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- Multiple bubbles (3)
- Autoregressive Conditional Duration (2)
- Companion matrix (2)
- Crashes (2)
- Credit risk (2)
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- Date stamping (2)
- Date-stamping strategy (2)
- Deviance information criterion (2)
- Eigenvalues (2)
- Eigenvectors (2)
- Financial bubbles (2)
- Flexible window (2)
- Generalized sup ADF test (2)
- MCMC (2)
- Market Microstructure (2)
- Microstructure noise (2)
- Mildly explosive process (2)
- Option Pricing (2)
- Periodically collapsing bubbles (2)
- Rational bubble (2)
- Structural models (2)
- Subprime crisis (2)
- Sup ADF test (2)
- Timeline (2)
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- Autoregression (1)
- Autoregressive conditional duration (1)
- Bias Reduction (1)
- Bond Pricing (1)
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Articles 1 - 23 of 23
Full-Text Articles in Finance and Financial Management
Specifying And Estimating Vector Autoregressions Using Their Eigensystem Representation, Leo Krippner
Specifying And Estimating Vector Autoregressions Using Their Eigensystem Representation, Leo Krippner
Sim Kee Boon Institute for Financial Economics
This article introduces the principles and mechanics of the eigensystem vector autoregression (EVAR) framework, where a VAR may be specified and estimated directly via its eigenvalue and eigenvector parameters. Using explicit constraints on the eigensystem permits control of a VAR ís allowable dynamics, which is illustrated empirically with standard and time-varying VAR estimations specified to be always non-explosive.
Are Bond Returns Predictable With Real-Time Macro Data?, Dashan Huang, Fuwei Jiang, Kunpeng Li, Guoshi Tong, Guofu Zhou
Are Bond Returns Predictable With Real-Time Macro Data?, Dashan Huang, Fuwei Jiang, Kunpeng Li, Guoshi Tong, Guofu Zhou
Research Collection Lee Kong Chian School Of Business
We investigate the predictability of bond returns using real-time macro variables and consider the possibility of a nonlinear predictive relationship and the presence of weak factors. To address these issues, we propose a scaled sufficient forecasting (sSUFF) method and analyze its asymptotic properties. Using both the existing and the new method, we find empirically that real-time macro variables have significant forecasting power both in-sample and out-of-sample. Moreover, they generate sizable economic values, and their predictability is not spanned by the yield curve. We also observe that the forecasted bond returns are countercyclical, and the magnitude of predictability is stronger during …
Estimating And Applying Autoregression Models Via Their Eigensystem Representation, Leo Krippner
Estimating And Applying Autoregression Models Via Their Eigensystem Representation, Leo Krippner
Sim Kee Boon Institute for Financial Economics
This article introduces the eigensystem autoregression (EAR) framework, which allows an AR model to be specified, estimated, and applied directly in terms of its eigenvalues and eigenvectors. An EAR estimation can therefore impose various constraints on AR dynamics that would not be possible within standard linear estimation. Examples are restricting eigenvalue magnitudes to control the rate of mean reversion, additionally imposing that eigenvalues be real and positive to avoid pronounced oscillatory behavior, and eliminating the possibility of explosive episodes in a time-varying AR. The EAR framework also produces closed-form AR forecasts and associated variances, and forecasts and data may be …
Disagreement In Market Index Options, Guilherme Salome, George Tauchen, Jia Li
Disagreement In Market Index Options, Guilherme Salome, George Tauchen, Jia Li
Research Collection School Of Economics
We generate new evidence on disagreement among traders in the S&P 500 options market from high-frequency intraday price and volume data. Inference on disagreement is based on a model where investors observe public information but agree to disagree on its interpretation; disagreement among investors is captured by the volume–volatility elasticity. For options, there are two natural variables related to disagreement: moneyness and tenor, which we relate to disagreement about the distribution of the market index at different quantiles and times. The estimated volume–volatility elasticity equals unity for options near the money and close to expiration, which is consistent with the …
Data Driven Value-At-Risk Forecasting Using A Svr-Garch-Kde Hybrid, Marius Lux, Wolfgang Karl Hardle, Stefan Lessmann
Data Driven Value-At-Risk Forecasting Using A Svr-Garch-Kde Hybrid, Marius Lux, Wolfgang Karl Hardle, Stefan Lessmann
Sim Kee Boon Institute for Financial Economics
Appropriate risk management is crucial to ensure the competitiveness of financial institutions and the stability of the economy. One widely used financial risk measure is value-at-risk (VaR). VaR estimates based on linear and parametric models can lead to biased results or even underestimation of risk due to time varying volatility, skewness and leptokurtosis of financial return series. The paper proposes a nonlinear and nonparametric framework to forecast VaR that is motivated by overcoming the disadvantages of parametric models with a purely data driven approach. Mean and volatility are modeled via support vector regression (SVR) where the volatility model is motivated …
Forecasting Realized Volatility Using A Nonnegative Semiparametric Model, Anders Eriksson, Daniel P. A. Preve, Jun Yu
Forecasting Realized Volatility Using A Nonnegative Semiparametric Model, Anders Eriksson, Daniel P. A. Preve, Jun Yu
Research Collection School Of Economics
This paper introduces a parsimonious and yet flexible semiparametric model to forecastfinancial volatility. The new model extends a related linear nonnegative autoregressive modelpreviously used in the volatility literature by way of a power transformation. It is semiparametric inthe sense that the distributional and functional form of its error component is partially unspecified.The statistical properties of the model are discussed and a novel estimation method is proposed.Simulation studies validate the new method and suggest that it works reasonably well in finitesamples. The out-of-sample forecasting performance of the proposed model is evaluated against anumber of standard models, using data on S&P 500 …
News Co-Occurrence, Attention Spillover, And Return Predictability, Li Guo, Lin Peng, Yubo Tao, Jun Tu
News Co-Occurrence, Attention Spillover, And Return Predictability, Li Guo, Lin Peng, Yubo Tao, Jun Tu
Research Collection School Of Economics
We examine the effect of investor attention spillover on stock return predictability. Using a novel measure, the News Network Triggered Attention index (NNTA), we find that NNTA negatively predicts market returns with a monthly in(out)-of-sample R-square of 5.97% (5.80%). In the cross-section, a long-short portfolio based on news co-occurrence generates a significant monthly alpha of 68 basis points. The results are robust to the inclusion of alternative attention proxies, sentiment measures, other news- and information-based predictors, across recession and expansion periods. We further validate the attention spillover effect by showing that news co-mentioning leads to greater increases in Google and …
Asset Pricing With Financial Bubble Risk, Ji Hyung Lee, Peter C. B. Phillips
Asset Pricing With Financial Bubble Risk, Ji Hyung Lee, Peter C. B. Phillips
Research Collection School Of Economics
This paper characterizes systematic risk stemming from the possible occurrence of price bubbles and measures the impact of this additional risk factor on asset prices. Historical stock market behavior and recent empirical experience have led economists and policy makers to acknowledge that price bubbles in financial markets do occur and need to be accounted for in risk analysis. New econometric tools for analyzing mildly explosive behavior (Phillips and Magdalinos, 2007; Phillips et al., 2011) have made it possible to detect the presence of bubbles in data and to date stamp their origination and collapse, providing empirical confirmation of such episodes …
Testing For Multiple Bubbles: Limit Theory Of Real-Time Detectors, Peter C. B. Phillips, Shuping Shi, Jun Yu
Testing For Multiple Bubbles: Limit Theory Of Real-Time Detectors, Peter C. B. Phillips, Shuping Shi, Jun Yu
Research Collection School Of Economics
This article provides the limit theory of real-time dating algorithms for bubble detection that were suggested in Phillips, Wu, and Yu (PWY; International Economic Review 52 [2011], 201-26) and in a companion paper by the present authors (Phillips, Shi, and Yu, 2015; PSY; International Economic Review 56 [2015a], 1099-1134. Bubbles are modeled using mildly explosive bubble episodes that are embedded within longer periods where the data evolve as a stochastic trend, thereby capturing normal market behavior as well as exuberance and collapse. Both the PWY and PSY estimates rely on recursive right-tailed unit root tests (each with a different recursive …
Testing For Multiple Bubbles: Historical Episodes Of Exuberance And Collapse In The S&P 500, Peter C. B. Phillips, Shuping Shi, Jun Yu
Testing For Multiple Bubbles: Historical Episodes Of Exuberance And Collapse In The S&P 500, Peter C. B. Phillips, Shuping Shi, Jun Yu
Research Collection School Of Economics
Recent work on econometric detection mechanisms has shown the effectiveness of recursive procedures in identifying and dating financial bubbles in real time. These procedures are useful as warning alerts in surveillance strategies conducted by central banks and fiscal regulators with real-time data. Use of these methods over long historical periods presents a more serious econometric challenge due to the complexity of the nonlinear structure and break mechanisms that are inherent in multiple-bubble phenomena within the same sample period. To meet this challenge, this article develops a new recursive flexible window method that is better suited for practical implementation with long …
Self-Exciting Jumps, Learning, And Asset Pricing Implications, Andras Fulop, Junye Li, Jun Yu
Self-Exciting Jumps, Learning, And Asset Pricing Implications, Andras Fulop, Junye Li, Jun Yu
Research Collection School Of Economics
The paper proposes a self-exciting asset pricing model that takes into account cojumps between prices and volatility and self-exciting jump clustering. We employ a dence of self-exciting jump clustering since the 1987 market crash, and its importance Bayesian learning approach to implement real time sequential analysis. We find evidence of self-exciting jump clustering since the 1987 market crash, and its importance becomes more obvious at the onset of the 2008 global financial crisis. It is found that learning affects the tail behaviors of the return distributions and has important implications for risk management, volatility forecasting and option pricing.
Testing For Multiple Bubbles 1: Historical Episodes Of Exuberance And Collapse In The S&P 500, Peter C. B. Phillips, Shu-Ping Shi, Jun Yu
Testing For Multiple Bubbles 1: Historical Episodes Of Exuberance And Collapse In The S&P 500, Peter C. B. Phillips, Shu-Ping Shi, Jun Yu
Research Collection School Of Economics
Recent work on econometric detection mechanisms has shown the effectiveness of recursive procedures in identifying and dating financial bubbles. These procedures are useful as warning alerts in surveillance strategies conducted by central banks and fiscal regulators with real time data. Use of these methods over long historical periods presents a more serious econometric challenge due to the complexity of the nonlinear structure and break mechanisms that are inherent in multiple bubble phenomena within the same sample period. To meet this challenge the present paper develops a new recursive flexible window method that is better suited for practical implementation with long …
Specification Sensitivity In Right-Tailed Unit Root Testing For Explosive Behavior, Peter C. B. Phillips, Shu-Ping Shi, Jun Yu
Specification Sensitivity In Right-Tailed Unit Root Testing For Explosive Behavior, Peter C. B. Phillips, Shu-Ping Shi, Jun Yu
Research Collection School Of Economics
Right-tailed unit root tests have proved promising for detecting exuberance in economic and financial activities. Like left-tailed tests, the limit theory and test performance are sensitive to the null hypothesis and the model specification used in parameter estimation. This paper aims to provide some empirical guidelines for the practical implementation of right-tailed unit root tests, focusing on the sup ADF test of Phillips, Wu and Yu (2011), which implements a right-tailed ADF test repeatedly on a sequence of forward sample recursions. We analyze and compare the limit theory of the sup ADF test under deferent hypotheses and model specifications. The …
Dating The Timeline Of Financial Bubbles During The Subprime Crisis, Peter C. B. Phillips, Jun Yu
Dating The Timeline Of Financial Bubbles During The Subprime Crisis, Peter C. B. Phillips, Jun Yu
Research Collection School Of Economics
A new recursive regression methodology is introduced to analyze the bubble characteristics of various financial time series during the subprime crisis. The methods modify a technique proposed in Phillips, Wu, and Yu (2011) and provide a technology for identifying bubble behavior with consistent dating of their origination and collapse. The tests serve as an early warning diagnostic of bubble activity and a new procedure is introduced for testing bubble migration across markets. Three relevant financial series are investigated, including a financial asset price (a house price index), a commodity price (the crude oil price), and one bond price (the spread …
Explosive Behavior In The 1990s Nasdaq: When Did Exuberance Escalate Asset Values?, Peter C. B. Phillips, Yangru Wu, Jun Yu
Explosive Behavior In The 1990s Nasdaq: When Did Exuberance Escalate Asset Values?, Peter C. B. Phillips, Yangru Wu, Jun Yu
Research Collection School Of Economics
A recursive test procedure is suggested that provides a mechanism for testing explosive behavior, date stamping the origination and collapse of economic exuberance, and providing valid confidence intervals for explosive growth rates. The method involves the recursive implementation of a right-side unit root test and a sup test, both of which are easy to use in practical applications, and some new limit theory for mildly explosive processes. The test procedure is shown to have discriminatory power in detecting periodically collapsing bubbles, thereby overcoming a weakness in earlier applications of unit root tests for economic bubbles. An empirical application to the …
Bayesian Analysis Of Structural Credit Risk Models With Microstructure Noises, Shirley J. Huang, Jun Yu
Bayesian Analysis Of Structural Credit Risk Models With Microstructure Noises, Shirley J. Huang, Jun Yu
Research Collection Lee Kong Chian School Of Business
In this paper a Markov chain Monte Carlo (MCMC) technique is developed for the Bayesian analysis of structural credit risk models with microstructure noises. The technique is based on the general Bayesian approach with posterior computations performed by Gibbs sampling. Simulations from the Markov chain, whose stationary distribution converges to the posterior distribution, enable exact finite sample inferences of model parameters. The exact inferences can easily be extended to latent state variables and any nonlinear transformation of state variables and parameters, facilitating practical credit risk applications. In addition, the comparison of alternative models can be based on devian information criterion …
Dating The Timeline Of Financial Bubbles During The Subprime Crisis, Peter C. B. Phillips, Jun Yu
Dating The Timeline Of Financial Bubbles During The Subprime Crisis, Peter C. B. Phillips, Jun Yu
Research Collection School Of Economics
A new recursive regression methodology is introduced to analyze the bubble characteristics of various financial time series during the subprime crisis. The methods modify a technique proposed in Phillips, Wu, and Yu (2011) and provide a technology for identifying bubble behavior with consistent dating of their origination and collapse. The tests serve as an early warning diagnostic of bubble activity and a new procedure is introduced for testing bubble migration across markets. Three relevant financial series are investigated, including a financial asset price (a house price index), a commodity price (the crude oil price), and one bond price (the spread …
Bayesian Analysis Of Structural Credit Risk Models With Microstructure Noises, Shirley J. Huang, Jun Yu
Bayesian Analysis Of Structural Credit Risk Models With Microstructure Noises, Shirley J. Huang, Jun Yu
Research Collection School Of Economics
In this paper a Markov chain Monte Carlo (MCMC) technique is developed for the Bayesian analysis of structural credit risk models with microstructure noises. The technique is based on the general Bayesian approach with posterior computations performed by Gibbs sampling. Simulations from the Markov chain, whose stationary distribution converges to the posterior distribution, enable exact ¯nite sample inferences of model parameters. The exact inferences can easily be extended to latent state variables and any nonlinear transformation of state variables and parameters, facilitating practical credit risk applications. In addition, the comparison of alternative models can be based on deviance information criterion …
Using High-Frequency Transaction Data To Estimate The Probability Of Informed Trading, Anthony S. Tay, Christopher Ting, Yiu Kuen Tse, Mitchell Warachka
Using High-Frequency Transaction Data To Estimate The Probability Of Informed Trading, Anthony S. Tay, Christopher Ting, Yiu Kuen Tse, Mitchell Warachka
Research Collection School Of Economics
This paper applies the asymmetric autoregressive conditional duration (AACD) model of Bauwens and Giot (2003) to estimate the probability of informed trading (PIN) using irregularly spaced transaction data. We model trade direction (buy versus sell orders) and the duration between trades jointly. Unlike the Easley, Hvidkjaer, and O'Hara (2002) approach, which uses the aggregate numbers of daily buy and sell orders to estimate PIN, our methodology allows for interactions between consecutive buy-sell orders and accounts for the duration between trades and the volume of trade. We extend the Easley–Hvidkjaer–O'Hara framework by allowing the probabilities of good news and bad news …
Modeling Transaction Data Of Trade Direction And Estimation Of Probability Of Informed Trading, Anthony S. Tay, Christopher Ting, Yiu Kuen Tse, Mitch Warachka
Modeling Transaction Data Of Trade Direction And Estimation Of Probability Of Informed Trading, Anthony S. Tay, Christopher Ting, Yiu Kuen Tse, Mitch Warachka
Research Collection School Of Economics
This paper implements the Asymmetric AutoregressiveConditional Duration (AACD) model of Bauwens and Giot (2003) to analyzeirregularly spaced transaction data of trade direction, namely buy versus sellorders. We examine the influence of lagged transaction duration, lagged volumeand lagged trade direction on transaction duration and direction. Our resultsare applied to estimate the probability of informed trading (PIN) based on theEasley, Hvidkjaer and O’Hara (2002) framework. Unlike the Easley-Hvidkjaer-O’Hara model, which uses the daily aggregate number of buy and sellorders, the AACD model makes full use of transaction data and allows forinteractions between buy and sell orders.
Intraday Stock Prices, Volume, And Duration: A Nonparametric Conditional Density Analysis, Anthony S. Tay, Christopher Ting
Intraday Stock Prices, Volume, And Duration: A Nonparametric Conditional Density Analysis, Anthony S. Tay, Christopher Ting
Research Collection School Of Economics
We investigate the distribution of high-frequency price changes, conditional on trading volume and duration between trades, on four stocks traded on the New York Stock Exchange. The conditional probabilities are estimated nonparametrically using local polynomial regression methods. We find substantial skewness in the distribution of price changes, with the direction of skewness dependent on the sign of trade. We also find that the probability of larger price changes increases with volume, but only for trades that occur with longer durations. The distribution of price changes vary with duration primarily when volume is high.
Jackknifing Bond Option Prices, Peter C. B. Phillips, Jun Yu
Jackknifing Bond Option Prices, Peter C. B. Phillips, Jun Yu
Research Collection School Of Economics
Prices of interest rate derivative securities depend crucially on the mean reversion parameters of the underlying diffusions. These parameters are subject to estimation bias when standard methods are used. The estimation bias can be substantial even in very large samples and much more serious than the discretization bias, and it translates into a bias in pricing bond options and other derivative securities that is important in practical work. This article proposes a very general and computationally inexpensive method of bias reduction that is based on Quenouille's (1956; Biometrika, 43, 353-360) jackknife. We show how the method can be applied directly …
Transaction-Data Analysis Of Marked Durations And Their Implications For Market Microstructure, Anthony S. Tay, Christopher Ting, Yiu Kuen Tse, Mitchell Warachka
Transaction-Data Analysis Of Marked Durations And Their Implications For Market Microstructure, Anthony S. Tay, Christopher Ting, Yiu Kuen Tse, Mitchell Warachka
Research Collection Lee Kong Chian School Of Business
We propose an Autoregressive Conditional Marked Duration (ACMD) model for the analysis of irregularly spaced transaction data. Based on the Autoregressive Conditional Duration (ACD) model, the ACMD model assigns marks to characterize events such as tick movements and trade directions (buy/sell). Applying the ACMD model to tick movements, we study the influence of trade frequency, direction and size on price dynamics, volatility and the permanent and transitory price impacts of trade. We also apply the ACMD model to analyze trade-direction data and estimate the probability of informed trading (PIN). We find that trade frequency has a critical role in price …