Does offshore NDF market influence onshore forex market? Evidence from India

Behera, Harendra; Ranjan, Rajiv; Chinoy, Sajjid

doi:10.1002/fut.22324

Cited by 3 publications

(3 citation statements)

References 23 publications

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“…(2) Technical Analysis and Signal Generation Module: This module conducts a comprehensive analysis and processing of market data utilizing technical analysis methods to derive trading signals. These signals are generated based on diverse technical indicators and rules, including but not limited to moving averages, trendlines, oscillators, and other pertinent factors [6]. Subsequently, the system can make well-informed decisions concerning position management, encompassing options to buy, sell, or hold positions.…”

Section: System Architecture and Key Componentsmentioning

confidence: 99%

“…The trade execution module needs to ensure the accuracy and timeliness of trade instructions and interact effectively with the broker's system. (6) Monitoring and recording of trades: Once a trade has been executed, the system needs to monitor the operation and results of the trade [9]. This includes monitoring the profit and loss of the current position, tracking the effect of trade execution, etc.…”

Section: Trading Signal Generation and Executionmentioning

confidence: 99%

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Forex Automated Trading System Establishment and Optimization Analysis

2023

infse

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The foreign exchange market's high complexity and risk demand advanced trading systems. This paper aims to explore the establishment and optimization of a foreign exchange automated trading system to enhance efficiency and risk management. Firstly, the paper introduces the characteristics of the foreign exchange market and existing trading methods. Then, it analyzes the advantages and challenges of automated trading systems. Next, it proposes a framework based on technical analysis and machine learning for the automated trading system, discussing its key components and functionalities. Finally, through backtesting with historical data and live trading validation, the system's performance and viability are evaluated.

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Section: System Architecture and Key Componentsmentioning

confidence: 99%

Section: Trading Signal Generation and Executionmentioning

confidence: 99%

Forex Automated Trading System Establishment and Optimization Analysis

2023

infse

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“…Various studies have discovered that there were interlinkages between onshore and offshore markets. Behera et al (2021) discovered a stable and long-run relationship between onshore and NDF markets. The interaction between the NDF and the onshore foreign exchange market limits the effectiveness of intervention in the exchange rate (Lau et al, 2020).…”

Section: Ndf Vs Domestic Exchange Rate Marketmentioning

confidence: 99%

Efficacy of central bank intervention in the foreign exchange market of the BRICS countries

Chaudhari¹,

Trivedi²

2022

brics-econ

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Central bank intervention plays a major role in managing exchange rate volatility. In comparison to advanced economies, emerging market economies are generally active in the forex market as excessive volatility of the local currency persists. The BRICS countries (Brazil, Russia, India, China and South Africa) are the major emerging economies influencing the international financial system. The paper empirically investigates the efficacy of central bank intervention in the case of the BRICS countries. It has been observed that intervention generally did not impact the exchange rate level; however, it reduced the volatility of the exchange rate. Furthermore, interventions in spot and derivatives markets are equally effective in containing exchange rate volatility, except in South Africa. It has been identified that sovereign yield spread impacts the exchange rate returns in China and South Africa and impacts the volatility in the returns in Brazil and Russia.

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The Interplay of Chinese Monetary Policy, the Offshore Renminbi Market, and Capital Flows: An Empirical Analysis via State Space Models

Zhang,

Zhang

2024

Applied Mathematics and Nonlinear Sciences

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This paper proposes state space modeling and Kalman filtering as the theoretical foundation to explore the impact of China’s monetary policy on the offshore RMB market and capital flows. In the state space model, the relationship between observable and unobservable variables is established, with the Hidden Markov Model representing the state vector as a first-order Markov process to estimate unobservable variables. Additionally, a variable parameter state space model is proposed to alter the relationship between explanatory variables and the dependent variable. Kalman filtering, as the core of the state space model solution algorithm, calculates the likelihood function through prediction error decomposition to estimate unknown parameters. The optimal lag of the model is determined to be 2 after constructing the VAR model in the empirical analysis, and the first-order autoregressive model is used to calculate variable parameters. The elasticity coefficient of the depth of development of the offshore RMB market on capital flows is the most significant variable, with a value of 0.96. Following this, the RMB exchange rate has an elasticity coefficient of 0.78.

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Does offshore NDF market influence onshore forex market? Evidence from India

Cited by 3 publications

References 23 publications

Forex Automated Trading System Establishment and Optimization Analysis

Forex Automated Trading System Establishment and Optimization Analysis

Efficacy of central bank intervention in the foreign exchange market of the BRICS countries

The Interplay of Chinese Monetary Policy, the Offshore Renminbi Market, and Capital Flows: An Empirical Analysis via State Space Models

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