scholarly journals Volatility Analysis of Select Blue Chip Companies Listed in Nifty 50 Index of India

2021 ◽  
Vol 11 (4) ◽  
pp. 104-115
Author(s):  
Dr. Shankar R
Keyword(s):  
Volatility Analysis ◽  
Blue Chip

An Introduction to Oil Market Volatility Analysis

2012 ◽  
Author(s):  
Walid Omar Matar ◽  
Saud Al-Fattah ◽  
Tarek N. Atallah ◽  
Axel Pierru
Keyword(s):  
Market Volatility ◽  
Oil Market ◽  
Volatility Analysis

Founders without Limits

2021 ◽  
Author(s):  
Bobby V. Reddy
Keyword(s):  
Stock Exchange ◽  
High Growth ◽  
Global History ◽  
Public Markets ◽  
The Us ◽  
History Of ◽  
The Uk ◽  
Dual Class ◽  
Blue Chip

Big Tech has flourished on the US public markets in recent years with numerous blue-chip IPOs, from Google and Facebook, to new kids on the block such as Snap, Zoom, and Airbnb. A key trend is the burgeoning use of dual-class stock. Dual-class stock enables founders to divest of equity and generate finance for growth through an IPO, without losing the control they desire to pursue their long-term, market-disrupting visions. Bobby Reddy scrutinises the global history of dual-class stock, evaluates the conceptual and empirical evidence on dual-class stock, and assesses the approach of the London Stock Exchange and ongoing UK regulatory reforms to dual-class stock. A policy roadmap is presented that optimally supports the adoption of dual-class stock while still protecting against its potential abuses, which will more effectively attract high-growth, innovative companies to the UK equity markets, boost the economy, and unleash the true potential of 'founders without limits'.

Investor Sentiment and Flows into Treasury Funds

2018 ◽  
Keyword(s):  
Mutual Funds ◽  
Financial Market ◽  
Mutual Fund ◽  
Explanatory Power ◽  
Investor Sentiment ◽  
Fund Flows ◽  
Inflation Expectations ◽  
Relative Response ◽  
Financial Market Risk ◽  
Blue Chip

The author compares the relative response of Treasury fund flows to the sentiment-prone Michigan Survey of Inflation Expectations and to the Blue Chip Survey of Financial Forecasts, a professional forecast of inflation. The Treasury market is an ideal subject for examining whether or not sentiment affects flows: it is highly liquid, making it unlikely that it is hard to arbitrage, and inflation is the primary factor affecting its returns. Using mutual fund inflows into TIPs and Treasury mutual funds that occurred between January 1991 and June 2011, the author finds that the Michigan Survey is insignificantly related to flows into inflation-indexed TIPs and is positively related to flows into nominal Treasury funds. The Blue Chip Survey does not have incremental explanatory power. The evidence is consistent with a combination of a hedging motive and a flight to liquidity triggered by information in the Michigan Survey about households’ perception of financial market risk. The two motives reinforce each other in driving flows into nominal Treasury funds when the Michigan forecast of inflation is high, while they appear to cancel each other out in determining flows into the illiquid TIPS market.

scholarly journals Challenges in currency derivatives management in the OTC market in Poland during the Covid-19 pandemic

2021 ◽  
pp. 254-263
Author(s):  
Piotr Wybieralski
Keyword(s):  
Market Volatility ◽  
Professional Literature ◽  
Market Participants ◽  
Preventive Actions ◽  
Force Majeure ◽  
Early Settlement ◽  
Currency Derivatives ◽  
Volatility Analysis ◽  
Crisis Conditions ◽  
The Impact

Purpose: The aim of the chapter is to analyze the impact of the Covid-19 pandemic and market volatility increase on risk management in the OTC derivatives market in Poland. Design/methodology/approach: The chapter describes the legal background of derivatives trading with non-financial enterprises, then identifies the main risks, and discusses possible actions of market participants. In this regard, the study conducts volatility analysis based on selected market data. Findings: Due to volatility increase and the resulting negative valuation of non-matured currency derivatives by Polish exporters, margin call clauses were triggered, entailing the need to post additional collateral or prematurely close contracts. The described situation is particularly difficult when the pre-settlement limit is fully utilized on deal date, usually in the case of long-lasting large open exposures in non-flexible transactions. Research implications: To determine market risk, studies often apply the VaR approach. Inthis way, the specific amount of risk is analyzed on adaily basis and used by banks both to determine the maximum amount of the contract and to control pre-settlement risk. Apart from many advantages of the VaR approach, there are some drawbacks, especially related to volatility estimation, which usually relies on historical market fluctuations. It may cause that the risk will not be properly valued under crisis conditions. In such situations, supplementary methods should be also implemented (stresstests). Practical implications: Under high market volatility, preventive actions should be prepared in advance, including treasury limit increase, additional funds for collaterals, or contracts modification (flexible products should be considered).Originality and value: The study covers a challenge that banks face, which is rarely described in professional literature but very serious for bank management. Under normal market conditions, if the margin call clause appears and no additional collateral is posted, the transaction should be closed to limit the counterparty’s loss. However, this type of action during the pandemic may impose the risk of force majeure. From the company perspective, using such instruments threatens their early settlement and the need to finance closeout amount.

scholarly journals Optimasi Deep Learning untuk Prediksi Saham di Masa Pandemi Covid-19

2021 ◽  
Vol 7 (2) ◽  
pp. 133
Author(s):  
Widi Hastomo ◽  
Adhitio Satyo Bayangkari Karno ◽  
Nawang Kalbuana ◽  
Ervina Nisfiani ◽  
Lussiana ETP
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Hidden Layer ◽  
Gated Recurrent Unit ◽  
Blue Chip

Penelitian ini bertujuan untuk meningkatkan akurasi dengan menurunkan tingkat kesalahan prediksi dari 5 data saham blue chip di Indonesia. Dengan cara mengkombinasikan desain 4 hidden layer neural nework menggunakan Long Short Term Memory (LSTM) dan Gated Recurrent Unit (GRU). Dari tiap data saham akan dihasilkan grafik rmse-epoch yang dapat menunjukan kombinasi layer dengan akurasi terbaik, sebagai berikut; (a) BBCA dengan layer LSTM-GRU-LSTM-GRU (RMSE=1120,651, e=15), (b) BBRI dengan layer LSTM-GRU-LSTM-GRU (RMSE =110,331, e=25), (c) INDF dengan layer GRU-GRU-GRU-GRU (RMSE =156,297, e=35 ), (d) ASII dengan layer GRU-GRU-GRU-GRU (RMSE =134,551, e=20 ), (e) TLKM dengan layer GRU-LSTM-GRU-LSTM (RMSE =71,658, e=35 ). Tantangan dalam mengolah data Deep Learning (DL) adalah menentukan nilai parameter epoch untuk menghasilkan prediksi akurasi yang tinggi.

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