Journal articles: '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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Zhang, Qiming, Xuemeng Guo, and Hongchang Li. "The Impact of Financial Risks on Financial Investment in Infrastructure: Based on a Two-Factor Stochastic Differential Equation." Discrete Dynamics in Nature and Society 2021 (October 21, 2021): 1–14. http://dx.doi.org/10.1155/2021/9112739.

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Financial risks, such as inflation and interest rate changes, significantly affect the costs and benefits of infrastructure projects. Nevertheless, there is a dearth of research concerning financial investment (government subsidies) for infrastructure projects in the context of inflation and interest rate changes. Accordingly, this study builds a stochastic differential equation model based on inflation rate and interest rate, through which the expression of government subsidies in public-private partnership is optimised. Specifically, the Monte Carlo simulation was used to undertake a calculation of the present value of operating loss subsidy and risk-sharing subsidy for the N City Metro Line 3. Subsequently, the effect of inflation, nominal interest rates, interest rate volatility, as well as inflation volatility, on the present value of operating loss subsidies was investigated. It was established that the dynamic random discount rate based on inflation rate and interest rate may effectively simulate the effect of inflation rate and interest rate changes on project operating loss. Moreover, it is feasible to calculate the present value of the risk-adjusted operating loss subsidy and the present value of the risk-sharing subsidy. Inflation rate, inflation volatility, and interest rate volatility are positively correlated with the present value of operating loss subsidies, whereas the interest rate is negatively correlated with the present value of inflation-adjusted operating loss subsidies. Inflation volatility has the greatest effect on the present value of subsidies, followed by interest rate volatility and inflation rate. Ultimately, this paper provides an effective tool for quantitative simulation of and risk-sharing in public-private partnership projects, which can facilitate a regional economy’s sustainable development.

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Alenezi, Mariam, Ahmad Alqatan, and Obby Phiri. "The sensitivity of GCC firms’ stock returns to exchange rate, interest rate, and oil price volatility." Corporate Ownership and Control 17, no. 4 (2020): 35–50. http://dx.doi.org/10.22495/cocv17i4art3.

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This study seeks to investigate the sensitivity of stock returns to exchange rate, interest rate and oil price volatility in the Gulf Cooperation Council (GCC) countries. It employs both the multivariate ordinary least square (OLS) regression and the exponential generalized autoregressive conditional heteroscedastic in mean (EGARCH-M) models to analyse the data collected from Bloomberg and DataStream on the GCC countries (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and the United Arab Emirates) for the period January 2007 to June 2012. The study shows that stock returns in GCC countries are influenced by the exchange rate risk, interest rate risk and oil price risk. However, the exposure is highest for exchange rate risk and lowest for interest rate risk. While the effects of these risks were mixed, overall, exchange rate risk and oil price risk showed a positive and significant relationship as compared to the interest rate risk that showed a negative significant effect on firm values. The level of the effect of these risks also differed from country to country. Further, foreign operations and firm size had a significant influence on the extent of the firms’ exposure to all three risks. The study findings suggest that the volatility of stock returns affected by changes in the risk factors could indicate non-prioritisation of risk management by firms. This has implications in terms of consideration of the long-term exposure of firms to these three risks and thus, the need for effective risk management strategies.

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Fernández-Villaverde, Jesús, Pablo Guerrón-Quintana, Juan F. Rubio-Ramírez, and Martin Uribe. "Risk Matters: The Real Effects of Volatility Shocks." American Economic Review 101, no. 6 (October 1, 2011): 2530–61. http://dx.doi.org/10.1257/aer.101.6.2530.

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We show how changes in the volatility of the real interest rate at which small open emerging economies borrow have an important effect on variables like output, consumption, investment, and hours. We start by documenting the strong evidence of time-varying volatility in the real interest rates faced by four emerging economies: Argentina, Brazil, Ecuador, and Venezuela. We estimate a stochastic volatility process for real interest rates. Then, we feed this process in a standard small open economy business cycle model. We find that an increase in real interest rate volatility triggers a fall in output, consumption, investment, hours, and debt. (JEL E13, E20, E32, E43, F32, F43, 011)

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Tahani, Nabil, and Xiaofei Li. "Pricing interest rate derivatives under stochastic volatility." Managerial Finance 37, no. 1 (January 31, 2011): 72–91. http://dx.doi.org/10.1108/03074351111092157.

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PurposeThe purpose of this paper is to derive semi‐closed‐form solutions to a wide variety of interest rate derivatives prices under stochastic volatility in affine‐term structure models.Design/methodology/approachThe paper first derives the Frobenius series solution to the cross‐moment generating function, and then inverts the related characteristic function using the Gauss‐Laguerre quadrature rule for the corresponding cumulative probabilities.FindingsThis paper values options on discount bonds, coupon bond options, swaptions, interest rate caps, floors, and collars, etc. The valuation approach suggested in this paper is found to be both accurate and fast and the approach compares favorably with some alternative methods in the literature.Research limitations/implicationsFuture research could extend the approach adopted in this paper to some non‐affine‐term structure models such as quadratic models.Practical implicationsThe valuation approach in this study can be used to price mortgage‐backed securities, asset‐backed securities and credit default swaps. The approach can also be used to value derivatives on other assets such as commodities. Finally, the approach in this paper is useful for the risk management of fixed‐income portfolios.Originality/valueThis paper utilizes a new approach to value many of the most commonly traded interest rate derivatives in a stochastic volatility framework.

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Park, Hun Y., and Anil K. Bera. "Interest-Rate Volatility, Basis Risk and Heteroscedasticity in Hedging Mortgages." Real Estate Economics 15, no. 2 (June 1987): 79–97. http://dx.doi.org/10.1111/1540-6229.00420.

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Díaz, Antonio, and Marta Tolentino. "Risk Management for Bonds with Embedded Options." Mathematics 8, no. 5 (May 13, 2020): 790. http://dx.doi.org/10.3390/math8050790.

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This paper examines the behavior of the interest rate risk management measures for bonds with embedded options and studies factors it depends on. The contingent option exercise implies that both the pricing and the risk management of bonds requires modelling future interest rates. We use the Ho and Lee (HL) and Black, Derman, and Toy (BDT) consistent interest rate models. In addition, specific interest rate measures that consider the contingent cash-flow structure of these coupon-bearing bonds must be computed. In our empirical analysis, we obtained evidence that effective duration and effective convexity depend primarily on the level of the forward interest rate and volatility. In addition, the higher the interest rate change and the lower the volatility, the greater the differences in pricing of these bonds when using the HL or BDT models.

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Bouzouita, Raja, and Arthur Young. "Recent Evidence on Insurance Stock Interest Rate Sensitivity." Journal of Finance Issues 8, no. 1 (June 30, 2010): 11–18. http://dx.doi.org/10.58886/jfi.v8i1.2364.

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A number of researchers analyzed the relationship between stock returns and interest rate risk for financial institutions with mixed results. The assets and liabilities holdings of insurance companies are regulated to reduce interest rate risk to policyholders, but not stockholders. Given the recent increase in the number of stock companies and the recent volatility in financial stocks, this paper reexamines the direction and characteristics of the relationship between interest rate risk and stock returns with more recent evidence. Our results have important policy implications for stock investors in insurance companies.

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Yoon, Ji-Hun, Jeong-Hoon Kim, Sun-Yong Choi, and Youngchul Han. "Stochastic volatility asymptotics of defaultable interest rate derivatives under a quadratic Gaussian model." Stochastics and Dynamics 17, no. 01 (December 15, 2016): 1750003. http://dx.doi.org/10.1142/s0219493717500034.

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Stochastic volatility of underlying assets has been shown to affect significantly the price of many financial derivatives. In particular, a fast mean-reverting factor of the stochastic volatility plays a major role in the pricing of options. This paper deals with the interest rate model dependence of the stochastic volatility impact on defaultable interest rate derivatives. We obtain an asymptotic formula of the price of defaultable bonds and bond options based on a quadratic term structure model and investigate the stochastic volatility and default risk effects and compare the results with those of the Vasicek model.

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Fornari, Fabio. "Assessing the compensation for volatility risk implicit in interest rate derivatives." Journal of Empirical Finance 17, no. 4 (September 2010): 722–43. http://dx.doi.org/10.1016/j.jempfin.2010.03.002.

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Markellos, Raphael N., and Dimitris Psychoyios. "Interest rate volatility and risk management: Evidence from CBOE Treasury options." Quarterly Review of Economics and Finance 68 (May 2018): 190–202. http://dx.doi.org/10.1016/j.qref.2017.08.005.

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Olaniyan, Sunday, and Hamadu Dallah. "MODELING THE VOLATILITY FOR LONG TERM INTEREST RATE RETURNS IN THE NIGERIA BOND MARKET USING CONDITIONALY HETEROSCEDASTIC MODELS." Jurnal Wahana Akuntansi 15, no. 1 (August 5, 2020): 46–56. http://dx.doi.org/10.21009/wahana.15.014.

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Investigating the volatility of ﬁnancial assets is fundamental to risk management. This study used generalized Autoregressive Conditional Heteroscedastic Volatility models to evaluate the volatility of the long term interest rate of Nigeria's financial market. We also incorporated three innovations distributions viz: the Gaussian, the student-t, and the Generalized Error Distribution (GED) in the modeling process under the maximum likelihood estimation method. The results show that GARCH (GED) is the most performing model for describing the volatility of three and twenty-year interest rate returns while TARCH (GED) is the most suitable model for describing the volatility of five and ten-year interest rate returns in Nigeria. The preferred models will help in the development of tools for effective risk management by monitoring the behavior of long term interest rates.

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Balter, Anne G., and Bas J. M. Werker. "THE EFFECT OF THE ASSUMED INTEREST RATE AND SMOOTHING ON VARIABLE ANNUITIES." ASTIN Bulletin 50, no. 1 (October 31, 2019): 131–54. http://dx.doi.org/10.1017/asb.2019.27.

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AbstractIn this paper, we consider the risk–return trade-off for variable annuities in a Black–Scholes setting. Our analysis is based on a novel explicit allocation of initial wealth over the payments at various horizons. We investigate the relationship between the optimal consumption problem and the design of variable annuities by deriving the optimal so-called assumed interest rate for an investor with constant relative risk aversion preferences. We investigate the utility loss due to deviations from this. Finally, we show analytically how habit-formation-type smoothing of financial market shocks over the remaining lifetime leads to smaller year-to-year volatility in pension payouts, but to increases in the longer-term volatility.

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Byun, Suk Joon, and Ki Cheon Chang. "Volatility risk premium in the interest rate market: Evidence from delta-hedged gains on USD interest rate swaps." International Review of Financial Analysis 40 (July 2015): 88–102. http://dx.doi.org/10.1016/j.irfa.2015.03.018.

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Akhmedov, Fakhraddin, Mhd Zeitoun, and Humssi Al. "Financial engineering to optimize risk management in banks based on Interest Rate Swaps to better hedge the exposure to interest rate fluctuations the case of banks in Syria." International Review, no. 1-2 (2021): 99–107. http://dx.doi.org/10.5937/intrev2102101a.

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The banking system is affected by uncertainties related to the evolution of pandemic. One of the identified risks is that of a fluctuation of rates. Volatility of Interest rates is one of the major risks for the banking system. Therefore, financial engineering can be used as a very important hedging practice for banks against such a risk. The aim of this study is to develop a risk hedging mechanism to better overcome market volatility by hedging position against the exposure to interest rate risk based on credit derivatives. Therefore, this study uses Interest Rate Swaps (IRS)s to better hedge the exposure of banks to interest rate fluctuations in stress conditions giving consideration to the case study of banks in Syria in optimizing hedging practices based on Interest Rate Swaps. The aim is to use financial engineering to provide banks with a hedging technique to better absorb shocks in times of stress conditions. This has been discussed and illustrated with visual model diagrams. The case study of banks in Syria is not just the story of individual banks but a window into how to hedge the exposure of banks in stress conditions. In the end, most banking crises are quite similar. The recommendations set out in this study provide banks with an optimized hedging practice which is not part of current financial engineering at banks in Syria.

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Ghosh, Renu, K. Latha, and Sunita Gupta. "Interest Rate Sensitivity of Non-banking Financial Sector in India." Vikalpa: The Journal for Decision Makers 43, no. 3 (August 20, 2018): 152–70. http://dx.doi.org/10.1177/0256090918792803.

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Executive Summary Before financial liberalization, interest rates were administered and exhibited near-zero volatility. The easing of financial repression in the 1990s generated experiences with interest rate volatility in India. Administrative restrictions on interest rates in India have been steadily eased since 1993. This has led to increased interest rate risk for financial firms. Most research studies have almost exclusively focused on the developed countries especially the banking sector of the United States. The present study attempts to examine the interest rate risk of non-banking financial institutions in India by using the methodology of panel regression and generalized autoregressive conditional heteroscedasticity (GARCH) (1, 1) model for the period from 1 April 1996 to 30 August 2014. The sample used in the study consists of all non-banking financial companies (NBFCs) listed in the S&P CNX 500 index which has continuous availability of share prices over the study period. The study also examines the impact of unanticipated changes in interest rate on stock returns of NBFCs. The Box–Jenkins methodology is applied to calculate unanticipated changes in interest rate variable, autoregressive integrated moving average (ARIMA) (24, 1, 0) model. The time series used in the present study is found to be stationary at the first logarithmic difference. Stock returns exhibit significant exposure with both market returns and interest rate changes. The interest rate sensitivity of large, medium, and small financial institutions is also found to be different. Estimation results for the variance equation in GARCH (1, 1) model suggest that the volatility for individual firm stock returns is time-variant. The ARCH and GARCH coefficients are found to be significant, providing evidence against using traditional model (ordinary least square (OLS)) that assumes time-invariant volatility. This implies that the market has a memory longer than one period and volatility is more sensitive to its own lagged values than it is to new surprises in the market. This study also investigates the possible determinants that account for cross-sectional variation in the interest rate sensitivity of NBFCs. It is found that the size of the firm is the preferred determinant that accounts for cross-sectional variation in the interest rate sensitivity of finance companies. When unanticipated changes in interest rate are used in lieu of actual interest rate changes, not much difference is observed in the significance coefficients. The only significant difference observed is in the magnitude. The impact of actual interest rate changes is more than the impact of unanticipated interest rate changes in absolute terms. This difference in the magnitude of impact arises because actual data incorporate movement in both anticipated and unanticipated components of interest rate. Hence, NBFCs managers and regulators should adopt policies and strategies to avoid the transmission of interest rate risk in their stock returns.

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Elyasiani, Elyas, and Iqbal Mansur. "International Spillover of Risk and Return among Major Banking Institutions: A Bivariate GARCH Model." Journal of Accounting, Auditing & Finance 18, no. 2 (April 2003): 303–30. http://dx.doi.org/10.1177/0148558x0301800207.

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Using the bivariate GARCH methodology, this study examines bank stock sensitivities to market, interest rate, and exchange rate, and investigates the spillover effects of interest rate volatility and unsystematic risk among the banking sectors of the United States and Japan, and the United States and Germany. Empirical results show that return-generating processes of the banking sectors considered can be properly described by GARCH models. Within this framework, banks are found to be highly sensitive to macroeconomic shocks such as the exchange rate and interest rate, with the latter exerting its impact at the volatility level. Moreover, stock volatilities in the banking sectors of the three countries are found to be highly interdependent. The direction and magnitude of the effects from interest rate volatility and unsystematic shocks in one country on other countries are sensitive to the origin of the shock, with the United States playing a leadership role. The findings have serious implications on international financial stability, international portfolio diversification, and policy formulation by central banks and fiscal authorities.

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Hammami, Haifa, and Younes Boujelbene. "FINANCIAL RISKS AND STOCK MARKET CRASHES: AN EMPIRICAL ANALYSIS OF THE TUNISIAN STOCK MARKET." Applied Finance Letters 10 (June 16, 2021): 10–23. http://dx.doi.org/10.24135/afl.v10i.379.

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This study aims to investigate the effect of financial risks on the stock market crashes occurrence from 1999 to 2020. Using the windows method, we detect two stock market crises in the Tunisian stock market. Based on the probit model, we find evidence that low stock return risk, low EUR/TND exchange rate risk, high interest rate risk, high credit risk and high liquidity risk increase the occurrence probability of stock market crashes. Our results suggest that the decrease in volatility, particularly in equity and exchange market, the increase in volatility in interest rate, the credit rating downgrades issued by Moody’s and the low liquidity market contribute to crashes in the Tunisian stock market. In summary, financial risks, which are the market risks, the credit risk and the liquidity risk could be leading indicators of crashes in the Tunisian stock market. Keywords: Stock market crashes; Liquidity risk; Credit risk; Market risks.

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Dewi, Syanti, and Ishak Ramli. "OPSI SAHAM PADA PASAR MODAL DI INDONESIA (STUDI PASAR OPSI SAAT PASAR OPSI MASIH BERLANGSUNG DI BURSA EFEK INDONESIA)." Jurnal Muara Ilmu Ekonomi dan Bisnis 2, no. 2 (March 28, 2019): 300. http://dx.doi.org/10.24912/jmieb.v2i2.1001.

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Stock option exchange market is not working anymore in the Indonesian Stock Exchange, using the data option exchange market for the running period 2007-2008, we analyzed the effect of stock price, strike price, time to maturity, volatility and risk- free interest rate on the stock option’s price of listed stock call or put option trading at the Indonesian Stock Exchange during 2007-2008. The results found that the stock price, strike price, time to maturity, volatility and risk-free interest rate are positive significantly affecting the stock option price either the buying option price or the selling option price in Indonesia Stock Exchange 2007-2008 period. While there were no variables that significantly affected the call option during the periode 2007-2008, furthermore stock prices and strike prices significantly affected the put option prices. Time to maturity, Volatility, and risk free interest rate did not significantly affect the put option prices.That is why the stock option exchange market stop since the investor were not sure to the stock option price versus the risk of the volatility, time to maturity, and riskfree rate.

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Haider, Syed Kamran Ali, Shujahat Haider Hashmi, and Ishtiaq Ahmed. "Systematic risk factors and stock return volatility." Applied Studies in Agribusiness and Commerce 11, no. 1-2 (June 30, 2017): 61–70. http://dx.doi.org/10.19041/apstract/2017/1-2/8.

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This study analyzes the transmission of systematic risk exhaling from macroeconomic fundamentals to volatility of stock market by using auto regressive generalized auto regressive conditional heteroskedastic (AR-GARCH) and vector auto regressive (VAR) models. Systematic risk factors used in this study are industrial production, real interest rate, inflation, money supply and exchange rate from 2000-2014. Results indicate that there exists relationship among the volatility of macroeconomic factors and that of stock returns in Pakistan. The relationship among the volatility of macroeconomic variables and that of stock returns is bidirectional; both affect each other in different dynamics. JEL code: C32, C58, G11, G12

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Arellano, Cristina. "Default Risk and Income Fluctuations in Emerging Economies." American Economic Review 98, no. 3 (May 1, 2008): 690–712. http://dx.doi.org/10.1257/aer.98.3.690.

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Recent sovereign defaults are accompanied by interest rate spikes and deep recessions. This paper develops a small open economy model to study default risk and its interaction with output and foreign debt. Default probabilities and interest rates depend on incentives for repayment. Default is more likely in recessions because this is when it is more costly for a risk averse borrower to repay noncontingent debt. The model closely matches business cycles in Argentina predicting high volatility of interest rates, higher volatility of consumption relative to output, and negative correlations of output with interest rates and the trade balance. (JEL E21, E23, E32, E43, F34, O11, O19)

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de Ferra, Sergio, and Enrico Mallucci. "Sovereign risk matters: Endogenous default risk and the time-varying volatility of interest rate spreads." Journal of International Economics 134 (January 2022): 103542. http://dx.doi.org/10.1016/j.jinteco.2021.103542.

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Brody, Dorje C., Lane P. Hughston, and Ewan Mackie. "General theory of geometric Lévy models for dynamic asset pricing." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 468, no. 2142 (February 29, 2012): 1778–98. http://dx.doi.org/10.1098/rspa.2011.0670.

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The geometric Lévy model (GLM) is a natural generalization of the geometric Brownian motion (GBM) model used in the derivation of the Black–Scholes formula. The theory of such models simplifies considerably if one takes a pricing kernel approach. In one dimension, once the underlying Lévy process has been specified, the GLM has four parameters: the initial price, the interest rate, the volatility and the risk aversion. The pricing kernel is the product of a discount factor and a risk aversion martingale. For GBM, the risk aversion parameter is the market price of risk. For a GLM, this interpretation is not valid: the excess rate of return is a nonlinear function of the volatility and the risk aversion. It is shown that for positive volatility and risk aversion, the excess rate of return above the interest rate is positive, and is increasing with respect to these variables. In the case of foreign exchange, Siegel's paradox implies that one can construct foreign exchange models for which the excess rate of return is positive for both the exchange rate and the inverse exchange rate. This condition is shown to hold for any geometric Lévy model for foreign exchange in which volatility exceeds risk aversion.

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BENTH, FRED ESPEN, and FRANK PROSKE. "UTILITY INDIFFERENCE PRICING OF INTEREST-RATE GUARANTEES." International Journal of Theoretical and Applied Finance 12, no. 01 (February 2009): 63–82. http://dx.doi.org/10.1142/s0219024909005117.

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We consider the problem of utility indifference pricing of a put option written on a non-tradeable asset, where we can hedge in a correlated asset. The dynamics are assumed to be a two-dimensional geometric Brownian motion, and we suppose that the issuer of the option have exponential risk preferences. We prove that the indifference price dynamics is a martingale with respect to an equivalent martingale measure (EMM) Q after discounting, implying that it is arbitrage-free. Moreover, we provide a representation of the residual risk remaining after using the optimal utility-based trading strategy as the hedge. Our motivation for this study comes from pricing interest-rate guarantees, which are products usually offered by companies managing pension funds. In certain market situations the life company cannot hedge perfectly the guarantee, and needs to resort to sub-optimal replication strategies. We argue that utility indifference pricing is a suitable method for analysing such cases. We provide some numerical examples giving insight into how the prices depend on the correlation between the tradeable and non-tradeable asset, and we demonstrate that negative correlation is advantageous, in the sense that the hedging costs become less than with positive correlation, and that the residual risk has lower volatility. Thus, if the insurance company can hedge in assets negatively correlated with the pension fund, they may offer cheaper prices with lower Value-at-Risk measures on the residual risk.

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Karlsson, P., K. F. Pilz, and E. Schlögl. "Calibrating a market model with stochastic volatility to commodity and interest rate risk." Quantitative Finance 17, no. 6 (December 21, 2016): 907–25. http://dx.doi.org/10.1080/14697688.2016.1254814.

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McKINNON, RONALD I. "INTEREST RATE VOLATILITY AND EXCHANGE RISK: NEW RULES FOR A COMMON MONETARY STANDARD." Contemporary Economic Policy 8, no. 2 (April 1990): 1–17. http://dx.doi.org/10.1111/j.1465-7287.1990.tb00587.x.

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Espinosa, Fernando, and Josep Vives. "A volatility-varying and jump-diffusion Merton type model of interest rate risk." Insurance: Mathematics and Economics 38, no. 1 (February 2006): 157–66. http://dx.doi.org/10.1016/j.insmatheco.2005.08.010.

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Gómez-Valle, L., and J. Martínez-Rodríguez. "The risk-neutral stochastic volatility in interest rate models with jump–diffusion processes." Journal of Computational and Applied Mathematics 347 (February 2019): 49–61. http://dx.doi.org/10.1016/j.cam.2018.07.048.

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Huang, Jianbo, Jian Liu, and Yulei Rao. "Binary Tree Pricing to Convertible Bonds with Credit Risk under Stochastic Interest Rates." Abstract and Applied Analysis 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/270467.

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The convertible bonds usually have multiple additional provisions that make their pricing problem more difficult than straight bonds and options. This paper uses the binary tree method to model the finance market. As the underlying stock prices and the interest rates are important to the convertible bonds, we describe their dynamic processes by different binary tree. Moreover, we consider the influence of the credit risks on the convertible bonds that is described by the default rate and the recovery rate; then the two-factor binary tree model involving the credit risk is established. On the basis of the theoretical analysis, we make numerical simulation and get the pricing results when the stock prices are CRR model and the interest rates follow the constant volatility and the time-varying volatility, respectively. This model can be extended to other financial derivative instruments.

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DEWI, MIRANDA NOVI MARA, KOMANG DHARMAWAN, and KARTIKA SARI. "ESTIMASI VALUE AT RISK MENGGUNAKAN VOLATILITAS DISPLACED DIFFUSION." E-Jurnal Matematika 8, no. 4 (November 30, 2019): 298. http://dx.doi.org/10.24843/mtk.2019.v08.i04.p268.

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Value at Risk (VaR) is a measure of risk that is able to calculate the worst possible loss that can occurs to stock prices with a certain level of confidence and within a certain period of time. The purpose of this study was to determine the VaR estimate from PT. Indonesian Telecommunications by using Displaced Diffusion volatility. The Displaced Diffusion Model is a stochastic volatility model that describes changes in a financial asset assuming volatility is not constant, but follows a stochastic process. Displaced Diffusion model are capable of modelling skewed implied volatility structures and frequently applied by interest rate quants. Based on the estimation of Displaced Diffusion volatility, it is found that volatility for PT. Indonesian Telecommunications is 0.010168 and VaR estimation using Displaced Diffusion volatility with a confidence level of 95 percent of 1.63%.

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Mendes, Victor, and Margarida Abreu. "Monetary and Financial Instability and European Bank Interest Margins." International Finance and Banking 5, no. 1 (April 13, 2018): 14. http://dx.doi.org/10.5296/ifb.v5i1.13000.

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This paper studies the extent to which financial instability and monetary and exchange rate policy influence European bank net interest margins, controlling for microeconomic variables and allowing for the heterogeneity of the banking industry. The sample is a broad cross-section of balance sheet and income statement information provided by banks from 12 European countries.We conclude that European banks are sensitive to exchange rate and interest rate volatility. They are also affected by their home country’s vulnerability to balance of payment and currency crises, but we find that banks feel differently about the associated risk of liquidity problems depending on their specialization. The instability of international financial markets is not good for banks, insofar as interest and exchange rate volatility both have a negative impact on the net interest margin.

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Harris, Geoffrey R., Tao L. Wu, and Jiarui Yang. "The relationship between counterparty default and interest rate volatility and its impact on the credit risk of interest rate derivatives." Journal of Credit Risk 11, no. 1 (March 2015): 93–127. http://dx.doi.org/10.21314/jcr.2015.190.

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Mohanty, Sunil K., Roar Aadland, Sjur Westgaard, Stein Frydenberg, Hilde Lillienskiold, and Cecilie Kristensen. "Modelling Stock Returns and Risk Management in the Shipping Industry." Journal of Risk and Financial Management 14, no. 4 (April 9, 2021): 171. http://dx.doi.org/10.3390/jrfm14040171.

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We estimate the impact of macroeconomic risk factors on shipping stock returns, using a quantile regression (QR) model. We regress the excess return of a portfolio for the container, dry bulk, chemical/gas, oil tanker, and diversified shipping sectors on the world market portfolio excess return, volatility index, and changes in the oil price, exchange rate, and interest rate. The sensitivities of stock returns to the risk factors differ across quantiles and shipping segments and are found to be significant for the volatility index, world market portfolio return, exchange rate, and changes in long-term interest rate with variation over quantiles. This provides evidence of asymmetric and heterogeneous dependence between stock returns and certain macroeconomic risk variables. The results of the study also suggest that standard OLS regression is inadequate to uncover the risk-return relation.

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Hattori, Masazumi, Andreas Schrimpf, and Vladyslav Sushko. "The Response of Tail Risk Perceptions to Unconventional Monetary Policy." American Economic Journal: Macroeconomics 8, no. 2 (April 1, 2016): 111–36. http://dx.doi.org/10.1257/mac.20140016.

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We examine the impact of unconventional monetary policy (UMP) on stock market tail risk and risks of extreme interest rate movements. We find that UMP announcements substantially reduced option-implied equity market tail risks and interest rate risks. Most of the impact derives from forward guidance rather than asset purchase announcements. Communication about the future path of policy rates reduced volatility expectations of long-term rates and the associated risk premia. The reaction of equity market tail risk, in turn, points to the risk-taking channel of monetary policy, as the commitment to low funding rates may have relaxed financial intermediaries’ risk-bearing constraints. (JEL E52, E58, G12, G13, G14)

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MATACZ, ANDREW, and JEAN-PHILIPPE BOUCHAUD. "AN EMPIRICAL INVESTIGATION OF THE FORWARD INTEREST RATE TERM STRUCTURE." International Journal of Theoretical and Applied Finance 03, no. 04 (October 2000): 703–29. http://dx.doi.org/10.1142/s0219024900000838.

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In this paper we study empirically the Forward Rate Curve (FRC) of 5 different currencies. We confirm and extend the findings of a previous investigation of the U.S. FRC. In particular, 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, in agreement with the idea of an extrapolated trend effect. We present a model which can be adequately calibrated to account for these effects.

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Wang, Xiandong, Jianmin He, and Shouwei Li. "Compound Option Pricing under Fuzzy Environment." Journal of Applied Mathematics 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/875319.

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Considering the uncertainty of a financial market includes two aspects: risk and vagueness; in this paper, fuzzy sets theory is applied to model the imprecise input parameters (interest rate and volatility). We present the fuzzy price of compound option by fuzzing the interest and volatility in Geske’s compound option pricing formula. For eachα, theα-level set of fuzzy prices is obtained according to the fuzzy arithmetics and the definition of fuzzy-valued function. We apply a defuzzification method based on crisp possibilistic mean values of the fuzzy interest rate and fuzzy volatility to obtain the crisp possibilistic mean value of compound option price. Finally, we present a numerical analysis to illustrate the compound option pricing under fuzzy environment.

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SOHEL AZAD, A. S. M., VICTOR FANG, and J. WICKRAMANAYAKE. "Low-Frequency Volatility of Yen Interest Rate Swap Market in Relation to Macroeconomic Risk*." International Review of Finance 11, no. 3 (April 20, 2011): 353–90. http://dx.doi.org/10.1111/j.1468-2443.2011.01129.x.

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Hautsch, Nikolaus, and Yangguoyi Ou. "Analyzing interest rate risk: Stochastic volatility in the term structure of government bond yields." Journal of Banking & Finance 36, no. 11 (November 2012): 2988–3007. http://dx.doi.org/10.1016/j.jbankfin.2012.06.020.

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LO, C. F., P. H. YUEN, and C. H. HUI. "OPTION RISK MEASUREMENT WITH TIME-DEPENDENT PARAMETERS." International Journal of Theoretical and Applied Finance 03, no. 03 (July 2000): 581–89. http://dx.doi.org/10.1142/s0219024900000668.

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In value-at-risk (VaR) methodology of option risk measurement, the determination of market values of the current option positions under various market scenarios is critical. Under the full revaluation and factor sensitivity approach which are accepted by regulators, accurate revaluation and precise factor sensitivity calculation of options in response to significant moves in market variables are important for measuring option risks in terms of VaR figures. This paper provides a method for pricing equity options in the constant elasticity variance (CEV) model environment using the Lie-algebraic technique when the model parameters are time-dependent. Analytical solutions for option values incorporating time-dependent model parameters are obtained in various CEV processes. The numerical results, which are obtained by employing a very efficient computing algorithm similar to the one proposed by Schroder [11], indicate that the option values are sensitive to the time-dependent volatility term structures. It is also possible to generate further results using various functional forms for interest rate and dividend term structures. From the analytical option pricing formulae, one can achieve more accuracy to compute factor sensitivities using more realistic term-structures in volatility, interest rate and dividend yield. In view of the CEV model being empirically considered to be a better candidate in equity option pricing than the traditional Black–Scholes model, more precise risk management in equity options can be achieved by incorporating term-structures of interest rates, volatility and dividend into the CEV option valuation model.

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Zhu, Jiaqi, and Shenghong Li. "Time-Consistent Investment and Reinsurance Strategies for Mean-Variance Insurers under Stochastic Interest Rate and Stochastic Volatility." Mathematics 8, no. 12 (December 7, 2020): 2183. http://dx.doi.org/10.3390/math8122183.

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This paper studies the time-consistent optimal investment and reinsurance problem for mean-variance insurers when considering both stochastic interest rate and stochastic volatility in the financial market. The insurers are allowed to transfer insurance risk by proportional reinsurance or acquiring new business, and the jump-diffusion process models the surplus process. The financial market consists of a risk-free asset, a bond, and a stock modelled by Heston’s stochastic volatility model. Interest rate in the market is modelled by the Vasicek model. By using extended dynamic programming approach, we explicitly derive equilibrium reinsurance-investment strategies and value functions. In addition, we provide and prove a verification theorem and then prove the solution we get satisfies it. Moreover, sensitive analysis is given to show the impact of several model parameters on equilibrium strategy and the efficient frontier.

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Li, Jinzhi, and Shixia Ma. "Pricing Options with Credit Risk in Markovian Regime-Switching Markets." Journal of Applied Mathematics 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/621371.

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This paper investigates the valuation of European option with credit risk in a reduced form model when the stock price is driven by the so-called Markov-modulated jump-diffusion process, in which the arrival rate of rare events and the volatility rate of stock are controlled by a continuous-time Markov chain. We also assume that the interest rate and the default intensity follow the Vasicek models whose parameters are governed by the same Markov chain. We study the pricing of European option and present numerical illustrations.

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

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