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1 – 10 of over 1000Takeaki Kariya, Fumiaki Ushiyama and Stanley R. Pliska
The purpose of this paper is to generalize the one‐factor mortgage‐backed securities (MBS)‐pricing model proposed by Kariya and Kobayashi to a three‐factor model. The authors…
Abstract
Purpose
The purpose of this paper is to generalize the one‐factor mortgage‐backed securities (MBS)‐pricing model proposed by Kariya and Kobayashi to a three‐factor model. The authors describe prepayment behavior due to refinancing and rising housing prices by discrete‐time, no‐arbitrage pricing theory, making an association between prepayment behavior and cash flow patterns.
Design/methodology/approach
The structure, rationality and potential for practical use of our model is demonstrated by valuing an MBS via Monte Carlo simulation and then conducting a comparative static analysis.
Findings
The proposed model is found to be effective for analysing MBS cash flow patterns, making a decision for bond investments and risk management due to prepayment.
Originality/value
While the one‐factor valuation model Kariya and Kobayashi treated is a basic framework, the generalized model presented in this paper is much more effective for analysing MBS cash flow patterns, making a decision for bond investments and risk management due to prepayment.
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Zhiwu Hong, Linlin Niu and Gengming Zeng
Using a discrete-time version of the arbitrage-free Nelson–Siegel (AFNS) term structure model, the authors examine how yield curves in the US and China react to exchange rate…
Abstract
Purpose
Using a discrete-time version of the arbitrage-free Nelson–Siegel (AFNS) term structure model, the authors examine how yield curves in the US and China react to exchange rate policy shocks as China introduces gradual reforms to make its exchange rate regime more flexible. The paper aims to discuss this issue.
Design/methodology/approach
The authors characterize the specification of the discrete-time AFNS model, prove the uniqueness of the solution for model identification, perform specification analysis on its canonical form and detail the MCMC estimation method with a fast and reliable prior extraction step.
Findings
Model decomposition reveals that in the US yield responses, changes in risk premia for medium- to long-term yields dominate changes in yield expectation for short- to medium-term yields, indicating that the portfolio rebalancing effect due to varying risk perception is stronger than the signaling effect due to policy rate expectation.
Practical implications
The results are helpful in diagnosing market sentiment and exchange rate risk pricing as China further internationalizes its currency.
Originality/value
The methodology can be easily extended to study yield curve responses to other scenarios of policy shocks or regime changes.
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Anyssa Trimech, Hedi Kortas, Salwa Benammou and Samir Benammou
The purpose of this paper is to discuss a multiscale pricing model for the French stock market by combining wavelet analysis and Fama‐French three‐factor model. The objective is…
Abstract
Purpose
The purpose of this paper is to discuss a multiscale pricing model for the French stock market by combining wavelet analysis and Fama‐French three‐factor model. The objective is to examine the relationship between stock returns and Fama‐French risk factors at different time‐scales.
Design/methodology/approach
Exploiting the scale separation property inherent to the maximal overlap discrete wavelet transform, the data set are decomposed into components associated with different time‐scales. This wavelet‐based decomposition scheme allows the three Fama‐French models to be tested over different investments periods.
Findings
The obtained results show that the explanatory power of the Fama‐French three‐factor model becomes stronger as the wavelet scale increases. Besides, the relationship between the portfolio returns and the risk factors (i.e. the market, size and value factors) depends significantly upon the considered time‐horizon.
Practical implications
The proposed methodology offers investors the opportunity to construct dynamic portfolio management strategies by taking into account the multiscale nature of risk and return. Moreover, it gives a new insight to fund rating and fund selection issues in relation to heterogeneous investments periods.
Originality/value
The paper uses wavelets as a relatively new and powerful tool for statistical analysis that allows a new understanding of pricing models. The paper will be of interest not only for academics in the field of asset pricing but also for fund managers and financial market investors.
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Wayne Ferson, Darren Kisgen and Tyler Henry
We evaluate the performance of fixed income mutual funds using stochastic discount factors motivated by continuous-time term structure models. Time-aggregation of these models for…
Abstract
We evaluate the performance of fixed income mutual funds using stochastic discount factors motivated by continuous-time term structure models. Time-aggregation of these models for discrete returns generates new empirical “factors,” and these factors contribute significant explanatory power to the models. We provide a conditional performance evaluation for US fixed income mutual funds, conditioning on a variety of discrete ex-ante characterizations of the states of the economy. During 1985–1999 we find that fixed income funds return less on average than passive benchmarks that do not pay expenses, but not in all economic states. Fixed income funds typically do poorly when short-term interest rates or industrial capacity utilization rates are high, and offer higher returns when quality-related credit spreads are high. We find more heterogeneity across fund styles than across characteristics-based fund groups. Mortgage funds underperform a GNMA index in all economic states. These excess returns are reduced, and typically become insignificant, when we adjust for risk using the models.
Robert J. Elliott, Tak Kuen Siu and Alex Badescu
The purpose of this paper is to consider a discrete‐time, Markov, regime‐switching, affine term‐structure model for valuing bonds and other interest rate securities. The proposed…
Abstract
Purpose
The purpose of this paper is to consider a discrete‐time, Markov, regime‐switching, affine term‐structure model for valuing bonds and other interest rate securities. The proposed model incorporates the impact of structural changes in (macro)‐economic conditions on interest‐rate dynamics. The market in the proposed model is, in general, incomplete. A modified version of the Esscher transform, namely, a double Esscher transform, is used to specify a price kernel so that both market and economic risks are taken into account.
Design/methodology/approach
The market in the proposed model is, in general, incomplete. A modified version of the Esscher transform, namely, a double Esscher transform, is used to specify a price kernel so that both market and economic risks are taken into account.
Findings
The authors derive a simple way to give exponential affine forms of bond prices using backward induction. The authors also consider a continuous‐time extension of the model and derive exponential affine forms of bond prices using the concept of stochastic flows.
Originality/value
The methods and results presented in the paper are new.
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Olasunkanmi James Kehinde, Jeff Walls, Amanda Mayeaux and Allison Comeaux
The purpose of this study is to propose and explore a conceptualization of decisional capital that is suitable for early career teachers.
Abstract
Purpose
The purpose of this study is to propose and explore a conceptualization of decisional capital that is suitable for early career teachers.
Design/methodology/approach
This study uses exploratory factor analysis on a sample of early career teachers to examine a literature-derived conceptualization of decisional capital.
Findings
The factors that emerged support the literature-derived conceptualization. A subsequent confirmatory factor analysis on a second sample of early career teachers offers additional evidence for the proposed conceptualization. An exploration of the underlying factor structure comparing results across four competing models (i.e. unidimensional, correlated factors, second order, and bifactor) suggests that a second order factor explains the variance across the three proposed factors well. We conclude that this second order factor is decisional capital.
Originality/value
This is the first study that examines the discrete elements of decisional capital. Understanding these discrete elements is an avenue for investigation into the development of decisional capital beyond the acknowledgment that it takes time to develop.
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Panagiotis Dontis-Charitos, Orla Gough, K. Ben Nowman and Sheeja Sivaprasad
We investigate the return and volatility spillovers from major UK banks to Financial Times Stock Exchange 100 (FTSE 100) index using Gaussian estimation and continuous time models…
Abstract
We investigate the return and volatility spillovers from major UK banks to Financial Times Stock Exchange 100 (FTSE 100) index using Gaussian estimation and continuous time models as well as discrete time multivariate GARCH (MGARCH) modelling approaches. Using daily, weekly and monthly data over the period December 1999–December 2010, which includes the recent 2007–2009 global financial crisis, empirical estimates of uni- and/or bi-directional return and volatility spillovers are provided. The bivariate MGARCH results reveal strong return spillovers from the FTSE to the banks, and no return spillover from the latter to the FTSE. Nevertheless, strong bi-directional volatility transmission is verified. The continuous time analysis provides mixed evidence of feedback effects over the different models.
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I survey applications of Markov switching models to the asset pricing and portfolio choice literatures. In particular, I discuss the potential that Markov switching models have to…
Abstract
I survey applications of Markov switching models to the asset pricing and portfolio choice literatures. In particular, I discuss the potential that Markov switching models have to fit financial time series and at the same time provide powerful tools to test hypotheses formulated in the light of financial theories, and to generate positive economic value, as measured by risk-adjusted performances, in dynamic asset allocation applications. The chapter also reviews the role of Markov switching dynamics in modern asset pricing models in which the no-arbitrage principle is used to characterize the properties of the fundamental pricing measure in the presence of regimes.
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Investors are inattentive to continuous information as opposed to discrete information, resulting in underreaction to continuous information. This paper aims to examine if the…
Abstract
Purpose
Investors are inattentive to continuous information as opposed to discrete information, resulting in underreaction to continuous information. This paper aims to examine if the well-documented return predictability of the strategies based on the ratio of short-term to long-term moving averages can be enhanced by conditioning on information discreteness. Anchoring bias has been the popular explanation for the source of underreaction in the context of moving averages-based strategies. This paper proposes and studies another possible source based on investor inattention that can potentially result in superior performance of these strategies.
Design/methodology/approach
The paper uses portfolio sorting as well as Fama-MacBeth cross-sectional regressions. For examining the role of information discreteness in the return predictability of the moving average ratio, the sample stocks are double-sorted based on the moving average ratio and information discreteness measure. The returns to these portfolios are computed using standard approaches in the literature. The regression approach controls for various well-known return predictors.
Findings
This study finds that the equally-weighted monthly returns to the long-short moving average ratio quintile portfolios increase monotonically from 0.54% for the discrete information portfolio to 1.37% for the continuous information portfolio over the 3-month holding period. This study observes a similar pattern in risk-adjusted returns, value-weighted portfolios, non-January returns, large and small stocks, for alternative holding periods and the ratio of 50-day to 200-day moving average. The results are robust to control for well-known return predictors in cross-sectional regressions.
Research limitations/implications
To the best of the authors’ knowledge, this is the first paper to document the significant role of investor inattention to continuous information in the return predictability of strategies based on the moving average ratios. There are many underreaction anomalies that have been reported in the literature, and the paper's results can be extended to those anomalies in subsequent research.
Practical implications
The findings of this paper have important practical implications. Strategies based on moving averages are an extremely popular component of a technical analyst's toolkit. Their profitability has been well-documented in the prior literature that attributes the performance to investors' anchoring bias. This paper offers a readily implementable approach to enhancing the performance of these strategies by conditioning on a straightforward measure of information discreteness. In doing so, this study extends the literature on the role of investor inattention to continuous information in anomaly profits.
Originality/value
While there is considerable literature on technical analysis, and especially on the performance of moving averages-based strategies, the novelty of this paper is the analysis of the role of information discreteness in strategy performance. Not only does the paper document robust evidence, but the findings suggest that the investor’s inattention to continuous information is a more dominant source of underreaction compared to anchoring. This is an important result, given that anchoring has so far been considered the source of return predictability in the literature.
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John Galakis, Ioannis Vrontos and Panos Xidonas
This study aims to introduce a tree-structured linear and quantile regression framework to the analysis and modeling of equity returns, within the context of asset pricing.
Abstract
Purpose
This study aims to introduce a tree-structured linear and quantile regression framework to the analysis and modeling of equity returns, within the context of asset pricing.
Design/Methodology/Approach
The approach is based on the idea of a binary tree, where every terminal node parameterizes a local regression model for a specific partition of the data. A Bayesian stochastic method is developed including model selection and estimation of the tree structure parameters. The framework is applied on numerous U.S. asset pricing models, using alternative mimicking factor portfolios, frequency of data, market indices, and equity portfolios.
Findings
The findings reveal strong evidence that asset returns exhibit asymmetric effects and non- linear patterns to different common factors, but, more importantly, that there are multiple thresholds that create several partitions in the common factor space.
Originality/Value
To the best of the authors' knowledge, this paper is the first to explore and apply a tree-structured and quantile regression framework in an asset pricing context.
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