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In this paper, we study the pricing of contracts in fixed income markets under volatility uncertainty in the sense of Knightian uncertainty or model uncertainty. The starting point is an arbitrage-free bond market under volatility uncertainty. The uncertainty about the volatility is modeled by a G-Brownian motion, which drives the forward rate dynamics. The absence of arbitrage is ensured by a drift condition. Such a setting leads to a sublinear pricing measure for additional contracts, which yields either a single price or a range of prices and provides a connection to hedging prices. Similar to the forward measure approach, we define the forward sublinear expectation to simplify the pricing of cashflows. Under the forward sublinear expectation, we obtain a robust version of the expectations hypothesis, and we show how to price options on forward prices. In addition, we develop pricing methods for contracts consisting of a stream of cashflows, since the nonlinearity of the pricing measure implies that we cannot price a stream of cashflows by pricing each cashflow separately. With these tools, we derive robust pricing formulas for all major interest rate derivatives. The pricing formulas provide a link to the pricing formulas of traditional models without volatility uncertainty and show that volatility uncertainty naturally leads to unspanned stochastic volatility. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has given rise to a spike in financial market volatility. In this paper, we attempt to assess the effects of financial & news-driven uncertainty shocks in growing Asian economies, using country-specific bond volatility shocks as a measure of local interest rate uncertainty. Also, we contrast the effects of local uncertainty with global stock market uncertainty. Using bond market data from nine Asian markets, we uncover a transmission mechanism of uncertainty shocks via the bond market. The mechanism works as a crowding-out effect due to government-led excessive market borrowing with supply-side consequences for the private sector, as opposed to economic policy or global stock market uncertainty which works more like a demand shock as in the literature. We conclude that countries with growing fiscal deficits that entail a larger government bond market or higher current account deficits, tend to experience an increase in the cost of borrowing due to this bond market volatility or interest rate uncertainty shocks. [ABSTRACT FROM AUTHOR]

This study addresses interest rate sensitivity of emerging market corporate debt. Previous research suggests that interest rate sensitivity of corporate bonds depends on residual maturity of issues, creditworthiness of issuers, embedded options and other idiosyncratic factors. However, the dependence of interest rate sensitivity on phases of the business cycle has not received an appropriate academic attention. This paper provides empirical evidence and theoretical interpretation of a dichotomy of interest rate sensitivity across the phases of the cycle, and sheds light on how credit spreads respond to interest rates. The historical span of the research covers the period of 2004–2016. The findings imply that hedging interest rate risk ought to be a dynamic process and take into consideration where the economy is positioned in the current business cycle. This research provides important insights on the nature of interest rate sensitivity, capable of enhancing financial stability and improving efficiency of financial system. [ABSTRACT FROM AUTHOR]

This paper seeks to identify the macroeconomic and financial factors that drive credit spreads on bond indices in the US credit market. To overcome the idiosyncratic nature of credit spread data reflected in time varying volatility, skewness and thick tails, it proposes asymmetric GARCH models with alternative probability density functions. The results show that credit spread changes are mainly explained by the interest rate and interest rate volatility, the slope of the yield curve, stock market returns and volatility, the state of liquidity in the corporate bond market and, a heretofore overlooked variable, the foreign exchange rate. They also confirm that the asymmetric GARCH models and Student-t distributions are systematically superior to the conventional GARCH model and the normal distribution in in-sample and out-of-sample testing. [ABSTRACT FROM AUTHOR]

Despite powerful advances in interest rate curve modeling for data-rich countries in the last 30 years, comparatively little attention has been paid to the key practical problem of estimation of the term structure of interest rates for emerging markets. This may be partly due to limited data availability. However, emerging bond markets are becoming increasingly important and liquid. It is, therefore, important to be understand whether conclusions drawn from developed countries carry over to emerging markets. We estimate model parameters of fully flexible Nelson-Siegel-Svensson term structures model which has become one of the most popular term structure model among academics, practitioners, and central bankers. We investigate four sets of bond data: U.S. Treasuries, and three major emerging market government bond data-sets (Brazil, Mexico and Turkey). By including both the very dense U.S. data and the comparatively sparse emerging market data, we ensure that are results are not specific to a particular data-set. We find that gradient and direct search methods perform poorly in estimating term structures of interest rates, while global optimization methods, particularly the hybrid particle swarm optimization introduced in this paper, do well. Our results are consistent across four countries, both in- and out-of-sample, and for perturbations in prices and starting values. For academics and practitioners interested in optimization methods, this study provides clear evidence of the practical importance of choice of optimization method and validates a method that works well for the NSS model. [ABSTRACT FROM AUTHOR]