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- How do you approach the outside world—in a planned, orderly way or a more flexible, spontaneous way? Your natural preferences in these four areas sort you into one of 16 distinct MBTI personality types. Understanding these types gives you...
- Corrosion means the deterioration of a metal resulting from a reaction with its environment. Corrosion expert means a person recognized by NACE International formerly the National Association of Corrosion Engineers as a corrosion specialist,...
- Free oil means the non-soluble, non-emulsified petroleum and allied petroleum product layer that accumulates in an oil-water separator. Handling means the storing, transmitting, transporting, or distributing of petroleum or allied petroleum products and includes putting petroleum products into a container or into the fuel tank of a motor vehicle, vessel, or aircraft. Impermeable barrier means a secondary storage tank wall, synthetic membrane liner, or other equivalent material in conformance with this code. Internal coating means a coating or lining of a noncorrodible material bonded firmly to the interior surface of a storage tank that does not chemically or physically degrade when in contact with the petroleum or allied petroleum products stored therein.
- Operator means the person who is responsible for the day-to-day operation of an installation where an aboveground or underground storage tank is located or, when referring to a vehicle, the driver in charge of the vehicle. Out-of-service means that a storage tank system or portion thereof is no longer serving its intended use. Overfill protection device means a mechanical device, electrical device, or fill procedure system that is intended to prevent a storage tank from being overfilled. Owner means the Crown, an institution, corporate entity, Indian band, government department or agency, or a person who has legal owner ship of the storage tank system or who has been assigned custody to control, care for, manage, or dispose of the storage tank system. Piping means fuel conduits, including fittings and valves that are necessary for the safe handling and storage of petroleum products and allied petroleum products and are specified by a nominal inside diameter.
Performance Evaluation Of Portfolio Insurance Strategies Using Stochastic Dominance Criteria
Precision leak detection test means a test capable of detecting a storage tank leak as small as 0. Pressure liquid media leak detection test means a test utilizing a device to pressurize piping with a suitable test liquid to determine the presence of leaks. Product transfer area means the area around the connection point between a delivery truck, railcar, or vessel and a storage tank system with a capacity of 2 L or more. Protected means having impact, projectile, and fire resistance qualities for an aboveground storage tank system.- Protective coating means a coating applied to a surface to protect the substrate from corrosion. Secondary containment means an impermeable barrier that prevents leaks from the primary storage tank system from reaching outside the containment area. Separated solid means the particulate material that settles at the bottom of an oil-water separator. Site means a lot or property where there is one or more underground storage tank systems within m of each other, or one or more aboveground storage tank systems within m of each other, and all storage tanks on the property are owned by the same owner s. Sludge means the petroleum or allied petroleum product residue or material that accumulates at the bottom of a storage tank. Spill means any loss of liquid petroleum or allied petroleum product from a storage tank system that is not attributable to a leak in the storage tank system. Spill containment device means a container fitted to the inlet of a storage tank or to the suction coupling of a used oil storage tank that helps prevent spills from entering the environment.
Back-test Analysis Of NIFTY 50 Stocks Using CPPI Strategy: A Data Science Approach
Static liquid media leak detection test means a leak detection test in which a suitable test liquid is placed into the containment device and is monitored for a change in the liquid level and the rate of change. Storage tank means a closed container for the storage of petroleum or allied petroleum products with a capacity of more than L that is designed to be installed in a fixed location. Storage tank system means a system for the storage and dispensing of petroleum or allied petroleum product and is not limited to storage tanks, associated piping, vents, pumps, and dispensing equipment. Tank bottom water means water that accumulates at the bottom of a storage tank. Underground storage tank means a storage tank with all of the storage tank volume below grade and the primary tank or double-wall completely surrounded by or in intimate contact with backfill. Underground storage tank system means one or more commonly connected underground storage tank s , including all underground and aboveground connections, piping, pumps, and dispensers.CPI / CPPI 2021 Training Programme For Insolvency Professionals
Used oil means oil from industrial and non-industrial sources that has been acquired for lubricating or other purposes and has become unsuitable for its original purpose due to the presence of impurities or the loss of original properties. Used oil does not include oils derived from animal or vegetable fats, crude oil or recovered fuel oils spilled onto land or water and wastes from petroleum-refining operations. The following categories of used oil are covered by this code See appendix B, note B. Vent means an opening in a storage tank system that is specifically designed to relieve excess internal pressure or vacuum within a storage tank system. Section 1.- In events such as the financial crisis in or the Covid pandemic which we are facing now, even a well-diversified portfolio will lose money. So how do you protect your wealth when the market crashes during such events but also participate in the upside and get the best of both worlds without losing out on your financial goals. Even though these kind of strategies are being used in the industry in many forms and techniques, here we attempt to look through a step-by-step, Data science methodology of Extracting adequate data from relevant sources, Cleaning, Transforming and Modelling them, implementing an on-the-fly CPPI algorithm, arriving at the back test results and interactively analyse the strategy using advanced data visualization methods.
- In the next few sections, we discuss more on the balance between user interactivity, intuitiveness and algorithmic complexity of the CPPI calculation rather than the nitty gritty of the CPPI as a Portfolio strategy. Raw data extracted from NSE will look like the following: These data points are basically various price points on a given day for an equity. Handling daily frequency data for a portfolio analysis may be an overkill and for the sake of simplicity we will aggregate the data at a weekly level. From the daily prices, we will find the weekly price points like open, close, high and low. We should also be careful about the column headings and make sure they look clean without any spaces or special characters in between.
- Once you convert to weekly time series data, the data frame should like like below: After this weekly grouping, calculate the weekly returns which is the percentage change of price from the previous week. A visual representation of the above data frame should look like the following figure in Tableau or for that matter any Data visualization tools. As we can see the weekly returns were very busy during the Feb-May time frame resulting in lot of volatility. There are basically less than a handful of steps to implement it and not complicated to implement in python. For a given set of time-series data of weekly returns, we need to traverse as many steps to compute and store the various indicators for each point in time.
- Let's find how to solve a real-world investment management problem by using a data science approach by analysing the impact of the Covid pandemic on NIFTY stocks. This article is an attempt to solve a real-world investment management problem by using a data science approach by analysing the impact of the Covid pandemic on NIFTY stocks. In events such as the financial crisis in or the Covid pandemic which we are facing now, even a well-diversified portfolio will lose money. So how do you protect your wealth when the market crashes during such events but also participate in the upside and get the best of both worlds without losing out on your financial goals. Even though these kind of strategies are being used in the industry in many forms and techniques, here we attempt to look through a step-by-step, Data science methodology of Extracting adequate data from relevant sources, Cleaning, Transforming and Modelling them, implementing an on-the-fly CPPI algorithm, arriving at the back test results and interactively analyse the strategy using advanced data visualization methods.
- In the next few sections, we discuss more on the balance between user interactivity, intuitiveness and algorithmic complexity of the CPPI calculation rather than the nitty gritty of the CPPI as a Portfolio strategy. Raw data extracted from NSE will look like the following: These data points are basically various price points on a given day for an equity. Handling daily frequency data for a portfolio analysis may be an overkill and for the sake of simplicity we will aggregate the data at a weekly level.
- From the daily prices, we will find the weekly price points like open, close, high and low. We should also be careful about the column headings and make sure they look clean without any spaces or special characters in between. Once you convert to weekly time series data, the data frame should like like below: After this weekly grouping, calculate the weekly returns which is the percentage change of price from the previous week.
- The idea behind it was that a strategy which would provide protection against market losses while preserving the upward potential should have considerable appeal to a wide range of investors. This payoff pattern can be achieved by synthetically creating a put option on a portfolio. Nowadays, PI techniques, such as stop-loss, synthetic put, and constant proportion portfolio insurance CPPI continue to be regularly applied in practical applications, such as capital guaranteed funds, which confirms that the principle still enjoys great popularity see e. Under the assumption that returns are lognormally distributed they show that very risk averse investors may prefer PI to a buy-and-hold strategy. Empirical results are also mixed. Hence, the continuing use of PI as well as the mixed research evidence suggests that so far no consensus has been reached about its effectiveness.
- In general, investment performance is traditionally measured from a mean-variance point of view. However, since PI strategies are developed to provide an upward potential combined with a downward protection, their evaluation should take into account their alleged improvement in the left tail of the return distribution. Although the VaR measure -indicating the worst case loss at a certain confidence level -has become one of the standard risk measures in the financial management industry cfr.
- Basel II , one of its main drawbacks remains that it is not a coherent risk measure. Therefore, the coherent risk measure ES has become a popular alternative to VaR. Nevertheless, performance analyses based on these measures are prone to the criticism that the choice of the confidence level remains an arbitrary choice. Given that the mean-variance framework is only partly useful in an insurance context, and that VaR and ES entail the arbitrary choice of a confidence level, we suggest using stochastic dominance rules for the performance analysis of PI strategies.
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This implies that we evaluate the performance of the strategies from the viewpoint of an expected utility maximizing investor. To our knowledge, comparing the strategies in a stochastic dominance framework -in which the whole return distribution is considered -is an application that has not been explored in a PI context so far. In fact, applying these rules in an empirical context has typically been marred by sampling error considerations see e.- However, recent theoretical work on sampling error in stochastic dominance tests with iid data e. Post Using these techniques, PI strategies can be evaluated more profoundly by comparing whole return distributions rather than just some selected moments or quantiles. Moreover, most research is centered on the synthetic put strategy, while little attention has been devoted to a profound comparison of this strategy with a stop-loss and CPPI strategy.
Back-test Analysis Of NIFTY 50 Stocks Using CCPI Strategy: A Data Science Approach | LaptrinhX
Furthermore, simulation exercises have mostly been limited to Monte Carlo simulations or backtesting. However, given the underlying normality assumption of the former and the limited number of possible scenarios of the latter, both approaches fail to correctly assess the performance of the strategies. Finally, only few studies have examined the impact of a different choice of the floor value, rebalancing time, and CPPI multiple so far e. This manifests itself in practice, since the choice of the implementation variables to be used in commercial applications of PI strategies still remains quite arbitrary. The present study reassesses the value of such techniques regardless of the underlying market condition.Portfolio Management - CPPI Returns For Different Floors - Quantitative Finance Stack Exchange
To this end, we consider stock return data from different markets. We use a block bootstrapping procedure in which we repeatedly select a one-year block randomly and test the performance of stop-loss, synthetic put and CPPI. In this way, the performance of each of the strategies can be examined for different market environments. This block bootstrapping technique is used in order to counter the problems that emerge when using a Monte Carlo simulation or backtesting. Indeed, applying bootstrapping implies that no assumptions have to be made about the distribution of market returns, and due to the resampling principle, we can generate a substantial number of scenarios.Code Of Practice For Storage Tank Systems Containing Petroleum And Allied Products: Part 1
Moreover, block bootstrapping preserves both the limited autocorrelation and the substantial heteroscedasticity of real world data in the sample. In addition, we extend the traditional performance analyses of dynamic insurance strategies by considering the VaR, ES, and stochastic dominance efficiency of these strategies. We follow a three-stage procedure in our effort to examine the desirability of dynamic insurance techniques. Second, we demonstrate how choosing a different floor value and CPPI multiple affects the performance of the strategies and test the importance of setting a specific floor value. Thirdly, the impact of a departure from the continuous rebalancing discipline is analyzed by testing whether an equally good protection can be obtained by using a lower rebalancing frequency. Our results indicate that portfolio insurance is useful for investment purposes, that is, all three strategies provide significantly better risk-return trade-offs and downside protection than a buy-and-hold strategy, albeit at the cost of a reduction in return.- The VaR and ES results confirm that the strategies are indeed able to truncate the downward tail of the return distribution. Moreover, relaxing the rebalancing discipline substantially harms the performance of the strategies. Not surprisingly, lowering the floor value increases the potential upward gain and seems to reduce the frequency of negative excess returns, but unfortunately implies larger in absolute value negative excess returns and worse VaR and ES results.
- In other words, the stochastic dominance tests show that for fixed CPPI floor values, the lowest initial equity allocation should be preferred. Finally, our results point out that both for the synthetic put and CPPI strategy daily rebalancing should be preferred to lower rebalancing frequencies, despite the higher transaction costs. This paper is organized as follows. In the next section, we discuss the three popular portfolio insurance alternatives stop-loss, synthetic put and CPPI. In section 3 the performance measures are described. Section 4 presents the empirical setup and section 5 contains the simulation results.
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In section 6 we conclude by giving some guidelines for practical implementations. Portfolio insurance strategiesUsing stop-loss portfolio insurance SL 2 the portfolio is fully invested in the risky asset which in this paper is referred to as 'equity' as long as its value is above the discounted value of the floor, which is the minimum target level the portfolio has to reach at the end of the investment horizon T. Once the portfolio drops below the discounted floor, the portfolio switches entirely to the risk-free asset, ensuring that the target is reached at the end of the period. Hence, this strategy is only subject to a single transaction cost, which can be substantial, given the fact that it is computed on the entire portfolio value. The synthetic put strategy SP is a dynamic portfolio strategy that replicates the payoff of a protective put i. The strike price F is set equal to the desired floor at T.- The portfolio invests changing proportions in the risky and risk-free assets. The proportion invested in the risky asset follows from an option pricing model see e. This strategy also implies portfolio positions in both a risky and a risk-free asset. The risky proportion is determined by the cushion, which is the difference between the portfolio value and the floor F, and by the multiple m. The latter is a constant that represents the desired sensitivity to market changes. CPPI requires investing an amount equal to the product of the multiple and the cushion in the risky asset, while investing the remainder in the risk-free asset.
- We impose short sale and credit constraints in order to follow the common practice in commercial applications. Similar to the SL strategy, a time subscript is added to the floor F to indicate the discounted value of the target minimum portfolio. As is clear from the discussion above, the investor has to choose a floor value, a rebalancing frequency and in the case of CPPI a multiple. Very risk averse investors will prefer relatively high floor values, which imply only limited upward potential for the strategy. Rebalancing can be initiated by a predefined minimal absolute market move or it can occur after a given time interval. In the present study, we consider rebalancing based on a time interval. Afterwards, we investigate the impact of weekly or monthly rebalancing frequencies on the strategies' performance. Finally, for the CPPI strategy a higher multiple implies a riskier strategy and can serve to achieve higher portfolio values.
- However, risk averse investors will rather apply low multiples in order to obtain a high protection. To facilitate comparisons, we will set the multiple in such a way that the initial exposure to the risky asset is similar across strategies. Performance measurementPortfolio theory assumes that investors select portfolios that are optimal to them. Optimality is often understood as maximizing expected utility of wealth. As such, choosing between portfolios amounts to choosing between return distributions.
- Of course, in the absence of knowing the exact return distributions, such choices are difficult to perform. In practice, investors therefore focus on specific moments or other statistics of these distributions. Examples are average returns in excess of a risk-free rate of return 'excess returns' and the standard deviation of return 'volatility'. The Sharpe ratio combines both by taking the ratio between the average excess return and volatility. Under some conditions, it can be shown that portfolio selection based on comparisons of these statistics is consistent with expected utility see e. Elton et al. Unfortunately, for PI techniques, where the focus is on value protection and upward potential, these statistics are not sufficient for adequate selection. High volatility can be due to positive return outliers, which would attract rather than shy away investors. Informal performance measures such as the occurrence of negative excess returns are therefore also contemplated.
- Recently, VaR, an asymmetric risk measure, has been put forward as an alternative to the symmetric volatility measure. VaR denotes the maximum loss at a certain confidence level. A major drawback of the VaR computation concerns the fact that no indication is given of the magnitude of the losses for the extreme situation in which the limit value is exceeded. The ES provides an answer to this disadvantage, because it expresses the average loss below this limit level. VaR and ES can be computed for different confidence levels. Of course, a higher confidence level will entail more negative VaR and ES values. Hence, choosing a higher confidence level indicates a higher degree of risk aversion.
- The skewness of the return distribution is another performance measure. Generally, investors prefer right-skewed distributions. Hence, a larger skewness makes a protection strategy more appealing; see e. The problem with all these performance measures is that they are difficult to associate with expected utility. Moreover, comparing different measures across strategies may lead to contradictory results.
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Stochastic dominance SD rules provide a framework that is explicitly based on expected utility. Moreover, they are based on the entire return distribution, rather than on some arbitrarily chosen statistics. Because of the asymmetric nature of PI techniques the SD framework seems particularly attractive. Yet, up to now it has not been popular because the theoretical distributions are unknown and all inference is plagued by severe sampling error. Short of a statistical framework to deal with this sampling error, researchers have relied on more informal measures like those introduced above.Certificate Of Proficiency In Insolvency (CPI) | Insolvency | BPP
In the remainder of this section, we first discuss the intuition behind SD rules and then summarize the testing procedure. Stochastic Dominance RulesThe goal of SD rules is to partition the investment opportunity set into an efficient and an inefficient set. The efficient set contains all investments that are not stochastically dominated by any other investment.
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