Stochastic filtering with applications in finance /:
This book provides a comprehensive account of stochastic filtering as a modeling tool in finance and economics. It aims to present this very important tool with a view to making it more popular among researchers in the disciplines of finance and economics. It is not intended to give a complete mathe...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Singapore ; Hackensack, NJ :
World Scientific,
©2010.
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| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | This book provides a comprehensive account of stochastic filtering as a modeling tool in finance and economics. It aims to present this very important tool with a view to making it more popular among researchers in the disciplines of finance and economics. It is not intended to give a complete mathematical treatment of different stochastic filtering approaches, but rather to describe them in simple terms and illustrate their application with real historical data for problems normally encountered in these disciplines. Beyond laying out the steps to be implemented, the steps are demonstrated in. |
| Beschreibung: | 1 online resource (xiv, 339 pages :) |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9789814304863 9814304867 1283144522 9781283144520 9786613144522 6613144525 |
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| 100 | 1 | |a Bhar, Ramaprasad. | |
| 245 | 1 | 0 | |a Stochastic filtering with applications in finance / |c Ramaprasad Bhar. |
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| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record. | |
| 520 | |a This book provides a comprehensive account of stochastic filtering as a modeling tool in finance and economics. It aims to present this very important tool with a view to making it more popular among researchers in the disciplines of finance and economics. It is not intended to give a complete mathematical treatment of different stochastic filtering approaches, but rather to describe them in simple terms and illustrate their application with real historical data for problems normally encountered in these disciplines. Beyond laying out the steps to be implemented, the steps are demonstrated in. | ||
| 505 | 0 | |a 1. Introduction : Stochastic filtering in finance. 1.1. Filtering problem. 1.2. Examples of filtering applications. 1.3. Linear Kalman filter. 1.4. Extended Kalman Filter (EKF). 1.5. Applying EKF to interest rate model. 1.6. Unscented Kalman Filter (UKF) for nonlinear models. 1.7. Background to particle filter for non Gaussian problems. 1.8. Particle filter algorithm. 1.9. Unobserved component models. 1.10. Concluding remarks -- 2. Foreign exchange market -- Filtering applications. 2.1. Mean reversion in real exchange rates. 2.2. Common and specific components in currency movements. 2.3. Persistent in real interest rate differentials. 2.4. Risk premia in forward exchange rate. 2.5. Concluding remarks -- 3. Equity market -- Filtering applications. 3.1. Introduction to equity price of risk. 3.2. Economic convergence in a filtering framework. 3.3. Ex-ante equity risk premium. 3.4. Concluding remarks -- 4. Filtering application -- Inflation and the macroeconomy. 4.1. Background and macroeconomic issues. 4.2. Inflation targeting countries and data requirement. 4.3. Model for inflation uncertainties. 4.4. Testing Fisher hypothesis. 4.5. Empirical results and analysis. 4.6. Concluding remarks -- 5. Interest rate model and non-linear filtering. 5.1. Background to HJM model and the related literature. 5.2. The basic HJM structure. 5.3. Forward rate volatility : Deterministic function of time. 5.4. Forward rate volatility : Stochastic. 5.5. Estimation via Kalman filtering. 5.6. Preference-free approach to bond pricing. 5.7. Concluding remarks -- 6. Filtering and hedging using interest rate futures. 6.1. Background details. 6.2. The futures price model in the HJM framework. 6.3. Non-linear filter for futures price system. 6.4. Data used in empirical study. 6.5. Empirical results. 6.6. Concluding remarks -- 7. A multifactor model of credit spreads. 7.1. Background and related research. 7.2. Variables influencing changes in credit spreads. 7.3. Credit spread and default risk. 7.4. Credit spread and liquidity. 7.5. Alternative approach to analyzing credit spread. 7.6. Data used. 7.7. Multifactor model for credit spread. 7.8. Fitting the model. 7.9. Results. 7.10. Concluding remarks -- 8. Credit default swaps -- Filtering the components. 8.1. Background to credit default swaps. 8.2. What is in the literature already? 8.3. Credit derivatives market and iTraxx indices. 8.4. CDS index data and preliminary analysis. 8.5. Focusing on explanatory variables. 8.6. Methodology for component structure. 8.7. Analyzing empirical results. 8.8. Concluding summary -- 9. CDS options, implied volatility and unscented Kalman filter. 9.1. Background to stochastic volatility. 9.2. Heston model in brief. 9.3. State space framework. 9.4. General state space model and filter revisited. 9.5. The application of unscented Kalman filter. 9.6. Empirical results. 9.7. Concluding remarks -- 10. Stochastic volatility model and non-linear filtering application. 10.1. Background to stochastic volatility models. 10.2. Stochastic volatility models of short-term interest rates. 10.3. Data for analysis. 10.4. Analysis of estimation results. 10.5. Comparison of volatility estimates. 10.6. Outline of state space model estimation via MCL. 10.7. Concluding summary -- 11. Applications for filtering with jumps. 11.1. Background to electricity market and prices. 11.2. A model for spot electricity prices. 11.3. State space model, Kalman filter and Poisson jumps. 11.4. Data and empirical results for electricity market. 11.5. Summarizing electricity market application. 11.6. Background to jumps in CDS indices. 11.7. CDS data and preliminary analysis. 11.8. Methodology for analyzing CDS jump risks. 11.9. Analysis of results from the CDS market. 11.10. Summarizing CDS market application. | |
| 546 | |a English. | ||
| 650 | 0 | |a Finance |x Mathematical models. |0 http://id.loc.gov/authorities/subjects/sh85048260 | |
| 650 | 0 | |a Stochastic analysis. |0 http://id.loc.gov/authorities/subjects/sh85128175 | |
| 650 | 6 | |a Finances |x Modèles mathématiques. | |
| 650 | 6 | |a Analyse stochastique. | |
| 650 | 7 | |a BUSINESS & ECONOMICS |x Finance. |2 bisacsh | |
| 650 | 7 | |a Finance |x Mathematical models |2 fast | |
| 650 | 7 | |a Stochastic analysis |2 fast | |
| 650 | 7 | |a Stochastische Analysis |2 gnd |0 http://d-nb.info/gnd/4132272-1 | |
| 650 | 7 | |a Kalman-Filter |2 gnd | |
| 650 | 7 | |a Kapitalmarkttheorie |2 gnd |0 http://d-nb.info/gnd/4137411-3 | |
| 650 | 7 | |a Volatilität. |2 idszbz | |
| 650 | 7 | |a Stochastischer Prozess. |2 idszbz | |
| 650 | 7 | |a Finanzinnovation. |2 idszbz | |
| 650 | 7 | |a Portfoliomanagement. |2 idszbz | |
| 776 | 0 | 8 | |i Print version: |a Bhar, Ramaprasad. |t Stochastic filtering with applications in finance. |d Singapore ; Hackensack, NJ : World Scientific, ©2010 |z 9789814304856 |w (DLC) 2010019089 |w (OCoLC)515479492 |
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Datensatz im Suchindex
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| author | Bhar, Ramaprasad |
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| contents | 1. Introduction : Stochastic filtering in finance. 1.1. Filtering problem. 1.2. Examples of filtering applications. 1.3. Linear Kalman filter. 1.4. Extended Kalman Filter (EKF). 1.5. Applying EKF to interest rate model. 1.6. Unscented Kalman Filter (UKF) for nonlinear models. 1.7. Background to particle filter for non Gaussian problems. 1.8. Particle filter algorithm. 1.9. Unobserved component models. 1.10. Concluding remarks -- 2. Foreign exchange market -- Filtering applications. 2.1. Mean reversion in real exchange rates. 2.2. Common and specific components in currency movements. 2.3. Persistent in real interest rate differentials. 2.4. Risk premia in forward exchange rate. 2.5. Concluding remarks -- 3. Equity market -- Filtering applications. 3.1. Introduction to equity price of risk. 3.2. Economic convergence in a filtering framework. 3.3. Ex-ante equity risk premium. 3.4. Concluding remarks -- 4. Filtering application -- Inflation and the macroeconomy. 4.1. Background and macroeconomic issues. 4.2. Inflation targeting countries and data requirement. 4.3. Model for inflation uncertainties. 4.4. Testing Fisher hypothesis. 4.5. Empirical results and analysis. 4.6. Concluding remarks -- 5. Interest rate model and non-linear filtering. 5.1. Background to HJM model and the related literature. 5.2. The basic HJM structure. 5.3. Forward rate volatility : Deterministic function of time. 5.4. Forward rate volatility : Stochastic. 5.5. Estimation via Kalman filtering. 5.6. Preference-free approach to bond pricing. 5.7. Concluding remarks -- 6. Filtering and hedging using interest rate futures. 6.1. Background details. 6.2. The futures price model in the HJM framework. 6.3. Non-linear filter for futures price system. 6.4. Data used in empirical study. 6.5. Empirical results. 6.6. Concluding remarks -- 7. A multifactor model of credit spreads. 7.1. Background and related research. 7.2. Variables influencing changes in credit spreads. 7.3. Credit spread and default risk. 7.4. Credit spread and liquidity. 7.5. Alternative approach to analyzing credit spread. 7.6. Data used. 7.7. Multifactor model for credit spread. 7.8. Fitting the model. 7.9. Results. 7.10. Concluding remarks -- 8. Credit default swaps -- Filtering the components. 8.1. Background to credit default swaps. 8.2. What is in the literature already? 8.3. Credit derivatives market and iTraxx indices. 8.4. CDS index data and preliminary analysis. 8.5. Focusing on explanatory variables. 8.6. Methodology for component structure. 8.7. Analyzing empirical results. 8.8. Concluding summary -- 9. CDS options, implied volatility and unscented Kalman filter. 9.1. Background to stochastic volatility. 9.2. Heston model in brief. 9.3. State space framework. 9.4. General state space model and filter revisited. 9.5. The application of unscented Kalman filter. 9.6. Empirical results. 9.7. Concluding remarks -- 10. Stochastic volatility model and non-linear filtering application. 10.1. Background to stochastic volatility models. 10.2. Stochastic volatility models of short-term interest rates. 10.3. Data for analysis. 10.4. Analysis of estimation results. 10.5. Comparison of volatility estimates. 10.6. Outline of state space model estimation via MCL. 10.7. Concluding summary -- 11. Applications for filtering with jumps. 11.1. Background to electricity market and prices. 11.2. A model for spot electricity prices. 11.3. State space model, Kalman filter and Poisson jumps. 11.4. Data and empirical results for electricity market. 11.5. Summarizing electricity market application. 11.6. Background to jumps in CDS indices. 11.7. CDS data and preliminary analysis. 11.8. Methodology for analyzing CDS jump risks. 11.9. Analysis of results from the CDS market. 11.10. Summarizing CDS market application. |
| ctrlnum | (OCoLC)738433287 |
| dewey-full | 332.01/51922 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 332 - Financial economics |
| dewey-raw | 332.01/51922 |
| dewey-search | 332.01/51922 |
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| discipline | Wirtschaftswissenschaften |
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It aims to present this very important tool with a view to making it more popular among researchers in the disciplines of finance and economics. It is not intended to give a complete mathematical treatment of different stochastic filtering approaches, but rather to describe them in simple terms and illustrate their application with real historical data for problems normally encountered in these disciplines. Beyond laying out the steps to be implemented, the steps are demonstrated in.</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="a">1. Introduction : Stochastic filtering in finance. 1.1. Filtering problem. 1.2. Examples of filtering applications. 1.3. Linear Kalman filter. 1.4. Extended Kalman Filter (EKF). 1.5. Applying EKF to interest rate model. 1.6. Unscented Kalman Filter (UKF) for nonlinear models. 1.7. Background to particle filter for non Gaussian problems. 1.8. Particle filter algorithm. 1.9. Unobserved component models. 1.10. Concluding remarks -- 2. Foreign exchange market -- Filtering applications. 2.1. Mean reversion in real exchange rates. 2.2. Common and specific components in currency movements. 2.3. Persistent in real interest rate differentials. 2.4. Risk premia in forward exchange rate. 2.5. Concluding remarks -- 3. Equity market -- Filtering applications. 3.1. Introduction to equity price of risk. 3.2. Economic convergence in a filtering framework. 3.3. Ex-ante equity risk premium. 3.4. Concluding remarks -- 4. Filtering application -- Inflation and the macroeconomy. 4.1. Background and macroeconomic issues. 4.2. Inflation targeting countries and data requirement. 4.3. Model for inflation uncertainties. 4.4. Testing Fisher hypothesis. 4.5. Empirical results and analysis. 4.6. Concluding remarks -- 5. Interest rate model and non-linear filtering. 5.1. Background to HJM model and the related literature. 5.2. The basic HJM structure. 5.3. Forward rate volatility : Deterministic function of time. 5.4. Forward rate volatility : Stochastic. 5.5. Estimation via Kalman filtering. 5.6. Preference-free approach to bond pricing. 5.7. Concluding remarks -- 6. Filtering and hedging using interest rate futures. 6.1. Background details. 6.2. The futures price model in the HJM framework. 6.3. Non-linear filter for futures price system. 6.4. Data used in empirical study. 6.5. Empirical results. 6.6. Concluding remarks -- 7. A multifactor model of credit spreads. 7.1. Background and related research. 7.2. Variables influencing changes in credit spreads. 7.3. Credit spread and default risk. 7.4. Credit spread and liquidity. 7.5. Alternative approach to analyzing credit spread. 7.6. Data used. 7.7. Multifactor model for credit spread. 7.8. Fitting the model. 7.9. Results. 7.10. Concluding remarks -- 8. Credit default swaps -- Filtering the components. 8.1. Background to credit default swaps. 8.2. What is in the literature already? 8.3. Credit derivatives market and iTraxx indices. 8.4. CDS index data and preliminary analysis. 8.5. Focusing on explanatory variables. 8.6. Methodology for component structure. 8.7. Analyzing empirical results. 8.8. Concluding summary -- 9. CDS options, implied volatility and unscented Kalman filter. 9.1. Background to stochastic volatility. 9.2. Heston model in brief. 9.3. State space framework. 9.4. General state space model and filter revisited. 9.5. The application of unscented Kalman filter. 9.6. Empirical results. 9.7. Concluding remarks -- 10. Stochastic volatility model and non-linear filtering application. 10.1. Background to stochastic volatility models. 10.2. Stochastic volatility models of short-term interest rates. 10.3. Data for analysis. 10.4. Analysis of estimation results. 10.5. Comparison of volatility estimates. 10.6. Outline of state space model estimation via MCL. 10.7. Concluding summary -- 11. Applications for filtering with jumps. 11.1. Background to electricity market and prices. 11.2. A model for spot electricity prices. 11.3. State space model, Kalman filter and Poisson jumps. 11.4. Data and empirical results for electricity market. 11.5. Summarizing electricity market application. 11.6. Background to jumps in CDS indices. 11.7. CDS data and preliminary analysis. 11.8. Methodology for analyzing CDS jump risks. 11.9. Analysis of results from the CDS market. 11.10. 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| id | ZDB-4-EBA-ocn738433287 |
| illustrated | Illustrated |
| indexdate | 2024-10-25T16:18:10Z |
| institution | BVB |
| isbn | 9789814304863 9814304867 1283144522 9781283144520 9786613144522 6613144525 |
| language | English |
| oclc_num | 738433287 |
| open_access_boolean | |
| owner | MAIN |
| owner_facet | MAIN |
| physical | 1 online resource (xiv, 339 pages :) |
| psigel | ZDB-4-EBA |
| publishDate | 2010 |
| publishDateSearch | 2010 |
| publishDateSort | 2010 |
| publisher | World Scientific, |
| record_format | marc |
| spelling | Bhar, Ramaprasad. Stochastic filtering with applications in finance / Ramaprasad Bhar. Singapore ; Hackensack, NJ : World Scientific, ©2010. 1 online resource (xiv, 339 pages :) text txt rdacontent computer c rdamedia online resource cr rdacarrier data file Includes bibliographical references and index. Print version record. This book provides a comprehensive account of stochastic filtering as a modeling tool in finance and economics. It aims to present this very important tool with a view to making it more popular among researchers in the disciplines of finance and economics. It is not intended to give a complete mathematical treatment of different stochastic filtering approaches, but rather to describe them in simple terms and illustrate their application with real historical data for problems normally encountered in these disciplines. Beyond laying out the steps to be implemented, the steps are demonstrated in. 1. Introduction : Stochastic filtering in finance. 1.1. Filtering problem. 1.2. Examples of filtering applications. 1.3. Linear Kalman filter. 1.4. Extended Kalman Filter (EKF). 1.5. Applying EKF to interest rate model. 1.6. Unscented Kalman Filter (UKF) for nonlinear models. 1.7. Background to particle filter for non Gaussian problems. 1.8. Particle filter algorithm. 1.9. Unobserved component models. 1.10. Concluding remarks -- 2. Foreign exchange market -- Filtering applications. 2.1. Mean reversion in real exchange rates. 2.2. Common and specific components in currency movements. 2.3. Persistent in real interest rate differentials. 2.4. Risk premia in forward exchange rate. 2.5. Concluding remarks -- 3. Equity market -- Filtering applications. 3.1. Introduction to equity price of risk. 3.2. Economic convergence in a filtering framework. 3.3. Ex-ante equity risk premium. 3.4. Concluding remarks -- 4. Filtering application -- Inflation and the macroeconomy. 4.1. Background and macroeconomic issues. 4.2. Inflation targeting countries and data requirement. 4.3. Model for inflation uncertainties. 4.4. Testing Fisher hypothesis. 4.5. Empirical results and analysis. 4.6. Concluding remarks -- 5. Interest rate model and non-linear filtering. 5.1. Background to HJM model and the related literature. 5.2. The basic HJM structure. 5.3. Forward rate volatility : Deterministic function of time. 5.4. Forward rate volatility : Stochastic. 5.5. Estimation via Kalman filtering. 5.6. Preference-free approach to bond pricing. 5.7. Concluding remarks -- 6. Filtering and hedging using interest rate futures. 6.1. Background details. 6.2. The futures price model in the HJM framework. 6.3. Non-linear filter for futures price system. 6.4. Data used in empirical study. 6.5. Empirical results. 6.6. Concluding remarks -- 7. A multifactor model of credit spreads. 7.1. Background and related research. 7.2. Variables influencing changes in credit spreads. 7.3. Credit spread and default risk. 7.4. Credit spread and liquidity. 7.5. Alternative approach to analyzing credit spread. 7.6. Data used. 7.7. Multifactor model for credit spread. 7.8. Fitting the model. 7.9. Results. 7.10. Concluding remarks -- 8. Credit default swaps -- Filtering the components. 8.1. Background to credit default swaps. 8.2. What is in the literature already? 8.3. Credit derivatives market and iTraxx indices. 8.4. CDS index data and preliminary analysis. 8.5. Focusing on explanatory variables. 8.6. Methodology for component structure. 8.7. Analyzing empirical results. 8.8. Concluding summary -- 9. CDS options, implied volatility and unscented Kalman filter. 9.1. Background to stochastic volatility. 9.2. Heston model in brief. 9.3. State space framework. 9.4. General state space model and filter revisited. 9.5. The application of unscented Kalman filter. 9.6. Empirical results. 9.7. Concluding remarks -- 10. Stochastic volatility model and non-linear filtering application. 10.1. Background to stochastic volatility models. 10.2. Stochastic volatility models of short-term interest rates. 10.3. Data for analysis. 10.4. Analysis of estimation results. 10.5. Comparison of volatility estimates. 10.6. Outline of state space model estimation via MCL. 10.7. Concluding summary -- 11. Applications for filtering with jumps. 11.1. Background to electricity market and prices. 11.2. A model for spot electricity prices. 11.3. State space model, Kalman filter and Poisson jumps. 11.4. Data and empirical results for electricity market. 11.5. Summarizing electricity market application. 11.6. Background to jumps in CDS indices. 11.7. CDS data and preliminary analysis. 11.8. Methodology for analyzing CDS jump risks. 11.9. Analysis of results from the CDS market. 11.10. Summarizing CDS market application. English. Finance Mathematical models. http://id.loc.gov/authorities/subjects/sh85048260 Stochastic analysis. http://id.loc.gov/authorities/subjects/sh85128175 Finances Modèles mathématiques. Analyse stochastique. BUSINESS & ECONOMICS Finance. bisacsh Finance Mathematical models fast Stochastic analysis fast Stochastische Analysis gnd http://d-nb.info/gnd/4132272-1 Kalman-Filter gnd Kapitalmarkttheorie gnd http://d-nb.info/gnd/4137411-3 Volatilität. idszbz Stochastischer Prozess. idszbz Finanzinnovation. idszbz Portfoliomanagement. idszbz Print version: Bhar, Ramaprasad. Stochastic filtering with applications in finance. Singapore ; Hackensack, NJ : World Scientific, ©2010 9789814304856 (DLC) 2010019089 (OCoLC)515479492 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=374772 Volltext CBO01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=374772 Volltext |
| spellingShingle | Bhar, Ramaprasad Stochastic filtering with applications in finance / 1. Introduction : Stochastic filtering in finance. 1.1. Filtering problem. 1.2. Examples of filtering applications. 1.3. Linear Kalman filter. 1.4. Extended Kalman Filter (EKF). 1.5. Applying EKF to interest rate model. 1.6. Unscented Kalman Filter (UKF) for nonlinear models. 1.7. Background to particle filter for non Gaussian problems. 1.8. Particle filter algorithm. 1.9. Unobserved component models. 1.10. Concluding remarks -- 2. Foreign exchange market -- Filtering applications. 2.1. Mean reversion in real exchange rates. 2.2. Common and specific components in currency movements. 2.3. Persistent in real interest rate differentials. 2.4. Risk premia in forward exchange rate. 2.5. Concluding remarks -- 3. Equity market -- Filtering applications. 3.1. Introduction to equity price of risk. 3.2. Economic convergence in a filtering framework. 3.3. Ex-ante equity risk premium. 3.4. Concluding remarks -- 4. Filtering application -- Inflation and the macroeconomy. 4.1. Background and macroeconomic issues. 4.2. Inflation targeting countries and data requirement. 4.3. Model for inflation uncertainties. 4.4. Testing Fisher hypothesis. 4.5. Empirical results and analysis. 4.6. Concluding remarks -- 5. Interest rate model and non-linear filtering. 5.1. Background to HJM model and the related literature. 5.2. The basic HJM structure. 5.3. Forward rate volatility : Deterministic function of time. 5.4. Forward rate volatility : Stochastic. 5.5. Estimation via Kalman filtering. 5.6. Preference-free approach to bond pricing. 5.7. Concluding remarks -- 6. Filtering and hedging using interest rate futures. 6.1. Background details. 6.2. The futures price model in the HJM framework. 6.3. Non-linear filter for futures price system. 6.4. Data used in empirical study. 6.5. Empirical results. 6.6. Concluding remarks -- 7. A multifactor model of credit spreads. 7.1. Background and related research. 7.2. Variables influencing changes in credit spreads. 7.3. Credit spread and default risk. 7.4. Credit spread and liquidity. 7.5. Alternative approach to analyzing credit spread. 7.6. Data used. 7.7. Multifactor model for credit spread. 7.8. Fitting the model. 7.9. Results. 7.10. Concluding remarks -- 8. Credit default swaps -- Filtering the components. 8.1. Background to credit default swaps. 8.2. What is in the literature already? 8.3. Credit derivatives market and iTraxx indices. 8.4. CDS index data and preliminary analysis. 8.5. Focusing on explanatory variables. 8.6. Methodology for component structure. 8.7. Analyzing empirical results. 8.8. Concluding summary -- 9. CDS options, implied volatility and unscented Kalman filter. 9.1. Background to stochastic volatility. 9.2. Heston model in brief. 9.3. State space framework. 9.4. General state space model and filter revisited. 9.5. The application of unscented Kalman filter. 9.6. Empirical results. 9.7. Concluding remarks -- 10. Stochastic volatility model and non-linear filtering application. 10.1. Background to stochastic volatility models. 10.2. Stochastic volatility models of short-term interest rates. 10.3. Data for analysis. 10.4. Analysis of estimation results. 10.5. Comparison of volatility estimates. 10.6. Outline of state space model estimation via MCL. 10.7. Concluding summary -- 11. Applications for filtering with jumps. 11.1. Background to electricity market and prices. 11.2. A model for spot electricity prices. 11.3. State space model, Kalman filter and Poisson jumps. 11.4. Data and empirical results for electricity market. 11.5. Summarizing electricity market application. 11.6. Background to jumps in CDS indices. 11.7. CDS data and preliminary analysis. 11.8. Methodology for analyzing CDS jump risks. 11.9. Analysis of results from the CDS market. 11.10. Summarizing CDS market application. Finance Mathematical models. http://id.loc.gov/authorities/subjects/sh85048260 Stochastic analysis. http://id.loc.gov/authorities/subjects/sh85128175 Finances Modèles mathématiques. Analyse stochastique. BUSINESS & ECONOMICS Finance. bisacsh Finance Mathematical models fast Stochastic analysis fast Stochastische Analysis gnd http://d-nb.info/gnd/4132272-1 Kalman-Filter gnd Kapitalmarkttheorie gnd http://d-nb.info/gnd/4137411-3 Volatilität. idszbz Stochastischer Prozess. idszbz Finanzinnovation. idszbz Portfoliomanagement. idszbz |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85048260 http://id.loc.gov/authorities/subjects/sh85128175 http://d-nb.info/gnd/4132272-1 http://d-nb.info/gnd/4137411-3 |
| title | Stochastic filtering with applications in finance / |
| title_auth | Stochastic filtering with applications in finance / |
| title_exact_search | Stochastic filtering with applications in finance / |
| title_full | Stochastic filtering with applications in finance / Ramaprasad Bhar. |
| title_fullStr | Stochastic filtering with applications in finance / Ramaprasad Bhar. |
| title_full_unstemmed | Stochastic filtering with applications in finance / Ramaprasad Bhar. |
| title_short | Stochastic filtering with applications in finance / |
| title_sort | stochastic filtering with applications in finance |
| topic | Finance Mathematical models. http://id.loc.gov/authorities/subjects/sh85048260 Stochastic analysis. http://id.loc.gov/authorities/subjects/sh85128175 Finances Modèles mathématiques. Analyse stochastique. BUSINESS & ECONOMICS Finance. bisacsh Finance Mathematical models fast Stochastic analysis fast Stochastische Analysis gnd http://d-nb.info/gnd/4132272-1 Kalman-Filter gnd Kapitalmarkttheorie gnd http://d-nb.info/gnd/4137411-3 Volatilität. idszbz Stochastischer Prozess. idszbz Finanzinnovation. idszbz Portfoliomanagement. idszbz |
| topic_facet | Finance Mathematical models. Stochastic analysis. Finances Modèles mathématiques. Analyse stochastique. BUSINESS & ECONOMICS Finance. Finance Mathematical models Stochastic analysis Stochastische Analysis Kalman-Filter Kapitalmarkttheorie Volatilität. Stochastischer Prozess. Finanzinnovation. Portfoliomanagement. |
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| work_keys_str_mv | AT bharramaprasad stochasticfilteringwithapplicationsinfinance |