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This study aims to investigate the determinants of credit spreads in hotel loans securitized into commercial mortgage-backed securities (CMBS) between 2010 and 2015.

The sample represents 1,579 US hotel fixed interest rate whole loans with an aggregate mortgage value of $26.6bn at loan origination. The relationship between credit spreads and property, loan and market characteristic is examined via multiple regression analysis. Additionally, the method of 2-stage least squares is used to control for endogeneity bias and identify the effect of the loan-to-value (LTV) ratio on credit spreads.

The multiple regression models explain 80 per cent of the variation in credit spreads and show a significant association of credit spreads with hotel and loan characteristics and market conditions. The findings indicate the debt coverage ratio to be the most important predictor of credit spreads followed by the loan maturity term, implied capitalization rate, LTV and yield curve. The results show the debt yield premium to be a stronger predictor of credit spreads than the debt yield ratio. The spread between the debt yield ratio and mortgage interest rate could be used in future research as an instrumental variable to identify the effect of the LTV on credit spreads.

This study is limited to the CMBS market and the period after the financial crisis. Additional limitations include sample selection bias, exclusion of multi-property loans and variable interest rate loans.

Interest rate increases in an expanding economy would likely increase the cost of borrowing for hotel owners leading to higher debt service payments and lower profitability. If an increase in interest rates is offset by a decline in credit spreads, hotel owners will still benefit from the ensuing stability in borrowing interest rates. The evidence also suggests that CMBS lenders favor select service and extended stay hotels. Owners and operators of these efficient and profitable hotels will likely obtain loans with lower credit spreads given their lower risk of default.

The current study provides evidence on the effects of loan and property characteristics in the pricing of loan risk and serves to inform CMBS market participants about the factors that drive credit spreads in hotel mortgage loans.

The paper is motivated by the paucity of empirical research on the determinants of capitalisation rates/yield in the commercial property market. Compared to property price determinants, the capitalisation rate has received significantly less attention. This is somewhat surprising given that the capitalisation rate is a more insightful indicator for investors on commercial property market performance than merely price changes or trends. The capitalisation rate, measured as the ratio of net operating income to the property’s capital value, captures the asset’s overall ability to generate income which is crucial for investors who typically invest in property for their income-generating capacity. The purpose of this paper is to address these issues.

To evaluate the determinants of capitalisation rates, time series analysis was used. The data capture performance in the Australian commercial property market between 2005 and 2018. All macroeconomic and financial data are freely available from official sources such as the Australian Bureau of Statistics and the nation’s central bank. Methodology wise, given the problematic nature of the data such as a mixed order of integration and the possibility of cointegration amongst some of the I (1) variables, the autoregressive distributed lag model was selected given its flexibility and relative lack of assumptions.

Bond rates, market risk premiums, stock market excess returns and other macroeconomic variables were found to drive capitalisation rates of Australian commercial properties. A 1% increase in the bond rate results in approximately 0.3–2.4% increase in capitalisation rates depending on the sub-market. Further, a 1% increase in excess market returns results in a 0.01–0.02% increase in capitalisation rates. Regarding risk premiums, a 100 basis point increase in the BBB spread results in approximately 0.92–1.27% reduction in cap rates in certain markets.

Asset managers will find these results useful in asset allocation strategies. Commercial properties offer attractive investment qualities such as yield stability in periods of economic uncertainty while allowing for the possibility of capital growth through appreciation of the underlying asset. By understanding the factors that affect the capitalisation rate, practitioners may predict emerging trends and identify threats to portfolio return and stability. This allows better integration of commercial property in the construction of portfolios that remain robust in a variety of market conditions.

The contribution to literature is significant given the lack of similar studies in the Australian market. The performance of real estate assets using cap rates as a comparative measure to equities and bonds influences decisions in asset allocation strategies. It provides crucial information for investors to estimate the performance of commercial property. This research supports the notion that both space and capital market indicators jointly affect capitalisation rates. The findings expand the knowledge base relating to commercial properties and validate the assessments of investors, developers and valuers who utilise yield as a performance benchmark for asset allocation strategies.

This study aims to extend the literature by exploring the degrees of integration of both fixed and adjustable mortgage rates and diverse riskless (Treasury) and risky (corporate) interest rates in the capital markets from January 1, 2010, until November 7, 2018. This period is uniquely characterized by a sharp conversion on January 20, 2017, from enhanced financial regulation during the Obama administration to major deregulatory ambitions during the first 22 months of the Trump administration.

The authors use the augmented Dickey and Fuller and the Phillips and Perron unit root tests to examine time series stationarity and the Johansen cointegration rank and the Stock-Watson common trends tests to inspect various cointegrations and regressions of time series pairs to explore different effects. The authors deploy these techniques over the entire time frame, as well as for distinct sub-periods of similar length.

The authors conclude that a deregulatory setting favors cointegration between mortgage and non-corporate capital markets. However, an enriched regulatory environment supports cointegration between mortgage and corporate capital markets. In addition, the Dodd-Frank Wall Street Reform and Consumer protection Act from July 21, 2010, created a unique though short-term effect on the relationships between Treasury and corporate bonds and fixed-rate mortgages.

The journey contributes to the overall understanding of the interactions among US financial markets. They are considered efficient, competitive and fully developed if their prices quickly adjust to economic changes and regulatory transformations.

The authors study the degrees of integration of various conventional and adjustable mortgage rates and different fixed and floating interest rates in the US capital markets from January 1, 2010, until November 7, 2018. This recent time frame has yet to be examined in the economic literature. This period is also characterized by a sharp transformation on January 20, 2017, from enhanced financial regulation during the Obama administration to major deregulatory drives during the first 22 months of the Trump administration.

This paper examines the cyclical regularities of macroeconomic, financial and property market aggregates in relation to the property stock price cycle in the UK. The Hodrick Prescott filter is employed to fit a long‐term trend to the raw data, and to derive the short‐term cycles of each series. It is found that the cycles of consumer expenditure, total consumption per capita, the dividend yield and the long‐term bond yield are moderately correlated, and mainly coincident, with the property price cycle. There is also evidence that the nominal and real Treasury Bill rates and the interest rate spread lead this cycle by one or two quarters, and therefore that these series can be considered leading indicators of property stock prices. This study recommends that macroeconomic and financial variables can provide useful information to explain and potentially to forecast movements of property‐backed stock returns in the UK.

To investigate the impact of the federal budget deficit (expressed as a per cent of the Gross Domestic Product, GDP) in the US on the ex ante real interest rate yield on Moody’s Baa-rated corporate bonds and to provide evidence that is both contemporary and covers an extended time period, namely, 1960 through 2015.

The analysis constructs a loanable funds model that involves a variety of financial and economic variables, with the ex ante real interest rate yield on Moody’s Baa-rated long-term corporate bonds as the dependent variable. The dependent variable is contemporaneous with the federal budget deficit and two other interest rate measures. Accordingly, instrumental variables are identified for each of these contemporaneous explanatory variables. The model also consists of four additional (lagged) explanatory variables. The model is then estimated using auto-regressive, i.e., AR(1), two-stage least squares.

The principal finding is that the ex ante real interest rate yield on Moody’s Baa rated corporate bonds is an increasing function of the federal budget deficit, expressed as a per cent of GDP. In particular, if the federal budget deficit were to rise by one per centage point, say from 3 to 4 per cent of GDP, the ex ante real interest rate would rise by 58 basis points.

There are other time-series techniques that could be applied to the topic, such as co-integration, although the AR(1) process is tailored for studying volatile series such as interest rates and stock prices.

The greater the US federal budget deficit, the greater the real cost of funds to firms. Hence, the high budget deficits of recent years have led to the crowding out of investment in new plant, new equipment, and new technology. These impacts lower economic growth and restrict prosperity in the US over time. Federal budget deficits must be substantially reduced so as to protect the US economy.

Higher budget deficits act to reduce investment in ew plant, new equipment and new technology. This in turn reduces job growth and real GDP growth and compromises the health of the economy.

This is the first study to focus on the impact of the federal budget deficit on the ex ante real long term cost of funds to firms in decades. Nearly all related studies fail to focus on this variable. Since, in theory, this variable (represented by the ex ante real yield on Moody’s Baa rated long term corporate bonds) is a key factor in corporate investment decisions, the empirical findings have potentially very significant implications for US firms and for the economy as a whole in view of the extraordinarily high budget deficits of recent years.

Crude oil, gold and interest rates are some of the key indicators of the health of domestic as well as global economy. The purpose of the study is to find the shock volatility and price volatility effects of gold and crude oil market on interest rates in India.

This study finds the mutual and directional association of the volatility of gold, crude oil and interest rates in India. The bi-variate GARCH models (Diagonal VEC GARCH and BEKK GARCH) are applied on the sample data of gold price, crude oil price and yield (interest rate) gathered from November 30, 2015 to November 16, 2020 (weekly basis) to investigate the volatility association including the volatility spillover effect in the three markets.

The main findings of the study focus on having a long-term conditional correlation between gold and interest rates, but there is no evidence of volatility spillover from gold and crude oil on the interest rates. The findings of the study are of great importance especially to the policymakers, as they state that the fluctuations in prices of gold and crude oil do not adversely impact the interest rates in India. Therefore, the fluctuations in prices of gold and crude may generally impact the economy, but it has nothing to do with interest rate in particular. This implies that domestic and foreign investments in the country will not be affected by gold and crude oil that are largely driven by interest rates in the country.

Gold and crude oil are two very important commodities that have their importance not only for domestic affairs but also for international business. They veritably influence the economy including forex exchange for any nation. In addition to this, the researchers believe the findings will provide insights to policymakers, stakeholders and investors.

Gold and crude oil undoubtedly influence the exchange rates but their impact on the interest rates in an economy is not definite and remains ambiguous owing to the mixed findings of the studies. The lack of studies related to the impact of gold and crude oil on the interest rates, despite them being essentials for the health of any economy is the main motivation of this study. This study is novel as it investigates the volatility impact of crude oil and gold on interest rates and contributes to the existing literature with its findings.

There is a debate on the excess volatility of long‐term bond yields. It is found that whether long‐term bond yields are excessively volatile or excessively smooth depends critically on the knowledge of the long‐run properties of the short‐term interest rate process. Uses a span of 200 years of data on interest rates and finds that the short rates from the USA and the UK are characterized by stationarity after the tests for unit root have accounted for structural breaks. Volatility tests reveal for the whole and sub‐sample periods that the long rates are excessively smooth.

The expanded sovereign bond portfolios from the sizeable public interventions in the financial sector during the current crisis need close monitoring and analysis of emerging vulnerabilities. This chapter presents some conventional and new measures of market, credit, and liquidity risks for government bond portfolios, considered from the perspective of a sovereign debt manager. In particular, it examines duration, convexity, and VaR statistics as measures of market exposure; the contingent-claims approach as the most promising measure of credit risk exposure; and a VaR statistic as a measure of liquidity risk.