Realized Volatility, Liquidity, and Corporate Yield Spreads

2014 ◽  
Vol 04 (01) ◽  
pp. 1450004 ◽  
Author(s):  
Marco Rossi
Keyword(s):  
Credit Risk ◽  
Strong Correlation ◽  
High Frequency ◽  
Realized Volatility ◽  
Round Trip ◽  
Substantial Fraction ◽  
Yield Spreads ◽  
Trading Decisions ◽  
Liquidity Costs ◽  
Investment Grade

I propose a friction measure of bond round-trip liquidity costs that is robust to outliers and accounts for the idiosyncratic information behind trading decisions. Particularly effective with investment-grade bonds, the proposed measure displays properties consistent with the credit risk puzzle. Using transactions from January 2004 to December 2011, I find that liquidity costs display a strong correlation with credit conditions and peaked during the sub-prime crisis. After controlling for equity volatility with high-frequency measures, liquidity costs explain a substantial fraction of the variation in the yield spreads of highly rated bonds, but become less important for speculative-grade bonds.

Estimating liquidity premium of corporate bonds using the spread information in on- and off-the-run Treasury securities

2017 ◽  
Vol 7 (2) ◽  
pp. 134-162 ◽  
Author(s):  
Haitao Li ◽  
Chunchi Wu ◽  
Jian Shi
Keyword(s):  
Credit Risk ◽  
Corporate Bonds ◽  
Corporate Bond ◽  
Liquidity Premium ◽  
Yield Spreads ◽  
Content Type ◽  
Novel Approach ◽  
Liquidity Measures ◽  
Investment Grade ◽  
Bond Spreads

Purpose The purpose of this paper is to estimate the effects of liquidity on corporate bond spreads. Design/methodology/approach Using a systematic liquidity factor extracted from the yield spreads between on- and off-the-run Treasury issues as a state variable, the authors jointly estimate the default and liquidity spreads from corporate bond prices. Findings The authors find that the liquidity factor is strongly related to conventional liquidity measures such as bid-ask spread, volume, order imbalance, and depth. Empirical evidence shows that the liquidity component of corporate bond yield spreads is sizable and increases with maturity and credit risk. On average the liquidity spread accounts for about 25 percent of the spread for investment-grade bonds and one-third of the spread for speculative-grade bonds. Research limitations/implications The results show that a significant part of corporate bond spreads are due to liquidity, which implies that it is not necessary for credit risk to explain the entire corporate bond spread. Practical implications The results show that returns from investments in corporate bonds represent compensations for bearing both credit and liquidity risks. Originality/value It is a novel approach to extract a liquidity factor from on- and off-the-run Treasury issues and use it to disentangle liquidity and credit spreads for corporate bonds.

Forecasting Exchange Rate Realized Volatility Through Decomposition Using High-Frequency Data

2017 ◽  
Author(s):  
Rim mname Lamouchi ◽  
Russell mname Davidson ◽  
Ibrahim mname Fatnassi ◽  
Abderazak Ben mname Maatoug
Keyword(s):  
Exchange Rate ◽  
High Frequency ◽  
Realized Volatility ◽  
High Frequency Data ◽  
Frequency Data

Credit risk assessment using purchase order information

2018 ◽  
Vol 05 (04) ◽  
pp. 1850041
Author(s):  
Suguru Yamanaka
Keyword(s):  
Risk Assessment ◽  
Credit Risk ◽  
Financial Institutions ◽  
High Frequency ◽  
Risk Model ◽  
Financial Statements ◽  
Order Information ◽  
Purchase Order ◽  
Credit Risk Assessment ◽  
Business Conditions

This paper proposes advanced credit risk assessment and lending operations using purchase order information from borrower firms. Purchase order information from a borrower firm is useful for financial institutions to evaluate the actual business conditions of the firm. This paper shows the application of purchase order information to lending operations and credit risk assessment, and reveals its effectiveness. First, we propose a “purchase order based” credit risk model for real-time credit risk monitoring of firms. Financial institutions can monitor the actual business conditions of borrower firms by evaluating the firm’s asset value using purchase order information. A combination of traditional firm monitoring using financial statements and high-frequency monitoring using purchase order information enables financial institutions to assess the business conditions of borrower firms more precisely and efficiently. Then, with high-frequency data, financial institutions can give borrower firms appropriate support if necessary on a timely basis. Second, we illustrate purchase order financing, which is the lending method backed by purchase order information from borrowers. With purchase order financing, firms that consistently receive purchase orders from credit-worthy firms can borrow money under more favorable lending terms than the usual lending terms based on the financial statements of the borrower firm.

Does VPIN provide predictive information for realized volatility forecasting: evidence from Chinese stock index futures market

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Conghua Wen ◽  
Fei Jia ◽  
Jianli Hao
Keyword(s):  
High Frequency ◽  
Realized Volatility ◽  
Futures Market ◽  
Stock Index ◽  
Stock Index Futures ◽  
Trading Behavior ◽  
Volatility Forecasting ◽  
Content Type ◽  
Index Futures ◽  
Intraday Data

PurposeUsing intraday data, the authors explore the forecast ability of one high frequency order flow imbalance measure (OI) based on the volume-synchronized probability of informed trading metric (VPIN) for predicting the realized volatility of the index futures on the China Securities Index 300 (CSI 300).Design/methodology/approachThe authors employ the heterogeneous autoregressive model for realized volatility (HAR-RV) and compare the forecast ability of models with and without the predictive variable, OI.FindingsThe empirical results demonstrate that the augmented HAR model incorporating OI (HARX-RV) can generate more precise forecasts, which implies that the order imbalance measure contains substantial information for describing the volatility dynamics.Originality/valueThe study sheds light on the relation between high frequency trading behavior and volatility forecasting in China's index futures market and reveals the underlying market mechanisms of liquidity-induced volatility.

CDS-based implied probability of default estimation

2020 ◽  
Vol 21 (4) ◽  
pp. 399-422
Author(s):  
Amira Abid ◽  
Fathi Abid ◽  
Bilel Kaffel
Keyword(s):  
Credit Risk ◽  
Term Structure ◽  
Transition Matrix ◽  
Risk Model ◽  
Default Probability ◽  
Probability Of Default ◽  
Content Type ◽  
Thomson Reuters ◽  
Investment Grade ◽  
Implied Default Probability

Purpose This study aims to shed more light on the relationship between probability of default, investment horizons and rating classes to make decision-making processes more efficient. Design/methodology/approach Based on credit default swaps (CDS) spreads, a methodology is implemented to determine the implied default probability and the implied rating, and then to estimate the term structure of the market-implied default probability and the transition matrix of implied rating. The term structure estimation in discrete time is conducted with the Nelson and Siegel model and in continuous time with the Vasicek model. The assessment of the transition matrix is performed using the homogeneous Markov model. Findings The results show that the CDS-based implied ratings are lower than those based on Thomson Reuters approach, which can partially be explained by the fact that the real-world probabilities are smaller than those founded on a risk-neutral framework. Moreover, investment and sub-investment grade companies exhibit different risk profiles with respect of the investment horizons. Originality/value The originality of this study consists in determining the implied rating based on CDS spreads and to detect the difference between implied market rating and the Thomson Reuters StarMine rating. The results can be used to analyze credit risk assessments and examine issues related to the Thomson Reuters StarMine credit risk model.

scholarly journals Regime-Dependent Good and Bad Volatility of Bitcoin

2020 ◽  
Vol 13 (12) ◽  
pp. 312
Author(s):  
Kislay Kumar Jha ◽  
Dirk G. Baur
Keyword(s):  
Stock Markets ◽  
High Frequency ◽  
Realized Volatility ◽  
Forecast Horizon ◽  
Volatility Asymmetry ◽  
Volatility Regimes ◽  
The Asymmetry

This paper analyzes high-frequency estimates of good and bad realized volatility of Bitcoin. We show that volatility asymmetry depends on the volatility regime and the forecast horizon. For one-day ahead forecasts, good volatility commands a stronger impact on future volatility than bad volatility on average and in extreme volatility regimes but not across all quantiles and volatility regimes. For 7-day ahead forecasting horizons the asymmetry is similar to that observed in stock markets and becomes stronger with increasing volatility. Compared with stock markets, the persistence and predictability of volatility is low indicating high variations of volatility.

Observation of bifurcation to chaos in a passive all-optical Fabry-Perot resonator: a high-frequency optical modulator

1984 ◽  
Vol 313 (1525) ◽  
pp. 405-409
Keyword(s):  
Excellent Agreement ◽  
High Frequency ◽  
Optical Bistability ◽  
Time Dependent ◽  
Optical Modulator ◽  
Round Trip Time ◽  
Round Trip ◽  
Trip Time ◽  
Fabry Perot ◽  
All Optical

We report the first observations of bifurcation routes to chaos in an all-optical resonator. Generation of associated deep and sustained Ikeda oscillation of the smooth CO 2 laser input pulses at twice the round-trip time of the Fabry-Perot resonator provides a high-frequency ( ca .0.1 GHz) passive optical modulator device. Results are in excellent agreement with our adaption of optical bistability theory to the time-dependent regime

scholarly journals Adaptive Heterogeneous Autoregressive Models of Realized Volatility Based on a Genetic Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Hui Qu ◽  
Ping Ji
Keyword(s):  
Genetic Algorithm ◽  
Stock Markets ◽  
High Frequency ◽  
Time Horizon ◽  
Realized Volatility ◽  
Heterogeneous Structure ◽  
Moving Window ◽  
Heterogeneous Structures ◽  
Chinese Stock Markets ◽  
New Framework

The heterogeneous autoregressive (HAR) models of high-frequency realized volatility are inspired by the Heterogeneous Market Hypothesis and incorporate daily, weekly and monthly realized volatilities in the volatility dynamics with a (1,5,22) time horizon structure. We build on the HAR models and propose a new framework, adaptive heterogeneous autoregressive (AHAR) models, whose time horizon structures are optimized by a genetic algorithm. Our models can be applied to markets with different heterogeneous structures, and their time horizon structures can be adjusted adaptively as the market's heterogeneous structure varies. Moving window tests with five-minute returns of the CSI 300 index indicate that the (1,5,22) structure originally proposed for American stock markets is not the best choice for Chinese stock markets, and Chinese stock markets’ heterogeneous structure does vary over time. Using four common loss functions, we find that the AHAR models outperform the corresponding HAR models in most of the forecast windows and thus are reasonable choices for volatility forecasting practices.

scholarly journals Modelando e Prevendo a Volatilidade dos Retornos de Ativos Brasileiros: uma Abordagem da Variância Realizada

2006 ◽  
Vol 4 (1) ◽  
pp. 55
Author(s):  
Marcelo C. Carvalho ◽  
Marco Aurélio S. Freire ◽  
Marcelo Cunha Medeiros ◽  
Leonardo R. Souza
Keyword(s):  
Long Memory ◽  
High Frequency ◽  
Value At Risk ◽  
Realized Volatility ◽  
High Frequency Data ◽  
Garch Models ◽  
Frequency Data ◽  
Normality Assumption ◽  
Out Of Sample ◽  
Management Area

The goal of this paper is twofold. First, using five of the most actively traded stocks in the Brazilian financial market, this paper shows that the normality assumption commonly used in the risk management area to describe the distributions of returns standardized by volatilities is not compatible with volatilities estimated by EWMA or GARCH models. In sharp contrast, when the information contained in high frequency data is used to construct the realized volatility measures, we attain the normality of the standardized returns, giving promise of improvements in Value-at-Risk statistics. We also describe the distributions of volatilities of the Brazilian stocks, showing that they are nearly lognormal. Second, we estimate a simple model of the log of realized volatilities that differs from the ones in other studies. The main difference is that we do not find evidence of long memory. The estimated model is compared with commonly used alternatives in out-of-sample forecasting experiment.

Realized FX Volatility : Statistical Properties and Applications

2018 ◽  
Vol 26 (1) ◽  
pp. 1-25
Author(s):  
Cheoljun Eom ◽  
Taisei Kaizoj ◽  
Jong Won Park ◽  
Enrico Scalas
Keyword(s):  
High Frequency ◽  
Government Intervention ◽  
Dynamic Properties ◽  
Correlation Coefficients ◽  
Realized Volatility ◽  
Statistical Properties ◽  
General Phenomenon ◽  
High Volatility ◽  
Fx Market ◽  
External Intervention

This paper empirically examines the statistical properties of realized volatility and the relationships between volatility and correlation measurements of realized volatility by using intraday high-frequency foreign exchange (FX) rates. Results regarding the distributional and dynamic properties of realized volatility are in agreement with the findings of previous studies. However, the positive correlation present in previous studies is not found in the case of JPY. On trading days with low volatility in the FX market, realized correlation coefficients between JPY and other currencies have positive values, while realized correlation coefficients on trading days with high volatility show negative values. These results are due to the Japanese government's intervention in the FX market, particularly during trading days with high volatility. In this regard, our results suggest that the positive relationships between volatility and correlations verified in previous studies are not a general phenomenon in the case of government intervention and government intervention may distort the efficiency of the FX market. In addition, we show that the multivariate measurement of realized volatility based on intraday high-frequency data can be a useful tool for determining the occurrence of external intervention in the FX market.

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