Relevant bibliographies by topics / Interest rate and volatility risk

Academic literature on the topic 'Interest rate and volatility risk'

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Published: 11 March 2023

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Journal articles on the topic "Interest rate and volatility risk"

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Ho, Thomas S. Y. "Managing Interest Rate Volatility Risk." Journal of Fixed Income 17, no. 3 (December 31, 2007): 6–17. http://dx.doi.org/10.3905/jfi.2007.700216.

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Yang, Steve Y., and Esen Onur. "Interest Rate Swap Market Complexity and Its Risk Management Implications." Complexity 2018 (October 24, 2018): 1–20. http://dx.doi.org/10.1155/2018/5470305.

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The primary objective of this paper is to study the post Dodd-Frank network structure of the interest rate swap market and propose a set of effective complexity measures to understand how the swap users respond to market risks. We use a unique swap dataset extracted from the swap data repositories (SDRs) to examine the network structure properties and market participants’ risk management behaviors. We find (a) the interest rate swap market follows a scale-free network where the power-law exponent is less than 2, which indicates that few of its important entities have a significant number of contracts within their subsidiaries (a.k.a. interaffiliated swap contracts); (b) swap rate volatility Granger-causes swap users to increase their risk sharing intensity at entity level, but market participants do not change their risk management strategies in general; (c) there is a significant contemporaneous correlation between the swap rate volatility and the underlying interest rate futures volatility. However, interest rate swap volatility does not cause the underlying interest rate futures volatility and vice versa. These findings provide the market regulators and swap users a better understanding of interest rate swap market participants’ risk management behaviors, and it also provides a method to monitor the swap market risk sharing dynamics.
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International Monetary Fund. "Interest Rate Volatility and Risk in Indian Banking." IMF Working Papers 04, no. 17 (2004): 1. http://dx.doi.org/10.5089/9781451843569.001.

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MATACZ, ANDREW, and JEAN-PHILIPPE BOUCHAUD. "EXPLAINING THE FORWARD INTEREST RATE TERM STRUCTURE." International Journal of Theoretical and Applied Finance 03, no. 03 (July 2000): 381–89. http://dx.doi.org/10.1142/s0219024900000243.

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We present compelling empirical evidence for a new interpretation of the Forward Rate Curve (FRC) term structure. We find that the average FRC follows a square-root law, with a prefactor related to the spot volatility, suggesting a Value-at-Risk like pricing. We find a striking correlation between the instantaneous FRC and the past spot trend over a certain time horizon. This confirms the idea of an anticipated trend mechanism proposed earlier and provides a natural explanation for the observed shape of the FRC volatility. We find that the one-factor Gaussian Heath–Jarrow–Morton model calibrated to the empirical volatility function fails to adequately describe these features.
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Hosokawa, Satoshi, and Koichi Matsumoto. "Pricing interest rate derivatives with model risk." Journal of Financial Engineering 02, no. 01 (March 2015): 1550003. http://dx.doi.org/10.1142/s2345768615500038.

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This paper studies an interest rate derivative when there is the model risk in an interest rate model. We consider a mean reverting interest rate process whose volatility model is not known. Most of prices of interest rate derivatives cannot be determined uniquely, based on this interest rate model. We study the price bounds of a derivative and propose how to calculate the price bounds by a trinomial model. Further, we analyze the model risk of derivatives and their portfolios numerically.
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Nouman, Muhammad, Maria Hashim, Vanina Adoriana Trifan, Adina Eleonora Spinu, Muhammad Fahad Siddiqi, and Farman Ullah Khan. "Interest rate volatility and financing of Islamic banks." PLOS ONE 17, no. 7 (July 26, 2022): e0268906. http://dx.doi.org/10.1371/journal.pone.0268906.

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Despite a direct ban on charging interest, interest-based benchmarks are used as a pricing reference by a majority of Islamic banks, due in part to the absence of stable and widely- published alternatives. Benchmarking interest rate exposes Islamic banks to the problems of conventional banks, particularly the interest rate risk. Against this backdrop, the present study empirically examines the dynamic linkage between the interest rate volatility and the financing of Islamic banks. The empirical analysis is carried using evidence from the Islamic banking industry of Pakistan during the time period 2006–2020. The multivariate Johansen and Jusiles Co-integration test and Vector Error Correction Model (VECM) are used as the baseline econometric models. Moreover, the DCC-GARCH model is employed for robustness and ensuring the consistency of results. The results indicate that a significant long-term and short-term relationship exists between the interest rate volatility and the financing of Islamic banking industry providing significant evidence for co-movements and convergence. These findings suggest that paradoxical as it may seem, the financing of Islamic banks operating within a dual banking system is subject to interest rate risk, mainly due to benchmarking interest rate, which in-turn makes Islamic banks vulnerable to the rate of return risk and withdrawal risk. Moreover, corporate financing, in particular, is more vulnerable to interest rate risk.
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Kim, Bomi, and Jeong-Hoon Kim. "Default risk in interest rate derivatives with stochastic volatility." Quantitative Finance 11, no. 12 (April 15, 2011): 1837–45. http://dx.doi.org/10.1080/14697688.2010.543426.

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Carcano, Nicola, and Silverio Foresi. "Hedging against interest rate risk: Reconsidering volatility-adjusted immunization." Journal of Banking & Finance 21, no. 2 (February 1997): 127–41. http://dx.doi.org/10.1016/s0378-4266(96)00031-3.

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Baños, David, Marc Lagunas-Merino, and Salvador Ortiz-Latorre. "Variance and Interest Rate Risk in Unit-Linked Insurance Policies." Risks 8, no. 3 (August 6, 2020): 84. http://dx.doi.org/10.3390/risks8030084.

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One of the risks derived from selling long-term policies that any insurance company has arises from interest rates. In this paper, we consider a general class of stochastic volatility models written in forward variance form. We also deal with stochastic interest rates to obtain the risk-free price for unit-linked life insurance contracts, as well as providing a perfect hedging strategy by completing the market. We conclude with a simulation experiment, where we price unit-linked policies using Norwegian mortality rates. In addition, we compare prices for the classical Black-Scholes model against the Heston stochastic volatility model with a Vasicek interest rate model.
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Yoon, Byung-Jo, Kook-Hyun Chang, and 홍. 민구. "Long Term Volatility of Interest Rate Swap and Macroeconomic Risk in Korean Market." Journal of Derivatives and Quantitative Studies 21, no. 3 (August 31, 2013): 255–73. http://dx.doi.org/10.1108/jdqs-03-2013-b0001.

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This paper tries to empirically investigate whether macroeconomic risk may be statistically useful in explaining long-term volatility of interest rate swap (IRS) in korean market. This paper uses the component-jump model to estimate long-term volatility of IRS from 1/2/2003 to 1/31/2013. By using the component-jump model, the IRS volatility is decomposed into a long-term and a short-term component. According to this study, slope of yield curve and foreign exchange volatility as a proxy of macroeconomic risk have been significant in explaining long-term volatility of IRS.
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Dissertations / Theses on the topic "Interest rate and volatility risk"

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Staikouras, Sotiris K. "Interest rate volatility and the risk of financial institutions." Thesis, City University London, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.287410.

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Abiola, Isaac Abiodun. "Modeling credit risk spread and interest rate volatility in the Eurodollar market." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq25214.pdf.

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Fernandes, Maria Helena. "Managing interest rate risk : a comparison of the effectiveness of forecasting and volatility models / M.H. Fernandes." Thesis, North-West University, 2005. http://hdl.handle.net/10394/26.

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lnterest rate risk is one of the most important types of risk to which banks are inherently exposed. lnterest rates determine a bank's profitability and have an effect on a bank's liquidity and investment portfolio. It is, therefore, extremely important to be able to predict interest rates accurately and manage interest rate risk effectively. In trying to manage interest rate risk, banks rely on Asset and Liability Committees (ALCOs). They also make use of several strategies, which are described (Gap, Earnings Sensitivity Analysis, Duration Gap and Market Value of Equity sensitivity analysis). The first step for these strategies, on which later steps depend, is to make interest rate forecasts. Forecasting plays such a crucial role because many significant decisions depend on the anticipated future values of specific variables. Forecasts may be produced in various different ways. The method chosen depends on the reason for and the importance of the forecasts as well as on the costs of alternative forecasting methods. In an attempt to manage interest rate risk by being able to predict the next rates correctly, several different models are used to try and predict interest rates for two data sets, namely: BA (Bankers' Acceptances, which is money market data) and Esc (Eskom, which is capital market data). They each have their place in the South African financial system, which is described in general. The chosen simple forecasting models that are used are: naive, moving average and exponential smoothing models. The aim is to try to predict the direction of the next interest rate (UP, CONSTANT, or DOWN) while supplying a point prediction of the next rate (one-step ahead). The "best" simple forecasting models are determined by specific set criteria (percentage of correct direction predictions, mean squared error and tracking signals). For the same time series, more advanced models are taken into account where the aim is to try to find an interval wherein the future interest rates (not only in the short-term but in the longer-term as well) are most likely to lie, using models based on the data, as well as first differences. For the long-term forecasts, two types of more advanced models are used, namely: Box-Jenkins models (where, specifically, nonseasonal second-order autoregressive or AR(2) models are examined); and volatility models that are found using a new technique that creates an interval by using different volatility estimates. The word 'volatility' used throughout the study refers to models with a fixed volatility function and not dynamic volatility as in models such as the ARCH and GARCH types. In this study, the range from simple to more complex time series models with constant volatility are considered. The former, simple models and AR(2) models are referred to as forecasting models, the latter more advanced models are referred to as volatility estimates. Short- and long-term predictions are, thus, made for each time series, at different specifically chosen points. A comparison of the effectiveness of the forecasting and volatility models is made.
Thesis (M.Sc. (Information Technology))--North-West University, Vaal Triangle Campus, 2006.
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Yuksel, Ayhan. "Credit Risk Modeling With Stochastic Volatility, Jumps And Stochastic Interest Rates." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/2/12609206/index.pdf.

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This thesis presents the modeling of credit risk by using structural approach. Three fundamental questions of credit risk literature are analyzed throughout the research: modeling single firm credit risk, modeling portfolio credit risk and credit risk pricing. First we analyze these questions under the assumptions that firm value follows a geometric Brownian motion and the interest rates are constant. We discuss the weaknesses of the geometric brownian motion assumption in explaining empirical properties of real data. Then we propose a new extended model in which asset value, volatility and interest rates follow affine jump diffusion processes. In our extended model volatility is stochastic, asset value and volatility has correlated jumps and interest rates are stochastic and have jumps. Finally, we analyze the modeling of single firm credit risk and credit risk pricing by using our extended model and show how our model can be used as a solution for the problems we encounter with simple models.
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FONSECA, RODRIGO ALMEIDA DA. "VOLATILITY FORECAST MODEL FOR MARKET INDEX USING FACTORS EXTRACTED FROM CREDIT RISK, INTEREST RATES, EXCHANGE RATES AND COMMODITIES PANELS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2017. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=33203@1.

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Esta Dissertação apresenta um modelo para extrair fatores capazes de prever a volatilidade do índice de ações IBOVESPA, representativo do mercado de ações brasileiro. Esta metodologia é diferenciada por utilizar fatores que não incluem ativos da classe de ações. São utilizados fatores extraídos de classes de ativos de crédito, taxas de juros, moedas e commodities para precificar a volatilidade de um índice de ações. Além disso, os fatores são extraídos de painéis de volatilidades filtradas por modelos do tipo GARCH.
It will be presented a model that is able to extract factors capable of predicting the volatility of IBOVESPA market index, which is representative of Brazilian equity market. This methodology is different from others because it won t use any inputs from equity asset classes. It will be used factors extracted from credit risk, interest rates, exchange rates and commodities data for pricing the volatility of an equity index. Besides that, those factors will be extracted from panels of volatility filtered by GARCH models.
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Chandorkar, Pankaj Avinash. "The determinants of UK Equity Risk Premium." Thesis, Cranfield University, 2016. http://dspace.lib.cranfield.ac.uk/handle/1826/11860.

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Equity Risk Premium (ERP) is the cornerstone in Financial Economics. It is a basic requirement in stock valuation, evaluation of portfolio performance and asset allocation. For the last decades, several studies have attempted to investigate the relationship between macroeconomic drivers of ERP. In this work, I empirically investigate the macroeconomic determinants of UK ERP. For this I parsimoniously cover a large body of literature stemming from ERP puzzle. I motivate the empirical investigation based on three mutually exclusive theoretical lenses. The thesis is organised in the journal paper format. In the first paper I review the literature on ERP over the past twenty-eight years. In particular, the aim of the paper is three fold. First, to review the methods and techniques, proposed by the literature to estimate ERP. Second, to review the literature that attempts to resolve the ERP puzzle, first coined by Mehra and Prescott (1985), by exploring five different types of modifications to the standard utility framework. And third, to review the literature that investigates and develops relationship between ERP and various macroeconomic and market factors in domestic and international context. I find that ERP puzzle is still a puzzle, within the universe of standard power utility framework and Consumption Capital Asset Pricing Model, a conclusion which is in line with Kocherlakota (1996) and Mehra (2003). In the second paper, I investigate the impact of structural monetary policy shocks on ex-post ERP. More specifically, the aim of this paper is to investigate the whether the response of UK ERP is different to the structural monetary policy shocks, before and after the implementation of Quantitative Easing in the UK. I find that monetary policy shocks negatively affect the ERP at aggregate level. However, at the sectoral level, the magnitude of the response is heterogeneous. Further, monetary policy shocks have a significant negative (positive) impact on the ERP before (after) the implementation of Quantitative Easing (QE). The empirical evidence provided in the paper sheds light on the equity market’s asymmetric response to the Bank of England’s monetary policy before and after the monetary stimulus. In the third paper I examine the impact of aggregate and disaggregate consumption shocks on the ex-post ERP of various FTSE indices and the 25 Fama-French style value-weighted portfolios, constructed on the basis of size and book-to-market characteristics. I extract consumption shocks using Structural Vector Autoregression (SVAR) and investigate its time-series and cross-sectional implications for ERP in the UK. These structural consumption shocks represent deviation of agent’s actual consumption path from its theoretically expected path. Aggregate consumption shocks seem to explain significant time variation in the ERP. At disaggregated level, when the actual consumption is less than expected, the ERP rises. Durable and Semi-durable consumption shocks have a greater impact on the ERP than non-durable consumption shocks. In the fourth and final paper I investigate the impact of short and long term market implied volatility on the UK ERP. I also examine the pricing implications of innovations to short and long term implied market volatility in the cross-section of stocks returns. I find that both the short and the long term implied volatility have significant negative impact on the aggregate ERP, while at sectoral level the impact is heterogeneous. I find both short and long term volatility is priced negatively indicating that (i) investors care both short and long term market implied volatility (ii) investors are ready to pay for insurance against these risks.
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Wu, Ting. "Essays on the Term Structure of Interest Rates and Long Run Variance of Stock Returns." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1276860580.

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Rosa, Francisco Eduardo Lopes Sousa. "Risk neutral probability density for currency options." Master's thesis, Instituto Superior de Economia e Gestão, 2019. http://hdl.handle.net/10400.5/20601.

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Mestrado em Finanças
Este trabalho tem o objectivo de facilitar a previsão para investidores em mercados financeiros. Embora possa ser usado em acções e futuros de petróleo, o principal objectivo é o mercado cambial, mais especificamente, opções de moeda, extraindo com risco neutro a densidade de probabilidade da função através de uma abordagem paramétrica e não paramétrica. Consequentemente, tal foi aplicado a um caso muito recente, em 2019, entre o dólar Norte americano e a libra inglesa, tornando assim mais atractiva a leitura do comportamento da densidade, especialmente com a saída do Reino unido da União Europeia.
This work has the purpose of easing the forecast for financial market investors. Although it can be used on equities and oil futures, the main aim is the Foreign exchange. More so, it is specialized on currency options, extracting then the closer Risk Neutral Probability Density Function through a parametric approach and a nonparametric approach. Subsequently, this was applied to a very recent case, in 2019, between the United States of America dollar and United Kingdom pound, making it more attractive to assess the behaviour of the density, specially linked to the withdrawal of United Kingdom from the European Union.
info:eu-repo/semantics/publishedVersion
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Fink, Holger Maria [Verfasser], Claudia [Akademischer Betreuer] Klüppelberg, Christoph [Akademischer Betreuer] Kühn, and Christian [Akademischer Betreuer] Bender. "Stochastic processes beyond semimartingales with application to interest rates, credit risk and volatility modeling / Holger Fink. Gutachter: Christoph Kühn ; Christian Bender. Betreuer: Claudia Klüppelberg." München : Universitätsbibliothek der TU München, 2012. http://d-nb.info/1021975931/34.

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Henrik, Hasseltoft. "Essays on the term structure of interest rates and long-run risks." Doctoral thesis, Handelshögskolan i Stockholm, Finansiell Ekonomi (FI), 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:hhs:diva-925.

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Stocks, Bonds, and Long-Run Consumption Risks. Bansal and Yaron (2004) show that long-run consumption risks and time-varying economic uncertainty in conjunction with recursive preferences can account for important features of equity markets. I bring the model to the term structure of interest rates and show that a calibrated version of the model can simultaneously explain properties of bonds and equities. Specifically, the model accounts for deviations from the expectations hypothesis, the upward sloping nominal yield curve, and the predictive power of the nominal yield spread. However, an estimation of the model using Simulated Method of Moments yields less convincing results and illustrates the difficulty of precisely estimating parameters of the model. Real (nominal) interest rates in the model are positively (negatively) correlated with consumption growth and real stock returns move inversely with inflation. The cyclicality of nominal interest rates and yield spreads is shown to depend on the relative values of the elasticity of intertemporal substitution and the correlation between real consumption growth and inflation. The “Fed-model” and the Changing Correlation of Stock and Bond Returns: An Equilibrium Approach. This paper presents an equilibrium model that provides a rational explanation for two features of data that have been considered puzzling: The positive relation between US dividend yields and nominal interest rates, often called the Fed-model, and the time-varying correlation of US stock and bond returns. Key ingredients are time-varying first and second moments of consumption growth, inflation, and dividend growth in conjunction with Epstein-Zin and Weil recursive preferences. Historically in the US, inflation has signaled low future consumption growth. The representative agent therefore dislikes positive inflation shocks and demands a positive risk premium for holding assets that are poor inflation hedges, such as equity and nominal bonds. As a result, risk premiums on equity and nominal bonds comove positively through their exposure to macroeconomic volatility. This generates a positive correlation between dividend yields and nominal yields and between stock and bond returns. High levels of macro volatility in the late 1970s and early 1980s caused stock and bond returns to comove strongly. The subsequent moderation in aggregate economic risk has brought correlations lower. The model is able to produce correlations that can switch sign by including the covariances between consumption growth, inflation, and dividend growth as state variables. International Bond Risk Premia. We extend Cochrane and Piazzesi (2005, CP) to international bond markets by constructing forecasting factors for bond excess returns across different countries. While the international evidence for predictability is weak using Fama and Bliss (1987) regressions, we document that local CP factors have significant predictive power. We also construct a global CP factor and provide evidence that it predicts bond returns with high R2 across countries. The local and global factors are jointly significant when included as regressors, which suggests that variation in bond excess returns are driven by country-specific factors and a common global factor. Shocks to US bond risk premia seem to be particularly important determinants for international bond premia. Motivated by these results, we estimate a parsimonious no-arbitrage affine term structure model in which risk premia are driven by one local and one global CP factor. We find that international bond risk premia are driven by a local slope factor and a world interest rate level factor.
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Books on the topic "Interest rate and volatility risk"

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Hanweck, Gerald A. Interest rate volatility: Understanding, analyzing, and managing interest rate risk and risk-based capital. Chicago: Irwin Professional Pub., 1996.

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Patnaik, Ila. Interest rate volatility and risk in Indian banking. Washington, D.C: International Monetary Fund, IMF Institute, 2004.

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Hördahl, Peter. Financial volatility and time-varying risk premia. Lund: Lund University, 1997.

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Matovu, John. Volatility and jump risk premia in emerging market bonds. [Washington, D.C.]: International Monetary Fund, Middle East and Central Asia Dept., 2007.

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Edwards, Sebastian. Interest rate volatility and contagion in emerging markets: Evidence from the 1990s. Cambridge, MA: National Bureau of Economic Research, 2000.

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Damian, Kissane, ed. Interest rate risk management. London: Eurostudy, 1988.

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Matz, Leonard M. Interest rate risk management. Austin, Tex: Sheshunoff, 2006.

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Nawalkha, Sanjay K. Interest Rate Risk Modeling. New York: John Wiley & Sons, Ltd., 2005.

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Managing interest rate risk. New York: Quorum Books, 1987.

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Managing interest rate risk. Cambridge: Woodhead-Faulkner, 1987.

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Book chapters on the topic "Interest rate and volatility risk"

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Bakshi, Gurdip, Charles Cao, and Zhiwu Chen. "Option Pricing and Hedging Performance Under Stochastic Volatility and Stochastic Interest Rates." In Handbook of Quantitative Finance and Risk Management, 547–74. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-0-387-77117-5_37.

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Gómez-Valle, Lourdes, and Julia Martínez-Rodríguez. "Real-World Versus Risk-Neutral Measures in the Estimation of an Interest Rate Model with Stochastic Volatility." In Mathematical and Statistical Methods for Actuarial Sciences and Finance, 397–401. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89824-7_71.

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Wu, Lixin. "Volatility and Correlation Adjustments." In Interest Rate Modeling, 225–51. 2nd edition. | Boca Raton, Florida : CRC Press, [2019]: CRC Press, 2019. http://dx.doi.org/10.1201/9781351227421-8.

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García, Francisco Javier Población. "Interest Rate Risk." In Financial Risk Management, 101–34. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-41366-2_5.

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Willsher, Richard. "Interest Rate Risk." In Export Finance, 143–44. London: Palgrave Macmillan UK, 1995. http://dx.doi.org/10.1007/978-1-349-13980-4_17.

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Bilan, Andrada, Hans Degryse, Kuchulain O’Flynn, and Steven Ongena. "Interest Rate Risk." In Banking and Financial Markets, 31–60. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26844-2_3.

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Ho, Thomas S. Y., and Sang Bin Lee. "Local Volatility Interest Rate Model." In Encyclopedia of Finance, 1901–18. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-91231-4_25.

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Mele, Antonio, and Yoshiki Obayashi. "Interest Rate Derivatives and Volatility." In Handbook of Fixed-Income Securities, 469–513. Hoboken, NJ, USA: John Wiley & Sons, Inc, 2016. http://dx.doi.org/10.1002/9781118709207.ch20.

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Zagst, Rudi. "Risk Measures." In Interest-Rate Management, 227–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-12106-1_6.

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Zagst, Rudi. "Risk Management." In Interest-Rate Management, 273–320. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-12106-1_7.

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Conference papers on the topic "Interest rate and volatility risk"

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Stádník, Bohumil. "IMPROVING THE QUANTIFICATION OF INTEREST RATE RISK." In 12th International Scientific Conference „Business and Management 2022“. Vilnius Gediminas Technical University, 2022. http://dx.doi.org/10.3846/bm.2022.762.

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The value of Macaulay duration, probably the most widely used quantification method for measuring interest rate sensitivity of bonds, could roughly be financially interpreted as a percentage change of the bond price if the paral-lel shift of the interest rate equals 1 percentage point along the entire zero-coupon curve and the initial bond price is equal to 100%. The main problem of its practical application lies in the fact that parallel curve shift is a very rare case, and we are more often concerned with predicting short-term rate shifts and considering their consequences for the rest of the yield curve and thus also for bonds with longer maturities. Therefore, it is useful to find a certain value that represents a quantification of the impact of short rate shifts on bond prices with respect to the parameters of bonds. So, the main contribution of this financial engineering research is to design a measure that can be used in the same way as Macaulay duration, but as a response to the change of the short interest rate, for example: in the equation for chang-ing ΔP of a bond, in the equation of the volatility ratio of two bonds, or in the equation for bond portfolio sensitivity. Such a measure is still lacking in finance. We refer to this measure as the “short rate-shift duration”. Since the effect of the short rate shift on the entire yield curve, and thus especially on the price of long-term bonds, is very difficult to predict analytically, we use empirical data to calculate the duration value of the short-term shift and also to calculate its values for the USD and EUR interest markets.
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Syarifuddin, Ferry. "The Exchange Rate Volatility in Indonesia and Policy Response." In International Conference on Eurasian Economies. Eurasian Economists Association, 2014. http://dx.doi.org/10.36880/c05.00886.

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High fluctuation of exchange rate in short horizon is obviously making economic activity more risky as uncertainty rises. Moreover, volatile exchange rates also make commodity prices, interest rates and a host of other variables more volatile as well. Although changes in long-run exchange rates tend to undergo relatively gradual shifts, in the shorter horizon, the exchange rate might be very volatile. Then there should be a systematic and measured policy to mitigate the foreign exchange fluctuations and to minimize the fluctuations as well as to drive it to its fundamental value. In this part, USD/IDR volatility is investigated using GARCH approach. The results reveal that, USD/IDR volatility in Indonesia is persistent. On the other hand, the following studies also present the outcomes of effectiveness of policy response by the Central Bank. Foreign-exchange sale interventions by the Central Bank lead conditional volatility of the USD/IDR to decrease slightly.
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Czech, Katarzyna. "Is a Japanese yen a safe haven? Relationship between Japanese currency and financial market uncertainty." In 3rd International Conference on Administrative & Financial Sciences. Cihan University - Erbil, 2021. http://dx.doi.org/10.24086/afs2020/paper.353.

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Japan's low-interest rates made the country's currency the primary funding currency in carry trade speculative strategies. Investors' activity in carry trade strategies has an enormous impact on the foreign exchange market volatility. A large inflow of capital to countries with higher interest rates contributes to their currency appreciation, and, in turn, a large outflow of capital from countries with a low-interest rate leads to a significant depreciation of their currency. However, in times of crisis and high uncertainty in the financial markets, investors massively withdraw from the carry trade. They sell financial assets purchased in a country with higher interest rates and then repay loans taken in a country with low-interest rates. A sudden increase in the supply of a country's currency with higher interest rates leads to its depreciation. On the other hand, the rise in demand for a country's currency with low-interest rates leads to its appreciation. The Japanese yen is one of the most popular funding currency in the carry trade and thus tends to appreciate during crisis periods. The paper aims to investigate the relationship between Japanese yen value and financial market uncertainty measured by the Volatility Index VIX and St. Louis FED Financial Stress Index. Based on the component generalized autoregressive conditional heteroscedasticity model CGARCH with asymmetric threshold term, it has been shown that the increase in financial markets uncertainty contributes to significant appreciation of the Japanese yen against the US dollar. It implies that the Japanese currency is an example of a safe-haven currency and can be applied to hedge financial stress for global equity investors.
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Ganchev, Alexander. "INVESTMENT CHARACTERISTICS OF INDONESIAN GOVERNMENT BOND MARKET DURING THE COVID-19 PANDEMIC." In 12th International Scientific Conference „Business and Management 2022“. Vilnius Gediminas Technical University, 2022. http://dx.doi.org/10.3846/bm.2022.825.

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The purpose of the study is to define the main investment characteristics of the Indonesian government bond market during the COVID-19 pandemic. The subject of the analysis is the yield to maturity yield curve of Indonesian government bonds, the dynamics for the period 2 January 2020–15 February 2022 is analyzed with various quanti-tative methods such as descriptive statistical analysis, time series analysis, correlation and autocorrelation analysis, probability distribution analysis, principal component analysis and graphical analysis. The study reveals that under the COVID-19 pandemic, the yield curve on Indonesian government bonds is highly stable and lacks the strong general volatility of highly developed debt markets during the same period. Quantitative analysis shows that the yield of the in-vestigated bonds has many of the well-studied characteristics that are present in the developed debt markets. However, there are some specifics and anomalies, such as a strong correlation along the entire yield curve and inhomogeneous volatility of medium-term yields. Therefore, despite the probable existence of incorrectly priced debt instruments, In-donesian government bonds should be considered by investors as an appropriate instrument for hedging interest rate risk in the COVID-19 environment.
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Foote, W. G., and J. Kraemer. "APL2 implementation of an interest rate volatility model." In Conference proceedings. New York, New York, USA: ACM Press, 1989. http://dx.doi.org/10.1145/75144.75163.

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Mosoiu, Ovidiu, Catalin Cioaca, and Ion Balaceanu. "USING THE CAPITAL ASSET PRICING MODEL IN INFORMATION SECURITY INVESTMENTS." In eLSE 2018. Carol I National Defence University Publishing House, 2018. http://dx.doi.org/10.12753/2066-026x-18-220.

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Interest in real option theory has intensified over the last decade due to the high uncertainty faced by some private and public organizations when deciding to make a strategic investment (competitive environment) or when faced with an external requirement of the organizational environment (ensuring security standards). Traditional methods of investment analysis define the existence of investment opportunity by net present value (NPV), ignoring the possibility that an investment will start from a certain moment in the future. In this way, it is not possible to capture the phenomenon in dynamics, which leads to limiting the possibility of solving the existing uncertainty over the time regarding the optimal use of resources. The need to optimize managerial strategies and give some flexibility to decision-makers in relation to the changes in the organization's external environment has triggered the real options analysis (ROA). By using ROA, a win-win situation is created in which the available policy options mitigate uncertainty fluctuations of updated net worth (based on new information available) and, at the same time, by applying the best strategy, maximize earnings. Information security systems are designed on a layered architecture and the decision to improve performance on each layer is the responsibility of strategic management. Being a modular system, it is recommended to build the architecture by stages, depending on the value of the assets. Also, the relatively long duration and costs of implementation, limited resources, irreversible character, and project risks determine the value and evaluation of the investment, involving its representation as a combined option associated with a succession of decisions. The proposed model is inspired from the theory of financial and real options, but also from the fuzzy logic. This approach seeks to anchor specific mechanisms for the study of asymmetric risk events in the security market (perfect market assumptions are of course limiting but provide a quick overview, which is essential for the proposed application). Using the capital asset pricing model (CAPM), the return on investments in the security of IT & C systems, by reference to the investment risk as the estimated value, is defined. Investors can take risks that can be broken down into two components: systematic risks and non-systemic risks. Systematic risk refers to the variability of income caused by external factors (macroeconomic conditions), being a measure of the relative market volatility of relative incomes. Unsystematic risk refers to income variability caused by unpredictable factors (mismanagement decisions, abrupt technologies overtaken). The depreciation of security investments is inherent and leads to the dilemma of small and frequent investments or major and rare investments. On this issue, the proposed model can provide solutions to decision-makers. Uncertainty, irreversibility, growth potential and competition are factors that influence the behavior and investment decision. We consider that by using the capital asset pricing model in the security investments associated with eLerning training systems, we can increase the precision of optimal investment in terms of risk and opportunity balancing.
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Georgiev, Slavi G., and Lubin G. Vulkov. "Simultaneous identification of time-dependent volatility and interest rate for European options." In THERMOPHYSICAL BASIS OF ENERGY TECHNOLOGIES (TBET 2020). AIP Publishing, 2021. http://dx.doi.org/10.1063/5.0041788.

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Yu, Yue, and Liu Lan. "The Impact of Interest Rate Marketization on the Interest Rate Risk of Commercial Banks." In 2019 3rd International Conference on Data Science and Business Analytics (ICDSBA). IEEE, 2019. http://dx.doi.org/10.1109/icdsba48748.2019.00045.

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He, Haixia. "Interest Rate Risk Management of Commercial Bank under the Background of Interest Rate Liberalization." In 2015 International Conference on Economics, Management, Law and Education. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/emle-15.2015.70.

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Gerni, Cevat, Selahattin Sarı, Dilek Özdemir, and Ömer Selçuk Emsen. "The Effects of Exchange Rate Volatility, Reserve Volatility and Real Interest Rates on Trade: Applications on Transition Economies." In International Conference on Eurasian Economies. Eurasian Economists Association, 2013. http://dx.doi.org/10.36880/c04.00711.

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On the basis of volatility or sharp fluctuations in macroeconomic variables, especially in the 1970s, it can be said to play a role in deepening the financial capital deepening. Deepening on volatility forms the basis of not only domestic and but also international economic deviations. With the collapse of the Eastern Bloc, a lot of countries have attempted to liberalize. This situation has caused volatility on mainly rate of exchange then many macroeconomics variables. In this aspect, the multi-relationship between volatility in foreign trade balance and the real interest rate, exchange rate and reserves’ volatility are investigated empirically with the appropriate set of data on 11 transition economies for the period 1996-2011. In this study, the effects of the volatility of foreign trade (netxvol) on the exchange rate volatility (kurvol), reserve volatility (rezvol), and real interest rates subjected with using panel data analysis. Moreover to regression analysis, centred on Granger Causality Test the volatility of the foreign trade balance, import and export volatility, exchange rate volatility, volatility of reserves and try to determine the causal relationship between the real interest rate. The findings have light on that the volatility of trade balance was mostly affected to the volatility of the reserve. It may well be said that the volatility of the interest rate and the exchange rate at the independence of the trade predispose to speculative movements.
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Reports on the topic "Interest rate and volatility risk"

1

Edwards, Sebastian. Interest Rate Volatility, Capital Controls, and Contagion. Cambridge, MA: National Bureau of Economic Research, October 1998. http://dx.doi.org/10.3386/w6756.

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Reinhart, Carmen, and Vincent Reinhart. What Hurts Most? G-3 Exchange Rate or Interest Rate Volatility. Cambridge, MA: National Bureau of Economic Research, October 2001. http://dx.doi.org/10.3386/w8535.

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Boudoukh, Jacob, Matthew Richardson, Richard Stanton, and Robert Whitelaw. A Multifactor, Nonlinear, Continuous-Time Model of Interest Rate Volatility. Cambridge, MA: National Bureau of Economic Research, July 1999. http://dx.doi.org/10.3386/w7213.

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Johri, Alok, Shahed Khan, and César Sosa-Padilla. Interest Rate Uncertainty and Sovereign Default Risk. Cambridge, MA: National Bureau of Economic Research, August 2020. http://dx.doi.org/10.3386/w27639.

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Carpenter, Jennifer, Fangzhou Lu, and Robert Whitelaw. The Price and Quantity of Interest Rate Risk. Cambridge, MA: National Bureau of Economic Research, February 2021. http://dx.doi.org/10.3386/w28444.

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Edwards, Sebastian, and Raul Susmel. Interest Rate Volatility and Contagion in Emerging Markets: Evidence from the 1990s. Cambridge, MA: National Bureau of Economic Research, July 2000. http://dx.doi.org/10.3386/w7813.

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Drechsler, Itamar, Alexi Savov, and Philipp Schnabl. Banking on Deposits: Maturity Transformation without Interest Rate Risk. Cambridge, MA: National Bureau of Economic Research, May 2018. http://dx.doi.org/10.3386/w24582.

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Collin-Dufresne, Pierre, Christopher Jones, and Robert Goldstein. Can Interest Rate Volatility be Extracted from the Cross Section of Bond Yields? An Investigation of Unspanned Stochastic Volatility. Cambridge, MA: National Bureau of Economic Research, September 2004. http://dx.doi.org/10.3386/w10756.

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Trolle, Anders, and Eduardo Schwartz. A General Stochastic Volatility Model for the Pricing and Forecasting of Interest Rate Derivatives. Cambridge, MA: National Bureau of Economic Research, June 2006. http://dx.doi.org/10.3386/w12337.

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Carrasquilla-Barrera, Alberto, Arturo José Galindo-Andrade, Gerardo Hernández-Correa, Ana Fernanda Maiguashca-Olano, Carolina Soto, Roberto Steiner-Sampedro, and Juan José Echavarría-Soto. Report of the Board of Directors to the Congress of Colombia - July 2020. Banco de la República de Colombia, February 2021. http://dx.doi.org/10.32468/inf-jun-dir-con-rep-eng.07-2020.

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In Colombia, as well as in the rest of the world, the Covid-19 pandemic has seriously damaged the health and well-being of the people. In order to limit the damage, local and national authorities have had to order large sectors of the population to be confined at their homes for long periods of time. An inevitable consequence of isolation has been the collapse of economic activity, expenditure, and employment, a phenomenon that has hit many countries of the world affected by the disease. It is an unprecedented crisis in modern times, not so much for its intensity (which is undoubtedly immense), but because its origin is not economic. That is what makes it so unpredictable and difficult to manage. Naturally, its economic consequences are enormous. Governments and central banks from all over the world are struggling to mitigate them, but the final solution is not in the hands of the economic authorities. Only science can provide a way out. In the meantime, the economic indicators in Colombia and in the rest of the world cause concern. The output falls, the massive loss of jobs, and the closure of businesses of all sizes have become daily news. Added to this, there is the deterioration in global financial conditions and the increase in the risk indicators. Financial volatility has increased and stock indexes have fallen. In the face of the lower global demand, export prices of raw materials have fallen, affecting the terms of trade for producing countries. Workers’ remittances have declined due to the increase of unemployment in developed countries. This crisis has also generated a strong reduction of global trade of goods and services, and effects on the global value chains. Central banks around the world have reacted decisively and quickly with strong liquidity injections and significant cuts to their interest rates. By mid-July, such determined response had succeeded to revert much of the initial deterioration in global financial conditions. The stock exchanges stopped their fall, and showed significant recovery in several countries. Risk premia, which at the beginning of the crisis took an unusual leap, recorded substantial corrections. Something similar happened with the volatility indexes of global financial markets, which exhibited significant improvement. Flexibilization of confinement measures in some economies, broad global liquidity, and fiscal policy measures have also contributed to improve global external financial conditions, albeit with indicators that still do not return to their pre-Covid levels.
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