Finite Dimensional Affine Realisations of HJM Models in Terms of Forward Rates and Yields

Finite dimensional Markovian HJM term structure models provide ideal settings for the study of term structure dynamics and interest rate derivatives where the flexibility of the HJM framework and the tractability of Markovian models coexist. Consequently, these models became the focus of a series of papers including Carverhill (1994), Ritchken and Sankarasubramanian (1995), Bhar and Chiarella (1997), Inui and Kijima (1998), de Jong and Santa-Clara (1999), Björk and Svensson (2001) and Chiarella and Kwon (2001a). However, these models usually required the introduction of a large number of state variables which, at first sight, did not appear to have clear links to the market observed quantities, and the explicit realisations of the forward rate curve in terms of the state variables were unclear. In this paper, it is shown that the forward rate curves for these models are affine functions of the state variables, and conversely that the state variables in these models can be expressed as affine functions of a finite number of forward rates or yields. This property is useful, for example, in the estimation of model parameters. The paper also provides explicit formulae for the bond prices in terms of the state variables that generalise the formulae given in Inui and Kijima (1998), and applies the framework to obtain affine representations for a number of popular interest rate models.     

Binomial Option Pricing with Skewed Asset Returns

This research presents a method for estimating the parameters of the binomial option pricing model necessary to appropriately price calls on assets with asymmetric end-of-period return distributions. Parameters of the binomial model are shown to be a function of the mean, variance, and skewness of the underlying return distribution. It is also shown that failure to incorporate skewness results in the mispricing of the call.     

Consistency conditions for affine term structure models

Sufficient conditions for the application of the Feynman-Kac formula for option pricing for wide classes of affine term structure models in the jump-diffusion case are derived by generalizing earlier results for bond pricing in the pure-diffusion case The author is grateful to Mikhail Chernov and Darrel Duffie for useful discussions and suggestions.     

A semi-analytic method for valuing high-dimensional options on the maximum and minimum of multiple assets

Valuing high-dimensional options has many important applications in finance but when the true distributions are unknown or complex, numerical approximations must be used. Approximation methods based on Monte-Carlo simulation show a steep trade-off between estimation accuracy and computational efficiency. This article presents an alternative semi-analytic approximation method for pricing options on the maximum or minimum of multiple assets with unknown distributions. Computational efficiency is shown to improve significantly without sacrificing estimation accuracy. The method is illustrated with applications to options on underlying assets with mean-reverting prices, time-dependent correlations, and stochastic volatility The authors would like to thank the two anonymous referees, the associate editor, and Dr. Jess H. Chua at the University of Calgary for valuable comments and insights on this research. This research was partly supported by NUS grant R-146-000-059-112     

The Dynamics of Location in Home Price

It is well established that house prices are dynamic. It is also axiomatic that location influences such selling prices, motivating our objective of incorporating spatial information in explaining the evolution of house prices over time. In this paper, we propose a rich class of spatio-temporal models under which each property is point referenced and its associated selling price modeled through a collection of temporally indexed spatial processes. Such modeling includes and extends all house price index models currently in the literature, and furthermore permits distinction between the effects of time and location. We study single family residential sales in two distinct submarkets of a metropolitan area and further categorize the data into single- and multiple-transaction observations. We find the spatial component is very important in explaining house price. Moreover, the relative homogeneity of homes within the submarket and the frequency with which homes sell affects the pattern of variation across space and time. Differences between single and repeat sale data are evident. The methodology is applicable to more general capital asset pricing when location is anticipated to be influential.     

Pricing default swaps: Empirical evidence

In this paper we compare market prices of credit default swaps with model prices. We show that a simple reduced form model outperforms directly comparing bonds' credit spreads to default swap premiums. We find that the model yields unbiased premium estimates for default swaps on investment grade issuers, but only if we use swap or repo rates as proxy for default-free interest rates. This indicates that the government curve is no longer seen as the reference default-free curve. We also show that the model is relatively insensitive to the value of the assumed recovery rate.     

Financial Stress Regimes and the Macroeconomy

Some financial stress events lead to macroeconomic downturns, while others appear to be isolated to financial markets. We identify financial stress regimes using a model that explicitly links financial variables to macro‐economic outcomes. The stress regimes are identified using an unbalanced panel of financial variables with an embedded method for variable selection. Our identified stress regimes are associated with corporate credit tightening and with NBER recessions. An exogenous deterioration in our financial conditions index has strong negative effects in economic activity, and negative amplification effects on inflation in the stress regime. These results are obtained with a novel factor‐augmented vector autoregressive model with smooth‐transition regimes (FASTVAR).     

中国与东南亚国家贸易潜力及影响因素研究——基于随机前沿引力模型

运用时变随机前沿引力模型和贸易非效率模型,以1998-2016年中国及东南亚九个国家面板数据为样本,测度中国对东南亚国家出口贸易潜力,并分析其主要影响因素。结果表明：1998-2016年中国对东南亚国家出口贸易潜力呈现先上升后下降趋势,依然有较大提升空间,中国对东南亚国家出口贸易潜力差异较大。航空运输货运量、货币自由度、财务自由度、商业自由度及世界贸易组织成员国均对出口贸易非效率具有显著的影响。     

Modified efficient importance sampling for partially non‐Gaussian state space models

The construction of an importance density for partially non‐Gaussian state space models is crucial when simulation methods are used for likelihood evaluation, signal extraction, and forecasting. The method of efficient importance sampling is successful in this respect, but we show that it can be implemented in a computationally more efficient manner using standard Kalman filter and smoothing methods. Efficient importance sampling is generally applicable for a wide range of models, but it is typically a custom‐built procedure. For the class of partially non‐Gaussian state space models, we present a general method for efficient importance sampling. Our novel method makes the efficient importance sampling methodology more accessible because it does not require the computation of a (possibly) complicated density kernel that needs to be tracked for each time period. The new method is illustrated for a stochastic volatility model with a Student's t distribution.     

Using clustering ensemble to identify banking business models

The business models of banks are often seen as the result of a variety of simultaneously determined managerial choices, such as those regarding the types of activities, funding sources, level of diversification, and size. Moreover, owing to the fuzziness of data and the possibility that some banks may combine features of different business models, the use of hard clustering methods has often led to poorly identified business models. In this paper we propose a framework to deal with these challenges based on an ensemble of three unsupervised clustering methods to identify banking business models: fuzzy c‐means (which allows us to handle fuzzy clustering), self‐organizing maps (which yield intuitive visual representations of the clusters), and partitioning around medoids (which circumvents the presence of data outliers). We set up our analysis in the context of the European banking sector, which has seen its regulators increasingly focused on examining the business models of supervised entities in the aftermath of the twin financial crises. In our empirical application, we find evidence of four distinct banking business models and further distinguish between banks with a clearly defined business model (core banks) and others (non‐core banks), as well as banks with a stable business model over time (persistent banks) and others (non‐persistent banks). Our proposed framework performs well under several robustness checks related with the sample, clustering methods, and variables used.