Search results

The purpose of this paper is to examine the variation and dispersion of prime retail yields in eight Asia‐Pacific centres. It seeks to provide empirical evidence on the significance of real estate and capital market influences as systematic drivers of retail yields in the sample of eight cities. The aim is to build a model that enables market participants to obtain base case yield forecasts.

A panel model is deployed in this study utilising a database of yields of eight years (2001‐2007). The small number of observations for retail yields across cities is addressed with this approach, which combines time‐series and cross‐section data. A fixed‐effect specification allows for city specific influences that partially capture the heterogeneity of cities in the sample. Within this framework the influence of time varying factors across markets and random effects on yields is examined.

The empirical estimates established significant influences from real rent growth and interest rates on retail yields explaining 78 per cent of their variation when allowed for fixed effects. Systematic time influences and market size are not significant. Retail yields are found fairly sensitive to long‐term interest (LTI) rates with 1 per cent change in LTI rates resulting in an over 80 basis points shift in yields. In general, investors should be aware of interest rate shocks as these can move retail yields in the region significantly. Based on the actual and simulated values for 2007 Shanghai and Hong Kong are broadly fairly priced. In Tokyo, Sydney and Singapore retail yields are somewhat lower than the simulated values, which are attributed to greater liquidity and transparency in these markets than indicating over‐pricing. In Delhi, the prime yield above the actual a sign of a possible outward movement is found. Beijing appears under‐priced. Finally, in Mumbai, which has the highest yield in the sample, the simulated yield is below actual as per 2007. An adjustment may not be expected as this difference is attributed to the pricing of supply risks in this market.

This study addresses the dearth of research work on retail yields in the Asia‐Pacific region. Through the panel methodology proposed market participants can obtain fundamentals‐based forecasts for prime retail yields in the sample of the eight cities, understand the exposure to interest rate movements and make calls as to whether markets are mispriced. The study shows that pooling data and panel techniques represent a good option to study market dynamics in situations of small datasets.

The purpose of this paper is to bring out the development of a flexible model for nonstationary crop yield distributions and its applications to decision-making in crop insurance.

The authors design a nonparametric Bayesian approach based on Gaussian process regressions to model crop yields over time. Further flexibility is obtained via Bayesian model averaging that results in mixed Gaussian processes.

Simulation results on crop insurance premium rates show that the proposed method compares favorably with conventional estimators, especially when the underlying distributions are nonstationary.

Unlike conventional two-stage estimation, the proposed method models nonstationary crop yields in a single stage. The authors further adopt a decision theoretic framework in its empirical application and demonstrate that insurance companies can use the proposed method to effectively identify profitable policies under symmetric or asymmetric loss functions.

Forecasting the future movement of yield curves contains valuable information for both academic and practical issues such as bonding pricing, portfolio management, and government policies. The purpose of this paper is to develop a dynamic factor approach that can provide more precise and consistent forecasting results under various yield curve dynamics.

The paper develops a unified dynamic factor model based on Diebold and Li (2006) and Nelson and Siegel (1987) three-factor model to forecast the future movement yield curves. The authors apply the state-space model and the Kalman filter to estimate parameters and extract factors from the US yield curve data.

The authors compare both in-sample and out-of-sample performance of the dynamic approach with various existing models in the literature, and find that the dynamic factor model produces the best in-sample fit, and it dominates existing models in medium- and long-horizon yield curve forecasting performance.

The authors find that the dynamic factor model and the Kalman filter technique should be used with caution when forecasting short maturity yields on a short time horizon, in which the Kalman filter is prone to trade off out-of-sample robustness to maintain its in-sample efficiency.

Bond analysts and portfolio managers can use the dynamic approach to do a more accurate forecast of yield curve movements.

The enhanced forecasting approach also equips the government with a valuable tool in setting macroeconomic policies.

The dynamic factor approach is original in capturing the level, slope, and curvature of yield curves in that the decay rate is set as a free parameter to be estimated from yield curve data, instead of setting it to be a fixed rate as in the existing literature. The difference range of estimated decay rate provides richer yield curve dynamics and is the key to stronger forecasting performance.

A simulation methodology is applied to the loan loss reserve process of an agricultural lender. Weaknesses of the point‐estimate approach to estimating loan loss reserves are addressed with a “bottom‐up” model. Modeling includes consideration of the producer’s and the lender’s diversification efforts. Implementation of this model will provide the lender a better understanding of the institution’s portfolio risk, as well as the credit risk associated with each loan. This study compares the lender’s loan loss estimates to a distribution of losses with associated probabilities. The comparative results could provide the lender a basis for setting probability levels for determining the regulatory required level of loan loss reserve.

The purpose of this paper is to extend the studies of commercial property yields by providing a cross-field approach through the implementation of methods used in physics.

Based on the equations used to describe real gases in physics, the commercial property yields are expressed through a model, as a product of two terms. The first term estimates the influence of the income change and investment on yields. The second estimates the yield variation as a function of property size. Additionally, the model combines the macroeconomic and microeconomic components influencing yield adjustment. Calculation of each component involves procedures developed in physics, with the investment volume being linked to the amount of gas and the microeconomic yield being linked to the gas compressibility.

The model was applied to the Auckland office and industrial markets, both to the historic and current cycle. At the macro-level, it was found that the use of accumulation of investment over a relevant cycle, results in a high data to model correlation. When modelling the yields at the micro-level, a relationship between the outlying properties and the yield softening was observed.

The paper provides an enhanced modelling power through association of the cyclic and investment activity with the yield change. Moreover, the model may be used to decouple the local and the international investment components and the extent of their influence on the local property market. Furthermore, it may be used to estimate the influence of the property size on the yield.

This research provides a new cross-field application of modelling techniques and enhances the understanding of factors influencing yield adjustments.

In the light of past financial and economic turmoil, there has been a marked increase in the volatility in real estate markets. This has impacted on the pricing of property assets, partly through market sentiment and particularly concerning risk. It also limits modelling accuracy model accuracy. The purpose of this paper is to create a new variable and model to enhance analysis of what drives real estate yields incorporating market sentiment to risk.

This paper specifically considers the modelling of property pricing within a volatile economic environment. The theoretical context begins by analysing the relationship between property yields and government bonds. The analytical context then moves on to specifically include a measurement of risk which stresses its role and importance in investment markets since the Global Financial Crisis. The model thus incorporates macroeconomic and real estate data, together with an international risk multiplier, which is calculated within the paper.

The paper finds the use of measurements of market sentiment and risk are more powerful tools for modelling yields than previous techniques alone.

This is an initial paper outlining the creation of sentiment and risk measurements in the financial market and showing an example of its application to a commercial real estate market. The implication is that this could add a major new explanatory variable to modelling of yields.

The paper highlights the importance of risk in the pricing of commercial real estate, over and above normal variables. It highlights how this can help explain over and undershooting of yields within commercial real estate which would be of great importance in the investment world.

This paper attempts to explicitly measure market sentiment, pricing of risk and how this impacts real estate pricing.

The internal (farmer-controlled) and external (non-farmer-controlled) factors affect crop yield. However, not a single study has identified and analyzed yield predictors in India using effective predictive models. Thus, this study aims to investigate how internal and external predictors impact pearl millet yield and Stover yield.

Descriptive analytics and artificial neural network are used to investigate the impact of predictors on pearl millet yield and Stover yield. From descriptive analytics, 473 valid responses were collected from semi-arid zone, and the predictors were categorized into internal and external factors. Multi-layer perceptron-neural network (MLP-NN) model was used in Statistical Package for the Social Sciences version 25 to model them.

The MLP-NN model reveals that rainfall has the highest normalized importance, followed by irrigation frequency, crop rotation frequency, fertilizers type and temperature. The model has an acceptable goodness of fit because the training and testing methods have average root mean square errors of 0.25 and 0.28, respectively. Also, the model has R² values of 0.863 and 0.704, respectively, for both pearl millet and Stover yield.

To the best of the authors’ knowledge, the current study is first of its kind related to impact of predictors of both internal and external factors on pearl millet yield and Stover yield.

The literature reveals that most studies have estimated crop yield using limited parameters and forecasting approaches. However, this research will examine the impact of various predictors such as internal and external of both yields. The outcomes of the study will help policymakers in developing strategies for stakeholders. The current work will improve pearl millet yield literature.

Recent U.S. Treasury yields have been constrained to some extent by the zero lower bound (ZLB) on nominal interest rates. Therefore, we compare the performance of a standard affine Gaussian dynamic term structure model (DTSM), which ignores the ZLB, to a shadow-rate DTSM, which respects the ZLB. Near the ZLB, we find notable declines in the forecast accuracy of the standard model, while the shadow-rate model forecasts well. However, 10-year yield term premiums are broadly similar across the two models. Finally, in applying the shadow-rate model, we find no gain from estimating a slightly positive lower bound on U.S. yields.

Following Ang and Piazzesi’s (2003) study, the authors use an affine term structure model to study the relevance of macroeconomic (domestic and foreign) factors for Peru’s sovereign yield curve in the period from November 2005 to December 2015. The paper aims to discuss this issue.

Risk premia are modeled as time-varying and depend on both observable and unobservable factors; and the authors estimate a vector autoregressive model considering no-arbitrage assumptions.

The authors find evidence that macro factors help to improve the fit of the model and explain a substantial amount of variation in bond yields. However, their influence is very sensitive to the specification model. Variance decompositions show that macro factors explain a significant share of the movements at the short and middle segments of the yield curve (up to 50 percent), while unobservable factors are the main drivers for most of the movements at the long end of the yield curve (up to 80 percent). Furthermore, the authors find that international markets are relevant for the determination of the risk premium in the short term. Higher uncertainty in international markets increases bond yields, although this effect vanishes quickly. Finally, the authors find that no-arbitrage restrictions with the incorporation of macro factors improve forecasts.

To the authors’ knowledge this is the first application of this type of models using data from an emerging country such as Peru.

The purpose of this paper is to propose an improved reinsurance pricing framework, which includes a crop yield forecasting model that integrates weather variables and crop production information from different geographically correlated regions using a new credibility estimator, and closed form reinsurance pricing formulas. A yield restatement approach to account for changing crop mix through time is also demonstrated.

The new crop yield forecasting model is empirically analyzed based on detailed farm-level data from Manitoba, Canada, covering 216 crop varieties from 19,238 farms from 1996 to 2011. As well, corresponding weather data from 30 stations, including daily temperature and precipitation, are considered. Algorithms that combine screening regression, cross-validation and principal component analysis are evaluated for the purpose of achieving efficient dimension reduction and model selection.

The results show that the new yield forecasting model provides significant improvements over the classical regression model, both in terms of in-sample and out-of-sample forecasting abilities.

The empirical analysis is limited to data from the province of Manitoba, Canada, and other regions may show different results.

This research is useful from a risk management perspective for insurers and reinsurers, and the framework may also be used to develop improved weather risk management strategies to help manage adverse weather events.

This is the first paper to integrate a credibility estimator for crop yield forecasting, and develop a closed form reinsurance pricing formula.