Estimating the Yield-Curve - An Implied Volatility Approach (in Danish)

1994 ◽  
Author(s):  
Claus Anderskov Madsen
Keyword(s):  
Implied Volatility ◽  
Yield Curve

scholarly journals SHORT RATE FORECASTING BASED ON THE INFERENCE FROM THE CIR MODEL FOR MULTIPLE YIELD CURVE DYNAMICS

2016 ◽  
Vol 11 (01) ◽  
pp. 1650004 ◽  
Author(s):  
LIN-YEE HIN ◽  
NIKOLAI DOKUCHAEV
Keyword(s):  
Implied Volatility ◽  
Yield Curve ◽  
Stock Option ◽  
Model Parameters ◽  
Option Prices ◽  
Cross Sectional ◽  
Federal Funds Rate ◽  
Market Participants ◽  
The Future ◽  
Rate Processes

In this paper, we propose a strategy to extract the information on the market participants’ expectation of the future short rate from the cross-sectional zero coupon bond prices. In line with the current market practice of building different yield curves for different tenors, we construct multiple one-factor short rate processes to pin down the salient features of the yield curve at different tenors. We represent this information in the form of the Cox–Ingersoll–Ross model implied parameters, and show that this information can be used to forecast the future short rate. This approach of representing the information on the market participants’ consensus in the form of implied model parameters and using these implied parameters for forecasting purposes resembles the approach of representing the market expectation of the underlying asset volatility reflected by stock option prices in the form of implied volatility, and using it to forecast the realized volatility. We illustrate the implementation of this method using historical US STRIPS prices and effective Federal Funds rate.

A NONPARAMETRIC ESTIMATOR FOR THE COVARIANCE FUNCTION OF FUNCTIONAL DATA

2014 ◽  
Vol 31 (6) ◽  
pp. 1359-1381 ◽  
Author(s):  
Alessio Sancetta
Keyword(s):  
Series Expansion ◽  
Functional Data ◽  
Covariance Function ◽  
Implied Volatility ◽  
Yield Curve ◽  
Estimation Procedure ◽  
Absolute Summability ◽  
Partially Observed ◽  
The Absolute ◽  
Minimax Rate

Many quantities of interest in economics and finance can be represented as partially observed functional data. Examples include structural business cycle estimation, implied volatility smile, the yield curve. Having embedded these quantities into continuous random curves, estimation of the covariance function is needed to extract factors, perform dimensionality reduction, and conduct inference on the factor scores. A series expansion for the covariance function is considered. Under summability restrictions on the absolute values of the coefficients in the series expansion, an estimation procedure that is resilient to overfitting is proposed. Under certain conditions, the rate of consistency for the resulting estimator achieves the minimax rate, allowing the observations to be weakly dependent. When the domain of the functional data is K(>1) dimensional, the absolute summability restriction of the coefficients avoids the so called curse of dimensionality. As an application, a Box–Pierce statistic to test independence of partially observed functional data is derived. Simulation results and an empirical investigation of the efficiency of the Eurodollar futures contracts on the Chicago Mercantile Exchange are included.

scholarly journals Research on Quantitative Investment Strategies Based on Deep Learning

Algorithms ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 35 ◽  
Author(s):  
Yujie Fang ◽  
Juan Chen ◽  
Zhengxuan Xue
Keyword(s):  
Deep Learning ◽  
Implied Volatility ◽  
Prediction Models ◽  
Short Term Memory ◽  
Yield Curve ◽  
Investment Strategy ◽  
Support Vector ◽  
Key Factors ◽  
Time Segment ◽  
Transaction Price

This paper takes 50 ETF options in the options market with high transaction complexity as the research goal. The Random Forest (RF) model, the Long Short-Term Memory network (LSTM) model, and the Support Vector Regression (SVR) model are used to predict 50 ETF price. Firstly, the original quantitative investment strategy is taken as the research object, and the 15 min trading frequency, which is more in line with the actual trading situation, is used, and then the Delta hedging concept of the options is introduced to control the risk of the quantitative investment strategy, to achieve the 15 min hedging strategy. Secondly, the final transaction price, buy price, highest price, lowest price, volume, historical volatility, and the implied volatility of the time segment marked with 50 ETF are the seven key factors affecting the price of 50 ETF. Then, two different types of LSTM-SVR models, LSTM-SVR I and LSTM-SVR II, are used to predict the final transaction price of the 50 ETF in the next time segment. In LSTM-SVR I model, the output of LSTM and seven key factors are combined as the input of SVR model. In LSTM-SVR II model, the hidden state vectors of LSTM and seven key factors are combined as the inputs of the SVR model. The results of the two LSTM-SVR models are compared with each other, and the better one is applied to the trading strategy. Finally, the benefit of the deep learning-based quantitative investment strategy, the resilience, and the maximum drawdown are used as indicators to judge the pros and cons of the research results. The accuracy and deviations of the LSTM-SVR prediction models are compared with those of the LSTM model and those of the RF model. The experimental results show that the quantitative investment strategy based on deep learning has higher returns than the traditional quantitative investment strategy, the yield curve is more stable, and the anti-fall performance is better.

scholarly journals A Joint Framework for Consistently Pricing Interest Rates and Interest Rate Derivatives

2009 ◽  
Vol 44 (3) ◽  
pp. 517-550 ◽  
Author(s):  
Massoud Heidari ◽  
Liuren Wu
Keyword(s):  
Interest Rate ◽  
Interest Rates ◽  
Term Structure ◽  
Implied Volatility ◽  
Yield Curve ◽  
Sequential Estimation ◽  
Estimation Procedure ◽  
Interest Rate Options ◽  
Curve Variation ◽  
The Interest Rate

AbstractDynamic term structure models explain the yield curve variation well but perform poorly in pricing and hedging interest rate options. Most existing option pricing practices take the yield curve as given, thus having little to say about the fair valuation of the underlying interest rates. This paper proposes an m + n model structure that bridges the gap in the literature by successfully pricing both interest rates and interest rate options. The first m factors capture the yield curve variation, whereas the latter n factors capture the interest rate options movements that cannot be effectively identified from the yield curve. We propose a sequential estimation procedure that identifies the m yield curve factors from the LIBOR and swap rates in the first step and the n options factors from interest rate caps in the second step. The three yield curve factors explain over 99% of the variation in the yield curve but account for less than 50% of the implied volatility variation for the caps. Incorporating three additional options factors improves the explained variation in implied volatilities to over 99%.

The Equilibrium Yield Curve for Government Securities

1979 ◽  
Vol 35 (3) ◽  
pp. 31-39 ◽  
Author(s):  
Herbert F. Ayres ◽  
John Y. Barry
Keyword(s):  
Yield Curve ◽  
Equilibrium Yield

Modeling the green bond yield on bond offering

2020 ◽  
Vol 26 (12) ◽  
pp. 2858-2878
Author(s):  
M.I. Emets
Keyword(s):  
Linear Regression ◽  
Yield Curve ◽  
Bond Market ◽  
Third Party ◽  
Bond Pricing ◽  
Market Development ◽  
Fair Price ◽  
Lower Yield ◽  
Yield Curves ◽  
Bond Yield

Subject. The article addresses the green bond pricing as compared to bonds other than green ones. Objectives. The aims are to determine how the fact that a bond is identified as a green one, the issue amount, and the availability of third-party verification, influence the yield to maturity; to make recommendations on effective green bond pricing. Methods. The study employs econometric testing of hypotheses, using the multiple linear regression. The sample includes 318 green and 1695 conventional bonds. Results. Green bonds have a lower yield to maturity in comparison with conventional bonds. The yield to maturity of green bonds with third-party verification is lower, as contrasted with green bonds without verification. Conclusions. The next step in the green bond market development is creating a benchmark yield curve for sovereign green bonds, with parallel issuance of conventional, non-green bonds. The yield curve is crucial for effective bond pricing. Two yield curves, i.e. for green and non-green bonds, will enable investors to estimate the fair price on issuance, as well as to define, if there is a difference in pricing.

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