Stochastic Processes and Economic Analysis: With Python (Richman Computational Economics)
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| Size | 23 MB (23,082 KB) |
|---|---|
| Format | |
| Downloaded | 612 times |
| Status | Available |
| Last checked | 10 Hour ago! |
| Author | Grant Richman |
“Book Descriptions: Unlock the power of economic analysis with a comprehensive guide to stochastic processes that integrates Python code for practical applications. This essential Kindle release caters to finance professionals, economists, data scientists, and students who seek to deepen their understanding of stochastic models and their pivotal role in economic forecasting and decision-making.Key Features:Exhaustive coverage of stochastic processes tailored for economic and financial analysis.Step-by-step Python code examples to enhance your practical skills in modeling.Insightful explanations that bridge theoretical concepts with real-world applications.Learn at your own pace with a structured approach covering fundamental to advanced topics.Book Description: This indispensable resource demystifies the complexities of stochastic processes, seamlessly linking theory with practical application through Python. From basic probability distributions to sophisticated economic models, this book provides a step-by-step journey through the stochastic processes essential for modern economic analysis. Whether you're navigating the intricacies of option pricing, interest rate modeling, or volatility forecasting, this expertly crafted guide will empower you with the knowledge and tools to excel in the ever-evolving field of economics.What You Will Learn:Master foundational concepts and properties of stochastic processes in economics.Explore a range of probability distributions crucial for economic systems modeling.Understand the mechanics behind random walks and their application to stock prices.Dive into Brownian motion and its use as a foundational model in finance.Apply Ito's Lemma to complex economic models and option pricing dilemmas.Analyze martingale properties and their implications within financial contexts.Formulate and solve stochastic differential equations in economics.Uncover the derivation and application of the Black-Scholes model for options.Characterize and apply Poisson processes in modeling economic events.Evaluate jump processes and their significance in abrupt price scenarios.Develop proficiency in discrete-time and continuous-time Markov chains.Calculate stationary distributions and understand their economic value.Utilize the Fokker-Planck equation for dynamic distribution modeling.Analyze mean-reverting processes in pricing models and economic cycles.Construct ARIMA models for effective economic time series forecasting.Harness GARCH models to analyze and predict market volatility.Model interest rates using the stochastic Cox-Ingersoll-Ross framework.Apply the Heston model to understand dynamic volatility changes.Integrate Kalman filters for accurate estimating and forecasting tasks.Harness stochastic control theory for decision-making in uncertain scenarios.Employ risk-neutral valuation for robust asset pricing strategies.Leverage dynamic programming for time-sensitive economic optimization.Master optimal stopping theory for strategic economic decision points.Discover the applications of Lévy processes in handling data discontinuities. Read more”
