary but not log-normal—is superior for long-term options, and a mixture of the two models does not lead to improvements.
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15

Ryu, Doojin. "Effects of Introducing Equity-Linked Warrants on Stock Market Behavior." Journal of Derivatives and Quantitative Studies 18, no. 4 (November 30, 2010): 23–50. http://dx.doi.org/10.1108/jdqs-04-2010-b0002.

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This paper investigates the effects of introducing equity-linked warrants (ELWs) on the stock price, trading volume, volatility, and systematic risk (beta) by using the event study methodology. The study defines the event date as the announcement date as well as the listing date. In addition, whereas previous research has investigated only call ELWs, this study analyzes the effects of introducing both call and put ELWs. The results provide no evidence of hedging effects of issuers before the announcement dates and information effects after the announcement dates. In addition, we can't find any significant changes of variables associated with the market completeness hypothesis near the listing dates. However, the trading volume of the stock tends to increase in the days immediately following the listing of call ELWs, which may be due to the “informed trading effect”. The empirical results also provide support for the “diminishing short-sales restrictions” hypothesis related to the listing of put ELWs, which implies that short-sale restrictions can be reduced because put ELWs can provide investors with short positions in the underlying stock.
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16

Han, Tzeu-Chen, and Chih-Min Wang. "Shipping Bunker Cost Risk Assessment and Management during the Coronavirus Oil Shock." Sustainability 13, no. 9 (April 29, 2021): 4998. http://dx.doi.org/10.3390/su13094998.

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This research explores ways to develop a risk management strategy that enables shipping companies to reduce unnecessary fuel cost risks, fuel price fluctuations and improve financial management. Through the Monte Carlo method, the study makes use of the simulation of the conditional value-at-risk (CVaR) model. First, the VaR of various shipping-fuel-cost combination over a ten-year period is simulated. Then, through the most appropriate probability distribution test, it is found that most of the VaR of shipping fuel cost combination are in Beta–Arcsine distribution. In other words, the high-frequency data are concentrated at both tails (minimum and maximum) with high volatility. Therefore, the best strategy is to install scrubbers on existing ships to purify their exhaust gas and choose natural gas-based marine fuel for new ships. This will benefit the shipping companies significantly more compared to the use of low-sulfur fuel and choosing forward bunker agreements. Bunker swaps and options of bunker prices to hedging the risk of bunker cost raised in the end of Coronavirus oil shock, the strategy could help achieve the goal of risk management in the sustainable supply chain.
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17

Dubey, Amlendu Kumar. "Instability and Time Scale Dependence of Beta in an Emerging Market Economy: Evidence from India." Vikalpa: The Journal for Decision Makers 39, no. 1 (January 2014): 41–56. http://dx.doi.org/10.1177/0256090920140103.

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This paper is an attempt in analysing time-scale dependence of systematic risk of stocks for an emerging market economy. Financial markets all over the world are characterized by heterogeneous investors. For example, different investors have different time horizons of investment which in turn is highly related to perception of risk of different investors in holding these stocks. Also, in emerging market economies, economic conditions are very fluid. Not only new firms are joining the market but existing firms themselves are changing rapidly; they are expanding into new markets, and at times with different products. Therefore, assuming that the risk in holding a firm's stock will be constant over a longer period is rather a restrictive assumption. Also, Indian equity markets are one of the most dynamic equity markets in the world today. The last decade has been the most eventful period for the Indian securities market. Resource mobilization in the primary market has increased dramatically, rising sixfold between 2000 and 2010 (NSE, 2010), which is having a very significant impact on the risk-return trade-off in the secondary market. Market capitalization has grown substantially over the period indicating that not only more companies are using the stock markets for resource mobilization today but overall market participation has also increased considerably. This paper tests for time-scale stability of beta of different trading stocks in the Indian equity market, using wavelet filters following Gencay et al (2002; 2005) and Fernandez (2006) and finds considerable instability in beta estimates. Based on this analysis, time-scale dependent beta estimates are provided for all the stocks under consideration. Time-scale dependent estimates of systematic risk embedded in different stocks will provide considerable information to practitioners in terms of benefits of diversification while constructing different portfolios using different stocks traded in Indian equity markets. Essentially, with the tools explained in this paper, practitioners will be able to incorporate their horizons of investment while planning for portfolio diversification. Also, the results emphasize the importance of a hedging strategy that varies over different time horizons of investments over a strategy where the hedge ratio is invariant to different time horizons.
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18

Glabadanidis, Paskalis. "Portfolio Strategies to Track and Outperform a Benchmark." Journal of Risk and Financial Management 13, no. 8 (August 1, 2020): 171. http://dx.doi.org/10.3390/jrfm13080171.

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I investigate the question of how to construct a benchmark replicating portfolio consisting of a subset of the benchmark’s components. I consider two approaches: a sequential stepwise regression and another method based on factor models of security returns’ first and second moments. The first approach produces the standard hedge portfolio that has the maximum feasible correlation with the benchmark. The second approach produces weights that are proportional to a “signal-to-noise” ratio of factor beta to idiosyncratic volatility. Using a factor model of securities returns allows the use of a larger number of securities than the number of time periods used to estimate the parameters of the factor model. I also consider a second objective that maximizes expected returns subject to a target tracking error variance. The security selection criterion naturally extends to the product of the information ratio and the signal-to-noise ratio. The optimal tracking portfolio is either a one-fund or a two-fund portfolio rule consisting of the optimal hedging portfolio, the tangent portfolio or the global minimum variance portfolio, depending on what constraints are imposed on the objective function. I construct buy-and-hold replicating portfolios using the algorithms presented in the paper to track a widely followed stock index with very good results both in-sample and out-of-sample.
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19

Zheng, Haitao, Junzhang Hao, Manying Bai, and Zhengjun Zhang. "Valuation of Guaranteed Unitized Participating Life Insurance under MEGB2 Distribution." Discrete Dynamics in Nature and Society 2019 (February 6, 2019): 1–16. http://dx.doi.org/10.1155/2019/9439786.

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Crisis events have significantly changed the view that extreme events in financial markets have negligible probability. Especially in the life insurance market, the price of guaranteed participating life insurance contract will be affected by a change in asset volatility which leads to the fluctuations in embedded option value. Considering the correlation of different asset prices, MEGB2 (multivariate exponential generalized beta of the second kind) distribution is proposed to price guaranteed participating life insurance contract which can effectively describe the dependence structure of assets under some extreme risks. Assuming the returns of two different assets follow the MEGB2 distribution, a multifactor fair valuation pricing model of insurance contract is split into four components: the basic contract, the annual dividend option, the terminal dividend option, and the surrender option. This paper studies the effect of death rate, minimum guaranteed yield rate, annual dividend ratio, terminal dividend ratio, and surrender on the embedded option values and calculates the single premium of the insurance contract under different influence factors. The Least-Squares Monte Carlo simulation method is used to simulate the pricing model. This article makes a comparison in the sensitivity of the pricing parameters under the MEGB2 distribution and Multivariate Normal distribution asset returns. Finally, an optimal hedging strategy is designed to cover the possible risks of the underlying assets, which can effectively hedge the risks of portfolio.
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20

Wang, Yudong, Chongfeng Wu, and Li Yang. "Hedging with Futures: Does Anything Beat the Naïve Hedging Strategy?" Management Science 61, no. 12 (December 2015): 2870–89. http://dx.doi.org/10.1287/mnsc.2014.2028.

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21

Chen, Haiwei, James Estes, and William Pratt. "Investing in the healthcare sector: mutual funds or ETFs." Managerial Finance 44, no. 4 (April 9, 2018): 495–508. http://dx.doi.org/10.1108/mf-08-2017-0280.

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Purpose The purpose of this paper is to investigate how healthcare funds differ from healthcare exchange-traded funds (ETFs) in terms of delivering positive alpha, beta, and hedging against a market downturn risk. The authors consider which vehicle is more effective in providing diversification within the healthcare sector and to what extent can investors gain by diverting a portion of their holdings in the S&P 500 index fund into either a value-weighted healthcare fund portfolio or ETFs. Design/methodology/approach Pooled and individual regressions are employed to estimate single and four-factor models of 132 healthcare mutual funds and 43 healthcare ETFs over the past four decades. The authors performed additional regressions to test the performance of mutual funds and ETFs relative to market volatility, market downturns, and policy influence. Findings The authors find that both healthcare funds and ETFs provide significantly positive average alpha and hedge against a market downturn risk. Holding an all-stock portfolio such as the S&P 500 index fund can be improved by simply adding a value-weighted healthcare portfolio, resulting in both a higher return and a lower standard deviation. However, returns for these funds and ETFs perform poorly in a very volatile market. ETF returns increased with the passing of the Obamacare. Healthcare sector funds and ETFs declined with the recent criticism from Donald Trump since he became the apparent GOP nominee in July of 2016. Originality/value Extending the literature in both sample size and scope of issues, this paper provides investors and financial advisors with practical guidance for achieving higher portfolio return while lowering standard deviation. Additionally, this study documents policy influence on the returns of healthcare mutual funds and healthcare ETFs.
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Subaedah, Siti, Tri Sulistyani, and Amirah. "Analisis Perbandingan Penggunaan Forward Contract Hedging dan Money Market Hedging dengan Open Position dalam Meminimalkan Pembayaran Hutang Impor." Permana : Jurnal Perpajakan, Manajemen, dan Akuntansi 11, no. 2 (August 28, 2019): 92–99. http://dx.doi.org/10.24905/permana.v11i2.49.

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Tujuan Penelitian ini adalah : 1) Untuk mengetahui ada atau tidaknya perbedaan antara penggunaan forward contract hedging dengan open position dalam meminimalkan pembayaran hutang impor. 2) Untuk mengetahui ada atau tidaknya perbedaan antara penggunaan money market hedging dengan open position dalam meminimalkan pembayaran hutang impor. Data yang dikumpulkan dalam penelitian ini adalah data kuantitatif. Sumber data dalam penelitian ini adalah sumber sekunder yang diperoleh dari laporan keuangan tahunan yang dipublikasikan dari Bursa Efek Indonesia periode 2014-2018. Sedangkan metode analisis data dan uji hipotesis yang digunakan adalah dengan uji beda T-test atau uji independent sampel T-test. Berdasarkan hasil penelitian diperoleh : 1) Tidak ada perbedaan yang signifikan antara penggunaan forward contract hedging dengan open position dalam meminimalkan pembayaran hutang impor. 2) Tidak ada perbedaan yang signifikan antara penggunaan money market hedging dengan open position dalam meminimalkan pembayaran hutang impor. Namun, di antara kedua teknik hedging tersebut, jumlah hutang impor yang paling rendah adalah ketika perusahaan menggunakan teknik money market hedging.
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23

Wu, Pengxiang, Qilin Ren, Wei Wang, Zhuo Ma, and Runzhi Zhang. "A bet-hedging strategy rather than just a classic fast life-history strategy exhibited by invasive fall armyworm." Entomologia Generalis 41, no. 4 (August 4, 2021): 337–44. http://dx.doi.org/10.1127/entomologia/2021/1154.

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24

Pasternack, Joel, and Stewart Venit. "Horse Racing Odds: Can You Beat the Track by Hedging Your Bets?" College Mathematics Journal 47, no. 4 (September 2016): 275–81. http://dx.doi.org/10.4169/college.math.j.47.4.275.

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25

Kurniawan, Muhammad. "Pendapatan Komprehensif Lain Perusahaan Sektor Aneka Industri di Indonesia." Jurnal Riset Akuntansi & Perpajakan (JRAP) 4, no. 02 (December 4, 2017): 258–73. http://dx.doi.org/10.35838/jrap.v4i02.200.

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ABSTRACT The purpose of this study was conducted to investigate how the implementation of the presentation of other comprehensive income after the implementation of the International Financial Reporting Standards. Research subjects are various industry sectors. Other comprehensive income components tested are foreign exchange differences, employee benefits, available financial instruments sold, hedges, asset revaluation, associations and venture. The research object is 42 company data of miscellaneous industry sector. The data analysis method used is cross-tabulation and test of Cramer-V difference. The results of the study conclude that the other components of comprehensive income that are presented differently in the research subjects are foreign exchange differences, available financial instruments for sale, hedging, asset revaluation and association. While the other comprehensive component of the presented income is no different is the employee benefits and joint venture. ABSTRAK Pujuan penelitian ini dilakukan untuk menginvestigasi bagaimana implementasi penyajian other comprehensive income setelah penerapan International Financial Reporting Standars. Subyek penelitian adalah sector aneka industry. Komponen penghasilan komprehensif lain yang diuji adalah selisih kurs, imbalan kerja, instrument keuangan yang tersedia dijual, lindung nilai, revaluasi aset, asosiasi, dan ventura. Obyek penelitian adalah 42 data perusahaan perusahaan sector aneka industry. Metode analisis data yang digunakan adalah tabulasi silang dan uji beda Cramer-V. Hasil penelitian menyimpulkan bahwa komponen penghasilan komprehensif lain yang tersaji berbeda pada subyek penelitian adalah selisih kurs, instrument keuangan yang tersedia dijual, hedging, revaluasi aset dan asosiasi. Sedangkan komponen penghasilan komprehensif lain yang tersaji tidak berbeda adalah imbalan kerja dan ventura bersama. JEL Classification: M41, M16, E42
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Kurniawan, Muhammad. "Pendapatan Komprehensif Lain Perusahaan Sektor Aneka Industri di Indonesia." Jurnal Riset Akuntansi & Perpajakan (JRAP) 4, no. 02 (December 4, 2017): 258–73. http://dx.doi.org/10.35838/jrap.2017.004.02.21.

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ABSTRACT The purpose of this study was conducted to investigate how the implementation of the presentation of other comprehensive income after the implementation of the International Financial Reporting Standards. Research subjects are various industry sectors. Other comprehensive income components tested are foreign exchange differences, employee benefits, available financial instruments sold, hedges, asset revaluation, associations and venture. The research object is 42 company data of miscellaneous industry sector. The data analysis method used is cross-tabulation and test of Cramer-V difference. The results of the study conclude that the other components of comprehensive income that are presented differently in the research subjects are foreign exchange differences, available financial instruments for sale, hedging, asset revaluation and association. While the other comprehensive component of the presented income is no different is the employee benefits and joint venture. ABSTRAK Pujuan penelitian ini dilakukan untuk menginvestigasi bagaimana implementasi penyajian other comprehensive income setelah penerapan International Financial Reporting Standars. Subyek penelitian adalah sector aneka industry. Komponen penghasilan komprehensif lain yang diuji adalah selisih kurs, imbalan kerja, instrument keuangan yang tersedia dijual, lindung nilai, revaluasi aset, asosiasi, dan ventura. Obyek penelitian adalah 42 data perusahaan perusahaan sector aneka industry. Metode analisis data yang digunakan adalah tabulasi silang dan uji beda Cramer-V. Hasil penelitian menyimpulkan bahwa komponen penghasilan komprehensif lain yang tersaji berbeda pada subyek penelitian adalah selisih kurs, instrument keuangan yang tersedia dijual, hedging, revaluasi aset dan asosiasi. Sedangkan komponen penghasilan komprehensif lain yang tersaji tidak berbeda adalah imbalan kerja dan ventura bersama. JEL Classification: M41, M16, E42
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27

De Boer, Sanne. "Smart Currency Hedging for Smart Beta Global Equities." SSRN Electronic Journal, 2014. http://dx.doi.org/10.2139/ssrn.2521640.

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28

de Boer, Sanne. "Smart Currency Hedging for Smart Beta Global Equities." Journal of Investing, November 8, 2016. http://dx.doi.org/10.3905/joi.2016.2016.1.056.

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29

Nadler, Daniel, and Anatoly B. Schmidt. "Beta hedging: performance measures, momentum weighting and rebalancing effects." Journal of Investment Strategies, 2019. http://dx.doi.org/10.21314/jois.2019.105.

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Nadler, Daniel, and Anatoly B. Schmidt. "Beta Hedging: Performance Measures, Momentum Weighting, and Rebalancing Effects." SSRN Electronic Journal, 2018. http://dx.doi.org/10.2139/ssrn.3166708.

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31

Bender, Jennifer. "To Beta or Not to Beta: A Comparison of Historical Versus Fundamental Betas for Hedging Market Risk." SSRN Electronic Journal, 2007. http://dx.doi.org/10.2139/ssrn.2543951.

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32

Zhang, Mengfei, and Frank J. Fabozzi. "On the Estimation of the SABR Model’s Beta Parameter:The Role of Hedging in Determining the Beta Parameter." Journal of Derivatives, August 10, 2016. http://dx.doi.org/10.3905/jod.2016.2016.1.052.

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33

JIANG, YUEXIANG, YIMING DAI, HUAIGANG LONG, and YANJIAN ZHU. "U.S. TRADE POLICY UNCERTAINTY AND EXPECTED STOCK RETURNS OF CHINESE LISTED COMPANIES." Singapore Economic Review, April 29, 2021, 1–24. http://dx.doi.org/10.1142/s0217590821500235.

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Our study is the first to examine the pricing effect of U.S. trade policy uncertainty (TPU) on Chinese stocks. We estimate the U.S. TPU beta, which measures Chinese stock exposure to the U.S. TPU index. Both portfolio analyses and cross-sectional regressions suggest a significantly negative relation between the U.S. TPU beta and expected returns, which cannot be explained by other pricing factors. The stocks in the lowest U.S. TPU beta quintile can generate 3.48% higher annual returns compared to stocks in the highest U.S. TPU beta quintile. Furthermore, we provide two potential mechanisms that include a real economy channel and a behavioral finance channel using vector autoregression models. Our results indicate that the negative premium can be explained by both demanding more of high TPU beta stocks in hedging against adverse effects from TPU and selling more of low TPU beta stocks due to pessimistic beliefs of noise trader.
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34

Zhang, Mengfei, and Frank J. Fabozzi. "On the Estimation of the SABR Model's Beta Parameter: The Role of Hedging in Determining the Beta Parameter." SSRN Electronic Journal, 2016. http://dx.doi.org/10.2139/ssrn.2961618.

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35

Shen, Chuan-He, and Yang Liu. "A verification model to capture option risk and hedging based on a modified underlying beta." Journal of Risk Model Validation, 2021. http://dx.doi.org/10.21314/jrmv.2020.233.

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36

Et al.,, Fauzia Mubarik. "Country Risk Analysis of Pakistan: Evidence from Karachi Stock Exchange." NICE Research Journal, December 30, 2017, 65–77. http://dx.doi.org/10.51239/nrjss.v0i0.18.

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This study analyzes the country risk of Pakistan by employing the international capital asset pricing model (ICAPM) framework for the time period of July 2000 to June 2015. This study has used the country beta approach to examine the country risk based on the local as well as the global economic indicators. The study includes one domestic variable; foreign exchange reserves (cash holdings) and three global variables; real foreign exchange rate, gold prices, and oil prices to examine the impact of these economic factors on country risk by employing Beta market approach. The empirical results of this study conclude that time-varying beta in Pakistan is affected by the global economic factors as well as the domestic economic factors and hence support the international capital asset pricing model in case of Pakistan. The findings of the study suggest that the international investors can frame out the hedging policy against country risk in measuring investment return and that an effective monetary and fiscal policy can be designed by considering the relationship of macroeconomic factors with country risk.
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37

Finkelshtein, Alin, Dalit Roth, Eshel Ben Jacob, and Colin J. Ingham. "Bacterial Swarms Recruit Cargo Bacteria To Pave the Way in Toxic Environments." mBio 6, no. 3 (May 12, 2015). http://dx.doi.org/10.1128/mbio.00074-15.

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ABSTRACTSwarming bacteria are challenged by the need to invade hostile environments. Swarms of the flagellated bacteriumPaenibacillus vortexcan collectively transport other microorganisms. Here we show thatP. vortexcan invade toxic environments by carrying antibiotic-degrading bacteria; this transport is mediated by a specialized, phenotypic subpopulation utilizing a process not dependent on cargo motility. Swarms of beta-lactam antibiotic (BLA)-sensitiveP. vortexused beta-lactamase-producing, resistant, cargo bacteria to detoxify BLAs in their path. In the presence of BLAs, both transporter and cargo bacteria gained from this temporary cooperation; there was a positive correlation between BLA resistance and dispersal.P. vortextransported only the most beneficial antibiotic-resistant cargo (including environmental and clinical isolates) in a sustained way.P. vortexdisplayed a bet-hedging strategy that promoted the colonization of nontoxic niches byP. vortexalone; when detoxifying cargo bacteria were not needed, they were lost. This work has relevance for the dispersal of antibiotic-resistant microorganisms and for strategies for asymmetric cooperation with agricultural and medical implications.IMPORTANCEAntibiotic resistance is a major health threat. We show a novel mechanism for the local spread of antibiotic resistance. This involves interactions between different bacteria: one species provides an enzyme that detoxifies the antibiotic (a sessile cargo bacterium carrying a resistance gene), while the other (Paenibacillus vortex) moves itself and transports the cargo.P. vortexused a bet-hedging strategy, colonizing new environments alone when the cargo added no benefit, but cooperating when the cargo was needed. This work is of interest in an evolutionary context and sheds light on fundamental questions, such as how environmental antibiotic resistance may lead to clinical resistance and also microbial social organization, as well as the costs, benefits, and risks of dispersal in the environment.
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38

Ma, Rufei, Bianxia Sun, Pengxiang Zhai, and Yi Jin. "Hedging stock market risks: can gold really beat bonds?" Finance Research Letters, December 2020, 101918. http://dx.doi.org/10.1016/j.frl.2020.101918.

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39

Mustiqa, Laila, and J. M. V. Mulyadi. "Penyajian Pendapatan Komprehensif Lain: Pada Industri Properti Real Estate dan Konstruksi." Jurnal Riset Akuntansi & Perpajakan (JRAP) 6, no. 01 (June 10, 2019). http://dx.doi.org/10.35838/jrap.2019.006.01.8.

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ABSTRACT The purpose of this study was to investigate how the application components of OCI after IFRS enacted. This study investigate for companies listed on the Indonesian Stock Exchange for the period from 2012 to 2015. The data analysis method used is Tabulation Cross (Cross Tabulation) and Cramer V with the help of SPSS statistical data processing program. Testing was conducted on 216 samples consisted of 184 samples for property and real estate industry and 32 samples for the construction industry. Results of research on industrial real estate properties and construction after different test on components of OCIie exchange rate differences, employee benefits, securities available for sale, hedging, revaluation of assets/intangible assets, associates and venture then there is a significant influence on foreign exchange and asset revaluation fixed/ intangible assets. ABSTRAK Tujuan penelitian ini adalah menginvestigasi bagaimana penerapan komponen-komponen OCI setelah IFRS di berlakukan. Penelitian ini meneliti untuk perusahaan-perusahaan yang terdaftar di Bursa Efek Indonesia untuk periode dari tahun 2012 sampai dengan 2015. Metode analisis data yang digunakan adalah Tabulasi Silang (Cross Tabulation) dan Cramer V dengan bantuan program pengolah data statistik SPSS. Pengujian ini dilakukan pada 216 sampel terdiri dari 184 sampel untuk industri properti dan real estate dan 32 sampel untuk industri konstruksi. Hasil penelitian pada industri properti real estate dan konstruksi ini setelah dilakukan uji beda pada komponen OCI yaitu selisih kurs, imbalan kerja, sekuritas tersedia dijual, hedging, revaluasi aset/aktiva tidak berwujud, asosiasi dan ventura maka terdapat pengaruh signifikan terhadap selisih kurs dan revaluasi aset tetap/aktiva tidak berwujud. JEL Classification: M40, M48
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