Please enable JavaScript.
Coggle requires JavaScript to display documents.
Literature Review Diagram - Coggle Diagram
Literature Review Diagram
Probability Theory
Monte Carlo Methods
Bayesian Monte Carlo
(Rasmussen & Ghahramani, 2003)
The Monte Carlo Method
(Metropolis & Ulam, 1949)
An Essay towards solving a Problem in the Doctrine of Chances
(Bayes, 1763)
Bayesian Inference
Thomas Bayes's Bayesian Inference
(Stigler, 1982)
:star:
Introduction to Bayesian Econometrics
(Greenberg, 2014)
Bayesian Inference
(Wasserman, 2012)
Stochastics
Historical Theory
Models using Stochastic Theory
Probability: Theory and Examples
(Durrett, 2019)
Martingales
Wiener Processes (Brownian Motion)
Sample Functions of the N-Parameter Wiener Process
(Orey & Pruitt, 1973)
Making Markov Martingales Meet Marginals: With Explicit Constructions
(Madan & Yor, 2002)
Markov Chains
MCMC Methods
An Introduction to MCMC for Machine Learning
(Andrieu et al, 2003)
Sampling-Based Approaches to Calculating Marginal Densities
(Gelfand & Smith, 1990)
:star:
Efficient Bayesian inference for stochastic volatility models with ensemble MCMC methods
(Shestopaloff et al, 2014)
emcee: The MCMC Hammer
(Mackey, 2013)
:star:
Markov chain Monte Carlo methods for stochastic volatility models
(Chib et al, 2002)
Boosting
Ancillarity-sufficiency interweaving strategy (ASIS) for boosting MCMC estimation of stochastic volatility models
(Kastner et al, 2014a)
An Ancillarity-Sufficiency Interweaving Strategy (ASIS) for Boosting MCMC Efficiency
(Yu and Meng, 2011)
Metropolis-Hastings Algorithm
The Metropolis Algorithm
(Beichl & Sullivan, 2000)
Monte Carlo sampling methods using markov chains and their applications
(Hastings, 1970)
Stochastic Filtration
An Introduction to the Kalman Filter
(Welch & Bishop, 1995)
Sequential MC (Particle Filtering)
:star:
The Iterated Auxiliary Particle Filter
(Guarniero, Johansen & Lee, 2016)
An Improved Particle Filter for Non-linear Problems
(Carpenter et al, 1999)
Markov Chains and Stochastic Stability
(Kaspi et al, 1997)
Foundations
Stochastic Processes and Statistics
(Doob, 1934)
Volatility
Econometric Application
Heston Model
On the Heston Model with Stochastic Interest Rates
(Grzelak & Oosterlee, 2011)
GARCH
Report Relevant Studies
Directly Relevant Studies
:star:
Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models
(Kastner et al, 2017)
Analysis of Exchange Rates via Multivariate Bayesian Factor Stochastic Volatility Models
(Kastner et al, 2014b)
Indirectly Relevent Studies
The economic value of co-movement between oil price and exchange rate using copula-based GARCH models
(Wu et al, 2012)
A decomposition formula for option prices in the Heston model and applications to option pricing approximation
(Alos, 2012)
The Copula-GARCH model of conditional dependencies: An international stock market application
(Jondeau & Rockinger, 2006)
Multivariate GARCH models: a survey
(Bauwens et al, 2006)
Ornstein–Uhlenbeck process
Volatility Clustering in Financial Markets: Empirical Facts and Agent-Based Models
(Cont, 2007)
Economic Application
Exchanges Rates
Market Volatility
Exchange rate volatility across financial crises
(Coudert et al, 2011)
High-Frequency Data and Volatility in Foreign-Exchange Rates
(Zhou, 2012)
Deterministic Factors
Purchasing Power Parity
The Purchasing Power Parity Puzzle
(Rogoff, 1996)
Empirical research on nominal exchange rates
(Frankel & Rose, 1995)
Purchasing Power Parity in the Long Run
(Abuaf & Jorion, 1990)
Terms of Trade
A Theory of Exchange Rate Determination
(Stockman, 1980)
Non-Exchange Rate Markets
Option Pricing
Option pricing: A review
(Smith Jr, 1976)
Black-Scholes Model
Merton modification
Pricing Warrants: An Empirical Study of the Black-Scholes Model and Its Alternatives
(Lauterbach & Schultz, 1990)
Theory of Rational Option Pricing
(Merton, 1973) :
The Pricing of Options and Corporate Liabilities
(Black & Scholes, 1973)
Data Science
Introductory Papers
The Impact of Machine Learning on Economics
(Agrawal et al, 2017)
Big Data: New Tricks for Econometrics
(Varian, 2014)
Fundamentals and exchange rate forecastability with simple machine learning methods
(Amat et al, 2018)
Exploring complex algorithms
Forecasting Daily and Monthly Exchange Rates with Machine Learning Techniques
(Plakandaras et al, 2015)
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks
(Wu et al, 2020)