научная статья по теме RISK MANAGEMENT FUEL-ENERGY SECTOR INVESTMENT PORTFOLIO Экономика и экономические науки

Текст научной статьи на тему «RISK MANAGEMENT FUEL-ENERGY SECTOR INVESTMENT PORTFOLIO»

Risk management fuel-energy sector Investment portfolio

E.V. Gandin,

Bachelor of International Economic Relations, Bachelor of Finance and investment Management, student, Financial university under the Government of the Russian Federation, Northumbria University (Россия, г. Москва, ГСП-3, 125993, Ленинградский проспект, 49; e-mail: Evgeniigandin@gmail.com)

Аннотация. Энергетика представляется значительной для сегодняшнего глобального финансового мира. Большие изменения в стоимости электроэнергии на сырьевые товары оказывают влияние на региональную и глобальную экономическую и финансовую производительность. Поскольку цены в 2000-е годы на акции топливно-энергетического сектора стали более неустойчивыми и рискованными,. по этой причине, прогнозируя будущие цены акций энергетических компаний, риски портфеля энергоресурсов становятся крайне важным вопросом для финансовых учреждений, центральных банков и корпораций. Наиболее используемым инструментом для количественной оценки рисков является известный параметр "Value at Risk" (VaR). VaR можно определить как величину потерь по портфелю с заданной вероятностью в течение фиксированного количества дней. Из-за интуитивности концепции VaR, его моделирование сталкивается с серьезными статистические проблемами. Это исследование обеспечивает управление рисков в энергетическом портфеле с помощью Bloomberg Terminal.

Abstract. Energy is a significant of today's global financial world. Large modification in energy commodity price can influence regional and global economic and financial performance. Energy commodities, as well as all other commodities, are subject to measure swings over time, particularly tied to overall business cycle. Since 2000s stock prices of fuel-energy sector become more volatile and risky. For this reason, forecasting future prices of energy stocks and managing energy portfolio risk associates with extremely crucial issue for financial institutions, central banks and corporations. The most used tool for risk quantification is the well-known "Value at Risk" (VaR). VaR could be defined as an amount of loss on a portfolio with a given probability over a fixed number of days. VaR pointed as a benchmark to measure market risk as it permit to reduce the risk associated with any kind of assets. Because of intuitiveness of a VaR concept, its modeling faces serious statistical problems. This research provides risk management of an energy portfolio by using Bloomberg Terminal.

Ключевые слова: управление портфелем, инвестиционный менеджмент, риск менеджмент, VaR.

Keywords: portfolio management, investment management, risk management, VaR.

Introduction.

Base of empirical researches due to this topic are Harry Markowitz [1], Di Clemente and Roma-no[18], Bekiros [23], Bystrom [21], Ghorbel and Trabelsi [26], Palaro and Hotta [24], Seymour and Polakow [19] and etc. In contrast to them, relatively little works were done to clarify energy commodities risk are Giot and Laurent [20], Hung et al [31], Sadeghi and Shavvalpour [25] and Sadorsky [16]. Modeling and forecasting volatility lies at the heart of modern finance because good estimates of correlation and volatility are needed for derivative pricing, portfolio optimization, risk management and hedging. To date, however, a few knowledge about the defense from dynamic volatility of an energy market and keep get profit.

This paper providing risk management process through a real portfolio management process. The outline of this research is as follows. The next section explains the different methodology types applied during portfolio management process. The following section explains the process of the one month portfolio management, analysis of main risks appeared during management process and conclusion.

2. Methodology types

Value-at-Risk (VaR) is the most popular and usual risk measurement tool. In this paper, VaR represents the quantile of the energy portfolio return distribution. The right quantile is used to measure the upside risk which means the extra expenses for energy commodity purchasers caused by the sharp rise of energy portfolio value. [28] [26] More formally, VaR is calculated based on the following equation:

Portfolio value at risk (VAR) for each stock was calculated in Excel using formula VaRa(L) = inf{l E R : P(L > I) < 1 - a} = inf{l £ :Fl(I) > a} [14]. Beta was calculated by division COVAR /VAR. Most of assets have Beta below 1 or near 1 (18 of 26 companies), which means that stocks are called neutral or in other words they move exactly the market in average [17].

Evaluation of risk measured decisions was measured by various methods. First one is Treynor's measure [4]. It compares portfolio excess return to Beta. It is calculated by the formula: Treynor's Measure

Total Portfolio Return - Risk Free Rate

Portfolio Beta

It means that the portfolio has higher premium per unit of non-diversifiable risk than the market.

The second one is Sharpe's measure [5], which reflects the incremental return for every increase of 1% of volatility and calculated by the formula:

Sharpe'sMeasure

Total Portfolio Return - Risk Free Rate

" Volatility (StDev) of Portfolio

The result number is quite high, that is why it means superior risk adjusted performance of my portfolio.

The third one is Jensen's measure [6]. For this method FTSE250 was chosen as index to compare with. The formula is the following: Jensen's Measure = (Total Portfolio Return - Risk Free Rate) - (Portfolio Beta*(Market Return - Risk Free Rate.

Dividend Discount Model (DDM) [10] of Kinder Morgan company as it has the highest dividends per share in my portfolio. I have taken Market Risk = 0.5%, Market Return = 5% and Beta = 1.025. By calculating Value of stock = (dividend per share + growth rate)/ (discount rate-growth rate) [2]. The theory of "Golden Cross of Market" [31] was applied for rebalancing strategy. Stochastics measures the velocity of equity's price movement in order to identify whether it is overbought or oversold. This indicator measures current price relative to highs and lows over a time period [9]. In a down-trend markets tend to close nearer to the lows, while in an up-trend they tend to close near the high. It is calculated by the formula:

%K (Slow Stochastic) = 100*Closing Range/Total Range

Where:

Closing Range = Close - Range Minimum

Total Range = Range Maximum - Range Minimum

%D = N-period moving average of %K where N is the %D period parameter, which was taken as 5 in case of this company.

Stochastics was used to recognize potential turning point for this company and to make decisions whether buy, hold or sell securities of this company.

According to Erta, Hunt and Brambley [32] there are 10 behavioural biases and effects in retail financial markets. My portfolio generally was based on reasoning model of thought as the decisions were made in slow, deliberately controlled, serial and flexible way. My only preference was reliability of portfolio as diversification was made for peace of mind and in order not to lose money. The belief, which I had was projection bias, as most of assets were bought on the bottom of their price within 3 month time-period with belief that they may arise in the future.as for decision-making short-cuts, 2 of them were used: Mental accounting and narrow

USA Eco

framing (investment and rebalancing decisions were made asset-by-asset rather than considering the whole investment portfolio) and Persuasion and social influence (financial advices of Bloomberg ANR command were analysed and followed).

According to data and instruments, all data for the portfolio was downloaded from Bloomberg and Bloomberg terminal was used to create and manage investment portfolio through <IDEA> command.

3. Estimation results

To begin with, the basement of my portfolio is diversification, asset allocation models and such kinds of analysis as: fundamental and macro-analysis of countries. The main aim of this outcome is to make asset allocation suggestions. All in all, this asset allocation strategy would be used as a guideline for building or adjusting my investment portfolio and risk management. Moreover, it is of vital importance to use classical theory of portfolio selection [1]. Due to this theory I decided to make diversified portfolio and invest in securities to reduce risk and chance of loss as the market is quite unstable. But it is necessary to allocate money in reasonable amount of equities (at least 20-25 stocks) according to Evans and Archer [7].

My second step is devoted to the top-down approach [40] and choice of countries to invest in. Leyuan You and Robert T. Daigler [28] recommend diversifying portfolio between USA or European markets and Asian ones. Firstly, I downloaded data about 15 countries which had the most profitable indexes. All data was collected and analysed with the help of Bloomberg [34] system. Main criteria of my final choice were macro-analysis of economic activity including Real GDP growth, Unemployment rate, CPI, etc. I decided to invest in the USA and India. Besides, the USA have good economic climate and India has signed the contract with Russia to get access to arctic oil [41].

Table 1.1.2

Data Watch

Country United States

Period Yearly

Year 2013 2014 2015 2016

Real GDP (YoY%) 2.2 2.1 3 2.9

CPI (YoY%) 1.48 1.9 2.1 2.2

Unemployment (%) 7.35 6.2 5.7 5.4

Curr. Acct. (% of GDP) -2.39 -2.5 -2.5 -2.3

Budget (% of GDP) -3.3 -2.9 -2.6 -2.9

Central Bank Rate (%) 0.25 0.25 0.95 -

3-Month Rate (%) 0.25 0.3 1.11 -

2-Year Note (%) 0.38 0.73 1.63 -

10-Year Note (%) 3.03 2.74 3.4 -

Table 1.1.3

India Economic Data Watch_

Country India

Period Yearly

Year 2013 2014 2015 2016

Real GDP (YoY%) 4.73 5.4 5.5 6.2

CPI (YoY%) 10.92 7.25 7.8 7

Unemployment (%) - - - -

Curr. Acct. (% of GDP) -2.81 - -2 -2.3

Budget (% of GDP) -5.89 - -4.4 -4.05

Central Bank Rate (%) 7.75 8 7.65 -

3-Month Rate (%) 9.06 - - -

2-Year Note (%) 8.42 - - -

10-Year Note (%) 9.23

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